Search results for: Boolean reasoning

Authors: Jieun Kim, Yongtae Park, Hakyeon Lee

Abstract:

The emergence of mobile application services and App Store has led to the explosive growth of user innovation, which users voluntarily contribute to. User innovation communities where end users freely reveal innovative ideas and needs with other community members are becoming increasingly influential in this area. However, user-s ideas in user innovation community are not enough to be new service opportunity, because some of them can already developed as existing services in App Store. Moreover, the existing services similar to new service opportunity can be significant references to apply analogy to develop service concept. In response, this research proposes Case-Based Reasoning approach to matching the user needs and existing services, identifying unmet opportunistic user needs, and retrieving similar services with opportunity. Due to its intuitive and transparent algorithm, users related to App Store innovation communities can easily employ Case-Based Reasoning based approach to their innovation.

Keywords: App Store, Case-Based Reasoning, Mobile Application Service, User innovation community.

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Authors: Neli P. Zlatareva

Abstract:

Ontology validation is an important part of web applications’ development, where knowledge integration and ontological reasoning play a fundamental role. It aims to ensure the consistency and correctness of ontological knowledge and to guarantee that ontological reasoning is carried out in a meaningful way. Existing approaches to ontology validation address more or less specific validation issues, but the overall process of validating web ontologies has not been formally established yet. As the size and the number of web ontologies continue to grow, more web applications’ developers will rely on the existing repository of ontologies rather than develop ontologies from scratch. If an application utilizes multiple independently created ontologies, their consistency must be validated and eventually adjusted to ensure proper interoperability between them. This paper presents a validation technique intended to test the consistency of independent ontologies utilized by a common application.

Keywords: Knowledge engineering, ontological reasoning, ontology validation, semantic web.

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Authors: Preeda Sansakorn, Min An

Abstract:

In order to be capable of dealing with uncertainties, subjectivities, including vagueness arising in building construction projects, the application of fuzzy reasoning technique based on fuzzy set theory is proposed. This study contributes significantly to the development of a fuzzy reasoning safety risk assessment model for building construction projects that could be employed to assess the risk magnitude of each hazardous event identified during construction, and a third parameter of probability of consequence is incorporated in the model. By using the proposed safety risk analysis methodology, more reliable and less ambiguities, which provide the safety risk management project team for decision-making purposes.

Keywords: Safety risks assessment, building construction safety, fuzzy reasoning, construction risk assessment model, building construction projects.

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Authors: Visit Hirankitti, Vuong Tran Xuan

Abstract:

Meta-reasoning is essential for multi-agent communication. In this paper we propose a framework of multi-agent communication in which agents employ meta-reasoning to reason with agent and ontology locations in order to communicate semantic information with other agents on the semantic web and also reason with multiple distributed ontologies. We shall argue that multi-agent communication of Semantic Web information cannot be realized without the need to reason with agent and ontology locations. This is because for an agent to be able to communicate with another agent, it must know where and how to send a message to that agent. Similarly, for an agent to be able to reason with an external semantic web ontology, it must know where and how to access to that ontology. The agent framework and its communication mechanism are formulated entirely in meta-logic.

Keywords: Semantic Web, agent communication, ontologies.

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Authors: Kamalendu Pal

Abstract:

This paper describes the main features of a knowledge-based system evaluation method. System evaluation is placed in the context of a hybrid legal decision-support system, Advisory Support for Home Settlement in Divorce (ASHSD). Legal knowledge for ASHSD is represented in two forms, as rules and previously decided cases. Besides distinguishing the two different forms of knowledge representation, the paper outlines the actual use of these forms in a computational framework that is designed to generate a plausible solution for a given case, by using rule-based reasoning (RBR) and case-based reasoning (CBR) in an integrated environment. The nature of suitability assessment of a solution has been considered as a multiple criteria decision-making process in ASHAD evaluation. The evaluation was performed by a combination of discussions and questionnaires with different user groups. The answers to questionnaires used in this evaluations method have been measured as a fuzzy linguistic term. The finding suggests that fuzzy linguistic evaluation is practical and meaningful in knowledge-based system development purpose. 

Keywords: Case-based reasoning, decision-support system, fuzzy linguistic term, rule-based reasoning, system evaluation.

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Authors: Suguru Yoshioka, Satoshi Tojo

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This paper introduces a temporal epistemic logic CBCTL that updates agent-s belief states through communications in them, based on computational tree logic (CTL). In practical environments, communication channels between agents may not be secure, and in bad cases agents might suffer blackouts. In this study, we provide inform* protocol based on ACL of FIPA, and declare the presence of secure channels between two agents, dependent on time. Thus, the belief state of each agent is updated along with the progress of time. We show a prover, that is a reasoning system for a given formula in a given a situation of an agent ; if it is directly provable or if it could be validated through the chains of communications, the system returns the proof.

Keywords: communication channel, computational tree logic, reasoning system, temporal epistemic logic.

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Authors: Michał Białek, Simon J. Handley

Abstract:

Moral decisions are considered as an intuitive process, while conscious reasoning is mostly used only to justify those intuitions. This problem is described in few different dual-process theories of mind, that are being developed e.g. by Frederick and Kahneman, Stanovich and Evans. Those theories recently evolved into tri-process theories with a proposed process that makes ultimate decision or allows to paraformal processing with focal bias.. Presented experiment compares the decision patterns to the implications of those models. In presented study participants (n=179) considered different aspects of trolley dilemma or its footbridge version and decided after that. Results show that in the control group 70% of people decided to use the lever to change tracks for the running trolley, and 20% chose to push the fat man down the tracks. In contrast, after experimental manipulation almost no one decided to act. Also the decision time difference between dilemmas disappeared after experimental manipulation. The result supports the idea of three co-working processes: intuitive (TASS), paraformal (reflective mind) and algorithmic process.

Keywords: Moral reasoning, moral decision, reflection, trolley problem, dual-process theory of reasoning, tri-process theory of cognition.

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Authors: Yaojun Wang, Yaoqing Wang

Abstract:

Stock selection is an important decision-making problem. Many machine learning and data mining technologies are employed to build automatic stock-selection system. A profitable stock-selection system should consider the stock’s investment value and the market timing. In this paper, we present a hybrid system including both engage for stock selection. This system uses a case-based reasoning (CBR) model to execute the stock classification, uses a decision-tree model to help with market timing and stock selection. The experiments show that the performance of this hybrid system is better than that of other techniques regarding to the classification accuracy, the average return and the Sharpe ratio.

Keywords: Case-based reasoning, decision tree, stock selection, machine learning.

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Authors: Bruno Trstenjak, Dzenana Donko

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Data mining and classification of objects is the process of data analysis, using various machine learning techniques, which is used today in various fields of research. This paper presents a concept of hybrid classification model improved with the expert knowledge. The hybrid model in its algorithm has integrated several machine learning techniques (Information Gain, K-means, and Case-Based Reasoning) and the expert’s knowledge into one. The knowledge of experts is used to determine the importance of features. The paper presents the model algorithm and the results of the case study in which the emphasis was put on achieving the maximum classification accuracy without reducing the number of features.

Keywords: Case based reasoning, classification, expert's knowledge, hybrid model.

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Authors: Padmanabhan Balasubramanian, R. Chinnadurai

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This paper deals with a power-conscious ANDEXOR- Inverter type logic implementation for a complex class of Boolean functions, namely Achilles- heel functions. Different variants of the above function class have been considered viz. positive, negative and pure horn for analysis and simulation purposes. The proposed realization is compared with the decomposed implementation corresponding to an existing standard AND-EXOR logic minimizer; both result in Boolean networks with good testability attribute. It could be noted that an AND-OR-EXOR type logic network does not exist for the positive phase of this unique class of logic function. Experimental results report significant savings in all the power consumption components for designs based on standard cells pertaining to a 130nm UMC CMOS process The simulations have been extended to validate the savings across all three library corners (typical, best and worst case specifications).

Keywords: Achilles' heel functions, AND-EXOR-Inverter logic, CMOS technology, low power design.

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Authors: Ebtehal Alawi Alsaggaf, Shehab A. Gamalel-Din

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Exploring an autistic child in Elementary school is a difficult task that must be fully thought out and the teachers should be aware of the many challenges they face raising their child especially the behavioral problems of autistic children. Hence there arises a need for developing Artificial intelligence (AI) Contemporary Techniques to help diagnosis to discover autistic people. In this research, we suggest designing architecture of expert system that combine Cognitive Maps (CM) with Case Based Reasoning technique (CBR) in order to reduce time and costs of traditional diagnosis process for the early detection to discover autistic children. The teacher is supposed to enter child's information for analyzing by CM module. Then, the reasoning processor would translate the output into a case to be solved a current problem by CBR module. We will implement a prototype for the model as a proof of concept using java and MYSQL. This will be provided a new hybrid approach that will achieve new synergies and improve problem solving capabilities in AI. And we will predict that will reduce time, costs, the number of human errors and make expertise available to more people who want who want to serve autistic children and their families.

Keywords: Autism, Cognitive Maps (CM), Case Based Reasoning technique (CBR).

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Authors: Rizauddin Ramli, Jaber Abu Qudeiri, Hidehiko Yamamoto

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Nowadays the market for industrial companies is becoming more and more globalized and highly competitive, forcing them to shorten the duration of the manufacturing system development time in order to reduce the time to market. In order to achieve this target, the hierarchical systems used in previous manufacturing systems are not enough because they cannot deal effectively with unexpected situations. To achieve flexibility in manufacturing systems, the concept of an Autonomous Decentralized Flexible Manufacturing System (AD-FMS) is useful. In this paper, we introduce a hypothetical reasoning based algorithm called the Algorithm for Future Anticipative Reasoning (AFAR) which is able to decide on a conceivable next action of an Automated Guided Vehicle (AGV) that works autonomously in the AD-FMS.

Keywords: Flexible Manufacturing System, Automated GuidedVehicle, Hypothetical Reasoning, Autonomous Decentralized.

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Authors: Manpreet Singh, Parvinder Singh Sandhu, Manjinder Singh Kahlon

Abstract:

There are many classical algorithms for finding routing in FPGA. But Using DNA computing we can solve the routes efficiently and fast. The run time complexity of DNA algorithms is much less than other classical algorithms which are used for solving routing in FPGA. The research in DNA computing is in a primary level. High information density of DNA molecules and massive parallelism involved in the DNA reactions make DNA computing a powerful tool. It has been proved by many research accomplishments that any procedure that can be programmed in a silicon computer can be realized as a DNA computing procedure. In this paper we have proposed two tier approaches for the FPGA routing solution. First, geometric FPGA detailed routing task is solved by transforming it into a Boolean satisfiability equation with the property that any assignment of input variables that satisfies the equation specifies a valid routing. Satisfying assignment for particular route will result in a valid routing and absence of a satisfying assignment implies that the layout is un-routable. In second step, DNA search algorithm is applied on this Boolean equation for solving routing alternatives utilizing the properties of DNA computation. The simulated results are satisfactory and give the indication of applicability of DNA computing for solving the FPGA Routing problem.

Keywords: FPGA, Routing, DNA Computing.

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Authors: Viet-Hoang Vu, Nhan Le-Thanh

Abstract:

Recently the usefulness of Concept Abduction, a novel non-monotonic inference service for Description Logics (DLs), has been argued in the context of ontology-based applications such as semantic matchmaking and resource retrieval. Based on tableau calculus, a method has been proposed to realize this reasoning task in ALN, a description logic that supports simple cardinality restrictions as well as other basic constructors. However, in many ontology-based systems, the representation of ontology would require expressive formalisms for capturing domain-specific constraints, this language is not sufficient. In order to increase the applicability of the abductive reasoning method in such contexts, we would like to present in the scope of this paper an extension of the tableaux-based algorithm for dealing with concepts represented inALCQ, the description logic that extends ALN with full concept negation and quantified number restrictions.

Keywords: Abductive reasoning, description logics, semantic matchmaking, non-monotonic inference, tableaux-based method.

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Authors: Fernando Alonso, Rafael Fernandez, Sonia Frutos, Javier Soriano

Abstract:

CIM is the standard formalism for modeling management information developed by the Distributed Management Task Force (DMTF) in the context of its WBEM proposal, designed to provide a conceptual view of the managed environment. In this paper, we propose the inclusion of formal knowledge representation techniques, based on Description Logics (DLs) and the Web Ontology Language (OWL), in CIM-based conceptual modeling, and then we examine the benefits of such a decision. The proposal is specified as a CIM metamodel level mapping to a highly expressive subset of DLs capable of capturing all the semantics of the models. The paper shows how the proposed mapping can be used for automatic reasoning about the management information models, as a design aid, by means of new-generation CASE tools, thanks to the use of state-of-the-art automatic reasoning systems that support the proposed logic and use algorithms that are sound and complete with respect to the semantics. Such a CASE tool framework has been developed by the authors and its architecture is also introduced. The proposed formalization is not only useful at design time, but also at run time through the use of rational autonomous agents, in response to a need recently recognized by the DMTF.

Keywords: CIM, Knowledge-based Information Models, Ontology Languages, OWL, Description Logics, Integrated Network Management, Intelligent Agents, Automatic Reasoning Techniques.

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Authors: Alicia Y. C. Tang, Sharifuddin M. Zain, Noorsaadah A. Rahman, Rukaini Abdullah

Abstract:

This paper discusses a qualitative simulator QRiOM that uses Qualitative Reasoning (QR) technique, and a process-based ontology to model, simulate and explain the behaviour of selected organic reactions. Learning organic reactions requires the application of domain knowledge at intuitive level, which is difficult to be programmed using traditional approach. The main objective of QRiOM is to help learners gain a better understanding of the fundamental organic reaction concepts, and to improve their conceptual comprehension on the subject by analyzing the multiple forms of explanation generated by the software. This paper focuses on the generation of explanation based on causal theories to explicate various phenomena in the chemistry subject. QRiOM has been tested with three classes problems related to organic chemistry, with encouraging results. This paper also presents the results of preliminary evaluation of QRiOM that reveal its explanation capability and usefulness.

Keywords: Artificial intelligence, explanation, ontology, organicreactions, qualitative reasoning, QPT.

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Authors: A. Lawanna, J. Daengdej

Abstract:

Case-Based Reasoning (CBR) is one of machine learning algorithms for problem solving and learning that caught a lot of attention over the last few years. In general, CBR is composed of four main phases: retrieve the most similar case or cases, reuse the case to solve the problem, revise or adapt the proposed solution, and retain the learned cases before returning them to the case base for learning purpose. Unfortunately, in many cases, this retain process causes the uncontrolled case base growth. The problem affects competence and performance of CBR systems. This paper proposes competence-based maintenance method based on deletion policy strategy for CBR. There are three main steps in this method. Step 1, formulate problems. Step 2, determine coverage and reachability set based on coverage value. Step 3, reduce case base size. The results obtained show that this proposed method performs better than the existing methods currently discussed in literature.

Keywords: Case-Based Reasoning, Case Base Maintenance, Coverage, Reachability.

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Authors: Takashi Mitsuishi, Kiyoshi Sawada, Yasunari Shidama

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The mathematical framework for studying of a fuzzy approximate reasoning is presented in this paper. Two important defuzzification methods (Area defuzzification and Height defuzzification) besides the center of gravity method which is the best well known defuzzification method are described. The continuity of the defuzzification methods and its application to a fuzzy feedback control are discussed.

Keywords: Fuzzy approximate reasoning, defuzzification, area method, height method.

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Authors: Fernando Alonso, Rafael Fernandez, Sonia Frutos, Javier Soriano

Abstract:

CIM is the standard formalism for modeling management information developed by the Distributed Management Task Force (DMTF) in the context of its WBEM proposal, designed to provide a conceptual view of the managed environment. In this paper, we propose the inclusion of formal knowledge representation techniques, based on Description Logics (DLs) and the Web Ontology Language (OWL), in CIM-based conceptual modeling, and then we examine the benefits of such a decision. The proposal is specified as a CIM metamodel level mapping to a highly expressive subset of DLs capable of capturing all the semantics of the models. The paper shows how the proposed mapping provides CIM diagrams with precise semantics and can be used for automatic reasoning about the management information models, as a design aid, by means of newgeneration CASE tools, thanks to the use of state-of-the-art automatic reasoning systems that support the proposed logic and use algorithms that are sound and complete with respect to the semantics. Such a CASE tool framework has been developed by the authors and its architecture is also introduced. The proposed formalization is not only useful at design time, but also at run time through the use of rational autonomous agents, in response to a need recently recognized by the DMTF.

Keywords: CIM, Knowledge-based Information Models, OntologyLanguages, OWL, Description Logics, Integrated Network Management, Intelligent Agents, Automatic Reasoning Techniques.

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Authors: Miloš Šeda, Tomáš Březina

Abstract:

In this paper, we focus on the use of knowledge bases in two different application areas – control of systems with unknown or strongly nonlinear models (i.e. hardly controllable by the classical methods), and robot motion planning in eight directions. The first one deals with fuzzy logic and the paper presents approaches for setting and aggregating the rules of a knowledge base. Te second one is concentrated on a case-based reasoning strategy for finding the path in a planar scene with obstacles.

Keywords: fuzzy controller, fuzzification, rule base, inference, defuzzification, genetic algorithm, neural network, case-based reasoning

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Authors: Hao-Hsiang Ku, Ching-Ho Chi

Abstract:

Big data applications have become an imperative for many fields. Many researchers have been devoted into increasing correct rates and reducing time complexities. Hence, the study designs and proposes an Ontology-based backpropagation neural network classification and reasoning strategy for NoSQL big data applications, which is called ON4NoSQL. ON4NoSQL is responsible for enhancing the performances of classifications in NoSQL and SQL databases to build up mass behavior models. Mass behavior models are made by MapReduce techniques and Hadoop distributed file system based on Hadoop service platform. The reference engine of ON4NoSQL is the ontology-based backpropagation neural network classification and reasoning strategy. Simulation results indicate that ON4NoSQL can efficiently achieve to construct a high performance environment for data storing, searching, and retrieving.

Keywords: Hadoop, NoSQL, ontology, backpropagation neural network, and high distributed file system.

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Authors: Javier Parra-Domínguez, Juan Manuel Corchado

Abstract:

At present, public efficiency is a concept that intends to maximize return on public investment focus on minimizing the use of resources and maximizing the outputs. The concept takes into account statistical criteria drawn up according to techniques such as DEA (Data Envelopment Analysis). The purpose of the current work is to consider, more precisely, the theoretical application of CBR (Case-Based Reasoning) from economics and computer science, as a preliminary step to improving the efficiency of law enforcement agencies (public sector). With the aim of increasing the efficiency of the public sector, we have entered into a phase whose main objective is the implementation of new technologies. Our main conclusion is that the application of computer techniques, such as CBR, has become key to the efficiency of the public sector, which continues to require economic valuation based on methodologies such as DEA. As a theoretical result and conclusion, the incorporation of CBR systems will reduce the number of inputs and increase, theoretically, the number of outputs generated based on previous computer knowledge.

Keywords: Case-based reasoning, knowledge, police, public efficiency.

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Authors: Padmanabhan Balasubramanian, C. Ardil

Abstract:

In this paper, we consider the problem of logic simplification for a special class of logic functions, namely complementary Boolean functions (CBF), targeting low power implementation using static CMOS logic style. The functions are uniquely characterized by the presence of terms, where for a canonical binary 2-tuple, D(mj) ∪ D(mk) = { } and therefore, we have | D(mj) ∪ D(mk) | = 0 [19]. Similarly, D(Mj) ∪ D(Mk) = { } and hence | D(Mj) ∪ D(Mk) | = 0. Here, 'mk' and 'Mk' represent a minterm and maxterm respectively. We compare the circuits minimized with our proposed method with those corresponding to factored Reed-Muller (f-RM) form, factored Pseudo Kronecker Reed-Muller (f-PKRM) form, and factored Generalized Reed-Muller (f-GRM) form. We have opted for algebraic factorization of the Reed-Muller (RM) form and its different variants, using the factorization rules of [1], as it is simple and requires much less CPU execution time compared to Boolean factorization operations. This technique has enabled us to greatly reduce the literal count as well as the gate count needed for such RM realizations, which are generally prone to consuming more cells and subsequently more power consumption. However, this leads to a drawback in terms of the design-for-test attribute associated with the various RM forms. Though we still preserve the definition of those forms viz. realizing such functionality with only select types of logic gates (AND gate and XOR gate), the structural integrity of the logic levels is not preserved. This would consequently alter the testability properties of such circuits i.e. it may increase/decrease/maintain the same number of test input vectors needed for their exhaustive testability, subsequently affecting their generalized test vector computation. We do not consider the issue of design-for-testability here, but, instead focus on the power consumption of the final logic implementation, after realization with a conventional CMOS process technology (0.35 micron TSMC process). The quality of the resulting circuits evaluated on the basis of an established cost metric viz., power consumption, demonstrate average savings by 26.79% for the samples considered in this work, besides reduction in number of gates and input literals by 39.66% and 12.98% respectively, in comparison with other factored RM forms.

Keywords: Reed-Muller forms, Logic function, Hammingdistance, Algebraic factorization, Low power design.

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Authors: Visit Hirankitti, Vuong Tran Xuan

Abstract:

Multi-agent communication of Semantic Web information cannot be realized without the need to reason with ontology and agent locations. This is because for an agent to be able to reason with an external semantic web ontology, it must know where and how to access to that ontology. Similarly, for an agent to be able to communicate with another agent, it must know where and how to send a message to that agent. In this paper we propose a framework of an agent which can reason with ontology and agent locations in order to perform reasoning with multiple distributed ontologies and perform communication with other agents on the semantic web. The agent framework and its communication mechanism are formulated entirely in meta-logic.

Keywords: Semantic Web, agent communication, ontologies.

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Authors: S. M. Seyedhosseini, A. Makui, M. Ghadami

Abstract:

Globalization and therefore increasing tight competition among companies, have resulted to increase the importance of making well-timed decision. Devising and employing effective strategies, that are flexible and adaptive to changing market, stand a greater chance of being effective in the long-term. In other side, a clear focus on managing the entire product lifecycle has emerged as critical areas for investment. Therefore, applying wellorganized tools to employ past experience in new case, helps to make proper and managerial decisions. Case based reasoning (CBR) is based on a means of solving a new problem by using or adapting solutions to old problems. In this paper, an adapted CBR model with k-nearest neighbor (K-NN) is employed to provide suggestions for better decision making which are adopted for a given product in the middle of life phase. The set of solutions are weighted by CBR in the principle of group decision making. Wrapper approach of genetic algorithm is employed to generate optimal feature subsets. The dataset of the department store, including various products which are collected among two years, have been used. K-fold approach is used to evaluate the classification accuracy rate. Empirical results are compared with classical case based reasoning algorithm which has no special process for feature selection, CBR-PCA algorithm based on filter approach feature selection, and Artificial Neural Network. The results indicate that the predictive performance of the model, compare with two CBR algorithms, in specific case is more effective.

Keywords: Case based reasoning, Genetic algorithm, Groupdecision making, Product management.

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Authors: Mohammad Darzi, Ali AsgharLiaei, Mahdi Hosseini, HabibollahAsghari

Abstract:

This article addresses feature selection for breast cancer diagnosis. The present process contains a wrapper approach based on Genetic Algorithm (GA) and case-based reasoning (CBR). GA is used for searching the problem space to find all of the possible subsets of features and CBR is employed to estimate the evaluation result of each subset. The results of experiment show that the proposed model is comparable to the other models on Wisconsin breast cancer (WDBC) dataset.

Keywords: Case-based reasoning; Breast cancer diagnosis; Genetic algorithm; Wrapper feature selection

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Authors: Hazem M. El-Bakry

Abstract:

In this paper a new fast simplification method is presented. Such method realizes Karnough map with large number of variables. In order to accelerate the operation of the proposed method, a new approach for fast detection of group of ones is presented. Such approach implemented in the frequency domain. The search operation relies on performing cross correlation in the frequency domain rather than time one. It is proved mathematically and practically that the number of computation steps required for the presented method is less than that needed by conventional cross correlation. Simulation results using MATLAB confirm the theoretical computations. Furthermore, a powerful solution for realization of complex functions is given. The simplified functions are implemented by using a new desigen for neural networks. Neural networks are used because they are fault tolerance and as a result they can recognize signals even with noise or distortion. This is very useful for logic functions used in data and computer communications. Moreover, the implemented functions are realized with minimum amount of components. This is done by using modular neural nets (MNNs) that divide the input space into several homogenous regions. Such approach is applied to implement XOR function, 16 logic functions on one bit level, and 2-bit digital multiplier. Compared to previous non- modular designs, a clear reduction in the order of computations and hardware requirements is achieved.

Keywords: Boolean Functions, Simplification, KarnoughMap, Implementation of Logic Functions, Modular NeuralNetworks.

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Authors: Hazem M. El-Bakry

Abstract:

In this paper a new fast simplification method is presented. Such method realizes Karnough map with large number of variables. In order to accelerate the operation of the proposed method, a new approach for fast detection of group of ones is presented. Such approach implemented in the frequency domain. The search operation relies on performing cross correlation in the frequency domain rather than time one. It is proved mathematically and practically that the number of computation steps required for the presented method is less than that needed by conventional cross correlation. Simulation results using MATLAB confirm the theoretical computations. Furthermore, a powerful solution for realization of complex functions is given. The simplified functions are implemented by using a new desigen for neural networks. Neural networks are used because they are fault tolerance and as a result they can recognize signals even with noise or distortion. This is very useful for logic functions used in data and computer communications. Moreover, the implemented functions are realized with minimum amount of components. This is done by using modular neural nets (MNNs) that divide the input space into several homogenous regions. Such approach is applied to implement XOR function, 16 logic functions on one bit level, and 2-bit digital multiplier. Compared to previous non- modular designs, a clear reduction in the order of computations and hardware requirements is achieved.

Keywords: Boolean functions, simplification, Karnough map, implementation of logic functions, modular neural networks.

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Authors: Hooriya Sadr Dadras, Shiva Seyed Erfani

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Speaking skills merit meticulous attention both on the side of the learners and the teachers. In particular, accuracy is a critical component to guarantee the messages to be conveyed through conversation because a wrongful change may adversely alter the content and purpose of the talk. Different types of tasks have served teachers to meet numerous educational objectives. Besides, negotiation of meaning and the use of different strategies have been areas of concern in socio-cultural theories of SLA. Negotiation of meaning is among the conversational processes which have a crucial role in facilitating the understanding and expression of meaning in a given second language. Conversational strategies are used during interaction when there is a breakdown in communication that leads to the interlocutor attempting to remedy the gap through talk. Therefore, this study was an attempt to investigate if there was any significant difference between the effect of reasoning gap tasks and information gap tasks on the frequency of conversational strategies used in negotiation of meaning in classrooms on one hand, and on the accuracy in speaking of Iranian intermediate EFL learners on the other. After a pilot study to check the practicality of the treatments, at the outset of the main study, the Preliminary English Test was administered to ensure the homogeneity of 87 out of 107 participants who attended the intact classes of a 15 session term in one control and two experimental groups. Also, speaking sections of PET were used as pretest and posttest to examine their speaking accuracy. The tests were recorded and transcribed to estimate the percentage of the number of the clauses with no grammatical errors in the total produced clauses to measure the speaking accuracy. In all groups, the grammatical points of accuracy were instructed and the use of conversational strategies was practiced. Then, different kinds of reasoning gap tasks (matchmaking, deciding on the course of action, and working out a time table) and information gap tasks (restoring an incomplete chart, spot the differences, arranging sentences into stories, and guessing game) were manipulated in experimental groups during treatment sessions, and the students were required to practice conversational strategies when doing speaking tasks. The conversations throughout the terms were recorded and transcribed to count the frequency of the conversational strategies used in all groups. The results of statistical analysis demonstrated that applying both the reasoning gap tasks and information gap tasks significantly affected the frequency of conversational strategies through negotiation. In the face of the improvements, the reasoning gap tasks had a more significant impact on encouraging the negotiation of meaning and increasing the number of conversational frequencies every session. The findings also indicated both task types could help learners significantly improve their speaking accuracy. Here, applying the reasoning gap tasks was more effective than the information gap tasks in improving the level of learners’ speaking accuracy.

Keywords: Accuracy in speaking, conversational strategies, information gap tasks, reasoning gap tasks.

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Authors: Ang Ling Weay, Mona Masood

Abstract:

This paper discusses the potential benefits of an interactive multimedia information representation in enhancing students’ critical thinking aligned with history reasoning in learning history amongst Secondary School students in Malaysia. Two modes of multimedia information representation were implemented; chronologic and thematic information representations. A qualitative study of an unstructured interview was conducted among two history teachers, one history education lecturer, two i-think experts, and five students from Form Four secondary school. The interview was to elicit their opinions on the implementation of thinking maps and interactive multimedia information representation in history learning. The key elements of the interactive multimedia (e.g. multiple media, user control, interactivity and use of timelines and concept maps) were then considered to improve the learning process. Findings of the preliminary investigation reveal that the interactive multimedia information representations have the potential benefits to be implemented as an instructional resource in enhancing students’ higher order thinking skills (HOTs). This paper concludes by giving suggestions for future work.

Keywords: Multimedia Information Representation, Critical Thinking, History Reasoning, Chronological and Thematic Information Representation.

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