A Global Environment Analysis and Visualization System with Semantic Computing for Multi-Dimensional World Map

Humankind, the dominant species on Earth, faces the most essential and indispensable mission; we must endeavor on a global scale to perpetually restore and improve our natural and social environments. The essential computation in environmental study is context-dependent-differential computation to analyze the changes of various situations (temperature, color, CO2, places of livings, sea level, coral area, etc.). It is important to realize global environmental computing methodology for analyzing difference and diversity of nature and livings in a context dependent way with a large amount of information resources in terms of global environments. It is also significant to memorize those situations and compute environment change in various aspects and contexts, in order to discover what is happening in the nature of our planet. We have various (almost infinite) aspects and contexts in environmental changes in our planet, and it is essential to realize a new analyzer for computing differences in those situations for discovering actual aspects and contexts existing in the nature. We propose a new method for Differential Computing in our Multi-dimensional World map. We utilize a multi-dimensional computing model, the Mathematical Model of Meaning (MMM), and a multi-dimensional space filtering method with an adaptive axis adjustment mechanism to implement differential computing. Computing environmental changes in multi-aspects and contexts using differential computing, important factors that change natural environment are highlighted. We also present a method to analyze and visualize the highlighted factors using our Multi-dimensional World Map (5-Dimensional World Map) System. We also introduce the concept of "SPA (Sensing, Processing and Analytical Actuation Functions)" for realizing a global environmental system, to apply it to Multi-dimensional World Map (5-Dimensional World Map) System. This concept is effective and advantageous to design environmental systems with Physical-Cyber integration to detect environmental phenomena as real data resources in a physical-space (real space), map them to cyber-space to make analytical and semantic computing, and actuate the analytically computed results to the real space with visualization for expressing environmental phenomena, causalities and influences

An Experience-Connected e-Learning System with a Personalization Mechanism for Learners’ Situations and Preferences

This paper presents an “experience-connected” e- Learning system that facilitates users to learn practical skills of foreign language by associating knowledge and daily-life experiences. “Experience-Connected” means that the users of this system receive personalized and situation-dependent learning materials automatically. Knowledge associated to users’ daily-life has the following advantages: 1) provides opportunities to learn frequently, and 2) provides clear and practical context information about foreign language usage. The unique feature of this system is a dynamic relevance computation mechanism that retrieves learning materials according to both preference relevance and spatiotemporal relevance. Users of this system obtain appropriate learning materials, without manual and time-consuming search processes. This paper proves the feasibility of the system by showing the actual system implementation that automatically broadcasts the media-data of foreign language learning materials to smart-phones

An Approach for Management of Regional Portal Sites through Project-Based Learning

In this paper, we present an approach for management of regional portal sites through project-based learning. In this approach, we have developed a framework to regularly provide information systems and contents based on needs of regional communities for the regional portal site by developing them on the project-based learning in our university. The waterfall model that is one of the software development techniques as the method of executing the project-based learning is practiced. This paper shows current status and effectiveness of our approach

A Time-Series Phrase Correlation Computing System With Acoustic Signal Processing For Music Media Creation

This paper presents a system that analyzes the time-series impression change in the acoustic signal by a unit of music phrase. The aim is to support the music creation using a computer (computer music) by bringing out composers' potentially existing knowledge and skills. Our goal is to realize the cross-genre/cross-cultural music creation. Our system realizes the automatic extraction of musical features from acoustic signals by dividing and decomposing them into “phrases†and “three musical elements†(rhythm, melody, and harmony), which are meaningful for human recognition. By calculating the correlation between the target “target music piece†and the “typical phrase†in each musical genre, composers are able to grasp the time-series impression change of music media by the unit of music phrase. The system leads to a new creative and efficient environment for cross-genre/cross-cultural music creation based on the potentially existing knowledge on the music phrase and structure

The rSPA Processes of River Water-quality Analysis System for Critical Contaminate Detection, Classification Multiple-water-quality-parameter Values and Real-time Notification

The water quality analysis is one of the most important aspects of designing environmental systems. It is necessary to realize detection and classification processes and systems for water quality analysis. The important direction is to lead to uncomplicated understanding for public utilization. This paper presents the river Sensing Processing Actuation processes (rSPA) for determination and classification of multiple-water- parameters in Chaophraya river. According to rSPA processes of multiple-water-quality-parameters, we find the pollutants of conductivity, salinity and total dissolved solid (TDS), which are accumulated from upstream to downstream. In several spots of the river, we have analyzed water quality in a maximum value of pollutants in term of oxidation-reduction potential (ORP). The first range effect of parameter is to express high to very high effects in term of dissolved oxygen, second is to express intermediate to very high effect in term of conductivity, third is to express low to very high effect in term of total dissolved solid, fourth is to express completely safe to very high effect in term of turbidity and the final is to express completely safe for effect in term of salinity

A Geo-Location Context-Aware Mobile Learning System with Adaptive Correlation Computing Methods

AbstractThis paper proposes a context-aware mobile learning system with adaptive correlation computing methods. This system enables users to enhance their knowledge by correlating it with daily experiences. The proposed system contains a hybrid metric vector space to define the correlation between heterogeneous metadata vectors of the user context and learning material. The system integrates heterogeneous metric vector spaces with definitions of the semantic relations between the vector spaces. The significant feature of this system is a hybrid adaptation mechanism for the calculation of correlation. The adaptation mechanism has multidirectional adaptation functions for various learning materials, situations, and learners. We propose a revise-localize-personalize (RLP) adaptation model. In the adaptation mechanism, users only have to improve the metadata or the relations just in their relevant field. The advantage of the system is that the system reduces the time-intensive efforts required for describing direct relations between user contexts and learning materials. This paper presents the feasibility of the context-aware heterogeneous information provision with the hybrid metric vector space, by implementing an actual mobile application system and examining real-world experiments on data provision

A Global Sign-Logo Recognition System

In the paper, we present a sign-logo recognition system for detecting meanings of signs and logos existing in a global real space. First, this system finds out the category of a sign-logo image input to the system by the similarity computations with images in the database focusing on the color and shape features of images. Second, the system searches for the information corresponding to the specific sign-logo image. This system makes it possible for a user to find out the meaning and the related information of sign-logos based on the user’s location. This paper also presents several experimental results for sign-logo recognition functions by using actual sign-logo images. Those results clarify the feasibility and the applicability of our system in real world spaces

A Semantically-Related Information-Extraction System of Living

This paper presents a semantically related information-extraction system of living things by the global analysis of spatial, temporal and color information of images. The progress of multimedia, mobile an GIS technology makes it possible to and share various information resources globally. Various kinds of information resources on natural environments of the real world are also existing in a cyber space, and it is becoming possible to support users to acquire the valuable knowledge that bridge user’s fragmentary information about the real world and adequate information on the cyber space. This system realizes the functions for identifying unknown living things contained in a picture image input by a user, through the global analysis of temporal, spatial and color information of this images within a user-selected domain, Given a picture image of a living thing with temporal and spatial information, this system evaluates possible candidates of living things. This system also analyzes color information by calculating correlations between the color distribution of an input image and corresponding sample image data. By these processes, users acquire detailed information such as the name, the habitant and the active period of the living things contained within the given images

A Similarity-Ranking Method on Semantic Computing for Providing Information-Services in Station-Concierge System

The prevalence of smartphones and wireless broadband networks have been progressing as a new Railway infomration environment.　According to the spread of such devices and information technology, various types of information can be obtained from databases connected to the Internet. One scenario of obtaining such a wide variety of information resources is in the phase of user’s transportation. This paper proposes an information provision system, named the Station Concierge System that matches the situation and intention of passengers. The purpose of this system is to estimate the needs of passengers like station staff or hotel concierge and to provide information resources that satisfy user’s expectations dynamically. The most important module of the system is constructed based on a new information ranking method for passenger intention prediction and service recommendation. This method has three main features, which are (1) projecting a user to semantic vector space by using her current context, (2) predicting the intention of a user based on selecting a semantic vector subspace, and (3) ranking the services by a descending order of relevant scores to the user’ intention. By comparing the predicted results of our method with those of two straightforward computation methods, the experimental studies show the effectiveness and efficiency of the proposed method. Using this system, users can obtain transit information and service map that dynamically matches their context

An Automatic Feature Extraction Method of Satellite Multispectral Images for Interpreting Deforestation Effects in Soil Degradation

Deforestation is still a major nature phenomenon in our society. For assessing deforestation effect, satellites remote sensing provides a fundamental data for observation. While new remote-sensing technologies are able to represent high-resolution forest mapping, the application is still limited only for detecting and mapping the deforestation area. In this paper, we proposed a new method for automatically extract features of Satellite Multispectral images for interpreting deforestation effect in the context of soil degradation. We proposed an idea to interpret reflected “substances (material)” of bare soil in deforested area in spectrum domain into human language. The objectives of this paper are to (1) recognize the deforestation activity automatically. (2) Identify deforestation causes and examines the deforestation effect based on deforestation causes. (3) Scrutinize deforestation effects on soil degradation. (4) Representing nature knowledge of deforestation effect in human language using semantic computing, to bring the clear, comprehensible knowledge even for people who are not familiar with forestry. As for the experimental study, Riau Tropical Forest has been selected as the study area, where the multispectral data was acquired by using Landsat 8 Satellite between 2013 and 2014; Where forest fire and logging activities are reported and detected