Abstract: A method, computer system, and a computer program product are provided for a context-aware relevancy modelling in conversational systems. A user query is received. A latent static content d is selected from a corpus of content D. A latent set of context C from a set of external context Cu is also selected. A result is generated using a scoring function and using the latent static content d from a corpus D and the latent set of context C from the set of external contexts CU so as to provide a most relevant context-base search response to said user query q. The result provides a most relevant context-base search response to said user query q. A response is then generated based on said result using said scoring function result to said user query q.

Abstract: A debris shield may include one or more microfoam units, the one or more microfoam units comprising internal voids. The debris shield may include one or more phase change materials positioned within the internal voids. The debris shield may include an advanced composite material layer positioned over a surface of the one or more microfoam units.

Abstract: A device for a creep test and a test system and a method using the device are provided. The device for the creep test includes a device frame, a test piece clamp, a load loading mechanism and linear displacement sensors. A test piece is arranged in a middle of the test piece clamp. A first end of the test piece clamp is a fixed end, a position of the fixed end is unchanged relative to the device frame. A second end of the test piece clamp is a movable end. The load loading mechanism includes a pulley block and a load. A system for a tensile and compressive creep test includes a box body which is sealable. The box body is provided with a box door which is provided with a constant temperature and humidity device. The device for the creep test is arranged in the box body.

Abstract: A method, computer system, and a computer program product are provided for a context-aware relevancy modelling in conversational systems. A user query is received. A latent static content d is selected from a corpus of content D. A latent set of context C from a set of external context Cu is also selected. A result is generated using a scoring function and using the latent static content d from a corpus D and the latent set of context C from the set of external contexts CU so as to provide a most relevant context-base search response to said user query q. The result provides a most relevant context-base search response to said user query q. A response is then generated based on said result using said scoring function result to said user query q.

Abstract: Techniques for generating a clarification to distinguish among retrieved content in interactive systems are provided. In one aspect, a method for generating a clarification prompt in an interactive system includes: obtaining a training dataset for generating the clarification prompt from existing question-answering datasets by modifying original queries in the existing question-answering datasets to obtain training examples of under-specified queries; and training a machine learning model using the training dataset how to select latent differentiating factors in content candidates obtained from an under-specified query from a user and, based on the latent differentiating factors, generate the clarification prompt to clarify an intent of the user.

Abstract: The present disclosure provides a method for recycling urea-formaldehyde (UF) from a wood-based panel. In the present disclosure, the UF is depolymerized by an ultrasonic treatment, and depolymerized UF can be reused for UF manufacture and wood-based panel production. The recycled and treated UF can be repeatedly used in wood-based panel manufacture without affecting performances of the wood-based panel. UF-glued wood-based panels can be recycled, and a recycled wood-based panel raw material can replace at least 50% of a non-recycled wood-based raw material for particle board production without affecting performances of the wood-based panel.

Abstract: A computer system, product, and method are provided. The computer system includes an artificial intelligence (AI) platform operatively coupled to a processor. The AI platform includes tools in the form of a machine learning model (MLM) manager, a metric manager, and a training manager. The MLM manager accesses a plurality of pre-trained source MLMs, and inputs a plurality of data objects of a test dataset into each of the source MLMs. The test dataset includes the plurality of data objects associated with respective labels. For each source MLM, associated labels are generated from the inputted data objects and a similarity metric is calculated. The MLM manager selects a base MLM to be used for transfer learning from the plurality of source MLMs based upon the calculated similarity metric. The training manager trains the selected base MLM with a target dataset for the target domain.

Abstract: From metadata of a corpus of natural language text documents, a relativity matrix is constructed, a row-column intersection in the relativity matrix corresponding to a relationship between two instances of a type of metadata. An encoder model is trained, generating a trained encoder model, to compute an embedding corresponding to a token of a natural language text document within the corpus and the relativity matrix, the encoder model comprising a first encoder layer, the first encoder layer comprising a token embedding portion, a relativity embedding portion, a token self-attention portion, a metadata self-attention portion, and a fusion portion, the training comprising adjusting a set of parameters of the encoder model.

Abstract: A device for a creep test and a test system and a method using the device are provided. The device for the creep test includes a device frame, a test piece clamp, a load loading mechanism and linear displacement sensors. A test piece is arranged in a middle of the test piece clamp. A first end of the test piece clamp is a fixed end, a position of the fixed end is unchanged relative to the device frame. A second end of the test piece clamp is a movable end. The load loading mechanism includes a pulley block and a load. A system for a tensile and compressive creep test includes a box body which is sealable. The box body is provided with a box door which is provided with a constant temperature and humidity device. The device for the creep test is arranged in the box body.

Abstract: Provided is a computer-implemented method, system, and computer program product for generating a goal-oriented dialogue from a grounding document. A processor may analyze a corpus of text. The processor may identify, based on the analyzing, one or more semantic structures that can be used to simulate a dialogue. The processor may generate, based on the identifying, a simulated dialogue, the simulated dialogue including one or more utterances from a simulated agent and one or more utterances from a simulated user to form a dialogue flow.

Abstract: The present disclosure provides a modified deodorant urea-formaldehyde (UF) resin and use thereof, as well as a particleboard and a preparation method thereof, and relates to the technical field of wood-based panels. Raw materials for preparing the modified deodorant UF resin provided by the present disclosure include: melamine-modified UF resin (MUF), aluminum ammonium sulfate dodecahydrate, wax, and water. Raw materials for preparing the MUF include: urea, formaldehyde, and melamine. When the modified deodorant UF resin provided by the present disclosure is used to prepare wood-based panels, the aluminum ammonium sulfate dodecahydrate losses 12 crystal water during hot pressing to form a metastable structure NH4Al(SO4)2, in which NH4+ can react with the formaldehyde to reduce formaldehyde emission, and NH4+ and A13+ can absorb hydroxyl groups of organic substances to form hydrogen bonds to reduce odor of the wood-based panels.

Abstract: Techniques facilitating interpretation of deep neural model based dialogue agents are provided. In one example, a computer-implemented method comprises extracting, by a device operatively coupled to a processor, features from a dialogue model independently from the dialogue model, the features comprising input features provided to the dialogue model and output features produced via the dialogue model in response to the input features, resulting in extracted features; and analyzing, by the device, a dialogue context associated with the extracted features by identifying pairwise interactions between respective ones of the extracted features.

Abstract: The present disclosure relates to the technical field of resource recycling and provides a processing method for a waste oriented strand board and waste oriented wood chips, and an oriented strand board preparation method thereof. The processing method for a waste oriented strand board provided in the present disclosure includes the following steps: softening a waste oriented strand board to obtain a softened waste oriented strand board; and flaking the softened waste oriented strand board pieces using a disc flaker to obtain waste oriented wood chips. According to the present disclosure, the waste oriented strand board is softened first to be chipped readily. It is specified in the present disclosure that the disc flaker is used such that relatively intact wood chips can be obtained.

Abstract: From metadata of a corpus of natural language text documents, a relativity matrix is constructed, a row-column intersection in the relativity matrix corresponding to a relationship between two instances of a type of metadata. An encoder model is trained, generating a trained encoder model, to compute an embedding corresponding to a token of a natural language text document within the corpus and the relativity matrix, the encoder model comprising a first encoder layer, the first encoder layer comprising a token embedding portion, a relativity embedding portion, a token self-attention portion, a metadata self-attention portion, and a fusion portion, the training comprising adjusting a set of parameters of the encoder model.

Abstract: Techniques facilitating interpretation of deep neural model based dialogue agents are provided. In one example, a computer-implemented method comprises extracting, by a device operatively coupled to a processor, features from a dialogue model independently from the dialogue model, the features comprising input features provided to the dialogue model and output features produced via the dialogue model in response to the input features, resulting in extracted features; and analyzing, by the device, a dialogue context associated with the extracted features by identifying pairwise interactions between respective ones of the extracted features.

Abstract: A computer system, product, and method are provided. The computer system includes an artificial intelligence (AI) platform operatively coupled to a processor. The AI platform includes tools in the form of a machine learning model (MLM) manager, a metric manager, and a training manager. The MLM manager accesses a plurality of pre-trained source MLMs, and inputs a plurality of data objects of a test dataset into each of the source MLMs. The test dataset includes the plurality of data objects associated with respective labels. For each source MLM, associated labels are generated from the inputted data objects and a similarity metric is calculated. The MLM manager selects a base MLM to be used for transfer learning from the plurality of source MLMs based upon the calculated similarity metric. The training manager trains the selected base MLM with a target dataset for the target domain.

Abstract: A method of providing real-time speech analysis to a user includes capturing a speech input, performing a real-time recognition of the speech input including converting the speech input to a text, analyzing the recognized speech input to identify an error in a voice of the user, the analyzing including comparing a voice of a correct text generated by an automated speech generation system with the captured speed input, and processing the text to extract a context dialog prompt.

Abstract: A method of translating information using crowdsourcing includes evaluating a metric related to a content to be translated, determining a priority for the content, queuing the content for the crowdsourcing based on the priority determined from the metric, and translating the information from a language to another language using the crowdsourcing.

Abstract: A method of providing real-time speech analysis to a user includes capturing a speech input, performing a real-time recognition of the speech input including converting the speech input to a text, analyzing the recognized speech input to identify an error in a voice of the user, the analyzing including comparing a voice of a correct text generated by an automated speech generation system with the captured speed input, and processing the text to extract a context dialog prompt.

Abstract: A method of providing real-time speech analysis for a user includes capturing a speech input, performing a real-time recognition of the speech input including converting the speech input to a text using an automatic speech recognition component, analyzing the recognized speech input, by a processing unit of a computer in a speech recognition and analyzing system, to identify an error in the user's speech, and by comparing a voice of a correct text generated by a speech generation and analyzing system with the captured speech input, and providing a real-time correction to the user based on a result of the comparing the voice of the correct text with the captured speech input. The comparing the voice of the correct text with the captured speech input includes comparing a standard pronunciation of the correct text with a pronunciation of the user in the captured speech input to identify the error.