Abstract: Aspects and embodiments herein pertain to global ML prediction applied in a data security context. In some examples, a global prediction pertaining to a data item as a whole (the ‘original’ data item) is generated using an ML predictor. A data element is masked by removing it from the data item, or obscuring it in some way (e.g., through modification or replacement). The same ML predictor is applied to the perturbed data item. A change in global confidence between the original data item and the perturbed data item denotes local significance of the individual data item (or subset of data items) that have been masked. Other significance indicators (such as start and end markers of significant sections) are also considered. Other methods of estimating local significance are considered. In some examples, one or more such methods are used to generate training data for training a self-interpretation ML model.

Abstract: The technology described herein determines whether a candidate text is in a requested class by using a generative model that may not be trained on the requested class. The present technology may use of a model trained primarily in an unsupervised mode, without requiring a large number of manual user-input examples of a label class. The may produce a semantically rich positive example of label text from a candidate text and label. Likewise, the technology may produce from the candidate text and the label a semantically rich negative example of label text. The labeling service makes use of a generative model to produce a generative result, which estimates the likelihood that the label properly applies to the candidate text. In another aspect, the technology is directed toward a method for obtaining a semantically rich example that is similar to a candidate text.

Abstract: The technology described herein determines whether a candidate text is in a requested class by using a generative model that may not be trained on the requested class. The present technology may use of a model trained primarily in an unsupervised mode, without requiring a large number of manual user-input examples of a label class. The may produce a semantically rich positive example of label text from a candidate text and label. Likewise, the technology may produce from the candidate text and the label a semantically rich negative example of label text. The labeling service makes use of a generative model to produce a generative result, which estimates the likelihood that the label properly applies to the candidate text. In another aspect, the technology is directed toward a method for obtaining a semantically rich example that is similar to a candidate text.

Abstract: Methods, systems, and computer storage media for providing a multi-attribute cluster-identifier that supports identifying malicious activity in computing environments. An instance of an activity having an attribute set can be assessed. The attribute set of the instance of the activity is analyzed to determine whether the instance of the activity is a malicious activity. The attribute set of the instance of the activity is compared to a plurality of multi-attribute cluster-identifiers of previous instances of the activity, such that, a determination that the instance of the activity is a malicious activity is made when the attribute set of the instance of the activity corresponds to an identified multi-attribute cluster-identifier. The identified multi-attribute cluster-identifier has a risk score and an attribute set that indicate a likelihood that the instance of the activity is a malicious activity. A visualization that identifies the instance of the activity as a malicious activity is generated.

Abstract: Methods, systems, and computer storage media for providing a multi-attribute cluster-identifier that supports identifying malicious activity in computing environments. An instance of an activity having an attribute set can be assessed. The attribute set of the instance of the activity is analyzed to determine whether the instance of the activity is a malicious activity. The attribute set of the instance of the activity is compared to a plurality of multi-attribute cluster-identifiers of previous instances of the activity, such that, a determination that the instance of the activity is a malicious activity is made when the attribute set of the instance of the activity corresponds to an identified multi-attribute cluster-identifier. The identified multi-attribute cluster-identifier has a risk score and an attribute set that indicate a likelihood that the instance of the activity is a malicious activity. A visualization that identifies the instance of the activity as a malicious activity is generated.

Abstract: The technology described herein visibly depicts hidden message traits to help users determine whether an email is genuine or deceptive. The hidden message traits are revealed by identifying and changing attributes that keep the hidden traits from being displayed in a rendered message. Spam messages, phishing messages, and messages that include or link to malicious programs (e.g., malware, ransomware) are examples of unwanted messages that can harm a recipient. These messages often rely on deception to get past email filtering systems and to trick a user into acting on content in a message. The deception often involves including hidden traits in a message that fool an automated filtering system. The technology described herein shows the visible traits to a user by including them in the rendered version of the message.

Abstract: A structure of a two-dimensional material-based device having an air-gap and a method for preparing same comprises a substrate, a gate, an dielectric, a two-dimensional material-based film, and source and drain electrodes; the air-gap is formed between the two-dimensional material-based film and the side wall of the dielectric on the gate, or the air-gap is formed between the dielectric and the gate; two-dimensional material-based devices of the above structure utilize the ductility and flexibility of two-dimensional materials to reduce the coupling between the gate and the source/drain electrodes, thereby reducing the parasitic capacitance and the circuit delay; also, the structure ensures that a part of the contact region of the source and drain electrodes with the two-dimensional material can be controlled by the gate, and can thus avoid the reduction in electrical properties and keep a low resistance of the device.

Abstract: Methods, systems, and computer storage media for providing a multi-attribute cluster-identifier that supports identifying malicious activity in computing environments. An instance of an activity having an attribute set can be assessed. The attribute set of the instance of the activity is analyzed to determine whether the instance of the activity is a malicious activity. The attribute set of the instance of the activity is compared to a plurality of multi-attribute cluster-identifiers of previous instances of the activity, such that, a determination that the instance of the activity is a malicious activity is made when the attribute set of the instance of the activity corresponds to an identified multi-attribute cluster-identifier. The identified multi-attribute cluster-identifier has a risk score and an attribute set that indicate a likelihood that the instance of the activity is a malicious activity. A visualization that identifies the instance of the activity as a malicious activity is generated.

Abstract: Aspects of this disclosure include fuzzy inclusion based impersonation detection technology. In one embodiment, a reverse n-gram map is created for the list of protected entities. A suspicious string may be broken into n-grams, and each n-gram of the suspicious string is to be searched in the reverse n-gram map for corresponding protected entities. A fuzzy inclusion of a protected entity may be detected in the suspicious string depending on the protected entities found during the search. Subsequently, impersonation can be identified based on the characteristics of the fuzzy inclusion. In this way, the communication system can detect impersonation techniques using visually similar text, and accordingly take various actions to help user mitigate risks caused by impersonation.

Abstract: The technology described herein determines whether a candidate text is in a requested class by using a generative model that may not be trained on the requested class. The present technology may use of a model trained primarily in an unsupervised mode, without requiring a large number of manual user-input examples of a label class. The may produce a semantically rich positive example of label text from a candidate text and label. Likewise, the technology may produce from the candidate text and the label a semantically rich negative example of label text. The labeling service makes use of a generative model to produce a generative result, which estimates the likelihood that the label properly applies to the candidate text. In another aspect, the technology is directed toward a method for obtaining a semantically rich example that is similar to a candidate text.

Abstract: It is a two-dimensional semiconductor-metal ohmic-contact structure, a preparation method therefor and use thereof, wherein the ohmic-contact structure comprises two-dimensional semiconductor having semimetal antimony or an alloy containing semimetal antimony deposited thereon to form an ideal van der Waals interface with strong orbital hybridization, and semimetal antimony is deposited on the two-dimensional semiconductor by high-vacuum evaporation; which is applied to semiconductor devices and realizes ultralow contact resistance between the metal and the two-dimensional semiconductor and significantly improve the performance of two-dimensional semiconductor devices.

Abstract: The technology described herein visibly depicts hidden message traits to help users determine whether an email is genuine or deceptive. The hidden message traits are revealed by identifying and changing attributes that keep the hidden traits from being displayed in a rendered message. Spam messages, phishing messages, and messages that include or link to malicious programs (e.g., malware, ransomware) are examples of unwanted messages that can harm a recipient. These messages often rely on deception to get past email filtering systems and to trick a user into acting on content in a message. The deception often involves including hidden traits in a message that fool an automated filtering system. The technology described herein shows the visible traits to a user by including them in the rendered version of the message.

Abstract: Embodiments of the present invention relate to a method for controlling temperature of a terminal, including: receiving, by the terminal, an instruction for triggering execution of a first task; obtaining, by the terminal, a current temperature of the terminal; and controlling, by the terminal, an execution manner of the first task based on the current temperature, to control temperature of the terminal. When the terminal obtains the current temperature of the terminal, the terminal may control, based on the current temperature of the terminal, the first task to be executed in different manners, so as to control temperature of the terminal and ensure that the mobile phone is in a safe state.

Abstract: The technology described herein visibly depicts hidden message traits to help users determine whether an email is genuine or deceptive. The hidden message traits are revealed by identifying and changing attributes that keep the hidden traits from being displayed in a rendered message. Spam messages, phishing messages, and messages that include or link to malicious programs (e.g., malware, ransomware) are examples of unwanted messages that can harm a recipient. These messages often rely on deception to get past email filtering systems and to trick a user into acting on content in a message. The deception often involves including hidden traits in a message that fool an automated filtering system. The technology described herein shows the visible traits to a user by including them in the rendered version of the message.

Abstract: Methods, systems, and computer storage media for providing a multi-attribute cluster-identifier that supports identifying malicious activity in computing environments. An instance of an activity having an attribute set can be assessed. The attribute set of the instance of the activity is analyzed to determine whether the instance of the activity is a malicious activity. The attribute set of the instance of the activity is compared to a plurality of multi-attribute cluster-identifiers of previous instances of the activity, such that, a determination that the instance of the activity is a malicious activity is made when the attribute set of the instance of the activity corresponds to an identified multi-attribute cluster-identifier. The identified multi-attribute cluster-identifier has a risk score and an attribute set that indicate a likelihood that the instance of the activity is a malicious activity. A visualization that identifies the instance of the activity as a malicious activity is generated.

Abstract: Disclosed is a process for producing biomass fiber molding material, and relates to the technical field of biological fibers. In the present disclosure, pretreated dried leaf fibers are softened by being subjected to a soaking process; the leaf fibers are grounded through a high-concentration grinder to obtain primary pulp of biomass fiber molding material; and then excess water is removed from the primary pulp of biomass fiber molding material through a pulp squeezing thickener to obtain the biomass fiber molding material. The primary pulp of biomass fiber molding material obtained by the production process of the present disclosure meets the specification for manufacturing safe paper, water is recycled in the production process to reduce water resource consumption; and the adding of auxiliary agents is reduced, which can reduce the cleaning of the product and improve the utilization rate of the leaf fibers.

Abstract: Implementations of the disclosure provide for methods, systems, and machine-readable media for image processing. In some embodiments, a method for image processing includes: generating, by a processor, a first histogram and a first parameter set for a first image; constructing, by the processor, a model representative of a second histogram of a second image based on an adjustment parameter set and the first parameter set; generating the second histogram based on the model; and generating a second image based on the first image and the second histogram. In some embodiments, the first histogram is a two-dimensional histogram.

Abstract: The technology described herein visibly depicts hidden message traits to help users determine whether an email is genuine or deceptive. The hidden message traits are revealed by identifying and changing attributes that keep the hidden traits from being displayed in a rendered message. Spam messages, phishing messages, and messages that include or link to malicious programs (e.g., malware, ransomware) are examples of unwanted messages that can harm a recipient. These messages often rely on deception to get past email filtering systems and to trick a user into acting on content in a message. The deception often involves including hidden traits in a message that fool an automated filtering system. The technology described herein shows the visible traits to a user by including them in the rendered version of the message.

Abstract: Aspects of this disclosure include fuzzy inclusion based impersonation detection technology. In one embodiment, a reverse n-gram map is created for the list of protected entities. A suspicious string may be broken into n-grams, and each n-gram of the suspicious string is to be searched in the reverse n-gram map for corresponding protected entities. A fuzzy inclusion of a protected entity may be detected in the suspicious string depending on the protected entities found during the search. Subsequently, impersonation can be identified based on the characteristics of the fuzzy inclusion. In this way, the communication system can detect impersonation techniques using visually similar text, and accordingly take various actions to help user mitigate risks caused by impersonation.

Abstract: Embodiments of the present invention relate to a method for controlling temperature of a terminal, including: receiving, by the terminal, an instruction for triggering execution of a first task; obtaining, by the terminal, a current temperature of the terminal; and controlling, by the terminal, an execution manner of the first task based on the current temperature, to control temperature of the terminal. When the terminal obtains the current temperature of the terminal, the terminal may control, based on the current temperature of the terminal, the first task to be executed in different manners, so as to control temperature of the terminal and ensure that the mobile phone is in a safe state.