A Global & Environmental Coral Analysis System with SPA-Based Semantic Computing for Integrating and Visualizing Ocean-Phenomena with “5-Dimensional World-Map”

Semantic computing is essentially significant for realizing the semantic interpretation of natural and social phenomena and analyzes the changes of various environmental situations. The 5D World Map (5DWM) System [4,6,8] has introduced the concept of “SPA (Sensing, Processing and Analytical Actuation Functions)” for global environmental system integrations [1–4], as a global environmental knowledge sharing, analysis and integration system. Environmental knowledge base creation with 5D World Map is realized for sharing, analyzing and visualizing various information resources to the map which can facilitate global phenomena-observations and knowledge discoveries with multi-dimensional axis control mechanisms. The 5DWM is globally utilized as a Global Environmental Semantic Computing System, in SDGs 9, 11, 14, United-Nations-ESCAP: (https://sdghelpdesk.unescap.org/toolboxes) for observing and analyzing disaster, natural phenomena, ocean-water situations with local and global multimedia data resources. This paper proposes a new semantic computing method as an important approach to semantic analysis for various environmental phenomena and changes in a real world. This method realizes “Self-Contained-Knowledge-Base-Image” & “Contextual-Semantic-Interpretation” as a new concept of “Coral-Health-level Analysis in Semantic-Space for Ocean-environment” for global ocean-environmental analysis [8,9,12,18]. This computing method is applied to automatic database creation with coral-health-level analysis sensors for interpreting environmental phenomena and changes occurring in the oceans in the world. We have focused on an experimental study for creating “Coral-Health-level Analysis Semantic-Space for Ocean-environment” [8,9,12,18]. This method realizes new semantic interpretation for coral health-level with “coral-images and coral-health-level knowledge-chart”.

Conceptualization of Aviation Cabin Crew’s Fatigue Risk Assessment

Airlines are of great importance to the transportation sector. With the increase in commercial air travel, airlines require extra flight crews. Aviation industry’s cabin crewmembers are faced with working overtime, working in shifts and long working hours. The shift system causes fatigue for flight crews. Fatigue is of critical importance in the aviation industry. Depending on the physical and psychological fatigue, explicit or implicit results appear. There are a number of approaches in the aviation industry to prevent fatigue. When previous studies are examined, there are few studies examine in the general, and aviation crew’s fatigue treat both pilots and cabin crew alike. The relationship between cabin crew’s fatigue-to-fatigue risk management systems, key fatigue-causing factors, tools to alarm fatigue, and outcome assessments are non-existent. However, various difficulties are encountered in measuring the cabin crews fatigue levels and measurements and are often subjective and not reliable. Therefore, the aim of this study is to create a concept map to be integrated into the aviation cabin crew’s fatigue risk assessment application design and implementation in order to arrive at a comprehensive fatigue risk assessment tool for the aviation industry.

Visitor Journey Application Development for Omni-Channels

The primary purpose of conducting this research is to determine how campus journey application development is progressing. As a result, this research proposes a conceptual model for visitor journey application development. The study included 100 top ranking educational institutes and additionally included Finnish and Estonian universities. 39 virtual campus tour applications and 36 visitor journey applications are benchmarked in total for this study. Provides an example of visitor journey mapping with features, complexities, and best practices that are influential for improving visitor experience during visitor journey application development.

Cross-Cultural Religious Tourism with Impression Distance Search System

Cross-cultural religious tourism is computational to promote cross-cultural communication and understanding according to impression distance. Our motivation to implement semantic search with an emotion-oriented context into the proposed system is to realize global tourism recommendations expressed in different cultures. The objectives of this paper are (1) to find the religious places by using the tourist’s emotional distance, (2) to find similar religious places not only in the same culture but also in the different cultures with the tourist’s emotional distance calculations. Experimental results demonstrate the feasibility and applicability of this method.

A Natural Logic System for Large Knowledge Bases

This paper describes principles and structure for a software system that implements a dialect of natural logic for knowledge bases. Natural logics are formal logics that resemble stylized natural language fragments, and whose reasoning rules reflect common-sense reasoning. Natural logics may be seen as forms of extended syllogistic logic. The paper proposes and describes realization of deductive querying functionalities using a previously specified natural logic dialect called Natura-Log. In focus here is the engineering of an inference engine employing as a key feature relational database operations. Thereby the inference steps are subjected to computation in bulk for scaling-up to large knowledge bases. Accordingly, the system eventually is to be realized as a general-purpose database application package with the database being turned logical knowledge base.

Global & Geographical Mapping and Visualization Method for Personal/Collective Health Data with 5D World Map System

This paper presents a new knowledge base creation method for personal/collective health data with knowledge of preemptive care and potential risk inspection with a global and geographical mapping and visualization functions of 5D World Map System. The final goal of this research project is a realization of a system to analyze the personal health/bio data and potential-risk inspection data and provide a set of appropriate coping strategies and alert with semantic computing technologies. The main feature of 5D World Map System is to provide a platform of collaborative work for users to perform a global analysis for sensing data in a physical space along with the related multimedia data in a cyber space, on a single view of time-series maps based on the spatiotemporal and semantic correlation calculations. In this application, the concrete target data for world-wide evaluation is (1) multi-parameter personal health/bio data such as blood pressure, blood glucose, BMI, uric acid level etc. and daily habit data such as food, smoking, drinking etc., for a health monitoring and (2) time-series multi-parameter collective health/bio data in the national/regional level for global analysis of potential cause of disease. This application realizes a new multidimensional data analysis and knowledge sharing for both a personal and global level health monitoring and disease analysis. The results are able to be analyzed by the time-series difference of the value of each spot, the differences between the values of multiple places in a focused area, and the time-series differences between the values of multiple locations to detect and predict a potential-risk of diseases.

TRAILSIGNER: A Conceptual Model of Hiking Trail Networks with Consistent Signage Planning and Management

Trail route networks provide an infrastructure for touristic and recreational walking activities worldwide. They can have a variety of layouts, signage systems, development and management patterns, involving multiple stakeholders and contributors, and tend to be determined by various interests on different levels and dynamically changing circumstances. This paper aims to develop the skeleton of TRAILSIGNER, a sound geospatial conceptual data model suite of trail networks, waymarked routes and their signage systems and assets, which can be used as a basis for creating an information system for the effective, organic and consistent planning, management, maintenance and presentation of trails and their signage. This reduces potential confusion, mistrust and danger for visitors caused by information mismatches including incomplete, incoherent or inconsistent route signposting. To ensure consistency of incrementally planned signposts with each other and with the (possibly changing) underlying trail network, a systematic, set-based approach is developed using generative logical rules and incorporated into the conceptual model suite as signpost logics. The paper also defines a reference ruleset for it. This approach may further be generalized, personalized and adapted to other fields or applications having similar requirements or phenomena.

Digital Intelligence Banking of Adaptive Digital Marketing with Life Needs Control

While individuals benefit from the goods and services provided by companies that enrich their lives and that have adapted to a dynamic environment that is always changing, these companies pay a high communication cost to access opportunities to provide these goods and services and to seek a better understanding of individual customers’ changing needs. Although vast amounts of information can be obtained, databases and machine learning are playing an increasingly important role in extracting meaning from this information, turning it into meaningful information assets that consider circumstances and contexts, and individualizing the economy of information. I propose an implementation method for providing information to enrich the profiles of individual customers by consolidating different data, calculating the individual customers’ needs through the relationships between customers and products, evaluating the change in relationships between individual customers and products over time, and providing goods and services to suit different intervals of change to factors such as lifestyle and living environment. As there are different factors involved in estimating the incidence of needs, and different frequencies and rates at which they occur, based on the special characteristics of products, different data are required to estimate such needs. By profiling individuals over the long term, it is possible to build an information provision environment that is conducive to companies’ customer acquisition.

Q-Learning as Failure

Reinforcement Learning allows us to acquire knowledge without any training data. However, for learning it takes time. In this work, we propose a method to perform Reverse action by using Retrospective Kalman Filter that estimates the state one step before. We show an experience by a Hunter Prey problem. And discuss the usefulness of our proposed method.

Application of a Heterogeneous Correlation Integration Method to a Context Cube Network Semantic Model for Railway Passengers

In recent years, with the development of information technology, many cyber-physical systems, in which real space and the information space are linked for data acquisition and analysis, have been constructed. The purpose of constructing a cyber-physical system is to solve and improve social and environmental problems. An important target is the railway space, which aims to provide safe and stable transportation services as part of the social infrastructure. In this paper, we propose a new data model, the “Context Cube Semantic Network”, for the railway space and a metric method that employs an integrated scale based on heterogeneous correlations of purpose, sensibility, and distance for the railway space. Furthermore, we constructed a station guidance system that implements the proposed method and evaluates subjects at the station. As a result, we clarified the effectiveness and applicability of the system.