Abstract: A sample rack manipulation device (10), a testing system (1) and a testing method using the sample rack manipulation device (10), and a computer-readable medium implementing the testing method. The sample rack manipulation device (10) comprises: a loading/unloading zone (TA) in which a plurality of sample racks (50) carrying sample containers (51) containing samples can be arranged side by side; a sampling zone (TD, TE) in which samples in each sample container (51) on the sample racks (50) are sampled by an automatic testing apparatus; and a docking device (114) configured to transfer the sample racks (50) between the loading/unloading zone (TA) and the sampling zone (TD, TE). The docking device (114) is configured to be able to remove the sample racks (50) loaded in the loading/unloading zone (TA) in any order and to transfer the removed test sample racks (50) to the sampling zone (TD, TE) for sampling.

Abstract: Embodiments of this specification provide account opening methods, systems, and apparatuses. An example method is performed by a terminal device that includes a trusted execution environment (TEE) and an encryption hardware-based secure element. When it is detected that a current user initiates an account opening request, the terminal device obtains a user public key of the current user in the TEE, and signs a user public key by using a predetermined manufacturer private key, to obtain a user certificate; signs, in the secure element by using a user private key corresponding to the user public key, opening information; generates an opening request in the TEE based on at least the user certificate and signed opening information; and, in the TEE, generates an opening request packet based on the opening request, and sends the opening request packet to a server that opens an account based on the opening request packet.

Abstract: A reshaping method for metal product includes acquiring position data information of a workpiece; converting the position data information into coordinate information, and fitting the coordinate information to obtain a surface contour curve of the workpiece; comparing the surface contour curve with a standard contour curve to generate a comparison result; obtaining reshaping information of the workpiece based on the comparison result; and controlling the reshaping system to reshape the workpiece. The disclosure acquires position data information of the workpiece by measuring, converts the position data information into coordinate information, fits the coordinate information to obtain the surface contour curve of the workpiece, compares the surface contour curve with the standard contour curve to obtain deformation situation, and reshapes the workpiece according to the reshaping information based on the deformation situation, which improves efficiency of workpiece reshaping and reducing labor costs.

Abstract: Provided are a method and apparatus for evaluating a degree of frequency regulation urgency of a generator set, a power system and a storage medium.

Abstract: A sample is analyzed to determine a set of events that should be selected for performing by a dynamic analyzer executing the sample in an instrumented, emulated environment. The set of selected events is performed. In some cases, at least one emulator detection resistance action is performed. A maliciousness verdict is determined for the sample based at least in part on one or more responses taken by the sample in response to the set of selected events being performed by the dynamic analyzer.

Abstract: Execution of an application in an application-level sandbox is disclosed. A request to launch an application is received by an operating system executing on a device. A determination is made that a stored copy of the application should be executed within an application-level sandbox. The stored copy of the application is executed in the application-level sandbox.

Abstract: A set of metadata associated with a plurality of samples is received. The samples are clustered. For members of a first cluster, a set of similarities shared among at least a portion of the members of the first cluster is determined. A cluster member is identified within the first cluster, and in response, additional analysis is caused to be performed on the outlier cluster member.

Abstract: Execution of an application in an application-level sandbox is disclosed. A request to launch an application is received by an operating system executing on a device. A determination is made that a stored copy of the application should be executed within an application-level sandbox. The stored copy of the application is executed in the application-level sandbox.

Abstract: The automatic generation of malware family signatures is disclosed. A set of metadata associated with a plurality of samples is received. The samples are clustered. For members of a first cluster, a set of similarities shared among at least a portion of the members of the first cluster is determined. The similarities are evaluated for suitability as a malware family signature. Suitability is evaluated based on how well the similarities uniquely identify the members of the first cluster. In the event the similarities are determined to be suitable as a malware family signature, a signature is generated.

Abstract: A sample is analyzed to determine a set of events that should be selected for performing by a dynamic analyzer executing the sample in an instrumented, emulated environment. In some cases, analyzing the sample includes extracting the sample's user interface layout into a tree hierarchy of user interface elements. The set of selected events is performed. In some cases, at least one emulator detection resistance action is performed. A maliciousness verdict is determined for the sample based at least in part on one or more responses taken by the sample in response to the set of selected events being performed by the dynamic analyzer.

Abstract: A sample is analyzed to determine a set of events that should be selected for performing by a dynamic analyzer executing the sample in an instrumented, emulated environment. The set of selected events is performed. In some cases, at least one emulator detection resistance action is performed. A maliciousness verdict is determined for the sample based at least in part on one or more responses taken by the sample in response to the set of selected events being performed by the dynamic analyzer.

Abstract: Disclosed is phishing classifier that classifies a URL and content page accessed via the URL as phishing or not is disclosed, with URL feature hasher that parses and hashes the URL to produce feature hashes, and headless browser to access and internally render a content page at the URL, extract HTML tokens, and capture an image of the rendering. Also disclosed are an HTML encoder, trained on HTML tokens extracted from pages at URLs, encoded, then decoded to reproduce images captured from rendering, that produces an HTML encoding of the tokens extracted, and an image embedder, pretrained on images, that produces an image embedding of the image captured. Further, phishing classifier layers, trained on the feature hashes, the HTML encoding, and the image embedding, process the URL feature hashes, HTML encoding and image embeddings to produce a likelihood score that the URL and the page accessed presents a phishing risk.

Abstract: A virtualized storage for use in performing dynamic analysis on a sample is configured, at least in part by copying the sample to the virtualized storage. A virtual machine emulator is launched using a snapshot of a virtualized platform. The virtualized platform is previously configured to use the virtualized storage, and the snapshot is configured to use a placeholder file to occupy space for later use when installing the sample. A location of the copied sample in an image corresponding to the virtualized storage is determined. The copied sample is installed and dynamic analysis is performed on the sample.

Abstract: A railway self-generating express freight car bogie has a wheel-axle assembly, frame, journal box suspension device, bolster, foundation brake, side bearing, empty/load auto-adjustment equipment, central suspension device, longitudinal traction device, vertical damper and lateral damper. An axle-end generator is connected at the outer end of an axle of the wheel-axle assembly, the rotor part of the axle-end generator is connected to the end bearing of the wheel-axle assembly by the axle-end bolts, and the stator part is connected to the mount of the journal box suspension devices. The bogie is designed according to the zero-wear concept, and a large number of rubber components and hydraulic dampers are used to improve the lateral stability, linear stability and curve passing performance of the car, so that the bogie is highly adaptable to railway lines, and can meet the operating requirements within 160 km/h and reduce the workload of bogie operation and maintenance.

Abstract: Systems and methods are disclosed for non-invasively measuring blood glucose levels in a biological sample based on spectral data. This includes utilizing at least one light source configured to strike a target area of a sample, utilizing at least one light filter positioned to receive light transmitted through the target area of the sample from the at least one light source, utilizing at least one light detector positioned to receive light from the at least one light source and filtered by the at least one light filter, and to generate an output signal, having a time dependent current, which is indicative of the power of light detected, receiving the output signal from the at least one light detector with a processor, calculating the attenuance attributable to blood with a ratio factor based on the received output signal, and determining a blood glucose level based on the calculated attenuance.

Abstract: Disclosed is classifying a URL and a page accessed via the URL as phishing or not. URL embedder extracts characters in a predetermined set from the URL to produce a character string trained using ground truth classification of the URL, producing a URL embedding. HTML parser accesses content at the URL and extracts HTML tokens from the page. Further, HTML encoder, trained on HTML tokens extracted from pages at example URLs, each example URL accompanied by a ground truth image captured from the page accessed via the example URL, produces an HTML encoding of the extracted tokens. Also, phishing classifier layers, trained on the URL embedding and the HTML encoding of example URLs, processes a concatenated input of the URL embedding and the HTML encoding to produce a score of a phishing risk.

Abstract: Disclosed is classifying a URL and a content page accessed via the URL as phishing or not. URL embedder extracts characters in a predetermined set from the URL to produce a character string trained using ground truth classification of the URL, producing a URL embedding. HTML parser accesses content at the URL and extracts HTML tokens from the content page. Further, HTML encoder, trained on HTML tokens extracted from content pages at example URLs, each example URL accompanied by a ground truth image captured from the content page accessed via the example URL, produces an HTML encoding of the tokens extracted from the page. Also, phishing classifier layers, trained on the URL embedding and the HTML encoding of example URLs, processes a concatenated input of the URL embedding and the HTML encoding to produce a likelihood score that the URL and content accessed via the URL presents a phishing risk.

Abstract: Disclosed is phishing classifier that classifies a URL and content page accessed via the URL as phishing or not is disclosed, with URL feature hasher that parses and hashes the URL to produce feature hashes, and headless browser to access and internally render a content page at the URL, extract HTML tokens, and capture an image of the rendering. Also disclosed are an HTML encoder, trained on HTML tokens extracted from pages at URLs, encoded, then decoded to reproduce images captured from rendering, that produces an HTML encoding of the tokens extracted, and an image embedder, pretrained on images, that produces an image embedding of the image captured. Further, phishing classifier layers, trained on the feature hashes, the HTML encoding, and the image embedding, process the URL feature hashes, HTML encoding and image embeddings to produce a likelihood score that the URL and the page accessed presents a phishing risk.