Fuzzy relational algebra for possibility-distribution-fuzzy-relational model of fuzzy data

In the real world, there exist a lot of fuzzy data which cannot or need not be precisely defined. We distinguish two types of fuzziness: one in an attribute value itself and the other in an association of them. For such fuzzy data, we propose a possibility-distribution-fuzzy-relational model, in which fuzzy data are represented by fuzzy relations whose grades of membership and attribute values are possibility distributions. In this model, the former fuzziness is represented by a possibility distribution and the latter by a grade of membership. Relational algebra for the ordinary relational database as defined by Codd includes the traditional set operations and the special relational operations. These operations are classified into the primitive operations, namely, union, difference, extended Cartesian product, selection and projection, and the additional operations, namely, intersection, join, and division. We define the relational algebra for the possibility-distribution-fuzzy-relational model of fuzzy databases.     

ConClass: a framework for real-time distributed knowledge-basedprocessing

We have developed a problem-solving framework, called ConClass, that is capable of classifying continuous real-time problems dynamically and concurrently on a distributed system. ConClass provides an efficient development environment for describing and decomposing a classification problem and synthesizing solutions. In ConClass, decomposed concurrent subproblems specified by the application developer effectively correspond to the actual distributed hardware elements. This scheme is useful for designing and implementing efficient distributed processing, making it easier to anticipate and evaluate system behavior. The ConClass system provides an object replication feature that prevents any particular object from being overloaded. In order to deal with an indeterminate amount of problem data, ConClass dynamically creates object networks that justify hypothesized solutions, and thus achieves a dynamic load distribution. A number of efficient execution mechanisms that manage a variety of asynchronous aspects of distributed processing have been implemented without using schedulers or synchronization schemes that are liable to develop bottlenecks. We have confirmed the efficiency of parallel distributed processing and load balancing of ConClass with an experimental application     

Cognitive model of team cooperation in en-route air traffic control

Since controller teams are in charge of en-route air traffic control, team cooperation is a key issue for good control performance. We conducted ethnographic field observation at the Tokyo Area Control Center and then analyzed the obtained data to develop a cognitive model of team cooperation in en-route air traffic control. We segmented conversational records, behavioral records, and so on by control unit, and then clarified relations between the segments and identified expert knowledge and judgment behind them. Cognitive processes of controller teams were reconstructed based on a concept of distributed cognition. The analysis revealed that the mutual belief model is applicable to team cooperation processes, the role assignment of tasks within a controller team is implicit and that control plans are implemented smoothly once team situation awareness has been established. A cognitive model of controllers?? team cooperation has been constructed based on these findings.     

Minimizing False Positives of a Decision Tree Classifier for Intrusion Detection on the Internet

Machine learning or data mining technologies are often used in network intrusion detection systems. An intrusion detection system based on machine learning utilizes a classifier to infer the current state from the observed traffic attributes. The problem with learning-based intrusion detection is that it leads to false positives and so incurs unnecessary additional operation costs. This paper investigates a method to decrease the false positives generated by an intrusion detection system that employs a decision tree as its classifier. The paper first points out that the information-gain criterion used in previous studies to select the attributes in the tree-constructing algorithm is not effective in achieving low false positive rates. Instead of the information-gain criterion, this paper proposes a new function that evaluates the goodness of an attribute by considering the significance of error types. The proposed function can successfully choose an attribute that suppresses false positives from the given attribute set and the effectiveness of using it is confirmed experimentally. This paper also examines the more trivial leaf rewriting approach to benchmark the proposed method. The comparison shows that the proposed attribute evaluation function yields better solutions than the leaf rewriting approach.

A formalization for the specification and systematic generation of computer graphics systems

A formalization of graphical processes in computer graphics systems is presented in terms of functions and their system of axioms. The concept of the viewing pipeline is formalized as operation sequence which is a sequential composition of graphical elementary operations. The formalization includes two kinds of operation sequences which are used as the formal specifications of graphics systems and display devices. In order to generate a graphics system using a display device, we introduced the concept of functionality-preserving transformation of operation sequences in terms of various types of commutations among primitive operations. A type of transformation, which is called extraction, plays a central role in the generation algorithm.     

Development of an epileptic seizure prediction algorithm using R–R intervals with self-attentive autoencoder

Epilepsy is a neurological disorder that may affect the autonomic nervous system (ANS) from 15 to 20 min before seizure onset, and disturbances of ANS affect R–R intervals (RRI) on an electrocardiogram (ECG). This study aims to develop a machine learning algorithm for predicting focal epileptic seizures by monitoring R–R interval (RRI) data in real time. The developed algorithm adopts a self-attentive autoencoder (SA-AE), which is a neural network for time-series data. The results of applying the developed seizure prediction algorithm to clinical data demonstrated that it functioned well in most patients; however, false positives (FPs) occurred in specific participants. In a future work, we will investigate the causes of FPs and optimize the developing seizure prediction algorithm to further improve performance using newly added clinical data.

     

Ab initio molecular orbital calculations of the NMR chemical shieldings for mannose and mannobiose

The ¹³C and ¹H NMR shielding constants for -methylmannose and -methylmannobiose have been calculated using the ab initio gauge-including atomic orbital (GIAO) method to study the conformational dependencies of the NMR chemical shifts of the sugars. The molecular structures were fully optimized using B3LYP/6-31G^* and the NMR shielding constants were calculated at both Hartree–Fock (HF) and density functional (DF) levels of theory with various kinds of basis sets. The values determined using the B3LYP hybrid functional were a little closer to those obtained experimentally than those determined at the HF level. Both, HF and B3LYP with the 6-311+G(2d,p) basis were found to give a very good correlation between the experimental and calculated shielding constants, especially for ¹³C.     

Hybrid Petri net based modeling for biological pathway simulation

Hybrid Petri net (HPN) is an extension of the Petri net formalism, which enables us to handle continuous information in addition to discrete information. Firstly, this paper demonstrates how biological pathways can be modeled by the integration of discrete and continuous elements, with an example of the λ phage genetic switch system including induction and retroregulation mechanisms. Although HPN allows intuitive modeling of biological pathways, some fundamental biological processes such as complex formation cannot be represented with HPN. Thus, this paper next provides the formal definition of hybrid functional Petri net with extension (HFPNe), which has high potential for modeling various kinds of biological processes. Cell Illustrator is a software tool developed on the basis of the definition of HFPNe. Hypothesis creation by Cell Illustrator is demonstrated with the example of the cyanobacterial circadian gene clock system. Finally, our ongoing tasks, which include the development of a computational platform for systems biology, are presented.     

Recursion Schemata for Slowly Growing Depth Circuit Classes

We characterize iterated log depth circuit classes between AC⁰ and AC¹ by Cobham-like bounded recursion schemata. We also give alternative characterizations which utilize the safe recursion method developed by Bellantoni and Cook.     

Ultraviolet bonding of perfluoropolyethers to carbon surfaces investigated using quantum chemical methods

For improving the tribological performance of hard disk drives, nanometer-thick perfluoropolyether (PFPE) lubricant films are generally treated with ultraviolet (UV) irradiation to bond them to the carbon overcoats of the disks. By modeling UV irradiation as an electron emission and attachment process, we investigate the UV bonding of nonfunctional PFPE Z and functional PFPE Zdol to hydrogenated and nitrogenated carbon surfaces with quantum chemical methods. Our calculation results show that, upon electron attachment, Z dissociates at its main chain to two fragments terminated by CF₂CF₂ and CF₂O groups, whereas Zdol dissociates to a hydrogen fluoride and a fragment. The perfluoromethoxy oxygen in one of the Z fragments and the carbon radical and the hydrogen-truncated end group in the Zdol fragment interact strongly with sp² and oxidized sites on carbon surfaces. Imine moieties on the CNx surface also contribute considerably to the UV bonding of Zdol.