A system and a method for automatically clustering sandbox analysis reports of similar malware samples. An automated malware analysis process includes receiving from a sandbox server the sandbox analysis reports of the similar malware samples at an application programming interface (API) of the clustering server, clustering similar Uniform Resource Locators (URLs) together and clustering the sandbox analysis reports of events in sandbox reports clusters (1-n) based on the URL clustering, static properties of the malware samples and dynamic properties of the malware samples.

A system and a method for automatically clustering sandbox analysis reports of similar malware samples. An automated malware analysis process includes receiving from a sandbox server the sandbox analysis reports of the similar malware samples at an application programming interface (API) of the clustering server, clustering similar Uniform Resource Locators (URLs) together and clustering the sandbox analysis reports of events in sandbox reports clusters (1-n) based on the URL clustering, static properties of the malware samples and dynamic properties of the malware samples.

A method and device for keyword extraction and a storage medium. The method includes receiving, at a terminal, an original document, acquiring, at the terminal, a candidate set by extracting at least one candidate phrase from the original document, acquiring, at the terminal, an association degree between the at least one candidate phrase in the candidate set and the original document, acquiring, at the terminal, a divergence degree of the at least one candidate phrase in the candidate set, and updating, at the terminal, a key phrase set of the original document by selecting the at least one candidate phrase from the candidate set as at least one key phrase based on the association degree and the divergence degree.

The present application discloses a method, an apparatus, a device, and a storage medium for intention recommendation, which relates to the field of big data, artificial intelligence, intelligent search, information flow and deep learning technologies in the field of computer technologies. A specific implementation scheme includes: receiving an intention query request carrying an intention keyword and a user identification, determining a first recommendation list according to the intention keyword and a pre-configured intention repository, where the intention repository includes at least one tree-shaped intention set, and each tree-shaped intention set includes at least one graded intention, processing intentions in the first recommendation list by using intention strategy information corresponding to the user identification to obtain a target recommendation list and output it.

Methods, apparatus, systems and articles of manufacture are disclosed for classification of unknown samples using agglomerative clustering. An apparatus includes an extractor to extract a feature from a sample source code, the feature including at least one of a register, a variable, or a library based on a threshold of occurrence in a corpus of samples, the corpus of samples including malware samples, a dendrogram generator to generate a dendrogram based on features extracted from the sample source code, the dendrogram representing a collection of samples clustered based on similarity among the samples, the samples including sample clusters belonging to known malware families, and an anchor point identifier to traverse the dendrogram to identify similarity of an unknown sample to the sample clusters based on a confidence score, and identify anchor point samples from the sample clusters identified as similar to the unknown sample, the anchor point samples to provide metadata for use in extrapolating information to classify the unknown sample.

Methods, apparatus, systems and articles of manufacture are disclosed for classification of unknown samples using agglomerative clustering. An apparatus includes an extractor to extract a feature from a sample source code, the feature including at least one of a register, a variable, or a library based on a threshold of occurrence in a corpus of samples, the corpus of samples including malware samples, a dendrogram generator to generate a dendrogram based on features extracted from the sample source code, the dendrogram representing a collection of samples clustered based on similarity among the samples, the samples including sample clusters belonging to known malware families, and an anchor point identifier to traverse the dendrogram to identify similarity of an unknown sample to the sample clusters based on a confidence score, and identify anchor point samples from the sample clusters identified as similar to the unknown sample, the anchor point samples to provide metadata for use in extrapolating information to classify the unknown sample.

In some embodiments, methods and systems for pushing audiovisual playlists based on a text-attentional convolutional neural network include a local voice interactive terminal, a dialog system server and a playlist recommendation engine, where the dialog system server and the playlist recommendation engine are respectively connected to the local voice interactive terminal. In some embodiments, the local voice interactive terminal includes a microphone array, a host computer connected to the microphone array, and a voice synthesis chip board connected to the microphone array. In some embodiments, the playlist recommendation engine obtains rating data based on a rating predictor constructed by the neural network; the host computer parses the data into recommended playlist information; and the voice terminal synthesizes the results and pushes them to a user in the form of voice.

A technology trend prediction method and system are provided. The method comprises acquiring paper data, and further comprises following steps: processing the paper data to generate a candidate technology lexicon; screening the candidate technology lexicon based on mutual information; calculating an independent word forming probability of an OOV word; extracting missed words in a title using a bidirectional long short-term memory network and a conditional random field (BI-LSTM+CRF) model; predicting a technology trend. The technology trend prediction method and system provided analyzes relationship of technology changes in a high-dimensional space, and predicts a development of technology trend based on time by extracting technical features of papers through natural language processing and time sequence algorithms.

A technology trend prediction method and system are provided. The method comprises acquiring paper data, and further comprises following steps: processing the paper data to generate a candidate technology lexicon; screening the candidate technology lexicon based on mutual information; calculating an independent word forming probability of an OOV word; extracting missed words in a title using a bidirectional long short-term memory network and a conditional random field (BI-LSTM+CRF) model; predicting a technology trend. The technology trend prediction method and system provided analyzes relationship of technology changes in a high-dimensional space, and predicts a development of technology trend based on time by extracting technical features of papers through natural language processing and time sequence algorithms.

There is provided a method and system for training and using a transformer language model (TLM) part of a recommendation engine. Natural language discussions about a category of items are received, the discussions comprising tags each indicative of a respective item belonging to the category of item. Information is received for each respective item. Based on the natural language discussions, the tags and the information about the respective item, the TLM is trained to: upon receipt of a user input, determine whether a given item should be recommended based on the user input, if the given item should be recommended, retrieving given information about the given item and generating a response to the user input, the response to the user input comprising the given item to be recommended and the given information, and output the response to the user input. The response is generated in natural language format.