Matching of Descriptive Labels to Glossary Descriptions

Semantic text similarity plays an important role in software engineering tasks in which engineers are requested to clarify the semantics of descriptive labels (e.g., business terms, table column names) that are often consists of too short or too generic words and appears in their IT systems. We formulate this type of problem as a task of matching descriptive labels to glossary descriptions. We then propose a framework to leverage an existing semantic text similarity measurement (STS) and augment it using semantic label enrichment and set-based collective contextualization where the former is a method to retrieve sentences relevant to a given label and the latter is a method to compute similarity between two contexts each of which is derived from a set of texts (e.g., column names in the same table). We performed an experiment on two datasets derived from publicly available data sources. The result indicated that the proposed methods helped the underlying STS correctly match more descriptive labels with the descriptions

MaestROB: A Robotics Framework for Integrated Orchestration of Low-Level Control and High-Level Reasoning

This paper describes a framework called MaestROB. It is designed to make the robots perform complex tasks with high precision by simple high-level instructions given by natural language or demonstration. To realize this, it handles a hierarchical structure by using the knowledge stored in the forms of ontology and rules for bridging among different levels of instructions. Accordingly, the framework has multiple layers of processing components; perception and actuation control at the low level, symbolic planner and Watson APIs for cognitive capabilities and semantic understanding, and orchestration of these components by a new open source robot middleware called Project Intu at its core. We show how this framework can be used in a complex scenario where multiple actors (human, a communication robot, and an industrial robot) collaborate to perform a common industrial task. Human teaches an assembly task to Pepper (a humanoid robot from SoftBank Robotics) using natural language conversation and demonstration. Our framework helps Pepper perceive the human demonstration and generate a sequence of actions for UR5 (collaborative robot arm from Universal Robots), which ultimately performs the assembly (e.g. insertion) task.Comment: IEEE International Conference on Robotics and Automation (ICRA) 2018. Video: https://www.youtube.com/watch?v=19JsdZi0TW

Utterance Classification with Logical Neural Network: Explainable AI for Mental Disorder Diagnosis

In response to the global challenge of mental health problems, we proposes a Logical Neural Network (LNN) based Neuro-Symbolic AI method for the diagnosis of mental disorders. Due to the lack of effective therapy coverage for mental disorders, there is a need for an AI solution that can assist therapists with the diagnosis. However, current Neural Network models lack explainability and may not be trusted by therapists. The LNN is a Recurrent Neural Network architecture that combines the learning capabilities of neural networks with the reasoning capabilities of classical logic-based AI. The proposed system uses input predicates from clinical interviews to output a mental disorder class, and different predicate pruning techniques are used to achieve scalability and higher scores. In addition, we provide an insight extraction method to aid therapists with their diagnosis. The proposed system addresses the lack of explainability of current Neural Network models and provides a more trustworthy solution for mental disorder diagnosis.Comment: ACL 202

A Class-Object Model for Program Transformations

The recent expert programmers who have been forced to develop large and complicated programs have strong desire to write “good codes ” from the viewpoints of both runtime efficiency and understandability, and they expect a translator to generate a good code just to fit their own needs if possible. This fitting usually requires complicated customization of the translator which could only be done by the experienced compiler experts, and the programmers cannot reflect their ideas on the translators so easily, especially when the programmer’s demands to the quality of practical programming tools are severe. These situations lead us to the necessity to develop a more flexible approach by which the specific programmers can reflect their experience and knowledge on the translators by themselves. The technology that enables the reuse of larger software components have become available with the emergence of the object-oriented paradigm, which have widened the applicability of reusable code pieces. However, the class-based objectoriente

Decomposition and Abstraction of Web Applications for Web Service Extraction and Composition

There are large demands for re-engineering humanoriented Web application systems for use as machineoriented Web application systems, which are called Web Services. This paper describes a framework named H2W, which can be used for constructing Web Service wrappers from existing, multi-paged Web applications. H2W's contribution is mainly for service extraction, rather than for the widely studied problem of data extraction. For the framework, we propose a page-transition-based decomposition model and a page access abstraction model with context propagation. With the proposed decomposition and abstraction, developers can flexibly compose a Web Service wrapper of their intent by describing a simple workflow program incorporating the advantages of previous work on Web data extraction. We show three successful wrapper application examples with H2W for real world Web applications