A relational tsetlin machine with applications to natural language understanding

Tsetlin machines (TMs) are a pattern recognition approach that uses finite state machines for learning and propositional logic to represent patterns. In addition to being natively interpretable, they have provided competitive accuracy for various tasks. In this paper, we increase the computing power of TMs by proposing a first-order logic-based framework with Herbrand semantics. The resulting TM is relational and can take advantage of logical structures appearing in natural language, to learn rules that represent how actions and consequences are related in the real world. The outcome is a logic program of Horn clauses, bringing in a structured view of unstructured data. In closed-domain question-answering, the first-order representation produces 10 × more compact KBs, along with an increase in answering accuracy from 94.83% to 99.48%. The approach is further robust towards erroneous, missing, and superfluous information, distilling the aspects of a text that are important for real-world understanding

Natural language query formalization to SPARQL for querying knowledge bases using Rasa

The idea of data to be semantically linked and the subsequent usage of this linked data with modern computer applications has been one of the most important aspects of Web 3.0. However, the actualization of this aspect has been challenging due to the difficulties associated with building knowledge bases and using formal languages to query them. In this regard, SPARQL, a recursive acronym for standard query language and protocol for Linked Open Data and Resource Description Framework databases, is a most popular formal querying language. Nonetheless, writing SPARQL queries is known to be difficult, even for experts. Natural language query formalization, which involves semantically parsing natural language queries to their formal language equivalents, has been an essential step in overcoming this steep learning curve. Recent work in the field has seen the usage of artificial intelligence (AI) techniques for language modelling with adequate accuracy. This paper discusses a design for creating a closed domain ontology, which is then used by an AI-powered chat-bot that incorporates natural language query formalization for querying linked data using Rasa for entity extraction after intent recognition. A precision–recall analysis is performed using in-built Rasa tools in conjunction with our own testing parameters, and it is found that our system achieves a precision of 0.78, recall of 0.79 and F1-score of 0.79, which are better than the current state of the art.

A Model for Stokes Flow in Domains with Permeable Boundaries

We derive a new computational model for the simulation of viscous incompressible flows bounded by a thin, flexible, porous membrane. Our approach is grid-free and models the boundary forces with regularized Stokeslets. The flow across the porous membranes is modeled with regularized source doublets based on the notion that the flux velocity across the boundary can be viewed as the flow induced by a fluid source/sink pair with the sink on the high-pressure side of the boundary and magnitude proportional to the pressure difference across the membrane. Several validation examples are presented that illustrate how to calibrate the parameters in the model. We present an example consisting of flow in a closed domain that loses volume due to the fluid flux across the permeable boundary. We also present applications of the method to flow inside a channel of fixed geometry where sections of the boundary are permeable. The final example is a biological application of flow in a capillary with porous walls and a protein concentration advected and diffused in the fluid. In this case, the protein concentration modifies the pressure in the flow, producing dynamic changes to the flux across the walls. For this example, the proposed method is combined with finite differences for the concentration field.

Planar vector fields with central symmetry: roughness and first degree of non-roughness

Рассматривается пространство гладких векторных полей, заданных в замкнутой области D на плоскости, инвариантных относительно центральной симметрии и трансверсальных границе D. Описано множество векторных полей, грубых относительно этого пространства; показано, что оно открыто и всюду плотно. Во множестве всех негрубых векторных полей выделено открытое всюду плотное подмножество, состоящее из векторных полей первой степени негрубости. We consider the space of smooth vector fields defined in a closed domain D on the plane, invariant under the central symmetry and transversal to the boundary D. The set of vector fields that are rough with respect to this space is described; it is shown that it is open and everywhere dense. In the set of all non-rough vector fields, an open everywhere dense subset consisting of vector fields of the first degree of non-roughness is distinguished.

Temporal Minimal-World Query Answering over Sparse ABoxes

Ontology-mediated query answering is a popular paradigm for enriching answers to user queries with background knowledge. For querying the absence of information, however, there exist only few ontology-based approaches. Moreover, these proposals conflate the closed-domain and closed-world assumption and, therefore, are not suited to deal with the anonymous objects that are common in ontological reasoning. Many real-world applications, like processing electronic health records, also contain a temporal dimension and require efficient reasoning algorithms. Moreover, since medical data are not recorded on a regular basis, reasoners must deal with sparse data with potentially large temporal gaps. Our contribution consists of two main parts: In the first part, we introduce a new closed-world semantics for answering conjunctive queries (CQs) with negation over ontologies formulated in the description logic $${\mathcal E}{\mathcal L}{{\mathcal H}_ \bot }$$ , which is based on the minimal canonical model. We propose a rewriting strategy for dealing with negated query atoms, which shows that query answering is possible in polynomial time in data complexity. In the second part, we extend this minimal-world semantics for answering metric temporal CQs with negation over the lightweight temporal logic and obtain similar rewritability and complexity results.

A Numerical Validation of 3D Experimental Dam-Break Wave Interaction with a Sharp Obstacle Using DualSPHysics

The presence downstream of a dam of either rigid or erodible obstacles may strongly affect the flood wave propagation, and this complex interaction may lead to further dramatic consequences on people and structures. The open-source Lagrangian-based DualSPHysics solver was used to simulate a three-dimensional dam-break in a closed domain including an oriented obstacle that deflects the flow, thus increasing the complexity of fluid dynamics. By comparing numerical results with experimental data, the effectiveness of the model was evaluated and demonstrated with an extensive sensitivity analysis based on several parameters crucial to the smoothed particle hydrodynamics method, such as the resolution, the boundary conditions, and the properties of the interaction weight function. Charts and summary tables highlight the most suitable conditions for simulating such occurrences in the DualSPHysics framework. The presence of the obstacle, being also an opportunity for observation and study of complex fluid dynamics, opens the way to investigate the fluid interaction with solid objects involved in dam-break events and, possibly, to predict their effect with respect to the relative position between them and the flood and other relevant parameters. Finally, the numerical model presents a good overall agreement.

LIA: A Mathbot Assistant for First-grade Students

Nowadays, chatbots are helpful in many areas, including education. This article presents a chatbot that aims to help first-grade students get acquainted with arithmetic operations. The architecture we propose has a general character, scalability and can build other closed-domain chatbots. The results of an initial pilot study are satisfactory and encourage us to continue the research.

Susceptible-Infected-Recovered model study using free particle dynamics

A study on the epidemiologic Susceptible-Infected-Recovered (SIR) model is presented using free particle dynamics. The study is performed using a computational model consisting of randomly allocated particles in a closed domain which are free to move inrandom directions with the ability to collide into each other. The transmission rules for the particle–particle interactions are based on the main viral infection mechanisms, resulting in real–time results of the number of susceptible, infected, and recovered particles within a population of N= 200 particles. The results are qualitatively compared with a differential equation SIR model in terms of the transmission rate β, recovery rate γ, and the basic reproductive number R0, yielding overall good results. The effect of the particle density ρ on R0 is also studied to analyze how an infectious disease spreads over different types of populations. The versatility of the proposed free–particle–dynamics SIR model allows to simulate different scenarios, such as social distancing, commonly referredto as quarantine, no social distancing measures, and a mixture of the former and the latter. It is found that by implementing early relaxation of social distancing measures before the number of infected particles reaches zero, could lead to subsequent outbreaks such as the particular events observed in different countries due to the ongoing COVID–19 health crisis

KoRASA: Pipeline Optimization for Open-Source Korean Natural Language Understanding Framework Based on Deep Learning

Since the emergence of deep learning-based chatbots for knowledge services, numerous research and development projects have been conducted in various industries. A high demand for chatbots has drastically increased the global market size; however, the limited functional scalability of open-domain chatbots is a challenge to their application to industries. Moreover, as most chatbot frameworks employ English, it is necessary to create chatbots customized for other languages. To address this problem, this paper proposes KoRASA as a pipeline-optimization method, which uses a deep learning-based open-source chatbot framework to understand the Korean language. KoRASA is a closed-domain chatbot that is applicable across a wide range of industries in Korea. KoRASA’s operation consists of four stages: tokenization, featurization, intent classification, and entity extraction. The accuracy and F1-score of KoRASA were measured based on datasets taken from common tasks carried out in most industrial fields. The algorithm for intent classification and entity extraction was optimized. The accuracy and F1-score were 98.2% and 98.4% for intent classification and 97.4% and 94.7% for entity extraction, respectively. Furthermore, these results are better than those achieved by existing models. Accordingly, KoRASA can be applied to various industries, including mobile services based on closed-domain chatbots using Korean, robotic process automation (RPA), edge computing, and Internet of Energy (IoE) services.