Abstract: A nucleic acid extraction composition, reagents and kits containing the same and uses thereof. Provided is a nucleic acid extraction and purification reagent free of volatile organic solvents, which prevents the damage of volatile organic solvents to people and greatly improves the timeliness of nucleic acid extraction and purification, making the operation extremely simple, and the nucleic acid can be obtained within 10 minutes. The obtained nucleic acid may be used for biological reactions such as PCR, NASBA, LAMP and RPA. Moreover, the reagents of the present disclosure may be used to extract nucleic acids of cells, bacteria, fungi, DNA viruses and RNA viruses from various samples such as blood, throat swab preserving fluid, saliva, urine, sputum, excrement and the like, and very suitable for clinical and scientific research uses.

Abstract: Conventional email filtering services are not suitable for recognizing sophisticated malicious emails, and therefore may allow sophisticated malicious emails to reach inboxes by mistake. Introduced here are threat detection platforms designed to take an integrative approach to detecting security threats. For example, after receiving input indicative of an approval from an individual to access past email received by employees of an enterprise, a threat detection platform can download past emails to build a machine learning (ML) model that understands the norms of communication with internal contacts (e.g., other employees) and/or external contacts (e.g., vendors). By applying the ML model to incoming email, the threat detection platform can identify security threats in real time in a targeted manner.

Abstract: Access to emails delivered to an employee of an enterprise is received. An incoming email addressed to the employee is acquired. A primary attribute is extracted from the incoming email by parsing at least one of: (1) content of the incoming email or (2) metadata associated with the incoming email. It is determined whether the incoming email deviates from past email activity, at least in part by determining, as a secondary attribute, a mismatch between a previous value for the primary attribute and a current value for the primary attribute, using a communication profile associated with the employee, and providing a measured deviation to at least one machine learning model.

Abstract: It is determined that a first email is present in a mailbox where emails deemed suspicious are placed for analysis. In response to determining that the first email is present in the mailbox, it is determined whether the first email is representative of a threat to an enterprise based at least in part by applying a trained model to the first email. In response to determining that the first email represents a threat to the enterprise, a record of the threat is generated by populating a data structure with information related to the first email. The data structure is applied to inboxes of a plurality of the employees to determine whether the first email is part of a campaign. In response to determining that the first email is part of a campaign, a filter associated with the data structure is applied to inbound emails addressed to employees of the enterprise.

Abstract: A message addressed to a user is received. A first model is applied to the message to produce a first output indicative of whether the message is representative of a non-malicious message. The first model is trained using past messages that have been verified as non-malicious messages. It is determined, based on the first output, that the message is potentially a malicious message. Responsive to determining that the message is potentially a malicious email based on the first output, apply a second model to the message to produce a second output indicative of whether the message is representative of a given type of attack. The second model is one of a plurality of models. At least one model included in the plurality of models is associated with characterizing a goal of the malicious message. An action is performed with respect to the message based on the second output.

Abstract: The present invention discloses a light detection and ranging (LIDAR) system ranging method, including: circularly allocating, by a light routing device, each periodic signal of a transmit signal to each light channel in a chronological order, monitoring a beat signal or a returned light pulse signal in each light channel, and calculating a target distance according to a frequency of the beat signal or a return delay time of the light pulse signal. The present invention further discloses a LIDAR, including: a laser source, a light routing device, an optical scanning system, a light detector, and a data processing module. A quantity of detection points per second of each beam is increased to N times, where N is a quantity of channels of the light routing device, so as to improve the detection efficiency and reduce the requirement on transmit resources; and a scanning mode and an angular resolution can be dynamically controlled according to needs.

Abstract: Conventional email filtering services are not suitable for recognizing sophisticated malicious emails, and therefore may allow sophisticated malicious emails to reach inboxes by mistake. Introduced here are threat detection platforms designed to take an integrative approach to detecting security threats. For example, after receiving input indicative of an approval from an individual to access past email received by employees of an enterprise, a threat detection platform can download past emails to build a machine learning (ML) model that understands the norms of communication with internal contacts (e.g., other employees) and/or external contacts (e.g., vendors). By applying the ML model to incoming email, the threat detection platform can identify security threats in real time in a targeted manner.

Abstract: Introduced here are computer programs and computer-implemented techniques for discovering malicious emails and then remediating the threat posed by those malicious emails in an automated manner. A threat detection platform may monitor a mailbox to which employees of an enterprise are able to forward emails deemed to be suspicious for analysis. This mailbox may be referred to as an “abuse mailbox” or “phishing mailbox.” The threat detection platform can examine emails contained in the abuse mailbox and then determine whether any of those emails represent threats to the security of the enterprise. For example, the threat detection platform may classify each email contained in the abuse mailbox as being malicious or non-malicious. Thereafter, the threat detection platform may determine what remediation actions, if any, are appropriate for addressing the threat posed by those emails determined to be malicious.

Abstract: Selectively rewriting URLs is disclosed. An indication is received that a message has arrived at a user message box. A determination is made that the message includes a first link to a first resource. The first link is analyzed to determine whether the first link is classified as a non-rewrite link. In response to determining that the first link is not classified as a non-rewrite link, a first replacement link is generated for the first link.

Abstract: Conventional email filtering services are not suitable for recognizing sophisticated malicious emails, and therefore may allow sophisticated malicious emails to reach inboxes by mistake. Introduced here are threat detection platforms designed to take an integrative approach to detecting security threats. For example, after receiving input indicative of an approval from an individual to access past email received by employees of an enterprise, a threat detection platform can download past emails to build a machine learning (ML) model that understands the norms of communication with internal contacts (e.g., other employees) and/or external contacts (e.g., vendors). By applying the ML model to incoming email, the threat detection platform can identify security threats in real time in a targeted manner.

Abstract: Conventional email filtering services are not suitable for recognizing sophisticated malicious emails, and therefore may allow sophisticated malicious emails to reach inboxes by mistake. Introduced here are threat detection platforms designed to take an integrative approach to detecting security threats. For example, after receiving input indicative of an approval from an individual to access past email received by employees of an enterprise, a threat detection platform can download past emails to build a machine learning (ML) model that understands the norms of communication with internal contacts (e.g., other employees) and/or external contacts (e.g., vendors). By applying the ML model to incoming email, the threat detection platform can identify security threats in real time in a targeted manner.

Abstract: An approach is provided in which the approach captures a first user activity log of a first user accessing multiple systems and captures a set of second user activity logs of a set of second users accessing the multiple systems. The approach determines a set of system monitoring preferences based the first user activity log and the set of second user activity logs, and scores the multiple systems based on the set of system monitoring preferences. The approach generates a recommended system monitoring list based on the scored multiple systems, and transmits the recommended system monitoring list to the first user.

Abstract: Deriving and surfacing insights regarding security threats is disclosed. A plurality of features associated with a message is determined. A plurality of facet models is used to analyze the determined features. Based at least in part on the analysis, it is determined that the message poses a security threat. A prioritized set of information is determined to be provided as output that is representative of why the message was determined to pose a security threat. At least a portion of the prioritized set of information is provided as output.

Abstract: An embodiment includes detecting an interface element and an element attribute of the interface element in a series of views of a user interface, and then after an update of the user interface, detecting a candidate element and a candidate element attribute in a series of views of the updated user interface. The embodiment then determines that the updated user interface lacks any errors using a decision tree that includes comparisons of all interface elements of the user interface to corresponding candidate elements of the updated user interface. The embodiment then generates an optimized decision tree based at least in part on an analysis of the comparisons of the user interface to the updated user interface resulting in a condition that allows for the determining of a lack of errors based on comparisons of a subset of the interface elements to corresponding candidate elements.

Abstract: Selectively rewriting URLs is disclosed. An indication is received that a message has arrived at a user message box. A determination is made that the message includes a first link to a first resource. The first link is analyzed to determine whether the first link is classified as a non-rewrite link. In response to determining that the first link is not classified as a non-rewrite link, a first replacement link is generated for the first link.

Abstract: Deriving and surfacing insights regarding security threats is disclosed. A plurality of features associated with a message is determined. A plurality of facet models is used to analyze the determined features. Based at least in part on the analysis, it is determined that the message poses a security threat. A prioritized set of information is determined to be provided as output that is representative of why the message was determined to pose a security threat. At least a portion of the prioritized set of information is provided as output.

Abstract: An approach is provided in which the approach captures a first user activity log of a first user accessing multiple systems and captures a set of second user activity logs of a set of second users accessing the multiple systems. The approach determines a set of system monitoring preferences based the first user activity log and the set of second user activity logs, and scores the multiple systems based on the set of system monitoring preferences. The approach generates a recommended system monitoring list based on the scored multiple systems, and transmits the recommended system monitoring list to the first user.

Abstract: A method for receiving an audiovisual data set that includes audio and/or visual content, receiving a topical data set that includes an indication of, at least, the plurality of topics and the plurality of subtopics, receiving a time mapping data set that maps each topic and subtopic to an associated time point in the audio and/or visual content, creating a mind map diagram, and presenting the mind map to a user.

Abstract: Deriving and surfacing insights regarding security threats is disclosed. A plurality of features associated with a message is determined. A plurality of facet models is used to analyze the determined features. Based at least in part on the analysis, it is determined that the message poses a security threat. A prioritized set of information is determined to be provided as output that is representative of why the message was determined to pose a security threat. At least a portion of the prioritized set of information is provided as output.

Abstract: Conventional email filtering services are not suitable for recognizing sophisticated malicious emails, and therefore may allow sophisticated malicious emails to reach inboxes by mistake. Introduced here are threat detection platforms designed to take an integrative approach to detecting security threats. For example, after receiving input indicative of an approval from an individual to access past email received by employees of an enterprise, a threat detection platform can download past emails to build a machine learning (ML) model that understands the norms of communication with internal contacts (e.g., other employees) and/or external contacts (e.g., vendors). By applying the ML model to incoming email, the threat detection platform can identify security threats in real time in a targeted manner.