Abstract: Disclosed are a lens structure, a photographic device and a focusing method of lens. The lens structure includes a lens barrel, a lens assembly, a drive mechanism, a position detection device and a control device. The lens barrel is extended along a first direction. The lens assembly includes a lens group configured to be movable along the first direction. The drive mechanism is fixedly connected to the lens group and configured to drive the lens group to move along the first direction. The position detection device configured to detect a position of the lens group in real time. The control device is electrically connected to the position detection device and the drive mechanism, and is configured to control the drive mechanism to work according to the position detection device to adjust the position of the lens group.

Abstract: Provided are a cell module stacking device and a cell module stacking method. The stacking device for the cell module includes a rack, a support mechanism, a pressing mechanism, and a tray. The support mechanism is configured to support a plurality of cells. The pressing mechanism is connected to the rack. A part of the pressing mechanism is movable in an axial direction of the support mechanism. The pressing mechanism is configured to press the plurality of cells into a cell module. The tray is disposed on the rack in such a manner that the tray is movable relative to the rack and is configured to support the cell module.

Abstract: The disclosure is directed to systems, methods, and apparatus, including non-transitory computer-readable media, for performing quota resolution on a cloud computing platform. A system can receive user account data from one or more user accounts representing a first user. The system can generate a plurality of features from the user account data characterizing interactions between the first user and the computing platform. From at least the plurality of features, the system can generate a score at least partially representing a predicted likelihood that the additional computing resources allocated to the first user account will be used in violation of one or more predetermined abusive usage parameters during a predetermined future time period.

Abstract: A battery positioning apparatus for positioning a battery. The battery includes a first grip surface and a second grip surface. Projections of the first grip surface and the second grip surface along a first direction do not overlap. The first direction is perpendicular to the first grip surface and the second grip surface. The battery positioning apparatus includes a pallet and a limiting mechanism. The pallet is configured to hold a battery. The limiting mechanism is disposed on the pallet and configured to release the first grip surface of the battery when the second grip surface of the battery is gripped along the first direction.

Abstract: Methods and embodiments of a high-performance parallel multi-literal matching algorithm called NeoHarry. A chunk of data comprising a character string comprising n bytes is sampled for a byte stream, and data in the sampled chunk are pre-shifted to create shifted copies of data at multiple sampled locations. A mask table is generated having column vectors containing match indicia identifying potential character matches. A look up of the mask table at multiple sampled locations using the pre-shifted data is performed for a target literal character pattern. The mask table lookup results are combined to generate match candidates and exact match verification is performed to identify any generated match candidates that match the target literal character pattern. NeoHarry uses a column-vector-based shift-or model and implements a cross-domain shift algorithm under which character patterns spanning two domains are identified.

Abstract: A device receives historical data associated with multiple cloud computing environments, trains one or more machine learning models, with the historical data, to generate trained machine learning models that generate outputs, and trains a model with the outputs to generate a trained model. The device receives particular data, associated with a cloud computing environment, that includes data identifying usage of resources associated with the cloud computing environment, and processes the particular data, with the trained machine learning models, to generate anomaly scores indicating anomalous usage of the resources associated with the cloud computing environment. The device processes the one or more anomaly scores, with the trained model, to generate a final anomaly score indicating anomalous usage of at least one of the resources associated with the cloud computing environment, and performs one or more actions based on the final anomaly score.

Abstract: A logistics conveying line for conveying a workpiece includes a frame including at least two stations and a driving mechanism including a driving portion and a connecting member. The driving portion is movably mounted at the frame. The connecting member is movably mounted at the driving portion. The connecting member is configured to switch between a connected state and a disconnected state with respect to a carrier. The carrier is configured to bear the workpiece. The driving portion is configured to, when the connecting member is in the connected state with respect to the carrier, drive the carrier to move from one of the stations to another one of the stations.

Abstract: Examples described herein relate to a programmable packet processing pipeline configured to: access a data corresponding to multiple bins, respective bins associated with multiple packet flows and for respective bins: identify a single flow associated with a bin of the multiple bins as a candidate heavy hitter flow and determine a different packet flow as the candidate heavy hitter flow for the bin of the multiple bins.

Abstract: A device may receive data that is related to historical reports associated with an organization, historical audits of the historical reports, and individuals associated with the historical reports. The device may determine a multi-entity profile for the data. The multi-entity profile may include a set of groupings of the data by a set of attributes included in the data. The device may determine, using the multi-entity profile, a set of supervised model features for the historical reports. The device may determine, using the multi-entity profile, a set of unsupervised model features for the historical reports independent of the historical audits. The device may determine, utilizing a model, a score for a report. The device may perform one or more actions.

Abstract: A vehicle power supply device is provided. The vehicle power supply device includes: a fixed base; a scissor-fork lifting mechanism, where the scissor-fork lifting mechanism is configured to be driven by a drive device to ascend or descend; and the scissor-fork lifting mechanism includes at least one scissor-fork swing rod set, the scissor-fork swing rod set includes at least one pair of swing rods, each pair of swing rods includes two swing rods that are cross-arranged and are hinged with each other, and two swing rods in a pair of swing rods that is closest to the fixed base are separately mounted on the fixed base; and a power supply head, where the power supply head is mounted on two swing rods in a pair of swing rods that is farthest from the fixed base, and the power supply head is driven by the scissor-fork lifting mechanism to ascend or descend.

Abstract: Methods and apparatus for a flexible Deterministic Finite Automata (DFA) tokenizer for AI-based malicious traffic detection. A DFA compiler is used to process profiles, such as SQLi, HTML5 and XSS profiles, as well as user-defined profiles, to generate corresponding DFA transition tables. The DFA tokenizer includes a DFA engine that employs the DFA transition table(s) to generate token sequences derived from input strings. The token sequences are converted into feature vectors using a feature extraction engine, and the feature vectors are used for training a machine learning/Artificial Intelligence (AI) model configured to perform binary classification (benign or malicious). During run-time, strings are extracted from input received via a network and tokenized with the DFA tokenizer to generate token sequences that are converted into feature vectors. The feature vectors are then classified using the AI model to determine whether the input is benign or malicious.

Abstract: A device may receive historical cloud data associated with resources of a cloud computing environment, and may receive historical customer data associated with requested resource usage by customers of the cloud computing environment. The device may determine a usage growth profile based on the historical cloud data and the historical customer data, and may determine, based on the historical cloud data and the historical customer data, usage deviation data indicating deviations between actual and planned resource usage. The device may train a model, with the usage growth profile and the usage deviation data, to generate a trained model, and may receive a request for new resource usage by a customer associated with the cloud computing environment. The device may process the request for the new resource usage, with the trained model, to generate projected resource usage data, and may perform actions based on the projected resource usage data.

Abstract: Examples include techniques to encode and decode for character class matching. A character class including a plurality of characters may be encoded in a mask table. A sampled chunk of input data is used to perform a decode operation to check, in a parallel manner, each character included in the input data with the encoded mask table. The checking of each character in the input data to determine whether at least one character in the input data matches a character included in the character class.

Abstract: A key distribution method based on broadband physical random sources includes: utilizing a driving semiconductor laser to generate an optical signal, passing the optical signal through a phase modulator driven by a random signal and then equally dividing the phase-modulated optical signal into two identical paths, injecting the two identical paths into slave semiconductor lasers at both communication parties Alice and Bob's sides, respectively, to generate initial synchronized signals, using the generated initial synchronized signals as driving signals to phase-modulate optical signals generated by continuous-wave (CW) light sources, and inputting the modulated optical signals to dispersion modules; wherein after the modulated CW optical signals pass through the dispersion modules, two synchronized broadband noise-like random signals are generated, and then high-speed synchronized keys are generated by a post-processing method.

Abstract: The disclosure is directed to systems, methods, and apparatus, including non-transitory computer-readable media, for performing quota resolution on a cloud computing platform. A system can receive user account data from one or more user accounts representing a first user. The system can generate a plurality of features from the user account data characterizing interactions between the first user and the computing platform. From at least the plurality of features, the system can generate a score at least partially representing a predicted likelihood that the additional computing resources allocated to the first user account will be used in violation of one or more predetermined abusive usage parameters during a predetermined future time period.

Abstract: Examples include techniques four use of a large scale multi-literal matching algorithm. Implementation of the large scale multi-literal matching algorithm includes processing a chunk of input data via performance of a SHIFT-OR operation using the chunk of input data to identify a match candidate for a target literal character pattern. A single input multiple data (SIMD) instruction may be utilized by a processor to perform the SHIFT-OR operation as a parallel table lookup of rows of SHIFT-OR mask table for the chunk of input data to facilitate identification of the match candidate.

Abstract: An ultrasound-based handheld animal and plant tissue ablation instrument includes a housing internally provided with a control board and a drive board, and an ultrasonic vibrator fixedly connected to a front end of the housing. The ultrasonic vibrator includes an ultrasonic transducer and an amplitude transformer. The amplitude transformer is fixedly connected to a front end of the ultrasonic transducer, changes an amplitude of vibration input by the ultrasonic transducer and transmits out the vibration. The control board is provided with a power supply, a DCDC conversion unit, a main control unit, a DCDC power adjustment unit and a sampling unit. The drive board is provided with a drive unit, a transformer unit and a resonance unit. The housing includes a first section and a second section. The end of the first section away from the second section is the front end of the housing.

Abstract: A key distribution method based on broadband physical random sources includes: utilizing a driving semiconductor laser to generate an optical signal, passing the optical signal through a phase modulator driven by a random signal and then equally dividing the phase-modulated optical signal into two identical paths, injecting the two identical paths into slave semiconductor lasers at both communication parties Alice and Bob's sides, respectively, to generate initial synchronized signals, using the generated initial synchronized signals as driving signals to phase-modulate optical signals generated by continuous-wave (CW) light sources, and inputting the modulated optical signals to dispersion modules; wherein after the modulated CW optical signals pass through the dispersion modules, two synchronized broadband noise-like random signals are generated, and then high-speed synchronized keys are generated by a post-processing method.

Abstract: A device receives historical data associated with multiple cloud computing environments, trains one or more machine learning models, with the historical data, to generate trained machine learning models that generate outputs, and trains a model with the outputs to generate a trained model. The device receives particular data, associated with a cloud computing environment, that includes data identifying usage of resources associated with the cloud computing environment, and processes the particular data, with the trained machine learning models, to generate anomaly scores indicating anomalous usage of the resources associated with the cloud computing environment. The device processes the one or more anomaly scores, with the trained model, to generate a final anomaly score indicating anomalous usage of at least one of the resources associated with the cloud computing environment, and performs one or more actions based on the final anomaly score.

Abstract: A device may receive historical cloud data associated with resources of a cloud computing environment, and may receive historical customer data associated with requested resource usage by customers of the cloud computing environment. The device may determine a usage growth profile based on the historical cloud data and the historical customer data, and may determine, based on the historical cloud data and the historical customer data, usage deviation data indicating deviations between actual and planned resource usage. The device may train a model, with the usage growth profile and the usage deviation data, to generate a trained model, and may receive a request for new resource usage by a customer associated with the cloud computing environment. The device may process the request for the new resource usage, with the trained model, to generate projected resource usage data, and may perform actions based on the projected resource usage data.