Language Interface Research Articles

Harnessing the power of electronic health records and open natural language data mining to capture meaningful patient experience during routine clinical care.

Electronic health records (EHR) are a rich data source for both quality improvement and clinical research. Natural language processing can be harnessed to extract data from these previously difficult to access sources. The objective of this study was to create and apply a natural language search query to extract EHR data to ask and answer quality improvement questions at a pediatric aerodigestive center. We developed a combined natural language search query to extract clinically meaningful data along with International Statistical Classification of Diseases (ICD10) and Current Procedural Terminology (CPT) code data. This search query was applied to a single pediatric aerodigestive center to answer key clinical questions asked by families. Data were extracted from EHR data from first clinic visit, operative note, microbiology lab report, and pathology report for all new patients from 2020 to 2021. Included as three queries were: 1) if I bring my child to a pediatric aerodigestive center, how often will my child obtain a medical diagnosis without needing an intervention? 2) if my child has a diagnostic procedure, how often will a diagnosis be made? 3) if a diagnosis is made, can it be addressed during that endoscopic intervention? For the 711 new patients coming to the pediatric aerodigestive center from 2020 to 2021, only 26-32% required an interventional triple endoscopy (rigid/flexible bronchoscopy with esophagoduodenoscopy). Of these triple endoscopies, 75.7% resulted in a positive finding that enabled optimization of that child's care. Of the 221 patients who underwent diagnostic triple endoscopies, 40.7% underwent intervention at the same time for laryngeal cleft (injection or suture, dependent upon age). Here we created an effective model of open language search query to extract meaningful metrics of patient experience from EHR data. This model easily allows the EHR to be harnessed to create retrospective and prospective databases that can be readily queried to answer clinical questions important to patients. Such databases are widely applicable not just to pediatric aerodigestive centers but to any clinical care setting using an EHR.

Question answering over knowledge graphs using BERT based relation mapping

AbstractA knowledge graph (KG) is a structured form of knowledge describing real‐world entities, properties and relationships as a graph. Question answering over knowledge graphs (KGQA) allows people to ask questions in natural language and extract answers from KG accurately and more quickly. The main task of a KGQA is to convert a natural language query to the corresponding structured query form like SPARQL. However, generating the precise SPARQL query from a question is challenging and highly error‐prone. Here we propose a question‐answering framework that uses KG to answer simple questions without using SPARQL. Question classification, dependency parsing, entity linking, BERT‐based relation finding and answer extraction constitute the main modules of the approach. We have used the DBpedia as the KG and tested the end‐to‐end system with a subset of QALD‐4, LC‐QuAD and SimpleQuestions datasets. Results show considerable improvement compared to other approaches in terms of F1‐score.

MapIntel: A visual analytics platform for competitive intelligence

AbstractCompetitive Intelligence allows an organization to keep up with market trends and foresee business opportunities. This practice is mainly performed by analysts scanning for any piece of valuable information in a myriad of dispersed and unstructured sources. Here we present MapIntel, a system for acquiring intelligence from vast collections of text data by representing each document as a multidimensional vector that captures its own semantics. The system is designed to handle complex Natural Language queries and visual exploration of the corpus, potentially aiding overburdened analysts in finding meaningful insights to help decision‐making. The system searching module uses a retriever and re‐ranker engine that first finds the closest neighbours to the query embedding and then sifts the results through a cross‐encoder model that identifies the most relevant documents. The browsing or visualization module also leverages the embeddings by projecting them onto two dimensions while preserving the multidimensional landscape, resulting in a map where semantically related documents form topical clusters which we capture using topic modelling. This map aims at promoting a fast overview of the corpus while allowing a more detailed exploration and interactive information encountering process. We evaluate the system and its components on the 20 newsgroups data set, using the semantic document labels provided, and demonstrate the superiority of Transformer‐based components. Finally, we present a prototype of the system in Python and show how some of its features can be used to acquire intelligence from a news article corpus we collected during a period of 8 months.

Generalisable Dialogue-based Approach for Active Learning of Activities of Daily Living

While Human Activity Recognition systems may benefit from Active Learning by allowing users to self-annotate their Activities of Daily Living (ADLs), many proposed methods for collecting such annotations are for short-term data collection campaigns for specific datasets. We present a reusable dialogue-based approach to user interaction for active learning in activity recognition systems, which utilises semantic similarity measures and a dataset of natural language descriptions of common activities (which we make publicly available). Our approach involves system-initiated dialogue, including follow-up questions to reduce ambiguity in user responses where appropriate. We apply this approach to two active learning scenarios: (i) using an existing CASAS dataset, demonstrating long-term usage; and (ii) using an online activity recognition system, which tackles the issue of online segmentation and labelling. We demonstrate our work in context, in which a natural language interface provides knowledge that can help interpret other multi-modal sensor data. We provide results highlighting the potential of our dialogue- and semantic similarity-based approach. We evaluate our work: (i) quantitatively, as an efficient way to seek users’ input for active learning of ADLs; and (ii) qualitatively, through a user study in which users were asked to compare our approach and an established method. Results show the potential of our approach as a hands-free interface for annotation of sensor data as part of an active learning system. We provide insights into the challenges of active learning for activity recognition under real-world conditions and identify potential ways to address them.

Market analysis and quality assessment of applications on Android-devices for patients with bronchial asthma and chronic obstructive pulmonary disease in Ukraine

Annotation. A modern and effective tool for managing patients with chronic obstructive pulmonary disease (COPD) and bronchial asthma (BA) are software applications for smartphones. In order to analyze and evaluate the quality of applications for ANDROID devices available in Ukraine, which can be used by patients with BA and COPD, relevant apps were searched in the Google Play app store. We analyzed their scope, including age restrictions, number of downloads, interface language, user rating. The quality of the apps was assessed according to the guidelines of the Health and Care Mobile App Assessment Organization (ORCHA), and the reliability checklist of an untested mobile health app. We found that Ukrainians have access to 47 mobile apps for ANDROID devices to help them adapt to living with the disease. Of these, only five applications are free and in Ukrainian, but only two are software applications in terms of quality and meet the requirements of ORCHA, so may be recommended for use in patients with AD and COPD, that is insufficient to meet the needs of Ukrainian patients.

Increased ventricular ectopy precedes Torsades de Pointes in patients with prolonged QT.

Torsades de Pointes (TdP) is a potentially lethal ventricular tachydysrhythmia. Prolonged heartrate corrected QT interval (QTc) predicts TdP; however, with poor specificity. We performed this study to identify other predictors of TdP among patients with prolonged QTc. We performed a retrospective case control study with 2:1 matching at an urban academic hospital. We searched our hospital electrocardiogram (ECG) database for tracings with heartrate ≤ 60, QTc≥500, and QRS<120, followed by a natural language search for electronic records with "Torsades," "polymorphic VT," or similar to identify TdP cases from 2005 to 19. We identified controls from a similar ECG database search matching for QTc, heartrate, age, and sex. We compared cardiologic and historical factors, medications, laboratory values, and ECG measurements including ectopy using univariate statistics. For those cases with saved telemetry strips that included preceding beats or TdP onset, we compared ectopy and TdP onset characteristics between the ECG and telemetry strips using mixed linear modeling. Seventy-five cases including 50 with telemetry strips and 150 controls were included. Historical, pharmacologic, laboratory, and cardiologic testing results were similar between cases and controls. The proportion of telemetry tracings with premature ventricular contractions (PVC's) preceding TdP was 0.78 compared to 0.16 for case ECG's (difference 0.62(95%CI 0.44-0.75)) and 0.10 for control ECGs (difference 0.68(95%CI 0.56-0.80)). Average telemetry heartrate was 72 and QTc 549 immediately preceding TdP, similar to the ECG values. Clinical factors don't differentiate patients with long QTc who develop TdP, however, an increase in PVC's in patients with prolonged QTc may usefully predict imminent TdP.

Dynamic prompt-based virtual assistant framework for BIM information search

Efficient information search from building information models (BIMs) requires deep BIM knowledge or extensive engineering efforts for building natural language (NL)-based interfaces. To address this challenge, this paper introduces a dynamic prompt-based virtual assistant framework dubbed “BIMS-GPT” that integrates generative pre-trained transformer (GPT) technologies, supporting NL-based BIM search. To understand users' NL queries, extract relevant information from BIM databases, and deliver NL responses along with 3D visualizations, a dynamic prompt-based process was developed. In a case study, BIMS-GPT's functionality is demonstrated through a virtual assistant prototype for a hospital building. When evaluated with a BIM query dataset, the approach achieves accuracy rates of 99.5% for classifying NL queries with incorporating 2% of the data in prompts. This paper contributes to the advancement of effective and versatile virtual assistants for BIMs in the construction industry as it significantly enhances BIM accessibility while reducing the engineering and training data prerequisites for processing NL queries.

A visual-language foundation model for pathology image analysis using medical Twitter.

The lack of annotated publicly available medical images is a major barrier for computational research and education innovations. At the same time, many de-identified images and much knowledge are shared by clinicians on public forums such as medical Twitter. Here we harness these crowd platforms to curate OpenPath, a large dataset of 208,414 pathology images paired with natural language descriptions. We demonstrate the value of this resource by developing pathology language-image pretraining (PLIP), a multimodal artificial intelligence with both image and text understanding, which is trained on OpenPath. PLIP achieves state-of-the-art performances for classifying new pathology images across four external datasets: for zero-shot classification, PLIP achieves F1 scores of 0.565-0.832 compared to F1 scores of 0.030-0.481 for previous contrastive language-image pretrained model. Training a simple supervised classifier on top of PLIP embeddings also achieves 2.5% improvement in F1 scores compared to using other supervised model embeddings. Moreover, PLIP enables users to retrieve similar cases by either image or natural language search, greatly facilitating knowledge sharing. Our approach demonstrates that publicly shared medical information is a tremendous resource that can be harnessed to develop medical artificial intelligence for enhancing diagnosis, knowledge sharing and education.

FindVehicle and VehicleFinder: a NER dataset for natural language-based vehicle retrieval and a keyword-based cross-modal vehicle retrieval system

Natural language (NL) based vehicle retrieval is a task aiming to retrieve a vehicle that is most consistent with a given NL query from among all candidate vehicles. Because NL query can be easily obtained, such a task has a promising prospect in building an interactive intelligent traffic system (ITS). Current solutions mainly focus on extracting both text and image features and mapping them to the same latent space to compare the similarity. However, existing methods usually use dependency analysis or semantic role-labelling techniques to find keywords related to vehicle attributes. These techniques may require a lot of pre-processing and post-processing work, and also suffer from extracting the wrong keyword when the NL query is complex. To tackle these problems and simplify, we borrow the idea from named entity recognition (NER) and construct FindVehicle, a NER dataset in the traffic domain. It has 42.3k labelled NL descriptions of vehicle tracks, containing information such as the location, orientation, type and colour of the vehicle. FindVehicle also adopts both overlapping entities and fine-grained entities to meet further requirements. To verify its effectiveness, we propose a baseline NL-based vehicle retrieval model called VehicleFinder. Our experiment shows that by using text encoders pre-trained by FindVehicle, VehicleFinder achieves 87.7% precision and 89.4% recall when retrieving a target vehicle by text command on our homemade dataset based on UA-DETRAC [1]. From loading the command into VehicleFinder to identifying whether the target vehicle is consistent with the command, the time cost is 279.35 ms on one ARM v8.2 CPU and 93.72 ms on one RTX A4000 GPU, which is much faster than the Transformer-based system. The dataset is open-source via the link https://github.com/GuanRunwei/FindVehicle, and the implementation can be found via the link https://github.com/GuanRunwei/VehicleFinder-CTIM.

Natural Language Interfaces for Databases with Deep Learning

In the age of the Digital Revolution, almost all human activities, from industrial and business operations to medical and academic research, are reliant on the constant integration and utilisation of ever-increasing volumes of data. However, the explosive volume and complexity of data makes data querying and exploration challenging even for experts, and makes the need to democratise the access to data, even for non-technical users, all the more evident. It is time to lift all technical barriers, by empowering users to access relational databases through conversation. We consider 3 main research areas that a natural language data interface is based on: Text-to-SQL, SQL-to-Text, and Data-to-Text. The purpose of this tutorial is a deep dive into these areas, covering state-of-the-art techniques and models, and explaining how the progress in the deep learning field has led to impressive advancements. We will present benchmarks that sparked research and competition, and discuss open problems and research opportunities with one of the most important challenges being the integration of these 3 research areas into one conversational system.

Information extraction from weakly structured radiological reports with natural language queries

ObjectivesProvide physicians and researchers an efficient way to extract information from weakly structured radiology reports with natural language processing (NLP) machine learning models.MethodsWe evaluate seven different German bidirectional encoder representations from transformers (BERT) models on a dataset of 857,783 unlabeled radiology reports and an annotated reading comprehension dataset in the format of SQuAD 2.0 based on 1223 additional reports.ResultsContinued pre-training of a BERT model on the radiology dataset and a medical online encyclopedia resulted in the most accurate model with an F1-score of 83.97% and an exact match score of 71.63% for answerable questions and 96.01% accuracy in detecting unanswerable questions. Fine-tuning a non-medical model without further pre-training led to the lowest-performing model. The final model proved stable against variation in the formulations of questions and in dealing with questions on topics excluded from the training set.ConclusionsGeneral domain BERT models further pre-trained on radiological data achieve high accuracy in answering questions on radiology reports. We propose to integrate our approach into the workflow of medical practitioners and researchers to extract information from radiology reports.Clinical relevance statementBy reducing the need for manual searches of radiology reports, radiologists’ resources are freed up, which indirectly benefits patients.Key Points• BERT models pre-trained on general domain datasets and radiology reports achieve high accuracy (83.97% F1-score) on question-answering for radiology reports.• The best performing model achieves an F1-score of 83.97% for answerable questions and 96.01% accuracy for questions without an answer.• Additional radiology-specific pretraining of all investigated BERT models improves their performance.Graphical

An empirically supported approach to the treatment of imprecision in vague reasoning

The aim of this paper is to propose a new approach for the automatic treatment of linguistic vagueness. Our motivation is the feeling that most existing approaches dealing with linguistic information are based on converting vague meaning into crisp meaning using some conversions to precise measurements. As a result, existing approaches are adequate and easy to implement, but do not closely model the human thought process. To help alleviate this deficiency, we propose the use of linguistic relations to provide a natural language interface to an end user. We show a possible linguistic Prolog model based on an extension of the syntactic unification algorithm using synonymy and antonymy, as well as the extension of the resolution principle. Our approach does not aim to provide a well-founded formal semantics for such a linguistic Prolog, but a simple model supported by two experiments focused on the use of vague language, both of them executed in Spanish (an analysis of the data of the first experiment it is also available in that language at [1]). Thus, the purpose of this paper is to contribute to the mechanization of approximate reasoning by being respectful of the semantics of the vague terms involved in it; i.e., by paying attention to how they are evaluated by linguistic users under experimentation.

Retrieving API Knowledge from Tutorials and Stack Overflow Based on Natural Language Queries

When encountering unfamiliar APIs, developers tend to seek help from API tutorials and Stack Overflow (SO). API tutorials help developers understand the API knowledge in a general context, while SO often explains the API knowledge in a specific programming task. Thus, tutorials and SO posts together can provide more API knowledge. However, it is non-trivial to retrieve API knowledge from both API tutorials and SO posts based on natural language queries. Two major problems are irrelevant API knowledge in two different resources and the lexical gap between the queries and documents. In this article, we regard a fragment in tutorials and a Question and Answering (Q&amp;A) pair in SO as a knowledge item (KI). We generate ⟨ API, FRA ⟩ pairs (FRA stands for fragment) from tutorial fragments and APIs and build ⟨ API, QA ⟩ pairs based on heuristic rules of SO posts. We fuse ⟨ API, FRA ⟩ pairs and ⟨ API, QA ⟩ pairs to generate API knowledge (AK for short) datasets, where each data item is an ⟨ API, KI ⟩ pair. We propose a novel approach, called PLAN, to automatically retrieve API knowledge from both API tutorials and SO posts based on natural language queries. PLAN contains three main stages: (1) API knowledge modeling, (2) query mapping, and (3) API knowledge retrieving. It first utilizes a deep-transfer-metric-learning-based relevance identification (DTML) model to effectively find relevant ⟨ API, KI ⟩ pairs containing two different knowledge items (⟨ API, QA ⟩ pairs and ⟨ API, FRA ⟩ pairs) simultaneously. Then, PLAN generates several potential APIs as a way to reduce the lexical gap between the query and ⟨ API, KI ⟩ pairs. According to potential APIs, we can select relevant ⟨ API, KI ⟩ pairs to generate potential results. Finally, PLAN returns a list of ranked ⟨ API, KI ⟩ pairs that are related to the query. We evaluate the effectiveness of PLAN with 270 queries on Java and Android AK datasets containing 10,072 ⟨ API, KI ⟩ pairs. Our experimental results show that PLAN is effective and outperforms the state-of-the-art approaches. Our user study further confirms the effectiveness of PLAN in locating useful API knowledge.

MILK: a Python scripting interface to MAUD for automation of Rietveld analysis.

Modern diffraction experiments (e.g. in situ parametric studies) present scientists with many diffraction patterns to analyze. Interactive analyses via graphical user interfaces tend to slow down obtaining quantitative results such as lattice parameters and phase fractions. Furthermore, Rietveld refinement strategies (i.e. the parameter turn-on-off sequences) tend to be instrument specific or even specific to a given dataset, such that selection of strategies can become a bottleneck for efficient data analysis. Managing multi-histogram datasets such as from multi-bank neutron diffractometers or caked 2D synchrotron data presents additional challenges due to the large number of histogram-specific parameters. To overcome these challenges in the Rietveld software Material Analysis Using Diffraction (MAUD), the MAUD Interface Language Kit (MILK) is developed along with an updated text batch interface for MAUD. The open-source software MILK is computer-platform independent and is packaged as a Python library that interfaces with MAUD. Using MILK, model selection (e.g. various texture or peak-broadening models), Rietveld parameter manipulation and distributed parallel batch computing can be performed through a high-level Python interface. A high-level interface enables analysis workflows to be easily programmed, shared and applied to large datasets, and external tools to be integrated with MAUD. Through modification to the MAUD batch interface, plot and data exports have been improved. The resulting hierarchical folders from Rietveld refinements with MILK are compatible with Cinema: Debye-Scherrer, a tool for visualizing and inspecting the results of multi-parameter analyses of large quantities of diffraction data. In this manuscript, the combined Python scripting and visualization capability of MILK is demonstrated with a quantitative texture and phase analysis of data collected at the HIPPO neutron diffractometer.

Simulation modeling and safety analysis of the mixed traffic flow with connected automated trucks platoon and human-driven vehicles

The rapid development of intelligent connected vehicles makes it possible to achieve breakthroughs in the application of technology in the connected automated trucks platoon(CATP) in the scenarios of ports, logistics parks, etc., but the safety of the mixed traffic flow formed by it and traditional human-driven vehicles has not been fully recognized. This paper builds a simulation scene based on SUMO(Simulation of Urban Mobility) simulation software, builds traditional human-driven vehicles and CATP car-following model, and studies the safety analysis of the mixed traffic flow of CATP and human-driven vehicles. According to the Traci interface of SUMO and PYTHON programming language, the functions of connected automated environment configuration and real-time data acquisition are realized, and the safety of mixed traffic flow is evaluated using multiple safety surrogate measures(SSM) such as time exposed time-to-collision(TET) and time-integrated time-to-collision(TIT). The simulation results show that when the CATP penetration rate is less than 0.5, the security risk of mixed traffic flow increases with the increase of penetration rate; When the CATP penetration rate exceeds 0.5, the safety risk of mixed traffic flow will be significantly reduced. The research conclusion of this paper has reference significance for the safety and practical implementation of CATP in ports and logistics parks.

Email Security Issues, Tools, and Techniques Used in Investigation

The email system is a globally distributed communication infrastructure service that involves multiple actors playing different roles to ensure end-to-end mail delivery. It is an indispensable method of communicating that is changing how people share data and information. As a result, it facilitates effective and efficient communication, especially in business, as well as convenience, accessibility, and replication. Today, email can send more than just text files; it can also send audio, video, photos, and other files with extensions. With email becoming ubiquitous in all aspects of our lives, enhancing its security, operating procedures, and forensic investigation has become essential. The purpose of this paper is to review some real email forensic incidents and the tools and techniques that have been proposed. A discussion of the major threats to email as well as techniques to mitigate them will also be provided. A comparison study was made of several techniques and analysis tools used in email forensics. In addition, this paper compares the available software tools for email forensics based on factors such as language interface, user interface, programming language, creation of image files, calculation of hash value, cost, and advantages.

Understanding Natural Language in Context

Recent years have seen an increasing number of applications that have a natural language interface, either in the form of chatbots or via personal assistants such as Alexa (Amazon), Google Assistant, Siri (Apple), and Cortana (Microsoft). To use these applications, a basic dialog between the assistant and the human is required. While this kind of dialog exists today mainly within static robots that do not make any movement in the household space, the challenge of reasoning about the information conveyed by the environment increases significantly when dealing with robots that can move and manipulate objects in our home environment. In this paper, we focus on cognitive robots, which have some knowledge-based models of the world and operate by reasoning and planning with this model. Thus, when the robot and the human communicate, there is already some formalism they can use -- the robot’s knowledge representation formalism. In this paper we describe an approach for translating natural language directives into the robot's formalism, allowing much more complicated household tasks to be completed. We do so by combining off-the-shelf SoTA large language models, planning tools, and the robot knowledge of the state of the world and of its own model. This results in much more accurate interpretation of directives in natural language.

Towards Hybrid Automation by Bootstrapping Conversational Interfaces for IT Operation Tasks

Process automation has evolved from end-to-end automation of repetitive process branches to hybrid automation where bots perform some activities and humans serve other activities. In the context of knowledge-intensive processes such as IT operations, implementing hybrid automation is a natural choice where robots can perform certain mundane functions, with humans taking over the decision of when and which IT systems need to act. Recently, ChatOps, which refers to conversation-driven collaboration for IT operations, has rapidly accelerated efficiency by providing a cross-organization and cross-domain platform to resolve and manage issues as soon as possible. Hence, providing a natural language interface to bots is a logical progression to enable collaboration between humans and bots. This work presents a no-code approach to provide a conversational interface that enables human workers to collaborate with bots executing automation scripts. The bots identify the intent of users' requests and automatically orchestrate one or more relevant automation tasks to serve the request. We further detail our process of mining the conversations between humans and bots to monitor performance and identify the scope for improvement in service quality.

Prototyping an Online Patient Registration Based on a Smartphone App at the Malang Primary Health Care

Currently, the patient registration system at the Primary Health Care (PHC) in Malang City still uses a manual registration system with various existing problems, like the emergence of queues and patients jostling in the waiting room, so it takes longer for health services. This study aimed to design a prototype application for online patient registration at the Malang City Health Center. The method used is Research and Development Prototyping. The manufacturing stages start from (1) the identification of basic needs, (2) developing a working prototype, (3) using a prototype (trial), and (4) revising and improving the prototype. The instrument used in this study was an interview guide conducted with two Heads of PHC (Dinoyo and Rampal Celaket) and nine Registration Officers at two Health Centers to determine the need for designing a prototype online registration application. Observations were made regarding the existing IT systems (Hardware and Software) in both PHC, the facilities and infrastructure that support IT systems (rooms, internet networks, etc.) and the documents supporting the processes. The results of this prototype design were in the form of a list of functional and non-functional requirements, business process flow diagrams, data flow diagrams, Entity Relationship Diagrams, and Interface Design, including programming language and user interface. Based on the results of the online patient registration prototype design, it enters the initial stage of using a system design that is by user needs but has yet to reach the implementation stage. However, this prototype can be used as a reference in developing an integrated online registration system with existing information systems at the PHC.&#x0D; Keywords: Prototype, Online Registration, Smartphone, Primary Health Care, Patient

An ontology-aided, natural language-based approach for multi-constraint BIM model querying

Construction project stakeholders often have to retrieve the required information in Building Information Models (BIMs) to support their design, engineering, and management activities. Natural language interface (NLI) systems are emerging as a time- and cost-effective way to query complex BIM models. However, the existing attempts cannot logically combine different constraints to perform fine-grained queries, dampening the usability of BIM-oriented NLIs. This paper presents a novel ontology-aided semantic parser to automatically map natural language queries (NLQs) that contain different attribute and relational constraints into computer-readable codes for BIM model retrieval in the context of building project development. A modular ontology was first developed to represent natural language expressions of Industry Foundation Classes (IFC) concepts, relationships, and reasoning rules; it was then populated with entities from target BIM models to assimilate project-specific information. After that, the ontology-aided semantic parser progressively extracts concepts, relationships, and value restrictions from NLQs to identify multi-level constraint conditions, resulting in standard SPARQL queries to successfully retrieve IFC-based BIM models. The approach was evaluated based on 225 NLQs collected from BIM users, with a 91% accuracy rate. Finally, a case study about the design-checking of a real-world residential building demonstrates the practicability of the proposed method in the construction industry.