Abstract: Systems and methods for malware detection using multiple neural networks are provided. According to one embodiment, for each training sample, a supervised learning process is performed, including: (i) generating multiple code blocks of assembly language instructions by disassembling machine language instructions contained within the training sample; (ii) extracting dynamic features corresponding to each of the code blocks by executing each of the code blocks within a virtual environment; (iii) feeding each code block into a first neural network and the corresponding dynamic features into a second neural network; (iv) updating weights and biases of the neural networks based on whether the training sample was malware or benign; and (v) after processing a predetermined or configurable number of the training samples, the neural networks criticize each other and unify their respective weights and biases by exchanging their respective weights and biases and adjusting their respective weights and biases accordingly.

Abstract: A drive system with both fixed-displacement hydraulic motors and variable-displacement hydraulic motors for a cutter head of a boring machine and a control method thereof are provided. The drive system includes a variable-displacement hydraulic motor group, a fixed-displacement hydraulic motor group and a variable-displacement hydraulic pump group. The variable-displacement hydraulic motor group, the fixed-displacement hydraulic motor group, and the variable-displacement hydraulic pump group are all connected to a main oil circuit of a cutter head system of the boring machine. The variable-displacement hydraulic pump group inputs flow to the main oil circuit; and, the variable-displacement hydraulic motor group and the fixed-displacement hydraulic motor group acquire flow from the main oil circuit. Displacements of the fixed-displacement hydraulic motor group and the variable-displacement hydraulic motor group are set in a way of specific displacement combination.

Abstract: A method for synthesizing a ketoxime is provided. In a system of an aqueous carbonate solution, a reaction is performed on a ketone, ammonia and hydrogen peroxide by using a titanium-silicon molecular sieve as a catalyst to obtain the ketoxime. Moreover, a reaction progress is judged and an optimal reaction ratio is determined by a real-time monitoring of a pH value in a reaction system during the reaction. In the present invention, by monitoring the pH value in the reaction system, the progress of the reaction is judged, thereby determining the optimal reaction ratio. The pH of the system is further adjusted by an aqueous carbonate solution to increase the reaction velocity and conversion rate of the ammonia.

Abstract: Disclosed are a pH-responsive amphoteric surfactant N-lauryl-(?-alkyl)-?-aminopropionic acid and a preparation method and uses thereof. An alkylamine and a solvent are added to a three-necked flask, stirred for complete dissolution, slowly dropwise added with methyl ?-alkyl-acrylate and reacted at room temperature after the ice-water bath is removed. The reaction mixture is refluxed in water bath and evaporated under rotation to remove the solvent and the residual methyl ?-alkyl-acrylateto give N-lauryl-(?-alkyl)-?-aminopropionic acid. Then N-lauryl-(?-alkyl)-?-aminopropionic acid and water are added to a three-necked flask, slowly dropwise added with a sodium hydroxide solution and reacted in water bath to give a crude product, which is filtered and dried to give a purified surfactant sodium N-lauryl-(?-alkyl)-?-aminopropionate. This surfactant is suitable for the foamed acid system and its performance can be switched by adjusting pH due to pH sensitivity.

Abstract: A human language analyzer receives, at the human language analyzer, text data representing information in a human language. The human language analyzer receives a computer command for identifying a text data component of the text data. The computer command comprises at least two requirements for the text data component. The human language analyzer, responsive to identifying that the first requirement and the second requirement is met, locates the text data component from one of two clauses. A clause analyzer receives a clause request to locate clauses within text data representing information in a human language. The clause analyzer receives, responsive to a dependency request, token information in a token data set. The clause analyzer determines a location for each clause of the sentence portion in a hierarchy of clauses. The clause analyzer generates and outputs a new data set based on the token data set and the hierarchy of clauses.

Abstract: Systems and methods are disclosed that facilitate obtaining two dimensional (2D) ultrasound images, using two or more ultrasound imaging modes or modalities, to generate 2D multi-parametric ultrasound (mpUS) images and/or to generate a three-dimensional (3D) mpUS image. The different ultrasound imaging modes acquire images in a common frame of reference during a single procedure to facilitate their registration. The mpUS images (i.e., 2D or 3D) may be used for enhanced detection of suspicious regions.

Abstract: A method and a system for reducing current noise of a touch terminal are provided. The method includes: determining that a user receives a voice call via an earpiece of the touch terminal, and controlling the touch terminal to switch from a first touch scan frequency to a second touch scan frequency, where the second touch scan frequency is less than the first touch scan frequency. As can be seen, if the user receives the voice call via the earpiece of the touch terminal, the touch terminal is controlled to switch from the first touch scan frequency to the second touch scan frequency, to effectively reduce the current noise heard by the user by decreasing the touch scan frequency of the touch terminal, so that the periodic current noise is outside a frequency band perceptible to a human ear, thereby improving user experience when receiving the voice call.

Abstract: A diphase stainless steel chemical vessel ballast cabin bulkhead includes a front cover, a rear cover and reinforced string boards. The front cover and the rear cover are distributed parallel with each other. The front cover and the rear cover are inter-connected by the reinforced string boards. The reinforced string board includes a deformation groove and connection plates, and the connection plates are distributed on two sides of the deformation groove symmetrically with respect to a central line of the deformation groove. The reinforced string boards are evenly distributed between the front cover and the rear cover; the reinforced string boards and an axis of the front cover and the rear cover are distributed parallel. Adjacent reinforced string boards are mutually connected. A thickness of the front cover is at least one time thicker than that of the rear cover. The processing method thereof includes three steps.

Abstract: A method for testing a display panel includes: applying a first level signal to a first sub-pixel and a third sub-pixel of a first pixel unit and applying a second level signal to a second sub-pixel of the first pixel unit; applying the second level signal to a first sub-pixel and a third sub-pixel of a second pixel unit and applying the first level signal to a second sub-pixel of the second pixel unit; and detecting a short circuit between adjacent sub-pixels. The first level signal has a voltage polarity opposite to a voltage polarity of the second level signal. Therefore, it is ensured that any two adjacent sub-pixels have opposite voltage polarities when the short circuit between adjacent sub-pixels of the display panel is detected. The method also provides improved testing abilities to detect an open circuit in a sub-pixel.

Abstract: Methods, systems, and devices for analyzing communication messages (e.g., emails) and selecting corresponding actions are described. In some database systems, a user may receive multiple messages at a user device. To efficiently determine responses to these messages, the user device may send the messages to a backend server for analysis. The server may perform natural language processing (NLP) to classify the message with one or more binary classifications and may extract metadata from each message. Based on the classifications and the metadata, the server may determine one or more actions the user device may perform to respond to each message. The server may send instructions to the user device indicating the suggested actions, and the user device may display these actions as options to a user. Additionally, the user device may use the classifications and metadata to automatically generate one or more communication templates in response to the message.

Abstract: There is provided an association analysis method and apparatus. An original database is divided into projection databases, each not contributing to a support count of a frequent item set of another. The projection databases are used for sequential-pattern association analysis performed respectively by nodes corresponding to the projection databases. Local frequent item sets and corresponding support counts obtained by the nodes are combined. Since an established projection database does not contribute a support count of a frequent item set of another projection database, different nodes can perform association mining, including pruning, on different projection databases respectively.

Abstract: Embodiments of the present disclosure provide a VNFM determining method and a network functions virtualization orchestrator. The method includes: receiving, by an NFVO, a first VNF instantiation request; determining, according to the first VNF instantiation request from VNFMs in a management domain of the NFVO, a VNFM managing the to-be-instantiated VNF; and sending a second VNF instantiation request to the determined VNFM, where the second VNF instantiation request is used to request the determined VNFM to instantiate the to-be-instantiated VNF. A correct VNFM is selected for the VNF, thereby ensuring that VNF deployment and life cycle management are performed correctly and highly efficiently.

Abstract: The disclosed embodiments provide a method and an apparatus for acquiring an evaluation index. The method comprises the steps of inputting samples into a classification model for classification training and acquiring output data of the classification model; acquiring probability statistics by performing probability distribution on the output data, wherein the probability statistics comprise probability intervals and a number of true positive samples and a number of true negative samples in each probability interval; and calculating the evaluation index of the classification model according to a threshold set and the acquired probability statistics. In the disclosed embodiments, probability statistics is performed for the output data of the classification model; and the evaluation index is calculated based on the acquired probability statistics, thereby solving the problem of scanning output data multiple times during the calculation of evaluation index.

Abstract: A resource allocating method for a VNF and an apparatus are used to resolve a prior-art problem that when a VNF is established. In the method, a resource allocation device in an NFV architecture selects a corresponding physical machine for a VDU based on a resource configuration information group of the VDU. The resource configuration information group includes a resource scheduling parameter, and the resource scheduling parameter is for indicating a requirement of a VNF on a resource selection and scheduling policy. In this way, when a plurality of factors need to be considered to schedule a resource for the VDU, the resource configuration information group may include resource scheduling parameters that indicate a plurality of factors, and the resource allocation device may schedule a resource for the VDU based on a plurality of factors, thereby improving resource scheduling flexibility in a VNF instantiation process.

Abstract: Methods and apparatuses for training model based on random forest are provided. The method includes: dividing worker nodes into one or more groups; performing random sampling, by worker nodes in each group, in the preset sample data to obtain the target sample data; and training, by the worker nodes in each group, one or more decision tree objects using the target sample data. Example embodiments of the present disclosure do not need to scan the complete sample data for once, thereby greatly reducing the amount of data to be read, the time cost, and further the iterative update time of the model. The efficiency of training is improved.

Abstract: A system for waking an electronic control unit upon movement of a controlled member, such as a vehicle lift gate or window, and protecting the electronic control unit from motor back EMF are provided. The system includes a power drive unit coupled to the controlled member and to the electronic control unit for moving the controlled member. A comparator subsystem is coupled to the power drive unit and is configured to compare an electrical output of the power drive unit generated due to motor back electromotive force in response to a manual movement of the controlled member to an electrical reference to determine whether to wake the electronic control unit. The system detects manual movement of the controlled member, e.g. from unauthorized entry, and causes an alarm and/or for the motor to brake or to drive to counteract the manual movement of the controlled member.

Abstract: A display panel and a display device are disclosed. The display panel includes at least one notch which includes a protrusion structure. A non-display region includes at least one first non-display region arranged along edges of the protrusion structure. At least one first pressure sensor is provided in the first non-display region. An included angle between a first strain detection direction of the first pressure sensor and a first edge of the display panel is greater than or equal to 65 degrees and less than or equal to 115 degrees. An included angle between a second strain detection direction and the first edge of the display panel is greater than or equal to ?25 degrees and less than or equal to 0 degree. A control chip is provided in the non-display region, and the first edge is an edge of the display panel close to the control chip.

Abstract: The present disclosure provides methods and an apparatuses for building a data identification model. One exemplary method for building a data identification model includes: performing logistic regression training using training samples to obtain a first model, the training samples comprising positive and negative samples; sampling the training samples proportionally to obtain a first training sample set; identifying the positive samples using the first model, and selecting a second training sample set from positive samples that have identification results after being identified using the first model; and performing Deep Neural Networks (DNN) training using the first training sample set and the second training sample set to obtain a final data identification model. The methods and the apparatuses of the present disclosure improve the stability of data identification models.

Abstract: Embodiments of the disclosure provide a text information clustering method and a text information clustering system. The method can include performing word segmentation on multiple pieces of text information to generate multiple words; performing an initial clustering on the multiple words to generate multiple first-level topics, each of the first-level topics comprising at least two pieces of text information; determining, for each of the first-level topics, a number of second-level topics based on a number of pieces of text information under the first-level topic; and performing, according to the number of second-level topics of each of the first-level topics, a secondary clustering on the multiple words of at least two pieces of text information comprised in the first-level topic to generate multiple second-level topics.

Abstract: Embodiments of the present disclosure provide a method and system for training a model by using training data. The training data includes a plurality of samples, each sample includes N features, and features in the plurality of samples form N feature columns, and the method includes: determining an importance value of each of the N feature columns; determining whether the importance value of each of the N feature columns satisfies a threshold condition; performing a dimension reduction on M feature columns to generate P feature columns in response to the determination that the importance values of the M feature columns do not satisfy the threshold condition, wherein M<N and P<M; merging (N?M) feature columns having importance values that satisfy the threshold condition and the generated P feature columns to obtain (N?M+P) feature columns; and training the model based on the training data including the (N?M+P) feature columns.