In order to use a program written in C++ or in another programming language, a compiler and an development environment are necessary. In this paper we will present a method of using a C++ program from a web page and one of showing the results by using HTML, the ASP.NET framework and the Windows version of GNU GCC compiler -MinGW. The programming model presented in this paper can also be used for other compilers. The described application allows the implementation, test and usage of some algorithms without installing a development environment, using only a browser to connect to the Internet.

Information surrounds us and keeping track of relevant details can be challenging. Although there are multiple applications to take notes, organize ideas, or set reminders, existing solutions are semantic-agnostic and rely on the user to manually search for desired information by keywords. We propose a novel method to help people store and retrieve such details with ease in Romanian language. Our conversational agent built on top of the RASA framework is capable to extract relevant information from the user's utterances, store them in a persistent knowledge graph, and ultimately, access them when requested. A set of specific intents regarding locations, timestamps, and properties were created and learned by the agent using manually built examples. In addition, an interaction logic based on a knowledge graph was added to enable the storage and retrieval of information, based on the identified semantic components from the input sentences. The performed tests showed a good accuracy for intent detection, and promising results for the sentence parser. Our conversational agent is accessible as a web application which can process text or speech inputs, and responds with a textual representation of the user's memorized facts.

We live in an era where time is a scarce resource and people enjoy the benefits of technological innovations to ensure prompt and smooth access to information required for our daily activities. In this context, conversational agents start to play a remarkable role by mediating the interaction between humans and computers in specific contexts. However, they turn out to be laborious for cross-domain use cases or when they are expected to automatically adapt throughout user dialogues. This paper introduces a method to plug in multiple domains of knowledge for a conversational agent localized in Romanian in order to facilitate the extension of the agent’s area of expertise. Furthermore, the agent is intended to become more domain-aware and learn new information dynamically from user conversations by means of a knowledge graph acting as a network of facts and information. We ensure high capabilities for natural language understanding by proposing a novel architecture that takes into account RoBERT-contextualized embeddings alongside syntactic features. Our approach leads to improved intent classification performance (F1 score = 82.6) when compared with a basic pipeline relying only on features extracted from the agent’s training data. Moreover, the proposed RDF knowledge representation is confirmed to provide flexibility in storing and retrieving natural language entities, values, and factoid relations between them in the context of each microworld.