This study tries to predict date of ear emergence of rice plants, based on cropping records over 25 years. Predicting ear emergence of rice plants is known to be crucial for practicing good harvesting quality, and has long been dependent upon old farmers who acquire skills of intuitive prediction based on their long term experiences. Facing with aging farmers, data driven approach for the prediction have been pursued. Nevertheless, they are not necessarily sufficient in terms of practical use. One of the issue is to adopt weather forecast as the feature so that the predictive performance is varied by the accuracy of the forecast. The other issue is that the performance is varied by region and the regional characteristics have not been used as the features for the prediction. With this background, we propose a feature engineering to quantify hidden regional characteristics as the feature for the prediction. Further the feature is engineered based only on observational data without any forecast. Applying our proposal to the data on the cropping records resulted in sufficient predictive performance, ±2.69days of RMSE.

This paper focuses on the collaborative learning of mathematics in which learners effectively acquire knowledge of common exercises through discussion with other learners. During collaborative learning, learners sometimes cannot solve exercises successfully, because they cannot derive answers by themselves or they hesitate to propose answers through discussion. To cope with such situations, this paper proposes two support functions using diagrams to encourage active discussion, since diagrams are often used to graphically illustrate mathematical concepts. One function indicates the differences between learner diagrams and the group diagram in order to encourage participation in discussions. To compare the characteristics of diagrams drawn by different learners, internal representation of the diagram, which consists of types of figures and remarkable relations to other figures, is introduced. The other function provides hints in the group diagram so that all learners can consider their answers collaboratively through discussions. Since preparing hints for all exercises is difficult, rules for drawing supplementary figures, which are general methods for drawing supplementary figures that correspond to individual answering methods/formulas, are also developed. By applying available rules to current group diagram, appropriate supplementary figures that can solve current learning situations may be generated. The experimental results showed that the generated hints successfully increased the number of utterances in the groups. Moreover, learners were also able to derive answers by themselves and tended to propose more opinions in discussions when the uniqueness of their diagrams was indicated.

This paper presents a knowledge acquisition method using sentence topics for question answering. We define templates for information extraction by the Korean concept network semi-automatically. Moreover, we propose the two-phase information extraction model by the hybrid machine learning such as maximum entropy and conditional random fields. In our experiments, we examined the role of sentence topics in the template-filling task for information extraction. Our experimental result shows the improvement of 18% in F-score and 434% in training speed over the plain CRF-based method for the extraction task. In addition, our result shows the improvement of 8% in F-score for the subsequent QA task.

This paper presents a method to automatically acquire a given noun's telic and agentive roles from corpus data. These relations form part of the qualia structure assumed in the generative lexicon, where the telic role represents a typical purpose of the entity and the agentive role represents the origin of the entity. Our proposed method employs a supervised machine-learning technique which makes use of template-based contextual features derived from token instances of each noun. The output of our method is a ranked list of verbs for each noun, across the different qualia roles. We also propose a variant of Spearman's rank correlation to evaluate the correlation of two top-N ranked lists. Using this correlation method, we represent the ability of the proposed method to identify qualia structure relative to a conventional template-based method.

Note taking is a fundamental activity for learning, and many software tools which enable students to take digitized notes have been proposed. Digitized notes are advantageous because they can be easily edited, rearranged, and shared. Although many note-taking tools have been proposed, there has been little research to examine the effect of note annotation and rearrangement with a digitized tool in terms of knowledge acquisition. Therefore, we have investigated the effect of note annotation and rearrangement on how well lecture content is remembered by learners. By annotation, we mean adding both handwritten and typed text, and rearrangement includes moving and deleting handwritten notes. We developed a simple note-taking application specialized for explanation, and evaluated it through a laboratory experiment with eight participants. The results show that note annotation and rearrangement significantly improved how well the participants remembered lecture content. Thus, the effect of annotation and rearrangement on remembrance was confirmed with respect to digitized notes.

An improved method for extracting translation equivalents from bilingual comparable corpora according to contextual similarity was developed. This method has two main features. First, a seed bilingual lexicon--which is used to bridge contexts in different languages--is adapted to the corpora from which translation equivalents are to be extracted. Second, the contextual similarity is evaluated by using a combination of similarity measures defined in opposite directions. An experiment using Wall Street Journal and Nihon Keizai Shimbun corpora, together with the EDR bilingual dictionary, demonstrated the effectiveness of the method; it produced lists of candidate translation equivalents with an accuracy of around 30% for frequently occurring unknown words. The method thus proved to be useful for improving the coverage of a bilingual lexicon.

This paper reports on an Intelligent CASE tool, applicable in a structured programming phase, or from detailed design to coding. This is automation of the bottom level in the hierarchical design process of detailed design and coding, where the largest man-hours are consumed. The main idea is that human designers use a CASE tool for the initial design of a software system, and the design knowledge is automatically acquired from the structured charts and stored in the knowledge base. The acquired design knowledge may be reused in designs. By reusing it, a similar software system may be designed automatically. It has been shown that knowledge acquired in this way has a Logarithmic Learning Effect. Based on this, a quantitative evaluation of productivity is made. By accumulating design experiences (e. g. 10 times), more than 80% of the detailing designs are performed automatically, and productivity increases by up to 4 times. This tool features universality, an essentially zero start-up cost for automatic design, and a substantial increase in software productivity after enough experiences have been accumulated. This paper proposes a new basic idea and its implementation, a quantitative evaluation applying techniques from Industrial Engineering, which proves the effectiveness of the proposed system.

This paper discusses some problems in Molecular Biology for which learning paradigms are strongly desired. We also present a framework of knowledge discovery by PAC-learning paradigm together with its theory and practice developed in our work for discovery from amino acid sequences.

This paper discusses the role of knowledge in document image understanding from the viewpoints of representation, utilization and acquisition. For the representation of knowledge, we propose two models, a layout model and a content model, which represent knowledge about the layout structure and content of a document, respectively. For the utilization of knowledge, we implement layout analysis and content analysis which utilize a layout model and a content model, respectively. The strategy of hypothesis generation and verification is introduced in order to integrate these two kinds of analysis. For the acquisition of knowledge, we propose a method of incremental acquisition of a layout model from a stream of example documents. From the experimental results of document image understanding and knowledge acquisition using 50 samples of visiting cards, we verified the effectiveness of the proposed method.

This paper gives a model to explain one phenomenon found in the process of creative concept formation, i.e. the phenomenon that people often get trapped in some state where the mental world remains nebulous and sometimes suddenly make a jump to a new concept. This phenomenon has been qualitatively explained mainly by the philosophers but there have not been models for explaining it quantitatively. Such model is necessary in a new research field to study the systems for aiding human creative activities. So far, the work on creation aid has not had theoretical background and the systems have been built based only on trial and error. The model given in this paper explains some aspects of the phenomena found in creative activities and give some suggestions for the future systems for aiding creative concept formation.

In general, the work on developing an expert system has relied on domain experts to provide all domain-specific knowledge. The method for acquiring knowledge directly from experts is inadequate in oriental medicine because it is hard to find an appropriate expert and the development cost becomes too high. Therefore, we have developed two effective methods for acquiring knowledge indirectly from sample cases. One is to refine a constructed knowledge base by using sample cases. The other is to train a neural network by using sample cases. To demonstrate the effectiveness of our methods, we have implemented two prototype systems; the Oriental Medicine Expert System (OMES) and the Oriental Medicine Neural Network (OMNN). These systems have been compared with the system with the knowledge base built directly by domain experts (OLDS). Among these systems, OMES are considered to be superior to other systems in terms of performances, development costs, and practicalness. In this paper, we present our methods, and describe our experimental and comparison results.