Yoshiharu Maeno

Stable Deterministic Crystallization for Discovering Hidden Hubs

Experts of chance discovery have recognized a new class of problems where the previous methods fail to reveal a latent structure behind observation. There are invisible events which play an important role in the dynamics of visible events. A hidden hub person (an invisible leader) in a communication network is a typical example. This paper presents a stable deterministic crystallization algorithm for discovering such hidden hub events. The algorithm is evaluated with the test data generated from a large scale-free random network. It is demonstrated that precision for discovering the hidden hub events remains as high as 80% to 100%, regardless of the prior knowledge and the network structure.

Reflective visualisation and verbalisation of unconscious preference

A method is presented that helps persons become aware of their unconscious preferences, and convey them to others in the form of verbal explanation. The method combines the concepts of reflection, visualisation, and verbalisation. The method was tested in an experiment where the unconscious preferences of the subjects for artworks were investigated. We learned two lessons. The persons become aware of their unconscious preferences by verbalising weak preferences as compared with strong preferences through discussion over preference diagrams. They overcome the personal differences and foster the mutual understanding by adjusting the granularity of visualisation.

Human-Interactive Annealing Process with Pictogram for Extracting New Scenarios for Patent Technology

The latent structure behind an observation often plays an important role in the dynamics of visible events. Such latent structure is composed of invisible events named dark events. Human-interactive annealing is developed to visualize and understand dark events. This paper presents an application of human-interactive annealing for extracting new scenarios for patent technology using the latent technology structure behind current patented technology.

Reflective visualization of dispute resolution

Mediation is a form of alternative dispute resolution which aims at assisting disputants in reaching an agreement on a disputed matter. Debate ensues on what skills an individual needs to play a mediators role effectively. Education and training for mediators become complex issues. Then how can mediators skills be trained in spite that the skills can not be defined clearly? In cognitive science visualization of and reflection on ones behavior is proven effective in such a situation. In this paper we explore a text processing method for reflective visualization of a dialogue. The dialogue is a time sequence of utterances from a mediator and disputants. The method visualizes an inter-topic association which foreshadows the intentional or unintentional subsequent development of topics indicated by temporal topics clusters far apart in time. The method is applied to a mediation case where a dispute between a seller and a buyer on cancelling an purchase transaction at an online auction site is resolved.

Human-interactive annealing for turning threat to opportunity in technology development

Human-interactive annealing is a new method for understanding invisible but relevant events, and inventing hypothetical scenarios. The human-interactive annealing is elaborated for texts. It is applied to patents for acquiring opportunity in technology development. An illustrative experiment on discovering opportunity by analyzing US patent texts on knowledge acquisition is demonstrated. A few sample hypothetical scenarios on emerging technological elements are obtained

Understanding of Dark Events for Harnessing Risk

Summary. There are invisible events which play an important role in the dynamics of visible events. Such an event is named a dark event. Understanding of the dark event is important for harnessing risk in modern social and business problems .An ew technique has been deveoped to understand dark events and to extend the chance discovery process. The technique is human-interactive annealing for revealing latent structures along with the algorithm for discovering dark events. Test data generated from a scale-free network shows that the precision of the algorithm is up to 90%. An experiment on discovering an invisible leader hidden under an on-line decisionmaking circumstance and a trial for the analysis on unknown emerging technology are demonstrated.

Discussion analysis using temporal data crystallization

This paper introduces a discussion analysis tool which extracts topic flow and important utterances from a discussion record based on word occurrences. We have proposed a discussion analysis method called Temporal Data Crystallization (TDC). This method divides the entire discussion record hierarchically at points where the topic changes, and analyzes some features of flow of topics for each period. In this paper, we showed the effect of hierarchical division by analyzing an example discussion record. Then, we introduced the extension of TDC by considering nonverbal information such as actions, facial expression, loudness of voice, and so on.

Node discovery in a networked organization

In this paper, I present a method to solve a node discovery problem in a networked organization. Covert nodes refer to the nodes which are not observable directly. They affect social interactions, but do not appear in the surveillance logs which record the participants of the social interactions. Discovering the covert nodes is defined as identifying the suspicious logs where the covert nodes would appear if the covert nodes became overt. A mathematical model is developed for the maximal likelihood estimation of the network behind the social interactions and for the identification of the suspicious logs. Precision, recall, and F measure characteristics are demonstrated with the dataset generated from a real organization and the computationally synthesized datasets. The performance is close to the theoretical limit for any covert nodes in the networks of any topologies and sizes if the ratio of the number of observation to the number of possible communication patterns is large.

Predicting relevant empty spots in social interaction

An empty spot refers to an empty hard-to-fill space which can be found in the records of the social interaction, and is the clue to the persons in the underlying social network who do not appear in the records. This contribution addresses a problem to predict relevant empty spots in social interaction. Homogeneous and inhomogeneous networks are studied as a model underlying the social interaction. A heuristic predictor function method is presented as a new method to address the problem. Simulation experiment is demonstrated over a homogeneous network. A test data set in the form of market baskets is generated from the simulated communication. Precision to predict the empty spots is calculated to demonstrate the performance of the presented method.

Catalyst personality for fostering communication among groups with opposing preference

The activity of an organization is excited by introducing new persons. Understanding such catalyst personality is an important basis for fostering communication among groups with opposing preference. In the prior understanding, the groups are independent segments. Cognition, resulted from seeing the overlaps revealed between the segments, is not the same as the prior understanding. This gap is a clue. We demonstrate an experiment using questionnaire on the preference of art pieces.