37 results about "Critical question" patented technology

Various embodiments of the invention address two critical problems that current email service providers face. First, there is the problem of maintaining high levels of customer service when email server systems are inundated with spam. Second, there is the problem of reducing the system-wide impact that spam has on the email delivery system. Current embodiments are directed to determining whether an email message is an unwanted bulk email message without necessarily considering the message that is conveyed by any portion of the email message. Through analyses of patterns of delivery of these email messages, profiles are built that allow an email server to ascertain whether there is a likelihood that any one particular email message constitutes an unwanted email message. If an email message is determined to likely constitute an unwanted email message, then memory-saving measures are implemented. In preferred embodiments, one copy of the email message is saved at a central, shared location that can be accessed by each of the intended recipients. This avoids having to replicate the email message across the system for each of the recipients.

The present disclosure describes a method of sentiment oriented slang for opinion mining. With increasing use of internet, many users can submit their review comments directly to the companies which can be automatically processed and summarized with critical issues from time to time and help the company get real time feedback from its customers. The method comprises, receiving at least one document comprising a plurality of sentiment oriented slang. The next step of the method comprises identifying the plurality of sentiment oriented slang in the at least one document. Further, a polarity score of each of a slang word identified is determined and sentiment information is displayed on an output device as an output.

The invention relates to a multi-lead correlation analysis electroencephalo-graph (EEG) feature extraction method. In multi-class motor imagery task recognition, EEG signal features of brain areas activated by a specific motor imagery task are effectively extracted, and effectively extracting the EEG signal features of the brain areas activated by the specific motor imagery task is a key problem to improve a recognition rate. With the multi-lead correlation analysis EEG feature extraction method, firstly multi-lead motor imagery EEG signals are extracted, then a correlation coefficient between every two lead EEG signals is analyzed to obtain a correlation parameter matrix, next a row variance of each correlation parameter matrix, the ratio values of the sum of all the row variances, and natural logarithms of all the row variances are calculated, obtained results are used as characteristic vectors of the EEG signals, and finally the characteristic vectors are input into a classifier to complete classifying recognition of multi-class motor imagery tasks. With the multi-lead correlation analysis EEG feature extraction method, not only can the EEG signal features of the brain areas activated by the specific motor imagery task at the same time can be fully extracted, influences on characteristic parameters can be reduced to a large extent, wherein the influences are caused by EEG signal individual differences, and further the problem that insufficience problem of electrode choosing can be solved.

A system and a computer program product are provided for evaluating question-answer pairs in an answer key by comparing a first answer key answer to a plurality of candidate answers to determine if the answer key may have a problem if the plurality of candidate answers are more similar to one another than to the first answer and to determine if the plurality of candidate answers has gradient information which may be used to update the answer key if not already included in the answer key.

The invention discloses a method for evaluating diffusion degree of regenerant in waste asphalt mixture, based on molecular dynamics simulation. The method includes the steps: establishing a molecularmodel of regenerant and waste asphalt mixture based on the molecular dynamics theory and simulation calculation software MS (Materials Studio Software) and LAMMPS (Large-scale Atomic Massively Parallel Simulator), simulating the diffusion process of the regenerant in the waste asphalt mixture, and after recording the molecular motion trajectory, calculating the diffusion coefficient through the concentration distribution curve. The calculation of the diffusion coefficient is based on Fick law. The method for evaluating diffusion degree of regenerant in waste asphalt mixture solves the problemthat the current macroscopic experiment is difficult to preferably characterize the diffusion condition of the regenerant and the asphalt, solves the key problem in the asphalt mixture regeneration technology, and has a good application prospect.

The invention discloses a recurrent neural network-based database query time prediction method. The method comprises the following steps of: firstly extracting query plans from database history query records so as to form original data, wherein each query plan comprises operation information and operation time; classifying the original data according to the lengths of the operation time to ensure that the quantities of the query plans in various types are the same; specially processing the query plans to obtain an operation sequence and an operation time sequence; taking the operation sequence as a feature vector, taking the operation time sequence as a label, inputting the feature vector and the label into a neural network, and carrying out training to obtain a model; for a to-be-predicted query plan, repeating the steps to obtain an operation sequence, inputting the operation sequence into the model, and outputting the operation time sequence to complete the prediction of a database query time. The method is capable of obtaining favorable effect in the aspect of relational database query time prediction, and the correctness of the model under simulated data training is higher than 78%. The method can be used for solving the key problems in query optimization and load management.

The invention provides an optical reference point recognition method and system, a computer readable medium and equipment. The optical reference point recognition method comprises the steps of recognizing all pads and pad attribution information in graphic design data of a PCB to be recognized; reading recognition parameter configuration used for recognizing an optical reference point; according to the pad attribution information, the recognition parameter configuration and predetermined screening conditions in combination with a preset screening sequence of the predetermined screening conditions, judging all the pads one to one or parallelly to mark the optical reference point. The optical reference point for prejudgment can be provided for a client for reference, the client does not needto watch the board to manually look for the optical reference point by himself/herself, thus the steps of artificial participation in the SMT manufacturing procedure can be reduced, a key problem inautomated program manufacturing is solved, and technical support is provided for intelligent manufacturing in the electronic industry.

An object clustering method in situation assessment includes the steps: firstly, generating a property priority list and a clustering factor matrix; secondly, segmenting objects in a battlefield into clusters according to the first priority selected from the property priority list and deleting the selected priority from the property priority list; thirdly, judging whether the object quantity in the clusters is greater than the preset threshold value of the object quantity or not, if so, then continuously selecting a priority from the property priority list to segment the clusters, if not or the property priority list is empty, then performing clustering analysis on object data in the bottom clusters to generate mini-clusters according to the object location relationship; and finally, utilizing a fusion algorithm to fuse the mini-clusters, and then acquiring an object group. The object clustering method in the situation assessment overcomes defects in the object clustering and solves the key problem that accuracy and speed of object clustering are low, so that the battlefield situation assessment and policies made by the commander in the battlefield can be effectively supported.

The present invention is generally directed to systems and methods for creating a new hybrid financial instrument, which addresses key concerns of investors and meets the financing requirements of investee companies. An embodiment of the present invention describes a new type of security called an “in-kind participating preferred security” (IPPS). The IPPS contract entitles the holder to a contingent in-kind dividend, payable periodically in the commodity produced by the issuer or in cash, which ever is of greater value. At expiration, the IPPS is redeemed by the company at its in-kind par value upon origination or in cash, which ever is greater.

The invention belongs to the technical field of software engineering, in particular to a system interface timing knowledge analysis system based on a fine-grained feature semantic network. Searching for the key interface problems in the test fault set of embedded system, analyzing and extracting the time-related faults, initially forming the time-related interface fault set; Searching for the keyfaults related to time sequence in embedded system test fault set, analyzing and extracting the faults related to interface to form the fault set related to time sequence; Two fault sets and forming an interface sequential fault set; According to the fault types in the sequential fault database, the key influencing factors of interface sequencing are extracted by using the semantic network based on fine-grained features, and the embedded system interface sequential knowledge set is formed. The sequential knowledge of embedded system interface can realize the inheritance of test knowledge, andit is scalable, easy to push the test knowledge and improve the test efficiency.

The invention proposes an information-environment-based network teaching and resource sharing management platform. On the basis of the existing information environment of the school, unified informatization standards and regulations are formulated and an infrastructure platform including a shared data center, unified identity authentication, and a unified information portal is built so as to solving a series of urgent key problems in current information construction; and existing dispersed teaching resources of the school are integrated based on data integration to form a comprehensive platform with integration of teaching resources integration and sharing, daily teaching activities supporting, and auxiliary teaching displaying and management, so that personalized information services areprovided for teachers and students of the school.

The invention discloses an indoor space semantic value calculation method based on spatial and social multimedia data. The semantic value of indoor independent subspace is divided into location and social contribution, and the semantic value of indoor independent subspace is fused. The method comprises analyzing the topological connection relation of each indoor independent subspace, constructingthe indoor space model, calculating the distance between each object, calculating the path matrix according to the user walking constraint, and calculating the location contribution degree; Accordingto the social multimedia data of indoor independent subspace, the sentence structure being analyzed and the key words being extracted to construct the emotion classification base classifier. Sentencesets are divided into key sentences and non-key sentences to classify social evaluation texts. The social contribution degree is calculated according to the classification of social evaluation texts.The indoor space value calculation method of the invention can be used for solving key problems such as value prediction of newly introduced shops, multi-dimensional shop evaluation and the like.