We propose a new method for predicting the travel-time along an arbitrary path between two locations on a map. Unlike traditional approaches, which focus only on particular links with heavy traffic, our method allows probabilistic prediction for arbitrary paths including links having no traffic sensors. We introduce two new ideas: to use string kernels for the similarity between paths, and to use Gaussian process regression for probabilistic prediction. We test our approach using traffic data generated by an agent-based traffic simulator.
In this study, we proposed a new text-mining methods for long-term market analysis. Using our method, we analyzed monthly price data of financial markets; Japanese government bond market, Japanese stock market, and the yen-dollar market. First we extracted feature vectors from monthly reports of Bank of Japan. Then, trends of each market were estimated by regression analysis using the feature vectors. As a result, determination coefficients were over 75%, and market trends were explained well by the information that was extracted from textual data. We compared the predictive power of our method among the markets. As a result, the method could estimate JGB market best and the stock market is the second.
``Embodied Evolution (EE)'' is a methodology in evolutionary robotics, in which, without simulations on a host computer, real robots evolve based on the interactions with actual environment. However, we had to accept robot behavior with low fitness especially in the early generations when adopting the EE framework. We introduced pre-evaluation into the EE framework so as to restrain robot behavior whose fitness is predicted to be low. This paper reports on the introduction of pre-evaluation into the Embodied-Evolution framework for a biped robot in order to reduce the risk of falling.
To realize an effective ITS(Intelligent Transport Systems) services, such as a traffic jam prediction system or car navigation system, the traffic information like average traffic speed is indispensable. However, current systems providing traffic information have serious problems about lack of data. Hence, we construct a system which provides traffic information, which complements lack data using incomplete probe and VICS(Vehicle Information and Communication System) data. The system utilizes multi-information such as real time/stored/diffusion/succession information effectively. We verified the performance of the system through experiments using probe/VICS data of Nagoya city, and confirmed beneficial results.
This paper reports the development of the simulator which helps for the introduction of On-demand Bus service. On-demand bus system is new convenient transportation system that passengers will be transported by the vehicles after they reserve a seat. The design of the introduction of On-demand Bus service is very important for the efficient operation, but there is no established theory because it is a new transportation system. The developed simulator performs well to design the On-demand Bus introduction. The result of the real field test in Moriyama City shows the answer from the simulator is realistic and it is useful information for designing efficient On-demand Bus introduction.
This paper proposes three new quantification measures of information diffusion characteristics in blogspace. These measures are calculated from the number of three basic structures, which are directed 2-edge connected subgraphs, in information diffusion networks. Each basic structure is related to information scattering, information gathering or information transmission. We analyze and visualize information diffusion networks extracted from six blog datasets. In the result, we show that the difference of information diffusion characteristics can be discriminated by the combination of three measures and human activity in blogspace can be explained by them.
The Distributed Constraint Optimization Problem (DCOP) is a fundamental framework of multi-agent systems. With DCOPs a multi-agent system is represented as a set of variables and a set of constraints/cost functions. Distributed task scheduling and distributed resource allocation can be formalized as DCOPs. In this paper, we propose an efficient method that applies directed soft arc consistency to a DCOP. In particular, we focus on DCOP solvers that employ pseudo-trees. A pseudo-tree is a graph structure for a constraint network that represents a partial ordering of variables. Some pseudo-tree-based search algorithms perform optimistic searches using explicit/implicit backtracking in parallel. However, for cost functions taking a wide range of cost values, such exact algorithms require many search iterations. Therefore additional improvements are necessary to reduce the number of search iterations. A previous study used a dynamic programming-based preprocessing technique that estimates the lower bound values of costs. However, there are opportunities for further improvements of efficiency. In addition, modifications of the search algorithm are necessary to use the estimated lower bounds. The proposed method applies soft arc consistency (soft AC) enforcement to DCOP. In the proposed method, directed soft AC is performed based on a pseudo-tree in a bottom up manner. Using the directed soft AC, the global lower bound value of cost functions is passed up to the root node of the pseudo-tree. It also totally reduces values of binary cost functions. As a result, the original problem is converted to an equivalent problem. The equivalent problem is efficiently solved using common search algorithms. Therefore, no major modifications are necessary in search algorithms. The performance of the proposed method is evaluated by experimentation. The results show that it is more efficient than previous methods.
Artificial Embryogeny (AE) is a strategy of evolutionary computation inspired by the developmental process of natural organisms. Yet while there are a few successful examples of generating network structures, existing AE models are insufficient to generate a network structure. The issue is that the possible links are limited to those connecting nodes with their predefined neighbors. Our novel AE model is capable of generating links connected to predefined neighbors as well as those to non-neighbors. In order to accelerate the convergence to a high fitness value, our AE model incorporates a heterogeneous mutation mechanism. We conduct experiments to generate not only a typical 2D grid pattern but robots with network structures consisting of masses, springs and muscles. The robots are evolved in various environments. The results show that our AE model has better convergence property, sufficient to search a larger space, than conventional AE models bounded by local neighborhood relationships.
This essay concerns the problems surrounding the use of the term ``concept'' in current ontology and terminology research. It is based on the constructive dialogue between realist ontology on the one hand and the world of formal standardization of health informatics on the other, but its conclusions are not restricted to the domain of medicine. The term ``concept'' is one of the most misused even in literature and technical standards which attempt to bring clarity. In this paper we propose to use the term ``concept'' in the context of producing defined professional terminologies with one specific and consistent meaning which we propose for adoption as the agreed meaning of the term in future terminological research, and specifically in the development of formal terminologies to be used in computer systems. We also discuss and propose new definitions of a set of cognate terms. We describe the relations governing the realm of concepts, and compare these to the richer and more complex set of relations obtaining between entities in the real world. On this basis we also summarize an associated terminology for ontologies as representations of the real world and a partial mapping between the world of concepts and the world of reality.
In this paper, we propose a communication framework which combined two types of communication among wheelchairs and mobile devices. Due to restriction of range of activity, there is a problem that wheelchair users tend to shut themselves up in their houses. We developed a navigational wheelchair which loads a system that displays information on a map through WWW. However, this wheelchair is expensive because it needs a solid PC, a precise GPS, a battery, and so on. We introduce mobile devices and use this framework to provide information to wheelchair users and to facilitate them to go out. When a user encounters other users, they exchange messages which they have by short-distance wireless communication. Once a message is delivered to a navigational wheelchair, the wheelchair uploads the message to the system. We use two types of pheromone information which represent trends of user's movement and existences of a crowd of users. First, when users gather, ``crowd of people pheromone'' is emitted virtually. Users do not send these pheromones to the environment but carry them. If the density exceeds the threshold, messages that express ``people gethered'' are generated automatically. The other pheromone is ``movement trend pheromone'', which is used to improve probability of successful transmissions. From results of experiments, we concluded that our method can deliver information that wheelchair users gathered to other wheelchairs.
Most existing works on network analysis mainly focus on only the existence of relations between entities. However, in trying to understand a real network, we naturally use not only the existence of relations but also information on the kind of relations, the attributes in the nodes, and the changes in time. In addition, we can observe some of the measures that are obtained as a result of the whole network structure. In order to extract some meaningful structural changes and integrity constraints from a dynamical network constructed from survey data, we are proposing a novel data mining framework in this paper that includes the above information that has not been used in previous studies. In the proposed framework, we start by detecting the change point in the dynamic network according to the change in the characteristic quantity. Then, by using the detected points, a dynamic network will be divided into two groups. In other words, we associate the class information to each network in a dynamic network. Finally, meaningful structural changes and integrity constraints can be obtained by applying inductive logic programming to a dynamic network and the related background knowledge represented in the first order logic. In experiments using real world data, we succeeded in obtaining meaningful results. Thus, we confirmed the usefulness of the proposed framework.
In this paper, we propose new methods and gave a system, called IFMAP , for extracting interesting patterns from a long sequential data based on frequency and self-information, and experimentally evaluate the proposed methods in the application of handling a newspaper article corpus. Sequential data mining methods based on frequency have intensively beenstudied so far. These methods, however, are not effective nor valuable for some applications where almost all high-frequent patterns should beregarded just as meaningless noisy patterns. An information-gain concept is quite important in order to restrain these noisy patterns, and was already studied for integrating it with a frequency criteria. Yang et.~al. gave a sequential mining system InfoMiner which can find periodic synchronous patterns being interesting and well-balanced from the both view-points of frequency and self-information. In this paper, we refine and extend the InfoMiner technologies in the following points: firstly, our method can handle ordinary, i.e., asynchronous and non-periodic patterns by using a sliding window mechanism, whereas InfoMiner cannot; secondly we give several combination measures for choosing valuable patterns based on frequency and self-information, while InfoMiner has just one measure which, we show in this paper, is not appropriate nor effective for handling newspaper article corpora; thirdly, we proposed a new unified method for pruning the search space of sequential data mining, which can uniformally be applied to any combination measures proposed here. We conduct experiments for evaluating the effectiveness and efficiency of the proposed method with respect to the runtime and the amount of excluding noisy patterns.
We propose an Expectation-Maximization (EM) algorithm which works on binary decision diagrams (BDDs). The proposed algorithm, BDD-EM algorithm, opens a way to apply BDDs to statistical learning. The BDD-EM algorithm makes it possible to learn probabilities in statistical models described by Boolean formulas, and the time complexity is proportional to the size of BDDs representing them. We apply the BDD-EM algorithm to prediction of intermittent errors in logic circuits and demonstrate that it can identify error gates in a 3bit adder circuit.
We have developed a table game named Innovation Game that supports users in thinking up ideas by combining existing products. There are two kinds of players in the Innovation Game, innovators and investors. While the innovators think up ideas and propose them, the investors criticize the ideas and make decisions whether they invest money to the ideas or not. In the Innovation Game, the innovators do not only propose ideas, but also improve the ideas reflecting comments from investors that represent negative impression to the ideas. Although it has been considered that ideas invested much money might be related to negative comments from investors, the relation has not been validated. We analyzed the communications in the Innovation Game. We have found features of communication in which ideas were invested much money. After a proposal of idea by a innovator, investors give negative comments to the innovator. The innovator accepts the negative comments with positive comments and improve their ideas. Finally, the investors satisfy the idea with positive comments and invest much money to them.
Structured prediction has become very important in recent years. A simple but notable class of structured prediction is one for sequences, so-called sequential labeling. For sequential labeling, it is often required to take a summation over all the possible output sequences, for instance when estimating the parameters of a probabilistic model. We cannot directly calculate such a summation from its definition in practice. Although the ordinary forward-backward algorithm provides an efficient way to do it, it is applicable to limited types of summations. In this paper, we propose a generalization of the forward-backward algorithm, by which we can calculate much broader types of summations than the conventional forward-backward algorithm. We show that this generalization subsumes some existing calculations required in past studies, and we also discuss further possibilities of this generalization.
In this study, we investigated the relationship between phases of meeting and non-verbal speech information. We considered that conversations at the meeting must show information to phases of the meeting as non-verbal features. We attempted to discriminate between the divergence phase and the convergence phase by the decision tree method using only non-verbal speech information. We performed an experiment with a group task based on a modification of the game Twenty-Questions and recorded participants' speech data. In a discrimination test, we used the recorded speech, and defined non-verbal speech features such as switching pauses (i.e. silent intervals between the utterance of two speakers), frequency for each turn-taking pattern and duration. We conducted the two discrimination tests for using parameters with friends group, with strangers group and with both groups. From the results, the accuracy of the open test is 77.3%, 85.2% and 77.3%, respectively. Taking into account only non-verbal speech information was used, we consider these results to be fairly good.
This paper proposes a design support framework, named DRIFT (Design Rationale Integration Framework of Three layers), which dynamically captures and manages hypothesis and verification in the design process. A core of DRIFT is a three-layered design process model of action, model operation and argumentation. This model integrates various design support tools and captures design operations performed on them. Action level captures the sequence of design operations. Model operation level captures the transition of design states, which records a design snapshot over design tools. Argumentation level captures the process of setting problems and alternatives. The linkage of three levels enables to automatically and efficiently capture and manage iterative hypothesis and verification processes through design operations over design tools. In DRIFT, such a linkage is extracted through the templates of design operations, which are extracted from the patterns embeded in design tools such as Design-For-X (DFX) approaches, and design tools are integrated through ontology-based representation of design concepts. An argumentation model, gIBIS (graphical Issue-Based Information System), is used for representing dependencies among problems and alternatives. A mechanism of TMS (Truth Maintenance System) is used for managing multiple hypothetical design stages. This paper also demonstrates a prototype implementation of DRIFT and its application to a simple design problem. Further, it is concluded with discussion of some future issues.