Humor processing is still a less studied issue, both in NLP and AI. In this paper we contribute to this field. In our previous research we showed that adding a simple pun generator to a chatterbot can significantly improve its performance. The pun generator we used generated only puns based on words (not phrases). In this paper we introduce the next stage of the system&#39;s development&#8212;an algorithm allowing generation of phrasal pun candidates. We show that by using only the Internet (without any hand-made humor-oriented lexicons), it is possible to generate puns based on complex phrases. As the output list is often excessively long, we also propose a method for reducing the number of candidates by comparing two web-query-based rankings. The evaluation experiment showed that the system achieved an accuracy of 72.5&#37; for finding proper candidates in general, and the reduction method allowed us to significantly shorten the candidates list. The parameters of the reduction algorithm are variable, so that the balance between the number of candidates and the quality of output can be manipulated according to needs.

Pawel Dybala

Michal Ptaszynski

Kohichi Sayama

Reducing Excessive Amounts of Data: Multiple Web Queries for Generation of Pun Candidates

We analyze the convergence time of particle swarm optimization (PSO) on the facet of particle interaction. We firstly introduce a statistical interpretation of social-only PSO in order to capture the essence of particle interaction, which is one of the key mechanisms of PSO. We then use the statistical model to obtain theoretical results on the convergence time. Since the theoretical analysis is conducted on the social-only model of PSO, instead of on common models in practice, to verify the validity of our results, numerical experiments are executed on benchmark functions with a regular PSO program.

Chao-Hong Chen

Ying-ping Chen

Convergence Time Analysis of Particle Swarm Optimization Based on Particle Interaction

It is proposed to use fuzzy similarity in fuzzy decision-making approach to deal with the supplier selection problem in supply chain system. According to the concept of fuzzy TOPSIS earlier methods use closeness coefficient which is defined to determine the ranking order of all suppliers by calculating the distances to both fuzzy positive-ideal solution (FPIS) and fuzzy negative-ideal solution (FNIS) simultaneously. In this paper we propose a new method by doing the ranking using similarity. New proposed method can do ranking with less computations than original fuzzy TOPSIS. We also propose three different cases for selection of FPIS and FNIS and compare closeness coefficient criteria and fuzzy similarity criteria. Numerical example is used to demonstrate the process. Results show that the proposed model is well suited for multiple criteria decision-making for supplier selection. In this paper we also show that the evaluation of the supplier using traditional fuzzy TOPSIS depends highly on FPIS and FNIS, and one needs to select suitable fuzzy ideal solution to get reasonable evaluation.

Pasi Luukka

Fuzzy Similarity in Multicriteria Decision-Making Problem Applied to Supplier Evaluation and Selection in Supply Chain Management

The present study demonstrates the application of artificial neural networks (ANNs) in predicting the weekly spring discharge. The study was based on the weekly spring discharge from a spring located near Ranichauri in Tehri Garhwal district of Uttarakhand, India. Five models were developed for predicting the spring discharge based on a weekly interval using rainfall, evaporation, temperature with a specified lag time. All models were developed both with one and two hidden layers. Each model was developed with many trials by selecting different network architectures and different number of hidden neurons; finally a best predicting model presented against each developed model. The models were trained with three different algorithms, that is, quick-propagation algorithm, batch backpropagation algorithm, and Levenberg-Marquardt algorithm using weekly data from 1999 to 2005. A best model for the simulation was selected from the three presented algorithms using the statistical criteria such as correlation coefficient (<svg style="vertical-align:-0.0pt;width:11.6875px;" id="M1" height="10.325" version="1.1" viewBox="0 0 11.6875 10.325" width="11.6875"  xmlns="http://www.w3.org/2000/svg">
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</svg>), determination coefficient, or Nash Sutcliff's efficiency (DC). Finally, optimized number of neurons were considered for the best model. Training and testing results revealed that the models were predicting the weekly spring discharge satisfactorily. Based on these criteria, ANN-based model results in better agreement for the computation of spring discharge. LMR models were also developed in the study, and they also gave good results, but, when compared with the ANN methodology, ANN resulted in better optimized values.

M. Mohan Raju

R. K. Srivastava

Dinesh C. S. Bisht

H. C. Sharma

Anil Kumar

Development of Artificial Neural-Network-Based Models for the Simulation of Spring Discharge

Rule-based reasoning (RBR) and case-based reasoning (CBR) are two complementary alternatives for building knowledge-based &#8220;intelligent&#8221; decision-support systems. RBR and CBR can be combined in three main ways: RBR first, CBR first, or some interleaving of the two. The N<small class="sc">EST</small> system, described in this paper, allows us to invoke both components separately and in arbitrary order. In addition to the traditional network of propositions and compositional rules, N<small class="sc">EST</small> also supports binary, nominal, and numeric attributes used for derivation of proposition weights, logical (no uncertainty) and default (no antecedent) rules, context expressions, integrity constraints, and cases. The inference mechanism allows use of both rule-based and case-based reasoning. Uncertainty processing (based on H&#225;jek's algebraic theory) allows interval weights to be interpreted as a union of hypothetical cases, and a novel set of combination functions inspired by neural networks has been added. The system is implemented in two versions: stand-alone and web-based client server. A user-friendly editor covering all mentioned features is included.

Petr Berka

NEST: A Compositional Approach to Rule-Based and Case-Based Reasoning

There is currently a growing body of research examining the effects of the fusion of domain knowledge and data mining. This paper examines the impact of such fusion in a novel way by applying validation techniques and training data to enhance the performance of knowledge-based expert systems. We present an algorithm for tuning an expert system to minimize the expected misclassification cost. The algorithm employs data reserved for training data mining models to determine the decision cutoff of the expert system, in terms of the certainty factor of a prediction, for optimal performance. We evaluate the proposed algorithm and find that tuning the expert system results in significantly lower costs. Our approach could be extended to enhance the performance of any intelligent or knowledge system that makes cost-sensitive business decisions.

Atish P. Sinha

Huimin Zhao

Tuning Expert Systems for Cost-Sensitive Decisions

This paper proposes and evaluates a multiagents system called NORA that predicts emotional attributes from learners' brainwaves within an intelligent tutoring system. The measurements from the electrical brain activity of the learner are combined with information about the learner's emotional attributes. Electroencephalogram was used to measure brainwaves and self-reports to measure the three emotional dimensions: pleasure, arousal, and dominance, the eight emotions occurring during learning: anger, boredom, confusion, contempt curious, disgust, eureka, and frustration, and the emotional valence positive for learning and negative for learning. The system is evaluated on natural data, and it achieves an accuracy of over 63%, significantly outperforming classification using the individual modalities and several other combination schemes.

Alicia Heraz

Claude Frasson

Towards a Brain-Sensitive Intelligent Tutoring System: Detecting Emotions from Brainwaves

The proposed by Meier and Staffelbach Self-Shrinking Generator (SSG) which has efficient hardware implementation only with a single Linear Feedback Shift Register is suitable for low-cost and fast stream cipher applications. In this paper we generalize the idea of the SSG for arbitrary Galois Field <svg style="vertical-align:-2.05875pt;width:39.712502px;" id="M2" height="12.6125" version="1.1" viewBox="0 0 39.712502 12.6125" width="39.712502"  xmlns="http://www.w3.org/2000/svg">
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</svg>. The proposed variant of the SSG is called the <svg style="vertical-align:-2.05875pt;width:6.9749999px;" id="M3" height="8.8625002" version="1.1" viewBox="0 0 6.9749999 8.8625002" width="6.9749999"  xmlns="http://www.w3.org/2000/svg">
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</svg>-ary Generalized Self-Shrinking Generator (pGSSG). We suggest a method for transformation of a non-binary self-shrunken pGSSG sequence into balanced binary sequence. We prove that the keystreams of the pGSSG have large period and good statistical properties. The analysis of the experimental results shows that the pGSSG sequences have good randomness properties. We examine the complexity of exhaustive search and entropy attacks of the pGSSG. We show that the pGSSG is more secure than SSG and Modified SSG against these attacks. We prove that the complexity of the used pGSSG attacks increases with increasing the prime <svg style="vertical-align:-2.05875pt;width:6.9749999px;" id="M4" height="8.8625002" version="1.1" viewBox="0 0 6.9749999 8.8625002" width="6.9749999"  xmlns="http://www.w3.org/2000/svg">
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</svg>. Previously mentioned properties give the reason to say that the pGSSG satisfy the basic security requirements for a stream chipper and can be useful as a part of modern stream ciphers.

Antoniya Todorova Tasheva

Zhaneta Nikolova Tasheva

Aleksandar Petrov Milev

Generalization of the Self-Shrinking Generator in the Galois Field <svg style="vertical-align:-3.50804pt" height="17.987499" version="1.1" viewBox="0 0 49.712502 17.987499" width="49.712502" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg"><g transform="matrix(.017,-0,0,-.017,.062,13.55)"><path id="x47" d="M692 302v-28q-45 -6 -54.5 -17t-9.5 -55v-86q0 -62 7 -95q-7 -2 -32 -7.5l-37 -8l-35.5 -7.5t-40.5 -7t-38.5 -4t-40.5 -2q-165 0 -266 93t-101 243q0 84 32 151t86 108t121.5 63t142.5 22q29 0 62.5 -4t52 -7.5t47.5 -10.5t31 -7l15 -156l-28 -6q-17 87 -66.5 121.5&#xA;t-128.5 34.5q-117 0 -191 -80.5t-74 -216.5q0 -56 17 -110t49.5 -100.5t87.5 -74.5t124 -28q53 0 87 17q17 9 24.5 23t7.5 43v85q0 54 -15.5 67.5t-89.5 18.5v28h254z" /></g><g transform="matrix(.017,-0,0,-.017,12.217,13.55)"><path id="x46" d="M493 503l-29 -4q-13 56 -28 80q-13 22 -35.5 29.5t-78.5 7.5h-76q-22 0 -27.5 -5t-5.5 -27v-238h103q66 0 82.5 12t25.5 62h29v-193h-29q-9 54 -25.5 67t-82.5 13h-103v-181q0 -64 15 -78.5t84 -19.5v-28h-272v28q61 5 74.5 19.5t13.5 78.5v399q0 64 -13 78t-72 19v28&#xA;h440q1 -40 10 -147z" /></g><g transform="matrix(.017,-0,0,-.017,21.209,13.55)"><path id="x28" d="M300 -147l-18 -23q-106 71 -159 185.5t-53 254.5v1q0 139 53 252.5t159 186.5l18 -24q-74 -62 -115.5 -173.5t-41.5 -242.5q0 -130 41.5 -242.5t115.5 -174.5z" /></g><g transform="matrix(.017,-0,0,-.017,27.091,13.55)"><path id="x1D45D" d="M570 304q0 -108 -87 -199q-40 -42 -94.5 -74t-105.5 -43q-41 0 -65 11l-29 -141q-9 -45 -1.5 -58t45.5 -16l26 -2l-5 -29l-241 -10l4 26q51 10 67.5 24t26.5 60l113 520q-54 -20 -89 -41l-7 26q38 28 105 53l11 49q20 25 77 58l8 -7l-17 -77q39 14 102 14q82 0 119 -36&#xA;t37 -108zM482 289q0 114 -113 114q-26 0 -66 -7l-70 -327q12 -14 32 -25t39 -11q59 0 118.5 81.5t59.5 174.5z" /></g><g transform="matrix(.012,-0,0,-.012,37.175,5.388)"><path id="x1D45B" d="M495 86q-46 -47 -87 -72.5t-63 -25.5q-43 0 -16 107l49 210q7 34 8 50.5t-3 21t-13 4.5q-35 0 -109.5 -72.5t-115.5 -140.5q-21 -75 -38 -159q-50 -10 -76 -21l-6 8l84 340q8 35 -4 35q-17 0 -67 -46l-15 26q44 44 85.5 70.5t64.5 26.5q35 0 10 -103l-24 -98h2&#xA;q42 56 97 103.5t96 71.5q46 26 74 26q9 0 16 -2.5t14 -11.5t9.5 -24.5t-1 -44t-13.5 -68.5q-30 -117 -47 -200q-4 -19 -3.5 -25t6.5 -6q21 0 70 48z" /></g><g transform="matrix(.017,-0,0,-.017,43.762,13.55)"><path id="x29" d="M275 270q0 -296 -211 -440l-19 23q75 62 116.5 174t41.5 243t-42 243t-116 173l19 24q211 -144 211 -440z" /></g></svg> 

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