This paper proposes a method to optimize human resource allocation for tasks involving human interaction. The method considers task success rates under temporal and spatial constraints, human experience and skills, and contextual factors. Human interaction is modeled in three types: autonomous, distributed, and collaborative. Additionally, individual skills, past performance, and biases are incorporated as variables to reflect real-world complexity. The approach enables formalize resource allocation processes traditionally managed through human communication. This paper presents the method’s formalization and compares five computational approaches. Experimental validation was conducted within the domain of systems engineering, utilizing a sample dataset comprising 100 tasks T and 1,000 individuals P. The proposed method demonstrated high reproducibility and effectiveness in task resolution.

This paper proposes a spatio-temporal and categorical data mining method for commercial transaction data. This method searches for elements from a set using three search methods: specific, concept, and pattern, which represent the level of abstraction of search conditions, and spatio-temporal information and category information, which correspond to domain knowledge. This method aims to obtain knowledge of combinations of events with substantial physical, temporal, and categorical correlations between two data sets based on the amount of correlation and frequent occurrence patterns. In other words, it is to realize the mechanism in the database by which humans try to obtain knowledge through memory recall about the location, trends, timing, and frequency of events. This method uses aggregation functions to extract spatio-temporal and categorical features of elements in two different sets contained in a single set. Furthermore, this method performs Numerization, which converts the features from linguistic to numerical and Linguization, which converts the features from numerical and linguistic formats. The set elements are represented as vector data consisting of spatio-temporal and categorical features by numerical and linguistic formats. Numerical and linguistic formats are used for specific and conceptual searches, while linguistic formats are used for pattern searches. This method uses a dynamic vector creation function at search to dynamically map only those set elements that satisfy the search conditions into a semantic orthogonal space with time, space, and category dimensions. This method calculates correlation computation by calculating the distance between elements for each feature, normalizing and integrating their scores. Additionally, this method extracts as correlation rules combinations of events with substantial physical, temporal, and category correlations by calculating support and confidence levels based on the frequency of occurrence of the elements. Namely, Apriori algorithm is contained in the calculation for correlation rules. In this paper, we present the details of the proposed method and its implementation as an application in business commerce.

One of the essential computations in ocean environmental study is semantic computing for interpreting and analyzing the changes of various situations (coral area, ocean water, places of ocean livings, sea level, etc.). It is important to realize global ocean-environmental computing methodology for analyzing difference and diversity of nature and livings in a context dependent way from a viewpoint of global environments. It is also significant to memorize those situations and compute ocean-environment change in various aspects and contexts, in order to discover what are happening in the nature of ocean. We have various aspects and contexts in ocean-environmental changes in our planet, and it is essential to realize a new analyzer for computing differences in those situations for finding actual aspects and contexts existing in oceans. We have proposed a new method for Differential Computing in our Multi-dimensional World map in [3,4]. We utilize a multi-dimensional semantic computing model, and a multi-dimensional space with an adaptive-axes selection mechanism to implement semantic computing [1,2]. Computing ocean-environmental changes in multi-aspects and contexts using semantic computing, important factors that change natural ocean-environment are highlighted. Semantic computing is an important and promising approach to semantic analysis for various environmental phenomena and changes in real world. This paper presents a new concept of “Coral-Health-Level Analysis Semantic-Space for Ocean-environment” for realizing global Ocean-environmental analysis. This space and computing method are based on environmental-database creation with coral-health-level-analysis sensors for analyzing and interpreting environmental phenomena and changes occurring in the oceans. This paper focuses on coral-health-level in South-East Asian Ocean area, as an experimental study for creating “Coral-Health-Level Analysis Semantic-Space for Ocean-environment”. We have created a semantic-space for coral-health-level analysis in the South-East Asian Ocean area with 24-dimensional axes (coral-health-level parameters). As the first step, this space is applied to South-East Asian Ocean area, and it is expandable to multiple spots in Ocean-areas to analyze and compare their coral-health-level situations in the global scope for Ocean-environmental analysis. In this paper, we also apply 5-Dimensional World Map System for semantic computing to analysis in South-East Asian Ocean area, as an international collaborative platform for Ocean-environment analysis with spatio-temporal and semantic dimensions.

This paper proposes a spatio-temporal and categorical correlation computing method for induction and deduction analysis. This method is a data analytics method to reveal spatial, temporal, and categorical relationships between two heterogeneous sets in past events by correlation calculation, thereby finding insights to build new connections between the sets in the future. The most significant feature of this method is that it allows inductive and deductive data analysis by applying context vectors to compute the relationship between the sets whose elements are time, space, and category. Inductive analysis corresponds to data mining, which composes a context vector as a hypothesis to extract meaningful relationships from trends and patterns of past events. Deductive analysis searches past events similar to a context vector’s temporal, spatial, and categorical conditions. Spatio-temporal information about the events and information such as frequency, scale, and category are used as parameters for correlation computing. In this method, a multi-dimensional vector space that consists of time, space, and category dimensions is dynamically created, and the data of each set expressed as vectors is mapped onto the space. The similarity degree of the computing shows the strength of relationships between the two sets. This context vector is also mapped onto the space and is calculated distances between the context vector and other vectors of the sets. This paper shows the details of this method and implementation method and assumed applications in commerce activities.

Dynamic routing with combinations of mobility and activity is expected as new methodology for supporting sensitivity to various contexts for traveling. It is important to realize the dynamics by integrating “mobility and activity” in physical and cyber spaces. This paper presents a mobility and activity integration system for making routing plans from an original point to a destination with a scenario as “sensitivity to context” on the route. The “sensitivity to context” expresses reactions to the intentions and situations of a moving user. This system applies semantic computing to find out the appropriate mobility and activity, that dynamically calculates semantic associations between user’s intentions and mobility services. This system makes a moving plan reflecting “sensitivity to context” created by query creation operators for synthesizing and expressing “everyday intention” and “mobility situation.” This system has a distance calculation function for “feature value vectors” expressing the means of mobility and the features of facility spots, and outputs some expected means of moving towards the destination with activities on the route.

Global warming and climate change affect not only all living things but also many non-living things. Furthermore, those phenomena caused extreme disasters that become impossible to ignore. Coral bleaching is a phenomenon to show ocean warming due to climate change. This paper presents the analysis and visualization of the coral health levels database by using 5D World Map System. Coral health levels are analyzed using a coral-knowledge image that includes coral with a coral health chart. We use image processing and color semantic distance to interpret coral health levels. We have implemented an actual space integration system to access environmental information resources with coral health levels and image analysis that the results have been shown on the 5D World Map System. As for the experiment study, the study areas of coral health levels analysis are located in the ocean close to Thailand’s islands as Ko Ha (Five Island), Ko Bon, Ko Hin Ngam, Ko Tarutao, Ko Thalu, and Ko Samaesarn.

In this paper, we present information providing a method to realize semantic representation of train service situations by a combination of sign-logo images. Through this method, multiple images having visual meanings such as pictograms, icons and sign-logos are combined to represent images to express train service situations in the railway space based on the knowledge of the railway company such as transfer ways in accidents. Features of this method are as follows: (1) To express events occurred in railway space by combining multiple images as visual communication based on “5W1H” that corresponds with human communication and information understanding. (2) To dynamically calculate the correlation between an event and meaning of the combined sign-logo images. (3) To be associated with the event of a railway space including an attractive tour guide to foreign visitors by combining sign-logo images. We have developed an experimental system as a digital signage and have applied the method in order to realize a sign-based guide at all train stations for foreign visitors in Japan. The effectiveness and feasibility of this method are shown in this paper.

We have put into practice the flipped classroom as a way of utilizing e-learning contents in our computer-programming course since 2013. The main feature of this practice is to use most of the time usually dedicated to lecture for practicing by assigning the students the e-learning materials as preparation before the class. We gave the students homework to learn vocabularies and grammar of the C programming language. This decreased the time a teacher spent lecturing and the students were assigned applied-problems to make practical software in addition to conventional basic problems in training. Our goal is to maintain the students' motivation to learn the computer programming through the sense of accomplishment that each student obtains by finishing practical assignments in the training. We confirmed the effectiveness of this approach by comparing examination scores between last year and this year, and putting questionnaires to the students. Additionally, we analyzed the learning situation of the students who were week in programming. The results are shown in this paper.

This paper presents a combined-image query creation method for expressing user's intentions by combining multiple digital images for image retrieval. This method uses image databases provided for query-creation and performs several set-operators to express user's imagination by combining user's imaginary images and real scenes. The user's intentions are expressed by the operation of subspace projection in the image feature space. This method makes it possible to create an imaginary image as the combined-image query for expressing user's intentions by combining several images and operators in the query creation process. The important feature of this method is to use shape and color features for expressing imaginations by extending our previously proposed method. This paper shows several experimental results to clarify the feasibility and effectiveness of our method.