Abstract: Methods and systems are presented for analyzing transactions conducted through user accounts with an online service provider based on a tube map structure. A transaction analysis system identifies a seed user account based on a set of risk factors, and generates a tube map based on a transaction flow originated from the seed user account. The tube map represents the transactions within the transaction flow using a multi-tier structure. The transaction analysis system disposes nodes representing different user accounts in different tiers of the tube map to illustrate when the transactions occur within the transaction flow. The transaction analysis system analyzes the transactions using the tube map to identify user accounts that likely involve in suspicious activities. One or more actions can be performed on the user accounts to improve the security of the online service provider.

Abstract: Disclosed is a preparation method for tricalcium silicate powder. The method includes the following steps: taking CaCO3 powder and SiO2 powder as raw materials for preparation, so as to obtain mixed powder having uniform components; pre-pressing the obtained mixed powder to make a blank; and performing sintering treatment on the obtained blank at a temperature of 1,200 DEG C-1,500 DEG C for 0.5 h-1.5 h, so as to obtain tricalcium silicate, where the sintering treatment is to perform mixed sintering by using auxiliary heating bodies and in cooperation with microwave treatment. According to the present disclosure, the tricalcium silicate is prepared through mixed sintering by using the auxiliary heating bodies and in cooperation with the microwave treatment, and a mixed heating mechanism enables a sample to be heated more easily at a low temperature, so that synthesis purity and efficiency are improved.

Abstract: Techniques are discussed for preparing and executing scanning plans for particular types of information, including personally identifiable information. A user indicates one or more datastores to be scanned for the particular type of information. A scanner determines scan objectives for the scanning plan and classifiers for use in scans conducted according to the scanning plan. The scanner estimates scan performance metrics and scan quality metrics. The scanner presents estimated results for the scanning plan based on the selected classifiers, scan objectives, estimate scan performance metrics, and estimated scan quality metrics. The user can modify the set of scan objectives or select between alternative sets of scan objectives. The scanning plan may be performed iteratively and the results of previous scan may be used to adjust classifiers or scan objectives to be used in subsequent scans.

Abstract: Methods and systems are presented for performing data scanning in a heterogenous data storage environment. A data scanning process on multiple different data storages may be performed based on a data scanning request, which specifies certain data of interest to be detected during the scanning. First, sampled data records are scanned from each of the data storages. Based on analyzing the data in the sampled data records and the metadata associated with each data storage, a fingerprint may be generated for each of the data storages that indicates a likelihood that the data storage includes the data of interest. A second scan may be performed on the data storage only if the fingerprint exceeds a threshold. The data of interest may be detected in the data storages during the second scan of the selected data storages.

Abstract: Methods and systems are presented for performing data scanning in a heterogenous data storage environment. A data scanning process on multiple different data storages may be performed based on a data scanning request, which specifies certain data of interest to be detected during the scanning. First, sampled data records are scanned from each of the data storages. Based on analyzing the data in the sampled data records and the metadata associated with each data storage, a fingerprint may be generated for each of the data storages that indicates a likelihood that the data storage includes the data of interest. A second scan may be performed on the data storage only if the fingerprint exceeds a threshold. The data of interest may be detected in the data storages during the second scan of the selected data storages.

Abstract: Methods and systems are presented for providing a computer platform that manages the impacts of government regulations on existing software processes of an online service provider. A regulation document is obtained from a government agency. The regulation document is processed, and legal obligations relevant to an online service provider are extracted from the regulation document. An ensemble machine learning model is used to recommend, for each of the legal obligations, software controls that can be implemented within one or more software processes of the online service provider to mitigate a risk of the legal obligations. The ensemble machine learning model may include an attribute-based model and a text-based model. An explainable visual interface is provided to present the recommended software controls and context that indicates to a user how the software controls are determined for the legal obligations.

Abstract: Methods and systems are presented for providing a computer platform that manages the impacts of government regulations on existing software processes of an online service provider. A regulation document is obtained from a government agency. The regulation document is processed, and legal obligations relevant to an online service provider are extracted from the regulation document. An ensemble machine learning model is used to recommend, for each of the legal obligations, software controls that can be implemented within one or more software processes of the online service provider to mitigate a risk of the legal obligations. The ensemble machine learning model may include an attribute-based model and a text-based model. An explainable visual interface is provided to present the recommended software controls and context that indicates to a user how the software controls are determined for the legal obligations.

Abstract: Methods and systems are presented for providing a computer platform that manages the impacts of government regulations on existing software processes of an online service provider. A regulation document is obtained from a government agency. The regulation document is processed, and legal obligations relevant to an online service provider are extracted from the regulation document. An ensemble machine learning model is used to recommend, for each of the legal obligations, software controls that can be implemented within one or more software processes of the online service provider to mitigate a risk of the legal obligations. The ensemble machine learning model may include an attribute-based model and a text-based model. An explainable visual interface is provided to present the recommended software controls and context that indicates to a user how the software controls are determined for the legal obligations.

Abstract: Methods and systems are presented for providing a computer platform that manages the impacts of government regulations on existing software processes of an online service provider. A regulation document is obtained from a government agency. The regulation document is processed, and legal obligations relevant to an online service provider are extracted from the regulation document. An ensemble machine learning model is used to recommend, for each of the legal obligations, software controls that can be implemented within one or more software processes of the online service provider to mitigate a risk of the legal obligations. The ensemble machine learning model may include an attribute-based model and a text-based model. An explainable visual interface is provided to present the recommended software controls and context that indicates to a user how the software controls are determined for the legal obligations.

Abstract: Techniques are discussed for preparing and executing scanning plans for particular types of information, including personally identifiable information. A user indicates one or more datastores to be scanned for the particular type of information. A scanner determines scan objectives for the scanning plan and classifiers for use in scans conducted according to the scanning plan. The scanner estimates scan performance metrics and scan quality metrics. The scanner presents estimated results for the scanning plan based on the selected classifiers, scan objectives, estimate scan performance metrics, and estimated scan quality metrics. The user can modify the set of scan objectives or select between alternative sets of scan objectives. The scanning plan may be performed iteratively and the results of previous scan may be used to adjust classifiers or scan objectives to be used in subsequent scans.

Abstract: Aspects of the present disclosure involve systems, methods, devices, and the like for fraudulent scheme detection. In one embodiment, a time-evolving graph-based solution is presented for the Ponzi scheme detection. For the solution, unbounded and time-based relational data is transformed to the time-evolving graph structure. Time-based aggregate metrics are computed and captured based in part on changes occurring within user accounts and transactions identified within the time-evolving graph structure. Then, with the aid of know pattern flows and the application of filtering rules, detection of such a fraudulent scheme may be accomplished.

Abstract: Aspects of the present disclosure involve, a customizable system and infrastructure which can receive privacy data from varying data sources for privacy scanning, containment, and reporting. In one embodiment, data received is scanned for privacy data extraction using various data connectors and decryption techniques. In another embodiment, the data extracted is transferred to a privacy scanning container where the data is analyzed by various deep learning models for the correct classification of the data. In some instances, the data extracted may be unstructured data deriving form emails, case memos, surveys, social media posts, and the like. Once the data is classified, the data may be stored or contained according to the classification of the data. Still in another embodiment, the classified data may be retrieved by an analytics container for use in reporting.

Abstract: Aspects of the present disclosure involve, a customizable system and infrastructure which can receive privacy data from varying data sources for privacy scanning, containment, and reporting. In one embodiment, data received is scanned for privacy data extraction using various data connectors and decryption techniques. In another embodiment, the data extracted is transferred to a privacy scanning container where the data is analyzed by various deep learning models for the correct classification of the data. In some instances, the data extracted may be unstructured data deriving form emails, case memos, surveys, social media posts, and the like. Once the data is classified, the data may be stored or contained according to the classification of the data. Still in another embodiment, the classified data may be retrieved by an analytics container for use in reporting.

Abstract: A method is provided that includes assigning one or more first records of a first table and one or more second records of a second table to corresponding range groups of a plurality of range groups. The method further includes comparing at least one record of the first table with at least one record of the second table, where the at least one record of the first table and the at least one record of the second table are assigned to a first range group of the plurality of range groups. The method also includes, based on the comparing, identifying a parent record and a child record and segmenting the parent record based on the child record. Further, the method includes subsequent to the segmenting the parent record, performing a join operation on the first table and the second table.

Abstract: Aspects of the present disclosure involve, a customizable system and infrastructure which can receive privacy data from varying data sources for privacy scanning, containment, and reporting. In one embodiment, data received is scanned for privacy data extraction using various data connectors and decryption techniques. In another embodiment, the data extracted is transferred to a privacy scanning container where the data is analyzed by various deep learning models for the correct classification of the data. In some instances, the data extracted may be unstructured data deriving form emails, case memos, surveys, social media posts, and the like. Once the data is classified, the data may be stored or contained according to the classification of the data. Still in another embodiment, the classified data may be retrieved by an analytics container for use in reporting.

Abstract: A method for preparing a pulp molded cup lid with buckles without overlapping curves on both surfaces, comprising: diluting plant fiber into pulp and placing it into a forming mold with a filter screen to form a paper embryo through vacuum adsorption; assembling the forming mold with a transfer mold under no pressure for vacuumization to obtain a paper embryo; transferring the paper embryo into a heat setting mold without mesh screen and assembling the mold under no pressure to obtain a cup lid body with stiffening ribs; then stamping into a finished cup lid. The cup lid has smooth internal and external surfaces without overlapping curves and no paper fiber dissolved out at the places where the internal surface and the liquid contact and the suction port and the labial part of the user contact; buckles improves the strength of the cup lid and the leakproofness.

Abstract: A method for preparing a pulp molded cup lid with buckles without overlapping curves on both surfaces, comprising: diluting plant fiber into pulp and placing it into a forming mold with a filter screen to form a paper embryo through vacuum adsorption; assembling the forming mold with a transfer mold under no pressure for vacuumization to obtain a paper embryo; transferring the paper embryo into a heat setting mold without mesh screen and assembling the mold under no pressure to obtain a cup lid body with stiffening ribs; then stamping into a finished cup lid. The cup lid has smooth internal and external surfaces without overlapping curves and no paper fiber dissolved out at the places where the internal surface and the liquid contact and the suction port and the labial part of the user contact; buckles improves the strength of the cup lid and the leakproofness.

Abstract: A process of manufacturing a pulp molded cup lid includes making a pulp slurry; distributing the pulp onto a continuous screen wherein the pulp slurry is converted into a blank; feeding the blank and the continuous screen onto a first blanking die; moving a first blanking punch onto the first blanking die to dry the blank at a temperature of 160-180° C. for 2-5 seconds; causing the first blanking punch to exert pressure of 27-40 N/cm2 on the blank for 8-20 seconds; conveying the blank onto a second blanking die which is disposed under a second blanking punch; drying the blank, the second blanking punch, and the second blanking die at a temperature of 160-180° C.; moving the second blanking punch onto the second blanking die in 2-8 seconds; and causing the second blanking punch to exert pressure of 80-120 N/cm2 on the blank for 30-45 seconds.