Search results for: Semantic Inference

Authors: Nigina Toktasynova, Sholpan Sagyndykova, Zhanat Kenzhebayeva, Maksat Kalimoldayev, Mariya Ishimova, Irbulat Utepbergenov

Abstract:

To solve these problems, we investigated the management system of heating enterprise, including strategic planning based on the balanced scorecard (BSC), quality management in accordance with the standards of the Quality Management System (QMS) ISO 9001 and analysis of the system based on expert judgment using fuzzy inference. To carry out our work we used the theory of fuzzy sets, the QMS in accordance with ISO 9001, BSC, method of construction of business processes according to the notation IDEF0, theory of modeling using Matlab software simulation tools and graphical programming LabVIEW. The results of the work are as follows: We determined possibilities of improving the management of heat-supply plant-based on QMS; after the justification and adaptation of software tool it has been used to automate a series of functions for the management and reduction of resources and for the maintenance of the system up to date; an application for the analysis of the QMS based on fuzzy inference has been created with novel organization of communication software with the application enabling the analysis of relevant data of enterprise management system. 

Keywords: Balanced scorecard, heat supply, quality management system, the theory of fuzzy sets.

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Authors: Haluk Eren, Mucahit Karaduman

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This paper presents 15-year trends for scientific studies in a scientific database considering 3D and vehicle words. Two words are selected to find their associated publications in IEEE scholar database. Both of keywords are entered individually for the years 2002, 2012, and 2016 on the database to identify the preferred subjects of researchers in same years. We have classified closer research fields after searching and listing. Three years (2002, 2012, and 2016) have been investigated to figure out progress in specified time intervals. The first one is assumed as the initial progress in between 2002-2012, and the second one is in 2012-2016 that is fast development duration. We have found very interesting and beneficial results to understand the scholars’ research field preferences for a decade. This information will be highly desirable in smart city-based research purposes consisting of 3D and vehicle-related issues.

Keywords: Vehicle, 3D, smart city, scholarly search, semantic.

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Authors: Marios Poulos, Sozon Papavlasopoulos, V. S. Belesiotis

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In the proposed method for Web page-ranking, a novel theoretic model is introduced and tested by examples of order relationships among IP addresses. Ranking is induced using a convexity feature, which is learned according to these examples using a self-organizing procedure. We consider the problem of selforganizing learning from IP data to be represented by a semi-random convex polygon procedure, in which the vertices correspond to IP addresses. Based on recent developments in our regularization theory for convex polygons and corresponding Euclidean distance based methods for classification, we develop an algorithmic framework for learning ranking functions based on a Computational Geometric Theory. We show that our algorithm is generic, and present experimental results explaining the potential of our approach. In addition, we explain the generality of our approach by showing its possible use as a visualization tool for data obtained from diverse domains, such as Public Administration and Education.

Keywords: Computational Geometry, Education, e-Governance, Semantic Web.

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Authors: Deon de Jager, Yahya Zweiri, Dimitrios Makris

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The three most important components in the cognitive architecture for cognitive robotics is memory representation, memory recall, and action-selection performed by the executive. In this paper, action selection, performed by the executive, is defined as a memory quantification and optimization process. The methodology describes the real-time construction of episodic memory through semantic memory optimization. The optimization is performed by set-based particle swarm optimization, using an adaptive entropy memory quantification approach for fitness evaluation. The performance of the approach is experimentally evaluated by simulation, where a UAV is tasked with the collection and delivery of a medical package. The experiments show that the UAV dynamically uses the episodic memory to autonomously control its velocity, while successfully completing its mission.

Keywords: Cognitive robotics, semantic memory, episodic memory, maximum entropy principle, particle swarm optimization.

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Authors: Louay M. Jeroudaih, Mohamed S. Hajji

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This paper looks into areas not covered by prominent Agent-Oriented Software Engineering (AOSE) methodologies. Extensive paper review led to the identification of two issues, first most of these methodologies almost neglect semantic web and ontology. Second, as expected, each one has its strength and weakness and may focus on some phases of the development lifecycle but not all of the phases. The work presented here builds extensions to a highly regarded AOSE methodology (MaSE) in order to cover the areas that this methodology does not concentrate on. The extensions include introducing an ontology stage for semantic representation and integrating early requirement specification from a methodology which mainly focuses on that. The integration involved developing transformation rules (with the necessary handling of nonmatching notions) between the two sets of representations and building the software which automates the transformation. The application of this integration on a case study is also presented in the paper. The main flow of MaSE stages was changed to smoothly accommodate the new additions.

Keywords: Agents, Intelligent Agents, Software Engineering(SE), UML, AUML, and Design Patterns.

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Authors: Lu Zhang, Chunping Li, Jun Liu, Hui Wang

Abstract:

Text similarity measurement is a fundamental issue in many textual applications such as document clustering, classification, summarization and question answering. However, prevailing approaches based on Vector Space Model (VSM) more or less suffer from the limitation of Bag of Words (BOW), which ignores the semantic relationship among words. Enriching document representation with background knowledge from Wikipedia is proven to be an effective way to solve this problem, but most existing methods still cannot avoid similar flaws of BOW in a new vector space. In this paper, we propose a novel text similarity measurement which goes beyond VSM and can find semantic affinity between documents. Specifically, it is a unified graph model that exploits Wikipedia as background knowledge and synthesizes both document representation and similarity computation. The experimental results on two different datasets show that our approach significantly improves VSM-based methods in both text clustering and classification.

Keywords: Text classification, Text clustering, Text similarity, Wikipedia

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Authors: Ganesan V.

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Application of Expert System in the area of agriculture would take the form of Integrated Crop Management decision aids and would encompass water management, fertilizer management, crop protection systems and identification of implements. In order to remain competitive, the modern farmer often relies on agricultural specialists and advisors to provide information for decision-making. An expert system normally composed of a knowledge base (information, heuristics, etc.), inference engine (analyzes knowledge base), and end user interface (accepting inputs, generating outputs). Software named 'CROP-9-DSS' incorporating all modern features like, graphics, photos, video clippings etc. has been developed. This package will aid as a decision support system for identification of pest and diseases with control measures, fertilizer recommendation system, water management system and identification of farm implements for leading crops of Kerala (India) namely Coconut, Rice, Cashew, Pepper, Banana, four vegetables like Amaranthus, Bhindi, Brinjal and Cucurbits. 'CROP-9-DSS' will act as an expert system to agricultural officers, scientists in the field of agriculture and extension workers for decision-making and help them in suggesting suitable recommendations.

Keywords: Diagnostic, inference engine, knowledge base and user interface.

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Authors: N. D. Karande, G. A. Patil

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Databases have become ubiquitous. Almost all IT applications are storing into and retrieving information from databases. Retrieving information from the database requires knowledge of technical languages such as Structured Query Language (SQL). However majority of the users who interact with the databases do not have a technical background and are intimidated by the idea of using languages such as SQL. This has led to the development of a few Natural Language Database Interfaces (NLDBIs). A NLDBI allows the user to query the database in a natural language. This paper highlights on architecture of new NLDBI system, its implementation and discusses on results obtained. In most of the typical NLDBI systems the natural language statement is converted into an internal representation based on the syntactic and semantic knowledge of the natural language. This representation is then converted into queries using a representation converter. A natural language query is translated to an equivalent SQL query after processing through various stages. The work has been experimented on primitive database queries with certain constraints.

Keywords: Natural language database interface, representation converter, syntactic and semantic knowledge

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Authors: Wanhyun Cho, Soonja Kang, Sangkyoon Kim, Soonyoung Park

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In this paper, we present the human action recognition method using the variational Bayesian HMM with the Dirichlet process mixture (DPM) of the Gaussian-Wishart emission model (GWEM). First, we define the Bayesian HMM based on the Dirichlet process, which allows an infinite number of Gaussian-Wishart components to support continuous emission observations. Second, we have considered an efficient variational Bayesian inference method that can be applied to drive the posterior distribution of hidden variables and model parameters for the proposed model based on training data. And then we have derived the predictive distribution that may be used to classify new action. Third, the paper proposes a process of extracting appropriate spatial-temporal feature vectors that can be used to recognize a wide range of human behaviors from input video image. Finally, we have conducted experiments that can evaluate the performance of the proposed method. The experimental results show that the method presented is more efficient with human action recognition than existing methods.

Keywords: Human action recognition, Bayesian HMM, Dirichlet process mixture model, Gaussian-Wishart emission model, Variational Bayesian inference, Prior distribution and approximate posterior distribution, KTH dataset.

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Authors: H. Bonakdari, I. Ebtehaj

Abstract:

The prediction of scour depth around bridge piers is frequently considered in river engineering. One of the key aspects in efficient and optimum bridge structure design is considered to be scour depth estimation around bridge piers. In this study, scour depth around bridge piers is estimated using two methods, namely the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN). Therefore, the effective parameters in scour depth prediction are determined using the ANN and ANFIS methods via dimensional analysis, and subsequently, the parameters are predicted. In the current study, the methods’ performances are compared with the nonlinear regression (NLR) method. The results show that both methods presented in this study outperform existing methods. Moreover, using the ratio of pier length to flow depth, ratio of median diameter of particles to flow depth, ratio of pier width to flow depth, the Froude number and standard deviation of bed grain size parameters leads to optimal performance in scour depth estimation.

Keywords: Adaptive neuro-fuzzy inference system, ANFIS, artificial neural network, ANN, bridge pier, scour depth, nonlinear regression, NLR.

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Authors: Tengku Mohd T. Sembok

Abstract:

Documents retrieval in Information Retrieval Systems (IRS) is generally about understanding of information in the documents concern. The more the system able to understand the contents of documents the more effective will be the retrieval outcomes. But understanding of the contents is a very complex task. Conventional IRS apply algorithms that can only approximate the meaning of document contents through keywords approach using vector space model. Keywords may be unstemmed or stemmed. When keywords are stemmed and conflated in retrieving process, we are a step forwards in applying semantic technology in IRS. Word stemming is a process in morphological analysis under natural language processing, before syntactic and semantic analysis. We have developed algorithms for Malay and Arabic and incorporated stemming in our experimental systems in order to measure retrieval effectiveness. The results have shown that the retrieval effectiveness has increased when stemming is used in the systems.

Keywords: Information Retrieval, Natural Language Processing, Artificial Intelligence.

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Authors: Tristan O’Neill, Trina Myers, Jarrod Trevathan

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The Tropical Data Hub (TDH) is a virtual research environment that provides researchers with an e-research infrastructure to congregate significant tropical data sets for data reuse, integration, searching, and correlation. However, researchers often require data and metadata synthesis across disciplines for cross-domain analyses and knowledge discovery. A triplestore offers a semantic layer to achieve a more intelligent method of search to support the synthesis requirements by automating latent linkages in the data and metadata. Presently, the benchmarks to aid the decision of which triplestore is best suited for use in an application environment like the TDH are limited to performance. This paper describes a new evaluation tool developed to analyze both features and performance. The tool comprises a weighted decision matrix to evaluate the interoperability, functionality, performance, and support availability of a range of integrated and native triplestores to rank them according to requirements of the TDH.

Keywords: Virtual research environment, Semantic Web, performance analysis, tropical data hub.

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Authors: Pitaktiratham J., Sinlan T., Anuntavoranich P., Sinthupinyo S.

Abstract:

The Kansei engineering is a technology which converts human feelings into quantitative terms and helps designers develop new products that meet customers- expectation. Standard Kansei engineering procedure involves finding relationships between human feelings and design elements of which many researchers have found forward and backward relationship through various soft computing techniques. In this paper, we proposed the framework of Kansei engineering linking relationship not only between human feelings and design elements, but also the whole part of product, by constructing association rules. In this experiment, we obtain input from emotion score that subjects rate when they see the whole part of the product by applying semantic differentials. Then, association rules are constructed to discover the combination of design element which affects the human feeling. The results of our experiment suggest the pattern of relationship of design elements according to human feelings which can be derived from the whole part of product.

Keywords: Association Rules Mining, Kansei Engineering, Product Design, Semantic Differentials

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Authors: Om Prakash Verma, Rajesh Singla, Rajesh Kumar

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Conventional controller’s usually required a prior knowledge of mathematical modelling of the process. The inaccuracy of mathematical modelling degrades the performance of the process, especially for non-linear and complex control problem. The process used is Water-Bath system, which is most widely used and nonlinear to some extent. For Water-Bath system, it is necessary to attain desired temperature within a specified period of time to avoid the overshoot and absolute error, with better temperature tracking capability, else the process is disturbed.

To overcome above difficulties intelligent controllers, Fuzzy Logic (FL) and Adaptive Neuro-Fuzzy Inference System (ANFIS), are proposed in this paper. The Fuzzy controller is designed to work with knowledge in the form of linguistic control rules. But the translation of these linguistic rules into the framework of fuzzy set theory depends on the choice of certain parameters, for which no formal method is known. To design ANFIS, Fuzzy-Inference-System is combined with learning capability of Neural-Network.

It is analyzed that ANFIS is best suitable for adaptive temperature control of above system. As compared to PID and FLC, ANFIS produces a stable control signal. It has much better temperature tracking capability with almost zero overshoot and minimum absolute error.

Keywords: PID Controller, FLC, ANFIS, Non-Linear Control System, Water-Bath System, MATLAB-7.

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Authors: Mohanad Alata , Moh'd Al-Nimr, Rami Al-Jarrah

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The present work demonstrates the design and simulation of a fuzzy control of an air conditioning system at different pressures. The first order Sugeno fuzzy inference system is utilized to model the system and create the controller. In addition, an estimation of the heat transfer rate and water mass flow rate injection into or withdraw from the air conditioning system is determined by the fuzzy IF-THEN rules. The approach starts by generating the input/output data. Then, the subtractive clustering algorithm along with least square estimation (LSE) generates the fuzzy rules that describe the relationship between input/output data. The fuzzy rules are tuned by Adaptive Neuro-Fuzzy Inference System (ANFIS). The results show that when the pressure increases the amount of water flow rate and heat transfer rate decrease within the lower ranges of inlet dry bulb temperatures. On the other hand, and as pressure increases the amount of water flow rate and heat transfer rate increases within the higher ranges of inlet dry bulb temperatures. The inflection in the pressure effect trend occurs at lower temperatures as the inlet air humidity increases.

Keywords: Air Conditioning, ANFIS, Fuzzy Control, Sugeno System.

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Authors: Ahmet Kayabasi, Ali Akdagli

Abstract:

In this study, three robust predicting methods, namely artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS) and support vector machine (SVM) were used for computing the resonant frequency of A-shaped compact microstrip antennas (ACMAs) operating at UHF band. Firstly, the resonant frequencies of 144 ACMAs with various dimensions and electrical parameters were simulated with the help of IE3D™ based on method of moment (MoM). The ANN, ANFIS and SVM models for computing the resonant frequency were then built by considering the simulation data. 124 simulated ACMAs were utilized for training and the remaining 20 ACMAs were used for testing the ANN, ANFIS and SVM models. The performance of the ANN, ANFIS and SVM models are compared in the training and test process. The average percentage errors (APE) regarding the computed resonant frequencies for training of the ANN, ANFIS and SVM were obtained as 0.457%, 0.399% and 0.600%, respectively. The constructed models were then tested and APE values as 0.601% for ANN, 0.744% for ANFIS and 0.623% for SVM were achieved. The results obtained here show that ANN, ANFIS and SVM methods can be successfully applied to compute the resonant frequency of ACMAs, since they are useful and versatile methods that yield accurate results.

Keywords: A-shaped compact microstrip antenna, Artificial Neural Network (ANN), adaptive Neuro-Fuzzy Inference System (ANFIS), Support Vector Machine (SVM).

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Authors: Joseph C. Chen, Venkata Mohan Kudapa

Abstract:

Development of two real-time surface roughness (Ra) prediction systems for milling operations was attempted. The systems used not only cutting parameters, such as feed rate and spindle speed, but also the cutting current generated and corrected by a clamp type energy sensor. Two different approaches were developed. First, a fuzzy inference system (FIS), in which the fuzzy logic rules are generated by experts in the milling processes, was used to conduct prediction modeling using current cutting data. Second, a neuro-fuzzy system (ANFIS) was explored. Neuro-fuzzy systems are adaptive techniques in which data are collected on the network, processed, and rules are generated by the system. The inference system then uses these rules to predict Ra as the output. Experimental results showed that the parameters of spindle speed, feed rate, depth of cut, and input current variation could predict Ra. These two systems enable the prediction of Ra during the milling operation with an average of 91.83% and 94.48% accuracy by FIS and ANFIS systems, respectively. Statistically, the ANFIS system provided better prediction accuracy than that of the FIS system.

Keywords: Surface roughness, input current, fuzzy logic, neuro-fuzzy, milling operations.

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Authors: Thabet Slimani, Boutheina Ben Yaghlane, Khaled Mellouli

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In the last few years, the Semantic Web gained scientific acceptance as a means of relationships identification in knowledge base, widely known by semantic association. Query about complex relationships between entities is a strong requirement for many applications in analytical domains. In bioinformatics for example, it is critical to extract exchanges between proteins. Currently, the widely known result of such queries is to provide paths between connected entities from data graph. However, they do not always give good results while facing the user need by the best association or a set of limited best association, because they only consider all existing paths but ignore the path evaluation. In this paper, we present an approach for supporting association discovery queries. Our proposal includes (i) a query language PmSPRQL which provides a multiparadigm query expressions for association extraction and (ii) some quantification measures making easy the process of association ranking. The originality of our proposal is demonstrated by a performance evaluation of our approach on real world datasets.

Keywords: Association extraction, query Language, relationships, knowledge base, multi-paradigm query.

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Authors: Agung Budi Muljono, I Made Ginarsa, I Made Ari Nrartha

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A large-scale power system (LSPS) consists of two or more sub-systems connected by inter-connecting transmission. Loading pattern on an LSPS always changes from time to time and varies depend on consumer need. The serious instability problem is appeared in an LSPS due to load fluctuation in all of the bus. Adaptive neuro-fuzzy inference system (ANFIS)-based power system stabilizer (PSS) is presented to cover the stability problem and to enhance the stability of an LSPS. The ANFIS control is presented because the ANFIS control is more effective than Mamdani fuzzy control in the computation aspect. Simulation results show that the presented PSS is able to maintain the stability by decreasing peak overshoot to the value of −2.56 × 10−5 pu for rotor speed deviation Δω2−3. The presented PSS also makes the settling time to achieve at 3.78 s on local mode oscillation. Furthermore, the presented PSS is able to improve the peak overshoot and settling time of Δω3−9 to the value of −0.868 × 10−5 pu and at the time of 3.50 s for inter-area oscillation.

Keywords: ANFIS, large-scale, power system, PSS, stability enhancement.

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Authors: Mohamed Amine Ouddan, Hassane Essafi

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Source code retrieval is of immense importance in the software engineering field. The complex tasks of retrieving and extracting information from source code documents is vital in the development cycle of the large software systems. The two main subtasks which result from these activities are code duplication prevention and plagiarism detection. In this paper, we propose a Mohamed Amine Ouddan, and Hassane Essafi source code retrieval system based on two-level fingerprint representation, respectively the structural and the semantic information within a source code. A sequence alignment technique is applied on these fingerprints in order to quantify the similarity between source code portions. The specific purpose of the system is to detect plagiarism and duplicated code between programs written in different programming languages belonging to the same class, such as C, Cµ, Java and CSharp. These four languages are supported by the actual version of the system which is designed such that it may be easily adapted for any programming language.

Keywords: Source code retrieval, plagiarism detection, clonedetection, sequence alignment.

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Authors: Ali Akbar Sadat Asl

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Diagnosis and deciding about diseases in medical fields is facing innate uncertainty which can affect the whole process of treatment. This decision is made based on expert knowledge and the way in which an expert interprets the patient's condition, and the interpretation of the various experts from the patient's condition may be different. Fuzzy logic can provide mathematical modeling for many concepts, variables, and systems that are unclear and ambiguous and also it can provide a framework for reasoning, inference, control, and decision making in conditions of uncertainty. In systems with high uncertainty and high complexity, fuzzy logic is a suitable method for modeling. In this paper, we use type-2 fuzzy logic for uncertainty modeling that is in diagnosis of leukemia. The proposed system uses an indirect-direct approach and consists of two stages: In the first stage, the inference of blood test state is determined. In this step, we use an indirect approach where the rules are extracted automatically by implementing a clustering approach. In the second stage, signs of leukemia, duration of disease until its progress and the output of the first stage are combined and the final diagnosis of the system is obtained. In this stage, the system uses a direct approach and final diagnosis is determined by the expert. The obtained results show that the type-2 fuzzy expert system can diagnose leukemia with the average accuracy about 97%.

Keywords: Expert system, leukemia, medical diagnosis, type-2 fuzzy logic.

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Authors: Parvinder S. Sandhu, Manisha Prashar, Pourush Bassi, Atul Bisht

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Software effort estimation is the process of predicting the most realistic use of effort required to develop or maintain software based on incomplete, uncertain and/or noisy input. Effort estimates may be used as input to project plans, iteration plans, budgets. There are various models like Halstead, Walston-Felix, Bailey-Basili, Doty and GA Based models which have already used to estimate the software effort for projects. In this study Statistical Models, Fuzzy-GA and Neuro-Fuzzy (NF) Inference Systems are experimented to estimate the software effort for projects. The performances of the developed models were tested on NASA software project datasets and results are compared with the Halstead, Walston-Felix, Bailey-Basili, Doty and Genetic Algorithm Based models mentioned in the literature. The result shows that the NF Model has the lowest MMRE and RMSE values. The NF Model shows the best results as compared with the Fuzzy-GA based hybrid Inference System and other existing Models that are being used for the Effort Prediction with lowest MMRE and RMSE values.

Keywords: Neuro-Fuzzy Model, Halstead Model, Walston-Felix Model, Bailey-Basili Model, Doty Model, GA Based Model, Genetic Algorithm.

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Authors: Yohei Saika, Tatsuya Uezu

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We constructed a method of phase unwrapping for a typical wave-front by utilizing the maximizer of the posterior marginal (MPM) estimate corresponding to equilibrium statistical mechanics of the three-state Ising model on a square lattice on the basis of an analogy between statistical mechanics and Bayesian inference. We investigated the static properties of an MPM estimate from a phase diagram using Monte Carlo simulation for a typical wave-front with synthetic aperture radar (SAR) interferometry. The simulations clarified that the surface-consistency conditions were useful for extending the phase where the MPM estimate was successful in phase unwrapping with a high degree of accuracy and that introducing prior information into the MPM estimate also made it possible to extend the phase under the constraint of the surface-consistency conditions with a high degree of accuracy. We also found that the MPM estimate could be used to reconstruct the original wave-fronts more smoothly, if we appropriately tuned hyper-parameters corresponding to temperature to utilize fluctuations around the MAP solution. Also, from the viewpoint of statistical mechanics of the Q-Ising model, we found that the MPM estimate was regarded as a method for searching the ground state by utilizing thermal fluctuations under the constraint of the surface-consistency condition.

Keywords: Bayesian inference, maximizer of the posterior marginal estimate, phase unwrapping, Monte Carlo simulation, statistical mechanics

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Authors: Sunitha Abburu, Golla Suresh Babu

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The issues that limit application interoperability is lack of common vocabulary, common structure, application domain knowledge ontology based semantic technology provides solutions that resolves application interoperability issues. Ontology is broadly used in diverse applications such as artificial intelligence, bioinformatics, biomedical, information integration, etc. Ontology can be used to interpret the knowledge of various domains. To reuse, enrich the available ontologies and reduce the duplication of ontologies of the same domain, there is a strong need to integrate the ontologies of the particular domain. The integrated ontology gives complete knowledge about the domain by sharing this comprehensive domain ontology among the groups. As per the literature survey there is no well-defined methodology to represent knowledge of a whole domain. The current research addresses a systematic methodology for knowledge representation using multiple sub-ontologies at different levels that addresses application interoperability and enables semantic information retrieval. The current method represents complete knowledge of a domain by importing concepts from multiple sub ontologies of same and relative domains that reduces ontology duplication, rework, implementation cost through ontology reusability.

Keywords: Knowledge acquisition, knowledge representation, knowledge transfer, ontologies, semantics.

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Authors: Erika Martinez Ramirez, Rene V. Mayorga

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In our modern world, more physical transactions are being substituted by electronic transactions (i.e. banking, shopping, and payments), many businesses and companies are performing most of their operations through the internet. Instead of having a physical commerce, internet visitors are now adapting to electronic commerce (e-Commerce). The ability of web users to reach products worldwide can be greatly benefited by creating friendly and personalized online business portals. Internet visitors will return to a particular website when they can find the information they need or want easily. Dealing with this human conceptualization brings the incorporation of Artificial/Computational Intelligence techniques in the creation of customized portals. From these techniques, Fuzzy-Set technologies can make many useful contributions to the development of such a human-centered endeavor as e-Commerce. The main objective of this paper is the implementation of a Paradigm for the Intelligent Design and Operation of Human-Computer interfaces. In particular, the paradigm is quite appropriate for the intelligent design and operation of software modules that display information (such Web Pages, graphic user interfaces GUIs, Multimedia modules) on a computer screen. The human conceptualization of the user personal information is analyzed throughout a Cascaded Fuzzy Inference (decision-making) System to generate the User Ascribe Qualities, which identify the user and that can be used to customize portals with proper Web links.

Keywords: Fuzzy Logic, Internet, Electronic Commerce, Intelligent Portals, Electronic Shopping.

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Authors: Yasmeen A. S. Essawy, Khaled Nassar

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With the rapid increase of complexity in the building industry, professionals in the A/E/C industry were forced to adopt Building Information Modeling (BIM) in order to enhance the communication between the different project stakeholders throughout the project life cycle and create a semantic object-oriented building model that can support geometric-topological analysis of building elements during design and construction. This paper presents a model that extracts topological relationships and geometrical properties of building elements from an existing fully designed BIM, and maps this information into a directed acyclic Elemental Graph Data Model (EGDM). The model incorporates BIM-based search algorithms for automatic deduction of geometrical data and topological relationships for each building element type. Using graph search algorithms, such as Depth First Search (DFS) and topological sortings, all possible construction sequences can be generated and compared against production and construction rules to generate an optimized construction sequence and its associated schedule. The model is implemented in a C# platform.

Keywords: Building information modeling, elemental graph data model, geometric and topological data models, and graph theory.

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Authors: Xiang Zhang, Daohong Yang, Ziyuan Wu, Lei Li, Wanting Zhou

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Visual Simultaneous Localization and Mapping (VSLAM) uses cameras to gather information in unknown environments to achieve simultaneous localization and mapping of the environment. This technology has a wide range of applications in autonomous driving, virtual reality, and other related fields. Currently, the research advancements related to VSLAM can maintain high accuracy in static environments. But in dynamic environments, the presence of moving objects in the scene can reduce the stability of the VSLAM system, leading to inaccurate localization and mapping, or even system failure. In this paper, a robust VSLAM method was proposed to effectively address the challenges in dynamic environments. We proposed a dynamic region removal scheme based on a semantic segmentation neural network and geometric constraints. Firstly, a semantic segmentation neural network is used to extract the prior active motion region, prior static region, and prior passive motion region in the environment. Then, the lightweight frame tracking module initializes the transform pose between the previous frame and the current frame on the prior static region. A motion consistency detection module based on multi-view geometry and scene flow is used to divide the environment into static regions and dynamic regions. Thus, the dynamic object region was successfully eliminated. Finally, only the static region is used for tracking thread. Our research is based on the ORBSLAM3 system, which is one of the most effective VSLAM systems available. We evaluated our method on the TUM RGB-D benchmark and the results demonstrate that the proposed VSLAM method improves the accuracy of the original ORBSLAM3 by 70%˜98.5% under a high dynamic environment.

Keywords: Dynamic scene, dynamic visual SLAM, semantic segmentation, scene flow, VSLAM.

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Authors: Azleena Mohd Kassim, Muhamad Rashidi A. Rahman, Yu-N. Cheah

Abstract:

Knowledge-based e-mail systems focus on incorporating knowledge management approach in order to enhance the traditional e-mail systems. In this paper, we present a knowledgebased e-mail system called KS-Mail where people do not only send and receive e-mail conventionally but are also able to create a sense of knowledge flow. We introduce semantic processing on the e-mail contents by automatically assigning categories and providing links to semantically related e-mails. This is done to enrich the knowledge value of each e-mail as well as to ease the organization of the e-mails and their contents. At the application level, we have also built components like the service manager, evaluation engine and search engine to handle the e-mail processes efficiently by providing the means to share and reuse knowledge. For this purpose, we present the KS-Mail architecture, and elaborate on the details of the e-mail server and the application server. We present the ontology mapping technique used to achieve the e-mail content-s categorization as well as the protocols that we have developed to handle the transactions in the e-mail system. Finally, we discuss further on the implementation of the modules presented in the KS-Mail architecture.

Keywords: E-mail rating, knowledge-based system, ontology mapping, text categorization.

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Authors: E. Karoui Chaabane, S. Hadouaj, K. Ghedira

Abstract:

Inter-organizational Workflow (IOW) is commonly used to support the collaboration between heterogeneous and distributed business processes of different autonomous organizations in order to achieve a common goal. E-government is considered as an application field of IOW. The coordination of the different organizations is the fundamental problem in IOW and remains the major cause of failure in e-government projects. In this paper, we introduce a new coordination model for IOW that improves the collaboration between government administrations and that respects IOW requirements applied to e-government. For this purpose, we adopt a Multi-Agent approach, which deals more easily with interorganizational digital government characteristics: distribution, heterogeneity and autonomy. Our model integrates also different technologies to deal with the semantic and technologic interoperability. Moreover, it conserves the existing systems of government administrations by offering a distributed coordination based on interfaces communication. This is especially applied in developing countries, where administrations are not necessary equipped with workflow systems. The use of our coordination techniques allows an easier migration for an e-government solution and with a lower cost. To illustrate the applicability of the proposed model, we present a case study of an identity card creation in Tunisia.

Keywords: E-government, Inter-organizational workflow, Multi-agent systems, Semantic web services.

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Authors: Wanhyun Cho, Soonja Kang, Sangkyoon Kim, Soonyoung Park

Abstract:

In this paper, we propose the variational EM inference algorithm for the multi-class Gaussian process classification model that can be used in the field of human behavior recognition. This algorithm can drive simultaneously both a posterior distribution of a latent function and estimators of hyper-parameters in a Gaussian process classification model with multiclass. Our algorithm is based on the Laplace approximation (LA) technique and variational EM framework. This is performed in two steps: called expectation and maximization steps. First, in the expectation step, using the Bayesian formula and LA technique, we derive approximately the posterior distribution of the latent function indicating the possibility that each observation belongs to a certain class in the Gaussian process classification model. Second, in the maximization step, using a derived posterior distribution of latent function, we compute the maximum likelihood estimator for hyper-parameters of a covariance matrix necessary to define prior distribution for latent function. These two steps iteratively repeat until a convergence condition satisfies. Moreover, we apply the proposed algorithm with human action classification problem using a public database, namely, the KTH human action data set. Experimental results reveal that the proposed algorithm shows good performance on this data set.

Keywords: Bayesian rule, Gaussian process classification model with multiclass, Gaussian process prior, human action classification, laplace approximation, variational EM algorithm.

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