Abstract: An automatic pipe clean system for massage bath equipment includes a bathtub, a water-filling pipe, a water-draining pipe, a suction pipe, a pump pipe, a pump connected between the suction pipe and the pump pipe, an automatic water-filling pipe, a water-filling valve and a controller. The controller is configured to: in a cleaning stage: turn on the pump to allow the water to pass and clean the suction pipe and the pump pipe; and in a drying stage: turn on the pump to allow the water to drain out from the suction pipe and the pump pipe; and turn off the pump.

Abstract: A method for improving the accuracy of speech recognition is disclosed. In one embodiment, such a method receives speech input from a user. The method further receives background inputs describing at least one of a webpage and an application from which the speech input was received. The method determines a language associated with the speech input and determines a weight and confidence level for each of the background inputs. A score is calculated for each of the background inputs based on the corresponding weight and confidence level. The method determines textual candidates for output in response to the speech input and ranks the textual candidates using a function that takes into account the scores and/or confidence levels of the background inputs. The textual candidate with the highest ranking may be returned to the user. A corresponding system and computer program product are also disclosed.

Abstract: A computer-implemented method, according to one embodiment, includes determining a domain and category information from metadata associated with a first webpage. The method further includes determining component types, hierarchies and grouping relationships for a plurality of text elements on the first webpage and constructing context information for the text elements. Word vectors are calculated based on the grouping relationships and the context information, and text feature types of the text elements are extracted based on the word vectors and the context information. The method further includes using the extracted text feature types to determine a re-ordering of a translation of the text elements. A computer program product, according to another embodiment, includes a computer readable storage medium having program instructions embodied therewith. The program instructions are readable and/or executable by a computer to cause the computer to perform the foregoing method.

Abstract: From an input image comprising a portion of text, the portion of text is extracted. The portion of text comprises text in a first natural language. The input image is classified into a candidate scene within a set of predefined scenes. The candidate scene is scored according to a quality measure. Using a predefined scene parameter of the scored candidate scene, the portion of text is translated. In the input image, the portion of text is replaced. with the translated portion of text.

Abstract: In one general embodiment, a computer-implemented method includes analyzing document object model code for generating locators of elements of a web page. The locators are output visibly on a user interface displaying the web page. A page object code template of the web page is generated. Mappings of the elements and/or the locators to page object variables are received from a user. Representations of the mappings are output visibly on the user interface displaying the web page. Mappings from page areas of the web page to page object functions are received from the user. Representations of the mappings from the page areas of the web page to the page object functions are output visibly on the user interface displaying the web page. Code references are rendered on the user interface displaying the web page.

Abstract: A method, computer system, and a computer program product are provided for responding to a language input query with ad hoc enriched term data. The technique comprises extracting information relating to the language input query using a Language Support Assistance Service. The extracted information includes one or more language terms requiring further support and an associated request type. This is identified from metadata relating to the language input query. Information is provided to a Term Related Corpus Data Service that includes one or more language terms requiring further support and the identified request type and any identified sources. The Term Related Corpus Data Service returns one or more ad hoc enriched terms that are tailored to the one or more language terms requiring further support and is according to the associated request type. The Language Support Assistance Service provides a response to the language input query.

Abstract: Embodiments of the present disclosure relate to a method, system, and computer program product for translation of rich text. In some embodiments, a method is disclosed. According to the method, one or more candidate formats are determined for source rich text. A target format for the source rich text is selected from the one or more candidate formats based on one or more corresponding images obtained from rendering the source rich text in the one or more candidate formats. Based on the target format, a translation editing environment is provided for editing a translation of the source rich text. In other embodiments, a system and a computer program product are disclosed.

Abstract: A method, computer system, and a computer program product for task assistance is provided. The present invention may include acquiring a request expression input by a user. The present invention may include identifying a request intent associated with a task based on the request expression. The present invention may include determining a response script corresponding to the request intent. The present invention may include executing the response script to complete the task and presenting the process of running the task in a user-interface (UI).

Abstract: Aspects of the present disclosure relate generally to UI automation in RPA and, more particularly, to automation of contextual UI element identification by RPA robots. For example, a computer-implemented method includes: receiving, by a processor set, user interface context identification information of attributes of user interface elements in user interface code of an application; constructing, by the processor set, a model of the attributes of the user interface elements including an attribute indicating functionality of at least one user interface element; obtaining from the model of the attributes of the user interface elements an identification of the at least one user interface element referenced in an action command for performing the functionality of the at least one user interface element in robotic process automation code; and storing, by the processor set, the model of the attributes of the user interface elements in persistent storage.

Abstract: Translating applications to a target language includes extracting program integrated information (PII) to be translated and creating translation context datasets based on interpretation of accessibility information associated with particular strings of PII. Translation pairs include PII and corresponding context datasets for context-based translation of application components. A two-stage index contains PII strings for first stage lookup and context datasets for distinguishing duplicate PII strings as a second stage lookup. Real-time translation is facilitated by the two-stage index, which is established by translation pairs and resulting translations.

Abstract: A method for identifying hard-coded strings in source code is disclosed. In one embodiment, such a method parses source code and associated localization resource files to identify hard-coded strings and their associated context. The method provides a confidence score for each hard-coded string that indicates whether the hard-coded string is translatable or non-translatable. Based on the confidence score for each hard-coded string, the method transforms each hard-coded string into a single equivalence word. The method then prepares training data by tagging the hard-coded strings in the source code and associated localization resource files as one of translatable and non-translatable. The method then trains a parts-of-speech (POS) tagging model using the training data. At runtime, the method fetches potential hard-coded strings and tags each hard-coded string as one of translatable and non-translatable using the POS tagging model. A corresponding system and computer program product are also disclosed.

Abstract: One or more computer processors identify a string within source code associated with a program integrated interface (PII). The one or more computer processors compare the identified string to a string entry comprised in a PII dictionary. The one or more computer processors, responsive to a match between the identified string and the string entry in the PII dictionary, classify the identified string as translatable or non-translatable based on a classification associated with the string entry. The one or more computer processors, responsive to a non-match between the identified string and the string entry in the PII dictionary, classify the identified string as translatable or non-translatable utilizing a hyperplane model trained with a margin (C) derived from unlimited search. The one or more computer processors, responsive to a classified translatable string, translate the classified translatable string.

Abstract: One or more computer processors identify a string within source code associated with a program integrated interface (PII). The one or more computer processors compare the identified string to a string entry comprised in a PII dictionary. The one or more computer processors, responsive to a match between the identified string and the string entry in the PII dictionary, classify the identified string as translatable or non-translatable based on a classification associated with the string entry. The one or more computer processors, responsive to a non-match between the identified string and the string entry in the PII dictionary, classify the identified string as translatable or non-translatable utilizing a hyperplane model trained with a margin (C) derived from unlimited search. The one or more computer processors, responsive to a classified translatable string, translate the classified translatable string.

Abstract: From an input image comprising a portion of text, the portion of text is extracted. The portion of text comprises text in a first natural language. The input image is classified into a candidate scene within a set of predefined scenes. The candidate scene is scored according to a quality measure. Using a predefined scene parameter of the scored candidate scene, the portion of text is translated. In the input image, the portion of text is replaced. with the translated portion of text.

Abstract: Embodiments of the present disclosure relate to attribute-based selectable item recommendation. A computer-implemented method is disclosed. A computer generates respective attribute profiles of selectable items based on one or more attribute elements included in the selectable items. The profiles include at least one attribute elements respectively correspond to the selectable items. The computer generates an attribute requirement description of an item requesting agent based on a historical search dataset of the item requesting agent. The attribute requirement description includes at least part of the one or more attribute elements. The computer determines recommended selectable items included in the selectable items for the item requesting agent based on the attribute profiles of the selectable items and the attribute requirement description of the item requesting agent.

Abstract: A pump control system and an operating method thereof are disclosed. The pump control system includes a pump; a motor mechanically connected to the pump; a motor driving controller electrically coupled to the motor, the motor driving controller configured to control a speed of the motor; a current detection unit electrically coupled between the motor and the motor driving controller or electrically coupled to a power supply input interface of the pump control system; and a main controller electrically coupled to the motor driving controller.

Abstract: Embodiments of the present disclosure relate to attribute-based selectable item recommendation. A computer-implemented method is disclosed. A computer generates respective attribute profiles of selectable items based on one or more attribute elements included in the selectable items. The profiles include at least one attribute elements respectively correspond to the selectable items. The computer generates an attribute requirement description of an item requesting agent based on a historical search dataset of the item requesting agent. The attribute requirement description includes at least part of the one or more attribute elements. The computer determines recommended selectable items included in the selectable items for the item requesting agent based on the attribute profiles of the selectable items and the attribute requirement description of the item requesting agent.

Abstract: Embodiments are disclosed for a method for dynamic text reading. The method includes performing pre-processing for a text document. Pre-processing includes determining the text document comprises an emotional statement based on an indicator of an emotion associated with the emotional statement. Pre-processing also includes identifying a speaker of the emotional statement. Further, pre-processing includes generating a role-to-voice map that associates the speaker with a digital representation of a voice for the speaker. The method additionally includes generating, based on the pre-processing, the voice for the speaker reading aloud a text of the text document using the digital representation of the voice with a tonal modulation that conveys the emotion.

Abstract: A method, computer system, and a computer program product for securing message transmission is provided. The present invention may include linking, by a first terminal device, a communication interface to a first communication channel for a target application. The present invention may include, in response to a first message being inputted in the communication interface, encrypting, by the first terminal device, the first message with a key for the first communication channel. The present invention may include, transmitting, by the first terminal device, the encrypted first message via the first communication channel.

Abstract: Orchestrated chat services utilizing a centralized chat service with access to a plurality of chatbots registered to the chat service. User's seeking support from the orchestrated chat service interact with a single user interface, while the backend of the chat service extracts the intents and entities from the user's input into the chat service. The orchestrated chat service identifies one or more classifications of chatbots suitable for responding to the user's input within a prescribed level of confidence dictated by one or more orchestration rules and selects a chatbot predicted to most likely respond to the user's input in a correct and accurate manner. The orchestrated chat service formats the user input and chat history into format of the selected chatbot's API, forwards user input and history to the selected chatbot and returns the response from the selected chatbot to the user interface of the orchestrated chat service.