Abstract: A camera with a privacy protection function and a camera privacy protection method therefor are provided, which relates to the field of image privacy protection. The camera includes an image acquisition module, a parameter storage module, an access control module, and an image post-processing module. An objective of the privacy protection method of the camera is to enable an image shot by the camera to naturally have a privacy protection capability to resist sniffing of a malicious third party to personal privacy, which includes, but is not limited to, face recognition. A system deeply participates in a generation process of an image from a plurality of levels, which can affect various procedures including image signal processing, image color adjustment, and an image encoding process, and provide a platform for the development and application of underlying privacy protection algorithms.

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: 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: 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: 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: A blade holder assembly for blender having a cup body arranged on a cup base; the cup base connects the cup body and a blade holder component; the blade holder component has a blade holder below and connected with the cup base; a handle is arranged on the bottom of the blade holder; connecting members are arranged on both ends of the handle; a locator is on the bottom of the blade holder; a connector is arranged at the center of the locator; a guide bone is on the edge of the blade holder, and a rotary button is in the guide bone; on the rotary fastener, two fastening members are arranged; the connecting members are connected with the rotary fastener; a guide slot is on the inner edge of the cup base; in the guide slot, a convex button is arranged for fastening the rotary fastener.

Abstract: A blade holder assembly for blender having a cup body arranged on a cup base; the cup base connects the cup body and a blade holder component; the blade holder component has a blade holder below and connected with the cup base; a handle is arranged on the bottom of the blade holder; connecting members are arranged on both ends of the handle; a locator is on the bottom of the blade holder; a connector is arranged at the center of the locator; a guide bone is on the edge of the blade holder, and a rotary button is in the guide bone; on the rotary fastener, two fastening members are arranged; the connecting members are connected with the rotary fastener; a guide slot is on the inner edge of the cup base; in the guide slot, a convex button is arranged for fastening the rotary fastener.