**8. References**

16 Will-be-set-by-IN-TECH

0.94 A quarter-ounce of chocolate = four pounds of fat.

Alice: I will think about it and get back to you.

0.82 Equal amounts of dark chocolate and white chocolate,

0.68 Praise your children openly, reprove them secretly.

0.68 When speaking to your children: say what you mean,

0.983 The Bermuda Triangle got tired of warm weather.

It moved to Finland. Now Santa Claus is missing.

on a suitable AIML-based chatbot, and an animation engine for the talking head.

0.746 There are 4 Santa Claus stages:1) When you believe in Santa. 2) When you donŠt believe in Santa.3) When you are Santa. 4) When you look like Santa.ve not seen it. What's it like?

A complete framework for an emotional talking head able to manage humor while conversing with the user has been presented along with its implementation. The whole architecture relies

The chatbot reasoner module is based on an extended AIML architecture where both humor, and emotions can be dealt with using suitable tags. A computational humor engine is able both to detect and to generate humorous sentences. Humor detection relies on the presence of alliteration, antinomy, or adult slang in the user's utterances, which are searched for using suitable thesauri like CMU and WordNet. Generation of humor makes use of a LSA based semantic space where humorous sentences have been placed along with the conversation

The system can also select the control parameters for the animation engine, regarding the mesh deformation due to the emotive state to be expressed, the prosody for controlling speech generation, and the coarticulation model that is used to morph a set of key visemes related to

The whole system has been tested on the humor recognition task with satisfactory results. However, our system is currently under development and much work has to be done in order to improve the whole architecture. Humor recognition algorithms can be enhanced, in order to capture different grades of humor, and to fully exploit the different levels of intensity in

The emotion database has to be completed al least with all the six Ekman basic emotions. Moreover, the most recent emotion models Ekman (1999) use more than six basis emotional

mean what you say,mbut don't be mean when you say it.

(Slick's Second Universe Law)

User: What can you say about children?

is a balanced diet.

User: What do you think of life? Alice: Life is the opposite of death.

0.986 Life is a roller coaster.

User: Have you ever seen Santa Claus?

EHeBby:

EHeBby:

EHeBby:

**7. Conclusion**

topics.

phonemes.

Talking Head emotional expressions.

(W. Cecil)

0.987 Life is an echo.

Alice: No I haven't.


Alice website: www.alicebot.org


**16** 

*Iran* 

**Study of the Reverse Converters for the Large Dynamic Range Four-Moduli Sets** 

The Residue Number System (RNS) is an efficient alternative number system which has been attracted researchers for over three decades. In RNS, arithmetic operations such as addition and multiplication can be performed on residues without carry-propagation between them; resulting in parallel arithmetic and high-speed hardware implementations (Parhami, 2000; Mohan, 2002; Omondi & Premkumar, 2007). Due to this feature, many Digital Signal Processing architectures based on RNS have been introduced in the literature (Soderstrand et al., 1986; Diclaudio et al., 1995; Chaves et al., 2004). In particular, RNS is an efficient method for the implementation of high-speed finite-impulse response (FIR) filters, where dominant operations are addition and multiplication. Implementation issues of RNSbased FIR filters show that performance can be considerably increased, in comparison with traditional two's complement binary number system (Jenkins et al., 1977; Conway et al.,

As described in (Navi et al., 2011) a typical RNS system is based on a moduli set which is included some pair-wise relatively prime integers. The product of the moduli is defined as the dynamic range, and it denotes the interval of integers which can be distinctively represented in RNS. The main components of an RNS system are a forward converter, parallel arithmetic channels and a reverse converter. The forward converter encodes a weighted binary number into a residue represented number, with regard to the moduli set; where it can be easily realized using modular adders or look-up tables. Each arithmetic channel includes modular adder, subtractor and multiplier for each modulo of set. The reverse converter decodes a residue represented number into its equivalent weighted binary number. The arithmetic channels are working in a completely parallel architecture without any dependency, and this results in a considerable speed enhancement. However; the overhead of forward and reverse converters can counteract this speed gain, if they are not designed efficiently. The forward converters can be designed using efficient methods. In contrast, design of reverse converters have many complexities with many important factors

An efficient moduli set with moduli of the form of powers of two can greatly reduce the complexity of the reverse converter as well as arithmetic channels. Due to this, many different moduli sets have been proposed for RNS which can be categorized based on their

**1. Introduction** 

2004; Cardarilli et al., 2007).

such as conversion algorithm, type and number of moduli.

Amir Sabbagh Molahosseini1 and Keivan Navi2

*1Kerman Branch, Islamic Azad University* 

*2Shahid Beheshti University* 

	- http://www.bdwebguide.com/jokes/1linejokes-1.htm.

Amir Sabbagh Molahosseini1 and Keivan Navi2 *1Kerman Branch, Islamic Azad University 2Shahid Beheshti University Iran* 
