Fuzzy Markup Language Research Articles

Human intelligence-based metaverse for co-learning of students and smart machines.

This paper proposes a Human Intelligence (HI)-based Computational Intelligence (CI) and Artificial Intelligence (AI) Fuzzy Markup Language (CI&AI-FML) Metaverse as an educational environment for co-learning of students and machines. The HI-based CI&AI-FML Metaverse is based on the spirit of the Heart Sutra that equips the environment with teaching principles and cognitive intelligence of ancient words of wisdom. There are four stages of the Metaverse: preparation and collection of learning data, data preprocessing, data analysis, and data evaluation. During the data preparation stage, the domain experts construct a learning dictionary with fuzzy concept sets describing different terms and concepts related to the course domains. Then, the students and teachers use the developed CI&AI-FML learning tools to interact with machines and learn together. Once the teachers prepare relevant material, students provide their inputs/texts representing their levels of understanding of the learned concepts. A Natural Language Processing (NLP) tool, Chinese Knowledge Information Processing (CKIP), is used to process data/text generated by students. A focus is put on speech tagging, word sense disambiguation, and named entity recognition. Following that, the quantitative and qualitative data analysis is performed. Finally, the students' learning progress, measured using progress metrics, is evaluated and analyzed. The experimental results reveal that the proposed HI-based CI&AI-FML Metaverse can foster students' motivation to learn and improve their performance. It has been shown in the case of young students studying Software Engineering and learning English.

Prevention of Falls from Heights in Construction Using an IoT System Based on Fuzzy Markup Language and JFML

The main cause of fatal accidents in the construction sector are falls from height (FFH) and the inappropriate use of a harness is commonly associated with these fatalities. Traditional methods, such as onsite inspections, safety communication, or safety training, are not enough to mitigate accidents caused by FFH associated with a poor management in the use of a harness. Although some technological solutions for the automated monitoring of workers could improve safety conditions, their use is not frequent due to the particularities of construction sites: complexity, dynamic environments, outdoor workplaces, etc. Then, the integration of expert knowledge with technology is a key issue. Fuzzy logic systems (FLS) and Internet of Things (IoT) present many potential benefits, such as real-time decisions being made based on FLS and data from sensors. In the current research, the development and test of an IoT system integrated with the Java Fuzzy Markup Language Library for FLS, to support experts’ decision making in FFH, is proposed. The proposal was checked in four construction scenarios based on working conditions with different levels of risk of FFH and obtained promising results.

An Internet of Things and Fuzzy Markup Language Based Approach to Prevent the Risk of Falling Object Accidents in the Execution Phase of Construction Projects.

The Internet of Things (IoT) paradigm is establishing itself as a technology to improve data acquisition and information management in the construction field. It is consolidating as an emerging technology in all phases of the life cycle of projects and specifically in the execution phase of a construction project. One of the fundamental tasks in this phase is related to Health and Safety Management since the accident rate in this sector is very high compared to other phases or even sectors. For example, one of the most critical risks is falling objects due to the peculiarities of the construction process. Therefore, the integration of both technology and safety expert knowledge in this task is a key issue including ubiquitous computing, real-time decision capacity and expert knowledge management from risks with imprecise data. Starting from this vision, the goal of this paper is to introduce an IoT infrastructure integrated with JFML, an open-source library for Fuzzy Logic Systems according to the IEEE Std 1855-2016, to support imprecise experts’ decision making in facing the risk of falling objects. The system advises the worker of the risk level of accidents in real-time employing a smart wristband. The proposed IoT infrastructure has been tested in three different scenarios involving habitual working situations and characterized by different levels of falling objects risk. As assessed by an expert panel, the proposed system shows suitable results.

Comparative Analysis of Real-Time Fall Detection Using Fuzzy Logic Web Services and Machine Learning

Falls are the main cause of susceptibility to severe injuries in many humans, especially for older adults aged 65 and over. Typically, falls are being unnoticed and interpreted as a mere inevitable accident. Various wearable fall warning devices have been created recently for older people. However, most of these devices are dependent on local data processing. Various algorithms are used in wearable sensors to track a real-time fall effectively, which focuses on fall detection via fuzzy-as-a-service based on IEEE 1855–2016, Java Fuzzy Markup Language (FML) and service-oriented architecture. Moreover, several approaches are used to detect a fall using machine learning techniques via human movement positional data to avert any accidents. For fuzzy logic web services, analysis is performed using wearable accelerometer and gyroscope sensors, whereas in machine learning techniques, k-NN, decision tree, random forest and extreme gradient boost are used to differentiate between a fall and non-fall. This study aims to carry out a comparative analysis of real-time fall detection using fuzzy logic web services and machine learning techniques and aims to determine which one is better for real-time fall detection. Research findings exhibit that the proposed fuzzy-as-a-service could easily differentiate between fall and non-fall occurrences in a real-time environment with an accuracy, sensitivity and specificity of 90%, 88.89% and 91.67%, respectively, while the random forest algorithm of machine learning achieved 99.19%, 98.53% and 99.63%, respectively.

FML-Based Reinforcement Learning Agent with Fuzzy Ontology for Human-Robot Cooperative Edutainment

The currently observed developments in Artificial Intelligence (AI) and its influence on different types of industries mean that human-robot cooperation is of special importance. Various types of robots have been applied to the so-called field of Edutainment, i.e., the field that combines education with entertainment. This paper introduces a novel fuzzy-based system for a human-robot cooperative Edutainment. This co-learning system includes a brain-computer interface (BCI) ontology model and a Fuzzy Markup Language (FML)-based Reinforcement Learning Agent (FRL-Agent). The proposed FRL-Agent is composed of (1) a human learning agent, (2) a robotic teaching agent, (3) a Bayesian estimation agent, (4) a robotic BCI agent, (5) a fuzzy machine learning agent, and (6) a fuzzy BCI ontology. In order to verify the effectiveness of the proposed system, the FRL-Agent is used as a robot teacher in a number of elementary schools, junior high schools, and at a university to allow robot teachers and students to learn together in the classroom. The participated students use handheld devices to indirectly or directly interact with the robot teachers to learn English. Additionally, a number of university students wear a commercial EEG device with eight electrode channels to learn English and listen to music. In the experiments, the robotic BCI agent analyzes the collected signals from the EEG device and transforms them into five physiological indices when the students are learning or listening. The Bayesian estimation agent and fuzzy machine learning agent optimize the parameters of the FRL agent and store them in the fuzzy BCI ontology. The experimental results show that the robot teachers motivate students to learn and stimulate their progress. The fuzzy machine learning agent is able to predict the five physiological indices based on the eight-channel EEG data and the trained model. In addition, we also train the model to predict the other students’ feelings based on the analyzed physiological indices and labeled feelings. The FRL agent is able to provide personalized learning content based on the developed human and robot cooperative edutainment approaches. To our knowledge, the FRL agent has not applied to the teaching fields such as elementary schools before and it opens up a promising new line of research in human and robot co-learning. In the future, we hope the FRL agent will solve such an existing problem in the classroom that the high-performing students feel the learning contents are too simple to motivate their learning or the low-performing students are unable to keep up with the learning progress to choose to give up learning.

Fuzzy Markup Language を用いたファジィシステムの開発

Fuzzy Markup Language を用いたファジィシステムの開発

A Fuzzy Markup Language-Based Approach for a Quality of Location Inference as An Environmental Health Awareness

In a recent study conducted over a two-month period, the authors have concluded that there exists a correlation between two environmental parameters, namely air quality and noise. The correlation efficiency results obtained were quantitative in nature, and thus cannot be intuitively interpreted by humans. In this article, the authors propose a Fuzzy-markup language (FML) model that aims to translate the degree of correlation that may exist among IoT environmental parameters into a linguistic set of indicators. For that purpose, the authors have developed a Fuzzy-Inference System (FIS) that infers the quality of location status according to people's surroundings and provide health-aware notifications accordingly. The initial FIS results show the significance of timeframe that should be considered according to each sensory data source. In other words, the correlation degree of the two sensory set is clearly affected by time frame variations.

PSO-Based Fuzzy Markup Language for Student Learning Performance Evaluation and Educational Application

This paper proposes an agent with particle swarm optimization (PSO) based on a Fuzzy Markup Language (FML) for students learning performance evaluation and educational applications, and the proposed agent is according to the response data from a conventional test and an item response theory. First, we apply a GS-based parameter estimation mechanism to estimate the items parameters according to the response data, and then to compare its results with those of an IRT-based Bayesian parameter estimation mechanism. In addition, we propose a static-IRT test assembly mechanism to assemble a form for the conventional test. The presented FML-based dynamic assessment mechanism infers the probability of making a correct response to the item for a student with various abilities. Moreover, this paper also proposes a novel PFML learning mechanism for optimizing the parameters between items and students. Finally, we adopt a K-fold cross validation mechanism to evaluate the performance of the proposed agent. Experimental results show that the novel PFML learning mechanism for the parameter estimation and learning optimization performs favorably. We believe the proposed PFML will be a reference for education research and pedagogy and an important co-learning mechanism for future human-machine educational applications.

Ontology-based GFML agent for patent technology requirement evaluation and recommendation

Patent technology requirement evaluation and recommendation are critical for patent strategy, patent management, and patent usage in an organization. This paper proposes a patent technology evaluation and recommendation agent based on a soft-computing approach to enhance patent expansibility and technology transfer. First, we investigate the relationship between patent technology and patent owners, such as academic institutes or organizations, and integrate the collected patent data with the characteristics of organizations to establish a popular patent ontology for general academic institutes or organizations. Then, the patent’s suitability for a specific organization is determined based on concepts extracted using Chinese Knowledge Information Processing. Next, we refer to the Japan Patent Office evaluation index and intellectual property quotient to describe the knowledge base and rule base of the patent quality evaluation agent by using fuzzy markup language (FML). A comprehensive patent quality evaluation mechanism is implemented, and the genetic algorithm is adopted to improve the performance of the proposed agent. Additionally, the patent requirement level evaluation mechanism infers the patent requirement level according to the basic information of an organization. Finally, we present a novel FML-based patent requirement recommendation agent to recommend a patent for an organization by considering the suitability of the patent technology for such an organization, the results of the comprehensive patent quality evaluation process, and the results of the evaluation of the demander’s patent requirements. According to the results, the proposed agent is feasible for patent recommendation. In the future, we will combine an intelligent robot with the GFML agent to assist humans or organizations in recommending an appropriate patent.

FML-based Dynamic Assessment Agent for Human-Machine Cooperative System on Game of Go

In this paper, we demonstrate the application of Fuzzy Markup Language (FML) to construct an FML-based Dynamic Assessment Agent (FDAA), and we present an FML-based Human–Machine Cooperative System (FHMCS) for the game of Go. The proposed FDAA comprises an intelligent decision-making and learning mechanism, an intelligent game bot, a proximal development agent, and an intelligent agent. The intelligent game bot is based on the open-source code of Facebook’s Darkforest, and it features a representational state transfer application programming interface mechanism. The proximal development agent contains a dynamic assessment mechanism, a GoSocket mechanism, and an FML engine with a fuzzy knowledge base and rule base. The intelligent agent contains a GoSocket engine and a summarization agent that is based on the estimated win rate, real-time simulation number, and matching degree of predicted moves. Additionally, the FML for player performance evaluation and linguistic descriptions for game results commentary are presented. We experimentally verify and validate the performance of the FDAA and variants of the FHMCS by testing five games in 2016 and 60 games of Google’s Master Go, a new version of the AlphaGo program, in January 2017. The experimental results demonstrate that the proposed FDAA can work effectively for Go applications.

Adaptive Personalized Diet Linguistic Recommendation Mechanism Based on Type-2 Fuzzy Sets and Genetic Fuzzy Markup Language

Many different real-world applications with a high-level of uncertainty proved the good performance of the type-2 fuzzy sets (T2 FSs). Balanced diet means that the intake of each necessary nutrient meets its adequate demand and actual caloric intake balances with calories burned. Additionally, making a diversity of choice from various types of food is also essential to reduce the risk of developing various chronic diseases. Different people have a different goal and it is hard to measure how healthy the eaten meal is for those who are not the domain experts on the diet. This paper presents an adaptive personalized diet linguistic recommendation mechanism based on type-2 fuzzy logic system (T2 FLS) and genetic fuzzy markup language (GFML). First, an adaptive dietary assessment and recommendation ontology is constructed by domain experts, and then a T2 FS-based GFML, describing the fuzzy knowledge base and the fuzzy rule base of the proposed mechanism, is evolved by using genetic algorithms. Next, a T2 FS-based fuzzy inference mechanism infers the result of the dietary health level based on the evolved type-2 GFML (T2GFML). In addition, the balanced computation mechanism is also proposed to reduce the computational complexity of the T2 FLS for the diet domain knowledge. Finally, the linguistic knowledge discovery mechanism presents the discovered linguistic meaning about the meal's health level to show the involved subjects how to make a personalized diet linguistic recommendation. This type of information about the eaten meal can provide the subjects with a reference to gradually improve their unhealthy eating habit and then become healthier and healthier. Experimental results show that the results of the proposed mechanism for the T2 FLS are better than those for the type-1 fuzzy logic system (T1 FLS).

Performance Verification Mechanism for Adaptive Assessment e-Platform and e-Navigation Application

Adaptive assessment e-platform is being promoted in the world to make teachers understand students’ e-learning performance on the Internet. However, system's load testing for an adaptive assessment is a very important issue during development of such an e-platform. In this paper, we have adopted the genetic fuzzy markup language (GFML) to infer the performance of an adaptive assessment e-platform. Firstly, we collected the data and information of the e-platform loading in two different mechanisms. With the collected data, the proposed CPU usage calculation mechanism is first implemented to acquire the CPU usage information from the screenshot of Ganglia. Next, we used the fuzzy c-means (FCM) clustering mechanism to construct the knowledge base according to the collected data. Then, number of threads, constant timer, MySQL parameter, CPU usage, and testing time of the e-platform were utilized to infer the e-platform load performance. Finally, the genetic learning algorithm was utilized to learn the knowledge and rule base to optimize the proposed approach. From these experimental results, the proposed method is feasible for verifying the performance of an adaptive assessment e-platform. In the future, the adaptive assessment e-platform can be utilized to e-Navigation systems and applications.

Fuzzy intelligent inference scheme for self-health estimation via blood pressure and body mass index in out-of-hospital

E-health for both chronic patients and wellness persons has recently attracted the interest of researchers and practitioners. The physical vital signs are one of the most important factors used to evaluate individual wellness. The variations of daily vital signs are even significant in analyzing physical health trends that can be applied in self-caring for individuals at home. In this paper, an Intelligent-Mamdani Inference Scheme (IMIS) based on fuzzy markup language (FML) is proposed to define approximate health conditions of the individuals via the blood pressure and the body mass index in out-of-hospital. The IMIS can fuse these vital signs and infer semantic health summary using the constructed fuzzy rules and the knowledge base. The main contributions of the this paper are: (1) to leverage personal vital signs by fuzzy logic technology for self-health management at home; (2) to design the novel scheme in detail for practicability and reproduction. The experimental results show that the scheme is feasible to infer personal health status. An individual can easily recognize self-health trend through semantic sentence generation, which further advances self-health management in out-of-hospital.

Healthy diet assessment mechanism based on fuzzy markup language for Japanese food

Owing to the change of our lifestyle, more people suffer from chronic diseases, such as obesity or diabetes. Making improvements in terms of diet and physical activity is helpful for people to reduce the risk of suffering from these chronic diseases. This paper presents a healthy diet assessment mechanism based on fuzzy markup language (FML) for Japanese food. This mechanism is based on the dietary standard, food exchange list (FEL), published by the Japan Diabetes Society, to assess one person's dietary healthy level according to his/her personal profile, his/her daily physical activity, and how balanced he/she consumes food items. We use FML to describe the knowledge base and the rule base of the constructed fuzzy logic system. Additionally, ontology stores the necessary knowledge base and rule base of the adopted fuzzy inference mechanism. The proposed mechanism operates as follows: (i) Dieticians first define the nutrient facts of the collected food items. (ii) The involved subjects record their personal information, eaten food items, and daily physical activity. Through the constructed FML-based personal profile ontology, the estimated energy requirement and the estimated unit requirement for each food group are obtained after inference. (iii) Finally, one-day dietary healthy level is inferred by the proposed fuzzy inference mechanism. From the experimental results, the proposed mechanism is feasible to apply to Japanese dietary assessment. In the future, we will integrate with genetic learning algorithm and collect more subjects' records to further improve the performance.

IT2FS-based ontology with soft-computing mechanism for malware behavior analysis

Antimalware application is one of the most important research issues in the area of cyber security threat. Nowadays, because hackers continuously develop novel techniques to intrude into computer systems for various reasons, many security researchers should analyze and track new malicious program to protect sensitive and valuable information in the organization. In this paper, we propose a novel soft-computing mechanism based on the ontology model for malware behavioral analysis: Malware Analysis Network in Taiwan (MAN in Taiwan, MiT). The core techniques of MiT contain two parts listed as follows: (1) collect the logs of network connection, registry, and memory from the operation system on the physical-virtual hybrid analysis environment to get and extract more unknown malicious behavior information. The important information is then extracted to construct the ontology model by using the Web Ontology Language and Fuzzy Markup Language. Additionally, MiT is also able to automatically provide and share samples and reports via the cloud storage mechanism; (2) apply the techniques of Interval Type-2 Fuzzy Set to construct the malware analysis domain knowledge, namely the Interval Type-2 Fuzzy Malware Ontology (IT2FMO), for malware behavior analysis. Simulation results show that the proposed approach can effectively execute the malware behavior analysis, and the constructed system has also released under GNU General Public License version 3. In the future, the system is expected to largely collect and analyze malware samples for providing industries or universities to do related applications via the established IT2FMO.

A query-oriented XML text summarization for mobile devices

Extensible Markup Language (XML) is a simple, flexible text format derived from SGML, which is originally designed to support large-scale electronic publishing. Nowadays XML plays a fundamental role in the exchange of a wide variety of data on the Web. As XML allows designers to create their own customized tags, enables the definition, transmission, validation, and interpretation of data between applications, devices and organizations, lots of works in soft computing employ XML to take control and responsibility for the information, such as fuzzy markup language, and accordingly there are lots of XML-based data or documents. However, most of mobile and interactive ubiquitous multimedia devices have restricted hardware such as CPU, memory, and display screen. So, it is essential to compress an XML document/element collection to a brief summary before it is delivered to the user according to his/her information need. Query-oriented XML text summarization aims to provide users a brief and readable substitution of the original retrieved documents/elements according to the user's query, which can relieve users' reading burden effectively. We propose a query-oriented XML summarization system QXMLSum, which extracts sentences and combines them as a summary based on three kinds of features: user's queries, the content of XML documents/elements, and the structure of XML documents/elements. Experiments on the IEEE-CS datasets used in Initiative for the Evaluation of XML Retrieval show that the query-oriented XML summary generated by QXMLSum is competitive.

Soft-computing-based emotional expression mechanism for game of computer Go

The game of Go is considered one of the most complicated games in the world. One Go game is divided into three stages: the opening, the middle, and the ending stages. Millions of people regularly play Go in countries around the world. The game is played by two players. One is White and another is Black. The players alternate placing one of their stones on an empty intersection of a square grid-patterned game board. The player with more territory wins the game. This paper proposes a soft-computing-based emotional expression mechanism and applies it to the game of computer Go to make Go beginners enjoy watching Go game and keep their tension on the game. First, the knowledge base and rule base of the proposed mechanism are defined by following the standards of the fuzzy markup language. The soft-computing mechanism for Go regional alarm level is responsible for showing the inferred regional alarm level to Go beginners. Based on the inferred board situation, the fuzzy inference mechanisms for emotional pleasure and arousal are responsible for inferring the pleasure degree and arousal degree, respectively. An emotional expression mapping mechanism maps the inferred degree of pleasure and degree of arousal into the emotional expression of the eye robot. The protocol transmission mechanism finally sends the pre-defined protocol to the eye robot via universal serial bus interface to make the eye robot express its emotional motion. From the experimental results, it shows that the eye robot can support Go beginners to have fun and retain their tension while watching or playing a game of Go.

A NOVEL GENETIC FUZZY MARKUP LANGUAGE AND ITS APPLICATION TO HEALTHY DIET ASSESSMENT

In this paper, we present a novel Genetic Fuzzy Markup Language (GFML)-based genetic fuzzy system, including the genetic learning base, the knowledge base and rule base of FML, the fuzzy inference engine, and the genetic learning mechanism. The GFML is applied to the genetic fuzzy system for dealing with the knowledge base, the rule base, and the genetic learning base of the healthy diet domain, including the ingredients and the contained servings of six food categories of some common food in Taiwan. Moreover, the proposed novel system is able to infer the healthy status of human's daily eating. In the proposed system, the domain experts first define the nutrient facts of the common food to construct the fuzzy food ontology. Meanwhile, the involved Taiwanese students of National University of Tainan (NUTN) record their daily meals for a constant period of time. Then, based on the built fuzzy profile ontology, fuzzy food ontology, and fuzzy personal food ontology, a GFML-based genetic fuzzy system is carried out to infer the possibility of dietary healthy level for one-day meals. The experimental results show that the proposed GFML-based genetic fuzzy system gives good results for the healthy diet assessment.

Apply fuzzy ontology and FML to knowledge extraction for university governance and management

The Higher Education Evaluation and Accreditation Council of Taiwan (HEEACT) supported by the Taiwan government has annually executed universities’ assessment process similar to the one in the United States since 2005. The HEEACT holds numerous assessment workshops and develops many basic standards to encourage each university to set its educational goals, measure the success in achieving these goals, ask some tough questions for the university, and meet these questions’ requirements to continuous improvement in the university. In this paper, we apply the fuzzy ontology and fuzzy markup language (FML) to perform the knowledge extraction for university governance and management, and adopt National University of Tainan (NUTN), Taiwan, as an example to construct the FML-based fuzzy ontology for knowledge management. In addition, the FML-based knowledge management system which processes a simulation for NUTN self-assessment and forecasts whether NUTN passes the 2011 HEEACT university assessment or not is also presented in this paper. Finally, NUTN fully passed 2011 university assessment in Taiwan and the results of our simulations are similar to the opinions of the HEEACT reviewers. It shows that the proposed fuzzy ontology and FML-based knowledge management system can work effectively for university governance and management.

Improving game bot behaviours through timed emotional intelligence

The video game industry is a very active economic sector focusing on the design and development of entertainment applications. In this sector, different enterprises compete to design innovative video games that exploit physical and emotional capabilities of video gamers in order to achieve high levels of realism. From this point of view, video games research should be enhanced by introducing: (1) novel methodologies for modeling emotions that depict the behaviour of different characters populating a virtual environment, and (2) some abstraction technologies useful to “move” this emotional intelligence on different game platforms without additional efforts. In this paper, innovative computational intelligence techniques, like the Timed Automata based Fuzzy Controllers, have been hybridized with emotional representation methodologies in order to provide game bots with human-like capabilities and, as a consequence, improve the realism of game under design. In order to allow different competitors to exploit our “emotional engine” on different hardware platforms, the Fuzzy Markup Language (FML) has been chosen to be the main technology for designing and implementing the bots behaviour. As shown in a case study based on Unreal Tournament 2004, the game bots exploiting our approach provide a more human-likeness behaviour if compared with simple finite state automaton based bots.