Computer-generated Conversation Using Newspaper Headline

In this paper, the authors propose a method that incorporates mechanisms for handling ambiguity in speech and the ability of humans to create associations, and for formulating conversations based on rule base knowledge and common knowledge. Go beyond the level that can be achieved, using only conventional natural language processing and vast repositories of sample patterns. In this paper, the authors propose a method for computer conversation sentences generated using newspaper headlines as an example of how the common knowledge and associative ability are applied.     

Stable growing neural gas: A topology learning algorithm based on player tracking in video games

Characters in video games usually use a manually-defined topology of the environment to navigate. To evolve in an open, unknown and dynamic world, characters should not have pre-existing representations of their environment. In this paper, characters learn this representation by imitating human players. We here put forward a modified version of the growing neural gas model (GNG) called stable growing neural gas (SGNG). The algorithm is able to learn how to use the environment from one or more teachers (players) by representing it with a graph. Unlike GNG, SGNG learning is in-line, reflecting the dynamic nature of the environment. The evaluation of the quality of the learned representations are detailed.     

The method of separation for evolutionary spectral density estimation of multi-variate and multi-dimensional non-stationary stochastic processes

The method of separation can be used as a non-parametric estimation technique, especially suitable for evolutionary spectral density functions of uniformly modulated and strongly narrow-band stochastic processes. The paper at hand provides a consistent derivation of method of separation based spectrum estimation for the general multi-variate and multi-dimensional case. The validity of the method is demonstrated by benchmark tests with uniformly modulated spectra, for which convergence to the analytical solution is demonstrated. The key advantage of the method of separation is the minimization of spectral dispersion due to optimum time- or space–frequency localization. This is illustrated by the calibration of multi-dimensional and multi-variate geometric imperfection models from strongly narrow-band measurements in I-beams and cylindrical shells. Finally, the application of the method of separation based estimates for the stochastic buckling analysis of the example structures is briefly discussed.     

New Uni-Resonance Approximation Technique for Reaction Rate of Higher Resonances in Activation Detectors

As a high efficiency solid sample analysis technique based on resonance ionization spectroscopy (RIS). Resonant Laser Ablation (RLA) is available. RLA combines Laser Ablation (LA) and RIS simultaneously with a single laser device. We have proposed the new dosimetry technique for a solid sample based on RLA.

We have attempted the detection of ²⁶Al (T _1/2: 7.2×10⁵yr) produced in the high purity Al irradiated by 14MeV neutrons at Fusion Neutronics Source (FNS). Japan Atomic Energy Research Institute. Some interference effects are observed in the trace element detection experiments. These interference effects interrupt the detection of ²⁶Al. In conclusion. it is difficult that ²⁶Al is recognized clearly. We have mainly discussed the origins and elimination methods of these interference effects to detect the trace elements produced by nuclear reactions.     

An analysis of specialist and non-specialist user requirements for geographic climate change information

The EU EuroClim project developed a system to monitor and record climate change indicator data based on satellite observations of snow cover, sea ice and glaciers in Northern Europe and the Arctic. It also contained projection data for temperature, rainfall and average wind speed for Europe. These were all stored as data sets in a GIS database for users to download. The process of gathering requirements for a user population including scientists, researchers, policy makers, educationalists and the general public is described. Using an iterative design methodology, a user survey was administered to obtain initial feedback on the system concept followed by panel sessions where users were presented with the system concept and a demonstrator to interact with it. The requirements of both specialist and non-specialist users is summarised together with strategies for the effective communication of geographic climate change information.     

FUZZY RELATIONAL EQUATIONS: BRIDGING THEORY,METHODOLOGY AND PRACTICE

This study delivers a general overview of the theory and practice of fuzzy relational equations. We discuss various methods leading to the solutions of these equations starting from analytical approaches, moving through semi-analytic methods and finally elaborating on the neural-like style of finding solutions to relational constructs. The paper addresses important aspects of knowledge representation worked out by these equations and proposes a number of structural enhancements of the existing relational architectures.     

Effect of solution representations on Tabu search in scheduling applications

This research investigates the application of meta-heuristic algorithms to a scheduling problem called permutation manufacturing-cell flow shop (PMFS) from two perspectives. First, we examine the effect of using different solution representations (S_new and S_old) while applying Tabu-search algorithm. Experimental results reveal that Tabu_S_new outperforms Tabu_S_old. The rationale why Tabu_S_new is superior is further examined by characterizing the intermediate outcomes of the evolutionary processes in these two algorithms. We find that the superiority of S_new is due to its relatively higher degree of freedom in modeling Tabu neighborhood. Second, we propose a new algorithm GA_Tabu_S_new, which empirically outperforms the state-of-the-art meta-heuristic algorithms in solving the PMFS problem. This research highlights the importance of solution representation in the application of meta-heuristic algorithm, and establishes a significant milestone in solving the PMFS problem.     

Extension of some MAP results to transient MAPs and Markovian binary trees

In this work we extend previous results on moment-based characterization and minimal representation of stationary Markovian arrival processes (MAPs) and rational arrival processes (RAPs) to transient Markovian arrival processes (TMAPs) and Markovian binary trees (MBTs).     

FPGA-based architecture to estimate the input glucose in a dark fermenter using a super-twisting observer

This article presents a digital architecture design for a Super-Twisting Observer (STO) implemented in a Field-Programmable Gate Array (FPGA). The STO is robust in the presence of uncertainties, which makes it suitable to estimate unknown inputs present in biological systems and applications based on sensorless control accurately. The proposed STO is used to estimate the glucose concentration in the inflow of a hydrogen production bioreactor (e.g., dark fermenter). The dark fermenter is not generally recognized as a swift process. Therefore, the observer digital architecture design criteria aim to optimize hardware resources and reduce power consumption through an iterative approach to perform internal arithmetic operations efficiently. Besides, an analysis of the errors produced by internal operations, observer discretization, and fixed-point data representation is presented and discussed. Numerical results showed that embedding the super-twisting observer into the FPGA is a reliable, low-power consumption, and efficient alternative to estimate the input glucose in a dark fermenter.     

Domain structure-based transfer learning for cross-domain word representation

Cross-domain word representation aims to learn high-quality semantic representations in an under-resourced domain by leveraging information in a resourceful domain. However, most existing methods mainly transfer the semantics of common words across domains, ignoring the semantic relations among domain-specific words. In this paper, we propose a domain structure-based transfer learning method to learn cross-domain representations by leveraging the relations among domain-specific words. To accomplish this, we first construct a semantic graph to capture the latent domain structure using domain-specific co-occurrence information. Then, in the domain adaptation process, beyond domain alignment, we employ Laplacian Eigenmaps to ensure the domain structure is consistently distributed in the learned embedding space. As such, the learned cross-domain word representations not only capture shared semantics across domains, but also maintain the latent domain structure. We performed extensive experiments on two tasks, namely sentiment analysis and query expansion. The experiment results show the effectiveness of our method for tasks in under-resourced domains.