144 results about "Activity detection" patented technology

A video gateway system at a worksite is coupled to multiple cameras on a network, and backs-up video streams generated by the cameras to a backend cloud backup video storage system and a frontend (cache) video storage system. The video gateway system generates an aggregated video asset from a plurality of streams of video from the multiple cameras, and generates metadata and a backup report associated with the video asset. The video asset, metadata, and backup report are stored on the backend cloud backup video storage system in an file system, and are also stored on the frontend (cache) video storage system such that for at least a period of time, the video asset and the associated the metadata and backup report are stored on both the cloud backup video storage system, facilitating quick access and retrieval to the stored video for retrieval for streaming, activity detection, and other uses.

Techniques are described herein that are capable of performing AI-based malicious activity detection using a threat actor profile. An alert is received. The alert indicates that a potentially anomalous event has occurred with regard to an entity. A profile of a threat actor is generated using information that describes behavior of the threat actor. An artificial intelligence (AI) model is triggered to determine whether the threat actor performs a malicious activity with regard to the entity by providing an AI prompt as an input to the AI model. The AI prompt includes the profile of the threat actor and a description of the potentially anomalous event. The AI prompt requests a determination whether the threat actor performs the malicious activity with regard to the entity.

In some embodiments, a fiber optic cable may be monitored while reducing false positives from disturbances in trusted areas. In some embodiments, a system includes a light source, a first remote termination unit in a trusted area, and a second remote termination unit in another trusted area. The light source emits light through the fiber optic cable, which passes through the first remote termination unit, an untrusted area, and then the second remote termination unit. The remote termination units reflect portions of the light back towards the light source. The system uses a prediction model to detect disturbances in the untrusted area based on the reflected light data.

Systems, methods, and techniques are directed to detecting potential anomalous activity based on changes in a security graph. In an example, a security system receives a first snapshot of a graph representative of a tenant account of a network-based system corresponding to a first timestamp. The security system receives a second snapshot of the graph corresponding to a second timestamp. The security system determines a first change in the graph based on the first and second snapshots and a second change related to the first change. The security system detects a potential anomaly based on the first and second changes. Responsive to detecting a potential anomaly, the security system causes a mitigation step to be performed with respect to the tenant account. In a further example, the security system determines relationships between a sequence of changes satisfies a cumulative anomaly criterion.

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 generates a first sparse similarity matrix for nearest neighbors of a plurality of data points. The data points each characterize a pattern of activity associated with an account. The method generates a second sparse similarity matrix for random neighbors of the plurality of data points. The method recursively clusters the plurality of data points based on the first sparse similarity matrix. The method quasi-supervises the recursive clustering based on the second sparse similarity matrix to stop the iterative clustering when the data points are split into N clusters. The value of N is not pre-determined. The method detects that the individual data point has changed clusters, indicating anomalous activity. And, the method generates an electronic alert that the anomalous activity is associated with the account.

There is described a computer-implemented method to monitor operational performance of a system based on sensor data associated with the operation of the system. Received sensor data is processed to provide a plurality of input signals for an Artificial Neural Network, ANN. This processing involves transforming the sensor data into a plurality of components that are characteristic of the operation of the system and normalised with respect to activity values within the ANN, each component having a corresponding component value, and for each component, determining a difference value between the normalised component value and a respective feedback value from the ANN and generating an input signal representative of the difference value. By monitoring activity associated with the ANN, anomalous activity indicative of a departure from normal operation of the system is detected.

The invention relates to the field of audio processing, and discloses a human voice activity detection method and device, computer equipment and a storage medium. The method comprises the following steps: collecting audio and video data of a user, and dividing the audio and video data into a video stream and an audio stream; detecting the video stream by using a FaceMesh model to obtain a lip opening degree and a head attitude angle, and determining a lip motion state according to the lip opening degree and the head attitude angle; blocking the audio stream to obtain a plurality of audio blocks, performing noise reduction on each audio block to obtain a plurality of noise-reduced audio blocks, and detecting each noise-reduced audio block by using a silhoVAD model to obtain a human voice activity probability and an audio cache queue; and inputting the lip motion state and the human voice activity probability into a state machine, introducing an audio cache queue by the state machine, and outputting a speaking identifier and a corresponding audio clip. Speaking recognition is cooperatively completed in combination with machine vision and hearing signals, and the accuracy of whole human voice activity detection is improved.

The invention discloses a CAN bus activity detection and wake-up circuit and method, and belongs to the technical field of CAN bus communication, the CAN bus activity detection and wake-up circuit comprises a CAN bus interface circuit, a signal detection and processing circuit and a controller, the input end of the CAN bus interface circuit is connected with a CAN bus; the signal detecting and processing circuit comprises a voltage comparator, the non-inverting input end and the inverting input end of the voltage comparator respectively receive a CANH signal and a CANL signal which are electrically isolated by the CAN bus interface circuit, and the output end of the voltage comparator is connected to a wake-up pin of the controller; the controller is used for monitoring the level state of the wake-up pin, and when the level of the wake-up pin is in an effective state, the electronic equipment is controlled to be switched from the sleep mode to the normal working mode. By introducing an electrical isolation and voltage difference detection mechanism, the problem of false wake-up caused by common-mode interference or bus noise in the prior art is solved, and reliable wake-up under low power consumption is realized.

The invention relates to the technical field of mandible activity detection, and discloses an AI-based mandible activity detection system. The system comprises a multi-area pressure sensing array used for collecting dynamic pressure distribution data in the mandibular movement process in real time; the initial activity analysis module extracts a mandibular activity intensity characterization value; the abnormal activity judgment module compares the characterization value with a preset interval to generate an initial activity abnormal signal; the influence collaborative analysis module obtains motion track data, calculates a track offset influence value, compares the track offset influence value with a threshold value, and generates a motion offset influence level signal; the behavior characteristic decomposition module decomposes the space-time characteristics of mandibular movement to obtain a movement behavior separation value; the dynamic partition processing module divides a mandibular movement limited area according to the separation value; a correlation evaluation module calculates a behavior cooperation trend value and a similarity value and generates a behavior cooperation correlation value; the dynamic adjustment module calculates an activity intensity correction factor based on the correlation value. According to the system, the comprehensiveness and accuracy of mandible motion range detection are improved.

A wearable multifunction device or earpiece or a pair of earpieces includes one or more processors, at least one microphone coupled to the one or more processors, a biometric sensor coupled to the one or more processors, and a memory coupled to the one or more processors, the memory having computer instructions causing the one or more processors to perform the operations of sensing a remaining battery life and based on the sensing, prioritizing one or more of the functions of always on recording, biometric measuring, biometric recording, sound pressure level measuring, voice activity detection, key word detection, key word analysis, personal audio assistant functions, transmission of data to a tethered phone, transmission of data to a server, transmission of data to a cloud device.

There are provided systems and methods of bot detection through explainable deep learning and rule violation codebooks from generative AI. A service provider, such as an electronic transaction processor for digital transactions, may provide computing services to users for processing various requests and interactions with those users. However, malicious users may utilize bots, such as automated scripts and software applications, that attempt to conduct fraud, compromise systems and data, and the like. To provide better bot and bot activity detection, the service provider may implement an explainable deep learning system that may generate rule violations of rules indicating bot activity or presence in computing logs and interactions using a generative AI. The violations may have a corresponding explanation in codebooks to automate bot detection. When bot activity is detected, the explanation may provide a reason for the bot activity detection.

Malware activity detection for networked computing systems is described. A network session record is provided to a machine learning (ML) model configured to generate an indication of whether the provided network session record evidences malware activity. The network session record indicates network traffic activity in a time period. Responsive to an indication by the ML model, correlation scores are calculated by, for each process session record in a process session record set, calculating a correlation score indicative of a correlation between the provided network session record and the process session record. Each process session record in the process session record set corresponds to a process executed by a computing device in the time period. A determination that a correlation score indicates a corresponding process session record is indicative of the evidenced malware activity is made. Responsive to the determination, a malware activity alert is generated.

The invention provides a multi-modal data real-time analysis and feedback method and system, and the method comprises the steps: collecting a video frame and an audio frame, reading a count value of the same monotonic timer when the collection of the video frame and the audio frame is completed, and generating an audio and video sequence; calculating the behavior popularity of each region in each time slice based on the sequence, and generating a region set for multi-modal event analysis in combination with a preset threshold and a quantity upper limit; scheduling the corresponding video sub-blocks to a visual computing power unit for target positioning and action classification, and executing voice activity detection and keyword category judgment on the time-aligned audio clips at the same time; visual and audio results are fused, and a structured event sequence is generated according to time slice and region compression; and constructing a classroom teaching chain through the sequence, identifying a key event, and finally generating a teaching event description. According to the invention, under the condition of domestic chip combination, cost, time delay, multi-modal consistency and data security controllability are considered, and real-time perception and feedback of teaching behaviors are effectively supported.

The invention discloses a full-duplex intelligent voice interaction system based on voice activity detection and intention recognition, and the system comprises a voice recognition module which is used for converting a voice stream into a text; the voice activity detection module is used for detecting voice activity; the intention recognition module is used for judging whether the user voice has a clear intention or not; the interruption control module is used for controlling pause and recovery of voice broadcast according to the voice activity detection result and the intention recognition result; the speech synthesis module is used for converting the text into a speech stream; the buffer area management module is used for managing a voice stream buffer area; and the resource management module is used for managing different tasks through the thread pool and processing voice recognition, voice activity detection and voice synthesis tasks in parallel. The invention further provides a full-duplex intelligent voice interaction method based on voice activity detection and intention recognition. Full-duplex voice interaction is realized; a mode of combining voice activity detection and intention recognition is adopted; and a high-concurrency scene is supported.

This invention relates to a portable on-site detection device and method for Lu-177 radionuclide activity. The detection device includes a gamma detection assembly and a circuit board installed within a housing. The gamma detection assembly comprises several pieces, each housing a gamma detection module. The space formed by the gamma detection assemblies serves as a sample chamber, with a central slot for holding a Lu-177 carrier bottle. The gamma detection module employs a combination structure of a GAGG:Ce scintillator and a silicon photomultiplier tube (SiPM). The circuit board houses a connection module between the circuit board and the gamma detection assembly, a power conversion module, a signal summing circuit for the gamma detection assembly, a digital multichannel module, and a data analysis and transmission module. The testing method includes five steps, including device initialization. The beneficial effects are that it provides technical support for accurate pre-administration dose verification of Lu-177, ensuring medication safety for patients undergoing radionuclide therapy and improving the quality of clinical nuclear medicine treatment.

The invention provides an automatic recording method, device and equipment, a storage medium and an automatic recording system, and relates to the technical field of computers, in particular to the technical field of sports science and technology and computer vision. According to the specific implementation scheme, the method comprises the steps of obtaining a current video stream collected by a visual collection device for a monitoring scene, and loading initial region configuration information of the monitoring scene; determining geometric transformation parameters according to the characteristic difference between the standard reference image of the monitoring scene and the current calibration image; calibrating coordinates of a functional region frame contained in the initial region configuration information by using the geometric transformation parameters to obtain a calibrated functional region frame; and based on the calibrated functional area frame, performing target activity detection on the current video stream, and generating a recording record when a detection result meets a preset triggering condition. According to the technical scheme, the frequency of mechanical calibration can be reduced, the operation and maintenance cost is reduced, and the long-term stability and accuracy of the recording system are ensured.

According to a first aspect of the present disclosure, there is provided a user authentication apparatus comprising: a face recognition unit configured to receive a visual input and extract a biometric marker from the visual input, the biometric marker being a face; a user identification unit configured to identify a user based on the biometric marker extracted by the face recognition unit; and an activity detection unit configured to detect whether the user identified by the user identification unit is a living person by extracting one or more additional biometric markers from the visual input received by the face recognition unit. According to other aspects of the present disclosure, a corresponding method of operation is conceived and a computer program for performing the method is provided.

Arrangements for cyber threat detection are provided. In some aspects, a request for an enterprise event may be received. The request may be received from a user computing device and may include a user agent string associated with the user computing device. The user agent string may be parsed to identify parameters associated with the request. The request may then be analyzed to determine whether it is unauthorized. Analyzing the request may include comparing the identified parameters to known viable parameters identified from historical hardware and / or software data. Based on the comparing, if the identified parameters match the known viable parameters, the requested event may be processed. If the identified parameters do not match the known viable parameters, the event may be flagged and transmitted to an enterprise organization computing system for further analysis and / or executing of one or more mitigation actions.

The invention provides a magnesium oxide activity detection device, and belongs to the technical field of detection instruments, the magnesium oxide activity detection device comprises a fixed base, a mounting groove is reserved in the fixed base, a heating electric furnace module is arranged in the mounting groove, an asbestosed wire gauze is arranged on the heating electric furnace module, a beaker is arranged on the asbestosed wire gauze, and three vertical rods are arranged on the fixed base. The three vertical rods are fixedly connected through a connecting cross rod, a thermometer is placed in the beaker, a support is arranged between the vertical rods and the thermometer, and the two ends of the support are connected with the thermometer through a fastener A and a fastener B and the vertical rods respectively. The magnesium oxide activity detection device solves the problems that when a thermodetector in an existing magnesium oxide activity detection device is assembled on a vertical rod through a bolt and an annular buckle, tools such as a wrench need to be used for assisting in the adjusting process, and use is inconvenient.

A computing platform may receive event processing requests. The computing platform may integrate, into the event processing requests, domain knowledge. The computing platform may reduce, using temporal sequence compression, the integrated event processing requests into high-signal segments. The computing platform may generate, using capsule-based representation learning, capsule structures representing the high-signal segments. The computing platform may access at least one unauthorized activity detection model. The computing platform may analyze, using the at least one unauthorized activity detection model, the event processing requests, which may include selectively retrieving, for the at least one unauthorized activity detection model and using contextual indexing, a subset of relevant capsule structures from the capsule structures. The computing platform may output, using the at least one unauthorized activity detection model, a unauthorized activity output for the event processing requests. The computing platform may initiate, based on the unauthorized activity output, one or more security actions.

The embodiment of the application discloses a kind of aircraft wing surface activity detection method, device, equipment and medium, it is related to work piece detection technical field, it solves the technical problem of lower efficiency of aircraft wing surface activity detection in prior art.The detection method comprises: obtaining the parameter information of camera measurement system based on the input information of camera measurement system;The camera measurement system is at preset frame rate to the multiple images of target movable component in the process of rotation, and the center coordinate value of the coded mark point on each image is obtained;Based on the center coordinate value of the coded mark point on each image, the angular velocity value of the shooting time point corresponding to each image is obtained;Based on the angular velocity value of the shooting time point corresponding to each image, the angular velocity matrix of the target movable component is obtained;Based on the angular velocity matrix of the target movable component, whether the activity degree of the target movable component is qualified is judged.

Rodent trap (100), comprehensive: - an activity detection arrangement (110) with a movable activity element (112) which is movable when subjected to a mechanical load by an animal; - a bait monitoring arrangement (120) with a bait receptacle (122) that is movable independently of the activity element (112) of the activity detection arrangement (110) and which assumes different positions depending on the amount of bait applied; - a first sensor arrangement (130) associated with the activity detection arrangement (110) and configured to detect the position of the movable activity element (112); and - a second sensor arrangement (140) which is assigned to the bait monitoring arrangement (120) and is configured to detect a position of the movable bait receptacle (122), - wherein the first sensor arrangement (130) is configured to detect animal activity independently of the second sensor arrangement (140), and - wherein the second sensor arrangement (140) is configured to detect a bait state independently of the first sensor arrangement (130).

The invention relates to an intelligent audio optimization method, and the method comprises the steps: inputting a voice signal and a network state parameter into a deep learning model for conjoint analysis, and generating a corresponding environment evaluation result; dynamically generating a voice activity detection threshold, and detecting the voice signal by using the voice activity detection threshold to determine whether the user is in a voice state; and if the user is not in the voice state, calling network state information to execute discontinuous transmission processing, and if the user is in the voice state and a voice gap exists in the voice signal, executing comfortable noise supplement processing, and outputting a target communication audio stream. Discontinuous transmission processing reduces bandwidth occupancy and energy consumption. The comfortable noise supplementary processing ensures the natural continuity and immersion of the communication process by inserting simulated background sound from a preset noise library or personalized configuration of a user into a voice gap. The stable voice communication quality is kept in a changeable environment, and the equipment power consumption is reduced.

A sound signal detection method and device, a computer readable storage medium and a terminal, the method comprising: determining a plurality of sound signals collected by a plurality of microphones from a same input signal within a first preset time period; determining a correlation coefficient, an error energy cumulative value and an energy difference cumulative value between each two sound signals of the plurality of sound signals; and if one or more of the following conditions are met, determining that wind noise exists in the input signal: at least one of the correlation coefficients is less than a preset correlation threshold, at least one of the error energy cumulative values is greater than a first preset difference value, and at least one of the energy difference cumulative values is greater than a second preset difference value. The present application can accurately determine whether wind noise exists in the sound signal collected by the microphone, and reduce unnecessary voice activity detection caused by excessive environmental wind noise.

The invention discloses a remote management method and system for a pet-friendly air purifier, and relates to the technical field of air purification, and the method comprises the following specific steps: S100, obtaining pet activity data, S200, obtaining air quality data, S300, generating a purifier control instruction based on pet activity and air quality data, and determining a working mode, the method comprises the steps of S400, sending purifier state data to a user terminal and receiving a user remote instruction, and S500, adjusting the working state of the purifier according to purifier control and the user remote instruction. By constructing a multi-mode environment perception and data fusion decision framework, the working logic of the air purifier is fundamentally changed, and the working efficiency of the air purifier is improved. Original independent pet activity detection and air quality monitoring information flow are deeply fused, the central processing module evaluates the comprehensive load degree of the current environment in real time, different scenes can be distinguished, corresponding purification strategies can be started, and it is ensured that the purification strategies are always matched with real pollution sources and pollution levels.

System and method for detecting a fraudulent activity at a barcode scanner is disclosed. The method issues an alert when the fraudulent activity is confirmed by comparing the visual signature of the item being transacted over the checkout terminal to the model visual signature. The model visual signature is obtained by averaging the collection of visual signature of the item gathered over a period of time. A human validation via a remote processor is employed to confirm the fraudulent activity verified by a computer.

The invention discloses an intelligent dialogue system and method supporting real-time voice interruption, and relates to the technical field of man-machine interaction. The system comprises a voice activity detection module, a voice recognition module, a dialogue management and LLM interaction module, a voice synthesis module and an interruption control module. Wherein the dialogue management and LLM interaction module is used for managing dialogue states and contexts and calling LLM to generate reply texts; when the dialogue is interrupted, re-integrating the context and the new instruction text, and then calling the large language model to generate a new reply text; the interruption control module is used for sending an interruption signal to the voice synthesis module when the voice activity detection module detects effective user input, and triggering the dialogue management and LLM interaction module to re-integrate the context to generate a new reply; the voice activity detection module and the voice synthesis module operate at the same time and are not blocked. The method supports interruption at any time, and ensures semantic coherence of the interrupted dialogue through a context management mechanism, and the user experience is smooth.

An object of a system according to an exemplary embodiment is to detect a security threat of a server in real time and quickly deal with the security threat.SOLUTION: A system according to an embodiment includes a real-time analysis unit, a suspicious activity detection unit, a threat handling unit, a report generation unit, and an automatic learning unit. The real-time analyzer analyzes the behavior of the server in real time using the generated AI. The suspicious activity detection unit detects a suspicious activity from the data analyzed by the real-time analysis unit. The threat handling unit identifies and handles a threat based on the suspicious activity detected by the suspicious activity detection unit. The report generation unit generates a security report based on the information on the threat addressed by the threat handling unit. The automatic learning unit automatically learns based on the report generated by the report generation unit to improve the security measure.SELECTED DRAWING: Figure 1

The invention discloses a method for detecting the activity of skin flap endonuclease 1 based on double cascade signal amplification. Belongs to the technical field of enzyme activity detection. According to the invention, the specific dumbbell probe is designed; when the skin flap endonuclease 1 exists in the sample to be detected, the skin flap endonuclease 1 can be detected; a flap endonuclease 1 specifically recognizes and cuts off a 5'overhanging flap in the dumbbell probe to generate a single-stranded DNA with a 3 '-OH terminal and a dumbbell-shaped DNA with a notch, two products generated by cutting respectively trigger rolling circle amplification and strand displacement amplification reactions, specific signals are amplified, the purpose of sensitive detection is achieved, the substrate utilization rate is 100%, no probe is wasted, and the detection cost is reduced. The method is carried out under isothermal and homogeneous conditions, does not need high-precision thermal circulation and separation steps, does not need to prepare a functional nano material, does not relate to a biological coupling procedure, effectively reduces the analysis cost and complexity, and provides a technical support for the detection of the activity of the skin flap endonuclease 1.