Abstract: An air conditioner control method, an air conditioner, and a storage medium. The air conditioner is provided with the detection apparatus group. The method includes controlling the detection apparatus group to be in different detection modes to detect a state of a target object; and controlling operation of the air conditioner based on the state of the target object.

Abstract: Provided herein are compounds of formula A-B, where A is selected from a monosaccharide or a disaccharide and B is a therapeutic moiety (e.g. selected from an anti-inflammatory moiety or chemotherapeutic moiety); as well as a pharmaceutically acceptable salt, stereoisomer, acid, or solvate thereof. Such a compound may be a substrate for glucosylceramidase beta 2 (GBA2), such that the compound is a prodrug that provides an active compound after cleavage of the monosaccharide or disaccharide by GBA2. Also provided are pharmaceutical compositions comprising the compounds, as well as various medical and non-medical uses of the compounds and compositions.

Abstract: The present invention provides a VOPRF protocol for post-quantum security, and a method and system for anonymous token authentication. The VOPRF protocol includes: a public parameter setting phase, an encoding and request phase, a re-encoding and response phase, and a decoding phase. The method for anonymous token authentication includes: an initialization phase: generating, by a trusted third-party, a common reference string; a key generation phase; a signing phase, executing jointly, by a client and a server, the VOPRF protocol to generate an anonymous token; and a token redemption phase: extracting, by the client, a token from a local list, and sending the token to the server for verification. The present invention implements the VOPRF protocol for post-quantum security and the method and system for anonymous token authentication, to still ensure security of an algorithm and a protocol under the threat of strong computing power of a quantum computer.

Abstract: Disclosed are a three-dimensional modeling system and modeling method based on multimodal fusion. The method includes: separately collecting feedback data of an electroencephalogram sensor, an electromyography sensor, an eye movement sensor, a gesture sensor, and a voice sensor, conducting multimodal fusion on the feedback data, obtaining multimodal-fused model data, matching the model data with a database instruction, obtaining and analyzing an instruction set, obtaining and identifying a relevant modeling parameter, obtaining a modeling method, automatically conducting modeling according to the modeling method, and obtaining a visual entity model.

Abstract: Various techniques for LLM powered detection reasoning solutions are disclosed. In some embodiments, a system, a process, and/or a computer program product for an LLM powered detection reasoning solution includes monitoring network traffic at a security platform, wherein the security platform generates a sample based on the monitored network traffic; sending the sample to a security service to generate a Large Language Model (LLM) powered detection and reason, wherein the LLM is prompted to automatically generate a malware or benign verdict and a reason for explaining the verdict; and reporting the LLM powered detection and reason.

Abstract: A security feed normalizer aggregates and normalizes threat intelligence data across security feeds and extracts threat descriptors of cybersecurity threats from the aggregated/normalized data. A first large language model (LLM) determines whether each threat descriptor is informative, i.e., comprises sufficient information for reproducing/generating network traffic of the corresponding cybersecurity threat. For informative threat descriptors, a second LLM generates network traffic for the corresponding cybersecurity threats. The generated network traffic is used for subsequent remediation of corresponding threats.

Abstract: A prompt generator receives descriptive data for a plug-in of a custom or open-source security testing tool. The prompt generator chooses a prompt template corresponding to the type of descriptive data and the security testing tool. For an open-source security testing tool, the prompt generator populates the prompt template with the descriptive data and prompts an open-source large language model (LLM) with the populated prompt to generate the plug-in for the open-source security testing tool. For a custom security testing tool, the prompt generator populates the prompt template with the descriptive data or an existing open source plugin, and prompts a custom LLM with the populated prompt to generate the plug-in for the custom security testing tool.

Abstract: According to one aspect of the present disclosure, a memory apparatus is provided. The memory apparatus may include a first memory die and a die group stacked in a first direction. The die group may include M second memory dies stacked in the first direction. Each of the second memory dies may be connected to a third memory die and configured to perform data exchange at a bandwidth of (N*X) bits with the third memory die through N data channels. The first memory die may be connected to the third memory die. The first memory die may be configured to perform data exchange at a bandwidth of (N*X) bits with the third memory die through N data channels. The first memory die may be configured to replace a first data channel of the die group when a storage portion corresponding to the first data channel fails.

Abstract: Disclosed is an intelligent interactive decision-making method for a discrete manufacturing system. The method includes the following steps: step 1, establishing a production scheduling optimization model and strategy for discrete manufacturing for an actual application scene; step 2, training the scheduling strategy with existing production data on the basis of a deep reinforcement learning algorithm, and storing a state having a high reward in a training process in a memory; step 3, updating the state according to prior knowledge in the memory; step 4, inputting the updated state into a deep reinforcement learning network, obtaining a corresponding reward, and updating the memory according to the reward; and step 5, repeating step 4 until model parameters converge, and saving and putting the model into an actual production scene.

Abstract: A system, method, and device for detecting Command and Control (C2) traffic is disclosed. The method includes (i) converting a monitored traffic sample to an image representation, (ii) querying a classifier based at least in part on the image representation to obtain a traffic classification, (iii) automatically detecting C2 traffic based at least in part on the traffic classification, and (iv) handling the network traffic based at least in part on the traffic classification.

Abstract: The present invention provides a VOPRF protocol for post-quantum security, and a method and system for anonymous token authentication. The VOPRF protocol includes: a public parameter setting phase, an encoding and request phase, a re-encoding and response phase, and a decoding phase. The method for anonymous token authentication includes: an initialization phase: generating, by a trusted third-party, a common reference string; a key generation phase; a signing phase, executing jointly, by a client and a server, the VOPRF protocol to generate an anonymous token; and a token redemption phase: extracting, by the client, a token from a local list, and sending the token to the server for verification. The present invention implements the VOPRF protocol for post-quantum security and the method and system for anonymous token authentication, to still ensure security of an algorithm and a protocol under the threat of strong computing power of a quantum computer.

Abstract: The present disclosure discloses a silicon wafer side edge detection device, comprising a first detection unit and a second detection unit, wherein the first detection unit and the second detection unit are of the same structure and are respectively configured to detect two mutually parallel side edges of a silicon wafer; and the first detection unit and the second detection unit each comprises a side edge detection assembly, the side edge detection assembly comprising a first point light source, a first optical path defining element, and a camera, wherein the first optical path defining element comprises a reflective structure and a slit, the reflective structure is configured to reflect light emitted by the first point light source and reflect the light to a side edge of a silicon wafer to be detected, and the light is reflected by the side edge of the silicon wafer.

Abstract: A stateful chatbot system leverages generative AI to provide an interface by which users can retrieve information from backend IoT databases of a security provider via natural language queries. Upon receiving a natural language query that corresponds to a request for information from the database, the chatbot generates a corresponding database query having a format compatible with the database. The chatbot comprises a generative model adapted to generate database queries based on natural language queries via prompt engineering using natural language and database query pairs. The chatbot queries the database with the generated database query, retrieves results comprising data/metadata that satisfy the query, and generates a summary of the results, both of which it presents as a response to the user's query. The chatbot also has access to a vulnerability database from which it can obtain information about known vulnerabilities documented therein to respond to user queries.

Abstract: An AI-based application uses AI models to expeditiously identify a product or service most relevant to an issue input to the application instead of a user navigating a platform or catalogue of products/services. The application prompts a first language model to convert the input description into a structured description of the issue. The application then, for each product/service category, prompts a second language model to identify a product/service most relevant to the structured description within each product/service category. The application then returns an identification of a product/service determined to be most relevant to the issue as represented by the structured description, confidence in the identification, and a reason the product/service was identified.

Abstract: An AI-based application uses AI models to expeditiously identify a product or service most relevant to an issue input to the application instead of a user navigating a platform or catalogue of products/services. The application prompts a first language model to convert the input description into a structured description of the issue. The application then, for each product/service category, prompts a second language model to identify a product/service most relevant to the structured description within each product/service category. The application then returns an identification of a product/service determined to be most relevant to the issue as represented by the structured description, confidence in the identification, and a reason the product/service was identified.

Abstract: A prompt injection attack can be used for data exfiltration. A security appliance can be programmed to monitor responses from an application that uses a generative AI model for uniform resource locators (URLs) that indicate a remote server. When a response is detected with a URL indicating a remote server, the security appliance determines whether the remote server is a suspicious server, which is a server not known to be benign and not known to be malicious. If deemed suspicious, the security appliance can block or hold the response to prevent possible data exfiltration.

Abstract: The present disclosure discloses a method for strengthening a biological manganese oxidation using a magnetic field and use thereof. The method includes steps of inoculating a manganese-oxidizing microorganism into a culture medium containing Mn2+, performing magnetization treatment in a culture process, and then collecting a biogenic manganese oxide. The method includes steps of performing a primary magnetic field treatment at a magnetic field intensity of 0.2-50 mT for 1-5 h when culturing is performed for 6-12 h, continuing culturing after the primary magnetization treatment, and performing magnetization treatment once every other 24 h for culture time of 72 h. A magnetic field is applied to accelerate an oxidation rate of a manganese-oxidizing microorganism to Mn2+ and a biological manganese oxidation rate is respectively improved by 36.4% and 23.8% under an action of an alternating magnetic field or a constant magnetic field within 72 h.

Abstract: A genetic algorithm is implemented to generate prompts that evade content filters of generative artificial intelligence (AI) systems. The genetic algorithm applies grammar operations to mutate candidate prompts, communicates the candidate prompts to generative AI systems, and selects candidate prompts that successfully evade content filters according to corresponding responses. A disambiguation model that corrects grammar in prompts is tested on the selected prompts to determine if grammar is properly corrected. Once tested, the disambiguation model is deployed in an ensemble with a classifier that outputs verdicts for prompts with grammar corrected by the disambiguation model.

Abstract: A system for delivery of liquid to and removal of liquid from a sample is provided. The system includes a primary device that contains the sample and at least one valve configured to allow liquid to be delivered to and removed from the sample; at least one reservoir that is configured to receive a volume of liquid; a secondary device including at least one port configured to receive the at least one reservoir and configured to be coupled to the primary device; and a tertiary device configured to receive the secondary device and comprising mechanical modules that deliver the volume of liquid from the at least one reservoir to the sample and remove the volume of liquid from the sample. Positioning the primary device in the secondary device causes the volume of liquid in the at least one reservoir to be delivered to the sample contained within the primary device.

Abstract: A modular adversarial training data generator (“generator”) generates adversarial training data for a machine learning (ML) model to detect malicious protocol data units (PDUs). The adversarial training data mimics high volume cyberattacks by perturbing PDUs to bypass malicious detection systems. For Hypertext Transfer Protocol (HTTP) PDUs, the generator rearranged, replaces, and grid searches values of HTTP header fields to generate the adversarial training data. The generator further biases grid search based on metrics for values of HTTP header fields that quantify impact of replacing the values on malicious verdicts by ML models trained on the adversarial training data.