Kouhei Takada

Toward Sensor-Based Context Aware Systems

This paper proposes a methodology for sensor data interpretation that can combine sensor outputs with contexts represented as sets of annotated business rules. Sensor readings are interpreted to generate events labeled with the appropriate type and level of uncertainty. Then, the appropriate context is selected. Reconciliation of different uncertainty types is achieved by a simple technique that moves uncertainty from events to business rules by generating combs of standard Boolean predicates. Finally, context rules are evaluated together with the events to take a decision. The feasibility of our idea is demonstrated via a case study where a context-reasoning engine has been connected to simulated heartbeat sensors using prerecorded experimental data. We use sensor outputs to identify the proper context of operation of a system and trigger decision-making based on context information.

A simple optimization method based on Backtrack and GA for delivery schedule

A delivery route optimization system greatly improves the real time delivery efficiency. To realize such an optimization, its distribution network requires solving several tens to hundreds (max. 1500–2000) cities Traveling Salesman Problems (TSP) within interactive response time (around 3 seconds) with expert-level accuracy (below 3% level of error rate). Moreover, as for the algorithms, understandability and flexibility are necessary because field experts and field engineers can understand and adjust it to satisfy the field conditions. To meet these requirements, a Backtrack and Restart Genetic Algorithm (Br-GA) is proposed. This method combines Backtracking and GA having simple heuristics such as 2-opt and NI (Nearest Insertion) so that, in case of stagflation, GA can restarts with the state of populations going back to the state in the generation before stagflation. Including these heuristics, field experts and field engineers can easily understand the way and use it. Using the tool applying their method, they can easily create/modify the solutions or conditions interactively depending on their field needs. Experimental results proved that the method meets the above-mentioned delivery scheduling requirements more than other methods from the viewpoint of optimality as well as simplicity.

Personalizing Learning Processes by Data Mining

A modeling approach for learning processes is utilized to process, evaluate and refine them. A formerly-developed concept called storyboarding has been applied at Tokyo Denki University (TDU) to model the various curricula for students to progress in their studies. Along with this particular storyboard, we developed a data mining technology to estimate chances for success for the students following each curricular path. Here, we introduce a concept of learner profiling. The profile represents the students’ individual properties, talents and preferences constructed through mining personal meta data about learning preferences.

Inner Random Restart Genetic Algorithm to optimize delivery schedule

A delivery route optimization system greatly improves the real time delivery efficiency. To realize such an optimization, its distribution network requires solving several tens to hundreds (maximum 2 thousands or so) cities Traveling Salesman Problems (TSP) within interactive response time (around 3 seconds) with expert-level accuracy (below 3% level of error rate). To meet these requirements, an Inner Random Restart Genetic Algorithm (Irr-GA) method is proposed. This method combines random restart and GA that has different types of simple heuristics such as 2-opt and NI (Nearest Insertion). Including these heuristics, field experts and field engineers can easily understand the way and use it. Using the tool applying their method, they can easily create/modify the solutions or conditions interactively depending on their field needs. Experimental results proved that the method meets the above-mentioned delivery scheduling requirements more than other methods from the viewpoint of optimality as well as simplicity.

Providing adaptive support in computer supported collaboration environments

Many misunderstandings can occur during remote interaction due to different user domain competency levels, different cognitive capacity of users as well as different user backgrounds. In this paper, we propose an adaptive keyword/summary presentation approach that aims at identifying potential misunderstandings of individual users and provide these users with effective and personalized content of the current discussion. Our approach is developed for virtual worlds and tested and implemented based on the Wonderland Project. In order to evaluate our approach, a practical scenario has been designed and tested, which demonstrates how the system enriches the cyberspace for collaboration by making adaptive use of keyword/summary presentation.

Enriching Web Based Computer Supported Collaborative Learning Systems by Considering Misunderstandings among Learners during Interactions

Remote collaboration has many benefits; however, due to the geographical distribution of learners, many misunderstandings can occur during interaction. For example, learners may miss the context of the discussion or do not hear parts of it, leading to inefficient discussions. In this paper, we propose an “Enriched Cyberspace” approach for dependable web based computer supported collaborative learning (CSCL) to overcome these problems. The system based on the approach assesses the situations of remote users through fusing information of multiple biological sensors and the related general contexts to enrich the cyberspace. A formative scenario evaluation demonstrates the feasibility and usefulness of the approach for developing effective web-based CSCL systems.

Using contexts to supervise a collaborative process

This paper describes a research project that investigated the feasibility of using contextual reasoning to supervise the collaborative work of knowledge workers. In complex projects that require contributions from various experts but whose interaction may be limited to a web-based collaborative tool, proper management of the project is essential to ensure that the project objectives are met. This is typically the job of a project manager. We assert that having situational awareness is likewise essential to managing a project, and we utilize Context-based Reasoning (CxBR) as the tool of choice for implementing situational awareness in an agent that assists project managers. We use rocket design and manufacture as the domain to evaluate our approach. We make use of public domain rocket design software developed by NASA as a guide to the domain. The paper describes the investigation and the related works involved in collaborative design project, as embodied by designing and building a small rocket.

A Case Study On Using Personalized Data Mining For University Curricula

In former work, the authors developed a modeling system for university learning processes, which aims at evaluating and refining university curricula to reach an optimum of learning success in terms of a best possible grade point average (GPA). This is performed by applying an Educational Data Mining (EDM) technology to former students curricula and their degree of success (GPA) and thus, uncovering golden didactic knowledge for successful education. We used learner profiles to personalize this technology. After a short introduction to this technology, we discuss the result of a practical application and draw conclusions. In particular, we could not obtain sufficient data to establish this kind of learner profiles. Therefore, we shifted our strategy from an “eager” one of holding an explicit model towards a “lazy” strategy of mining with data, which is really available without making “guesses” what they mean (profiles). In particular, we utilize the educational history of the students and vocational ambitions for student modeling.

Ensuring Diversity in a Backtrack and GA Optimization Method for Delivery Schedule

Delivery route optimization greatly improves the delivery efficiency in terms of all resources including human resources and energy consumption. In our application scenario the distribution network requires solving several tens to hundreds (max. 1500-2000) cities Traveling Salesman Problems (TSP) within interactive response time (around 3 seconds) with expert-level accuracy (below 3% level of error rate). Moreover, since domain experts have to adjust the solutions due to boundary conditions that can not be formally expressed (such as human convenience, road conditions, and social aspects), understandability and flexibility of the applied heuristics are necessary. To meet all these requirements, a Backtrack and Restart Genetic Algorithm (Br-GA) was proposed. This method combines Backtracking and GA having simple heuristics such as 2-opt and NI (Nearest Insertion) so that, in case of stagflation, GA can restarts with the state of populations going back to the state in the generation before stagflation. Here, a refinement of this algorithm is introduced, which aims at ensuring diversity of initial solutions, which are subject to mutation. We introduce a formal definition of a diversity degree as well as a technique to compose new random individuals, which follow a required degree of diversity.

Enriched Cyberspace Through Adaptive Multimedia Utilization for Dependable Remote Collaboration

Due to the geographical distribution, different cognitive capacity, and different domain competency of workers or learners, many misunderstandings can occur during distributed remote collaboration, leading to inefficient discussions and undesired results. To make remote collaboration more efficient and dependable, enriching cyberspace through adaptively utilizing multimedia information is proposed and evaluated. This assesses situations of remote users through information fusion of multiple biomedical sensors and the related contexts such as user profiles. Transmitting and using such information, the system adaptively supports the distributed remote collaboration by stressing, warning, and presenting keywords/summaries in multimedia. Effects of presenting keywords/summaries adaptively depending on situations and cognitive profiles of remote members are evaluated as to the decrease of not-/misunderstanding possibilities during the explanation on the Cyberspace. The evaluation demonstrates the feasibility and usefulness of the proposed method.