Search results for: retrieval algorithms

Authors: Ali Habiboghli, Rouhollah Mostafaei, Vasif Nabiyev

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Optical network uses a tool for routing called Latin router. These routers use particular algorithms for routing. For example, we can refer to LDF algorithm that uses backtracking (one of CSP methods) for problem solving. In this paper, we proposed new approached for completion routing table (DRA&CRA algorithm) and compare with pervious proposed ways and showed numbers of backtracking, blocking and run time for DRA algorithm less than LDF and CRA algorithm.

Keywords: Latin Router, Constraint Satisfaction Problem, Wavelength Routing.

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Authors: Mohammed Salahat, Steve Wade

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This paper presents and compares the SSDDD “Systematic Soft Domain Driven Design Framework” to DDD “Domain Driven Design Framework” as a soft system approach of information systems development. The framework use SSM as a guiding methodology within which we have embedded a sequence of design tasks based on the UML leading to the implementation of a software system using the Naked Objects framework. This framework has been used in action research projects that have involved the investigation and modelling of business processes using object-oriented domain models and the implementation of software systems based on those domain models. Within this framework, Soft Systems Methodology (SSM) is used as a guiding methodology to explore the problem situation and to develop the domain model using UML for the given business domain. The framework is proposed and evaluated in our previous works, a comparison between SSDDD and DDD is presented in this paper, to show how SSDDD improved DDD as an approach to modelling and implementing business domain perspectives for Information Systems Development. The comparison process, the results, and the improvements are presented in the following sections of this paper.

Keywords: SSM, UML, domain-driven design, soft domain-driven design, naked objects, soft language, information retrieval, multimethodology.

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Authors: Angelo Nodari, Ignazio Gallo

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The novelty proposed in this study is twofold and consists in the developing of a new color similarity metric based on the human visual system and a new color indexing based on a textual approach. The new color similarity metric proposed is based on the color perception of the human visual system. Consequently the results returned by the indexing system can fulfill as much as possibile the user expectations. We developed a web application to collect the users judgments about the similarities between colors, whose results are used to estimate the metric proposed in this study. In order to index the image's colors, we used a text indexing engine to facilitate the integration of visual features in a database of text documents. The textual signature is build by weighting the image's colors in according to their occurrence in the image. The use of a textual indexing engine, provide us a simple, fast and robust solution to index images. A typical usage of the system proposed in this study, is the development of applications whose data type is both visual and textual. In order to evaluate the proposed method we chose a price comparison engine as a case of study, collecting a series of commercial offers containing the textual description and the image representing a specific commercial offer.

Keywords: Color Extraction, Content-Based Image Retrieval, Indexing

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Authors: Matthias Dehmer, Frank Emmert Streib, Jürgen Kilian, Andreas Zulauf

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Methods for organizing web data into groups in order to analyze web-based hypertext data and facilitate data availability are very important in terms of the number of documents available online. Thereby, the task of clustering web-based document structures has many applications, e.g., improving information retrieval on the web, better understanding of user navigation behavior, improving web users requests servicing, and increasing web information accessibility. In this paper we investigate a new approach for clustering web-based hypertexts on the basis of their graph structures. The hypertexts will be represented as so called generalized trees which are more general than usual directed rooted trees, e.g., DOM-Trees. As a important preprocessing step we measure the structural similarity between the generalized trees on the basis of a similarity measure d. Then, we apply agglomerative clustering to the obtained similarity matrix in order to create clusters of hypertext graph patterns representing navigation structures. In the present paper we will run our approach on a data set of hypertext structures and obtain good results in Web Structure Mining. Furthermore we outline the application of our approach in Web Usage Mining as future work.

Keywords: Clustering methods, graph-based patterns, graph similarity, hypertext structures, web structure mining

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Authors: Narasimham Challa, Jayaram Pradhan

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Cryptographic algorithms play a crucial role in the information society by providing protection from unauthorized access to sensitive data. It is clear that information technology will become increasingly pervasive, Hence we can expect the emergence of ubiquitous or pervasive computing, ambient intelligence. These new environments and applications will present new security challenges, and there is no doubt that cryptographic algorithms and protocols will form a part of the solution. The efficiency of a public key cryptosystem is mainly measured in computational overheads, key size and bandwidth. In particular the RSA algorithm is used in many applications for providing the security. Although the security of RSA is beyond doubt, the evolution in computing power has caused a growth in the necessary key length. The fact that most chips on smart cards can-t process key extending 1024 bit shows that there is need for alternative. NTRU is such an alternative and it is a collection of mathematical algorithm based on manipulating lists of very small integers and polynomials. This allows NTRU to high speeds with the use of minimal computing power. NTRU (Nth degree Truncated Polynomial Ring Unit) is the first secure public key cryptosystem not based on factorization or discrete logarithm problem. This means that given sufficient computational resources and time, an adversary, should not be able to break the key. The multi-party communication and requirement of optimal resource utilization necessitated the need for the present day demand of applications that need security enforcement technique .and can be enhanced with high-end computing. This has promoted us to develop high-performance NTRU schemes using approaches such as the use of high-end computing hardware. Peer-to-peer (P2P) or enterprise grids are proven as one of the approaches for developing high-end computing systems. By utilizing them one can improve the performance of NTRU through parallel execution. In this paper we propose and develop an application for NTRU using enterprise grid middleware called Alchemi. An analysis and comparison of its performance for various text files is presented.

Keywords: Alchemi, GridNtru, Ntru, PKCS.

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Authors: Olfa.Ben Ahmed, Mahmoud. Mejdoub, Chokri. Ben Amar

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Recognizing human action from videos is an active field of research in computer vision and pattern recognition. Human activity recognition has many potential applications such as video surveillance, human machine interaction, sport videos retrieval and robot navigation. Actually, local descriptors and bag of visuals words models achieve state-of-the-art performance for human action recognition. The main challenge in features description is how to represent efficiently the local motion information. Most of the previous works focus on the extension of 2D local descriptors on 3D ones to describe local information around every interest point. In this paper, we propose a new spatio-temporal descriptor based on a spacetime description of moving points. Our description is focused on an Accordion representation of video which is well-suited to recognize human action from 2D local descriptors without the need to 3D extensions. We use the bag of words approach to represent videos. We quantify 2D local descriptor describing both temporal and spatial features with a good compromise between computational complexity and action recognition rates. We have reached impressive results on publicly available action data set

Keywords: Accordion, Bag of Features, Human action, Motion, Moving point, Space-Time Descriptor, SIFT, Video.

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Authors: Pınar Tüfekçi, Yılmaz Kılıçaslan

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In this paper we present a computational model for pronominal anaphora resolution in Turkish. The model is based on Hobbs’ Naїve Algorithm [4, 5, 6], which exploits only the surface syntax of sentences in a given text.

Keywords: Anaphora Resolution, Pronoun Resolution, Syntax based Algorithms, Naїve Algorithm.

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Authors: Jiashang Jiang, Hao Liu, Yongxin Yuan

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In this paper the gradient based iterative algorithms are presented to solve the following four types linear matrix equations: (a) AXB = F; (b) AXB = F, CXD = G; (c) AXB = F s. t. X = XT ; (d) AXB+CYD = F, where X and Y are unknown matrices, A,B,C,D, F,G are the given constant matrices. It is proved that if the equation considered has a solution, then the unique minimum norm solution can be obtained by choosing a special kind of initial matrices. The numerical results show that the proposed method is reliable and attractive.

Keywords: Matrix equation, iterative algorithm, parameter estimation, minimum norm solution.

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Authors: Tsuyoshi Okita, Tatsuya Niwa

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For a given specific problem an efficient algorithm has been the matter of study. However, an alternative approach orthogonal to this approach comes out, which is called a reduction. In general for a given specific problem this reduction approach studies how to convert an original problem into subproblems. This paper proposes a formal modeling language to support this reduction approach. We show three examples from the wide area of learning problems. The benefit is a fast prototyping of algorithms for a given new problem.

Keywords: Formal language, statistical inference problem, reduction.

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Authors: Konstantinos Liagkouras, Konstantinos Metaxiotis

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In this paper we present a classification of the various technologies applied for the solution of the portfolio selection problem according to the discipline and the methodological framework followed. We provide a concise presentation of the emerged categories and we are trying to identify which methods considered obsolete and which lie at the heart of the debate. On top of that, we provide a comparative study of the different technologies applied for efficient portfolio construction and we suggest potential paths for future work that lie at the intersection of the presented techniques.

Keywords: Portfolio selection, optimization techniques, financial models, stochastics, heuristics.

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Authors: Razib M. Othman, Safaai Deris, Rosli M. Illias, Hany T. Alashwal, Rohayanti Hassan, FarhanMohamed

Abstract:

The most important property of the Gene Ontology is the terms. These control vocabularies are defined to provide consistent descriptions of gene products that are shareable and computationally accessible by humans, software agent, or other machine-readable meta-data. Each term is associated with information such as definition, synonyms, database references, amino acid sequences, and relationships to other terms. This information has made the Gene Ontology broadly applied in microarray and proteomic analysis. However, the process of searching the terms is still carried out using traditional approach which is based on keyword matching. The weaknesses of this approach are: ignoring semantic relationships between terms, and highly depending on a specialist to find similar terms. Therefore, this study combines semantic similarity measure and genetic algorithm to perform a better retrieval process for searching semantically similar terms. The semantic similarity measure is used to compute similitude strength between two terms. Then, the genetic algorithm is employed to perform batch retrievals and to handle the situation of the large search space of the Gene Ontology graph. The computational results are presented to show the effectiveness of the proposed algorithm.

Keywords: Gene Ontology, Semantic similarity measure, Genetic algorithm, Ontology search

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Authors: O. Sotolongo-Grau, D. Rodriguez-Perez, J. A. Santos-Miranda, M. M. Desco, O. Sotolongo-Costa, J. C. Antoranz

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The use of the oncologic index ISTER allows for a more effective planning of the radiotherapic facilities in the hospitals. Any change in the radiotherapy treatment, due to unexpected stops, may be adapted by recalculating the doses to the new treatment duration while keeping the optimal prognosis. The results obtained in a simulation model on millions of patients allow the definition of optimal success probability algorithms.

Keywords: Mathematical model, radiation oncology, dynamical systems applications.

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Authors: C. Chahin, M. C. Hsu, Y. H. Lin, C. Y. Huang

Abstract:

Electronics Products that achieve high levels of integrated communications, computing and entertainment, multimedia features in small, stylish and robust new form factors are winning in the market place. Due to the high costs that an industry may undergo and how a high yield is directly proportional to high profits, IC (Integrated Circuit) manufacturers struggle to maximize yield, but today-s customers demand miniaturization, low costs, high performance and excellent reliability making the yield maximization a never ending research of an enhanced assembly process. With factors such as minimum tolerances, tighter parameter variations a systematic approach is needed in order to predict the assembly process. In order to evaluate the quality of upcoming circuits, yield models are used which not only predict manufacturing costs but also provide vital information in order to ease the process of correction when the yields fall below expectations. For an IC manufacturer to obtain higher assembly yields all factors such as boards, placement, components, the material from which the components are made of and processes must be taken into consideration. Effective placement yield depends heavily on machine accuracy and the vision of the system which needs the ability to recognize the features on the board and component to place the device accurately on the pads and bumps of the PCB. There are currently two methods for accurate positioning, using the edge of the package and using solder ball locations also called footprints. The only assumption that a yield model makes is that all boards and devices are completely functional. This paper will focus on the Monte Carlo method which consists in a class of computational algorithms (information processed algorithms) which depends on repeated random samplings in order to compute the results. This method utilized in order to recreate the simulation of placement and assembly processes within a production line.

Keywords: Monte Carlo simulation, placement yield, PCBcharacterization, electronics assembly

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Authors: R.V Pawar, P.P.Kajave, S.N.Mali

Abstract:

The speech signal conveys information about the identity of the speaker. The area of speaker identification is concerned with extracting the identity of the person speaking the utterance. As speech interaction with computers becomes more pervasive in activities such as the telephone, financial transactions and information retrieval from speech databases, the utility of automatically identifying a speaker is based solely on vocal characteristic. This paper emphasizes on text dependent speaker identification, which deals with detecting a particular speaker from a known population. The system prompts the user to provide speech utterance. System identifies the user by comparing the codebook of speech utterance with those of the stored in the database and lists, which contain the most likely speakers, could have given that speech utterance. The speech signal is recorded for N speakers further the features are extracted. Feature extraction is done by means of LPC coefficients, calculating AMDF, and DFT. The neural network is trained by applying these features as input parameters. The features are stored in templates for further comparison. The features for the speaker who has to be identified are extracted and compared with the stored templates using Back Propogation Algorithm. Here, the trained network corresponds to the output; the input is the extracted features of the speaker to be identified. The network does the weight adjustment and the best match is found to identify the speaker. The number of epochs required to get the target decides the network performance.

Keywords: Average Mean Distance function, Backpropogation, Linear Predictive Coding, MultilayeredPerceptron,

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Authors: Jae Wook Shin, Ju-man Song, Hyun-Taek Choi, Poo Gyeon Park

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This paper introduces a new variable step-size APA with decorrelation of AR input process is based on the MSD analysis. To achieve a fast convergence rate and a small steady-state estimation error, he proposed algorithm uses variable step size that is determined by minimising the MSD. In addition, experimental results show that the proposed algorithm is achieved better performance than the other algorithms.

Keywords: adaptive filter, affine projection algorithm, variable step size.

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Authors: Satoshi Shimizu, Kazuaki Yamaguchi, Toshiki Saitoh, Sumio Masuda

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Some fast exact algorithms for the maximum weight clique problem have been proposed. Östergard’s algorithm is one of them. Kumlander says his algorithm is faster than it. But we confirmed that the straightforwardly implemented Kumlander’s algorithm is slower than O¨ sterga˚rd’s algorithm. We propose some improvements on Kumlander’s algorithm.

Keywords: Maximum weight clique, exact algorithm, branch-andbound, NP-hard.

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Authors: Young-Seok Choi

Abstract:

We present a normalized LMS (NLMS) algorithm with robust regularization. Unlike conventional NLMS with the fixed regularization parameter, the proposed approach dynamically updates the regularization parameter. By exploiting a gradient descent direction, we derive a computationally efficient and robust update scheme for the regularization parameter. In simulation, we demonstrate the proposed algorithm outperforms conventional NLMS algorithms in terms of convergence rate and misadjustment error.

Keywords: Regularization, normalized LMS, system identification, robustness.

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Authors: Sheng-Gwo Chen, Wen-Haw Chen

Abstract:

It is an important problem to compute the geodesics on a surface in many fields. To find the geodesics in practice, however, the traditional discrete algorithms or numerical approaches can only find a list of discrete points. The first author proposed in 2010 a new, elegant and accurate method, the geodesic-like method, for approximating geodesics on a regular surface. This paper will present by use of this method a computation of the Bezier geodesic-like curves on spheres.

Keywords: Geodesics, Geodesic-like curve, Spheres, Bezier.

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Authors: L. Bennaceur Farah, I. R. Farah, R. Bennaceur, Z. Belhadj, M. R. Boussema

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The overall objective of this paper is to retrieve soil surfaces parameters namely, roughness and soil moisture related to the dielectric constant by inverting the radar backscattered signal from natural soil surfaces. Because the classical description of roughness using statistical parameters like the correlation length doesn't lead to satisfactory results to predict radar backscattering, we used a multi-scale roughness description using the wavelet transform and the Mallat algorithm. In this description, the surface is considered as a superposition of a finite number of one-dimensional Gaussian processes each having a spatial scale. A second step in this study consisted in adapting a direct model simulating radar backscattering namely the small perturbation model to this multi-scale surface description. We investigated the impact of this description on radar backscattering through a sensitivity analysis of backscattering coefficient to the multi-scale roughness parameters. To perform the inversion of the small perturbation multi-scale scattering model (MLS SPM) we used a multi-layer neural network architecture trained by backpropagation learning rule. The inversion leads to satisfactory results with a relative uncertainty of 8%.

Keywords: Remote sensing, rough surfaces, inverse problems, SAR, radar scattering, Neural networks and Fractals.

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Authors: Syerina Azlin Md Nasir, Nor Laila Md Noor, Suriyati Razali

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The growing interest on national heritage preservation has led to intensive efforts on digital documentation of cultural heritage knowledge. Encapsulated within this effort is the focus on ontology development that will help facilitate the organization and retrieval of the knowledge. Ontologies surrounding cultural heritage domain are related to archives, museum and library information such as archaeology, artifacts, paintings, etc. The growth in number and size of ontologies indicates the well acceptance of its semantic enrichment in many emerging applications. Nowadays, there are many heritage information systems available for access. Among others is community-based e-museum designed to support the digital cultural heritage preservation. This work extends previous effort of developing the Traditional Malay Textile (TMT) Knowledge Model where the model is designed with the intention of auxiliary mapping with CIDOC CRM. Due to its internal constraints, the model needs to be transformed in advance. This paper addresses the issue by reviewing the previous harmonization works with CIDOC CRM as exemplars in refining the facets in the model particularly involving TMT-Artifact class. The result is an extensible model which could lead to a common view for automated mapping with CIDOC CRM. Hence, it promotes integration and exchange of textile information especially batik-related between communities in e-museum applications.

Keywords: automated mapping, cultural heritage, knowledgemodel, textile practice

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Authors: Monjed Ezzeddinne, Nicolas Lefebvre, Régis Lengellé

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This paper presents a new version of the SVM mixture algorithm initially proposed by Kwok for classification and regression problems. For both cases, a slight modification of the mixture model leads to a standard SVM training problem, to the existence of an exact solution and allows the direct use of well known decomposition and working set selection algorithms. Only the regression case is considered in this paper but classification has been addressed in a very similar way. This method has been successfully applied to engine pollutants emission modeling.

Keywords: Identification, Learning systems, Mixture ofExperts, Support Vector Machines.

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Authors: Alessandro A. Zizzari, Francesca Calabrese, Giovanni Indiveri, Andrea Coraddu, Diego Villa

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The aim of this paper is to present a comparative study on two different methods for the evaluation of the equilibrium point of a ship, core issue for designing an On Board Stability System (OBSS) module that, starting from geometry information of a ship hull, described by a discrete model in a standard format, and the distribution of all weights onboard calculates the ship floating conditions (in draught, heel and trim).

Keywords: Algorithms, Computer applications, Equilibrium, Marine applications, Stability System.

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Authors: J. Ramírez A., A. Rubiano F.

Abstract:

In this paper the direct kinematic model of a multiple applications three degrees of freedom industrial manipulator, was developed using the homogeneous transformation matrices and the Denavit - Hartenberg parameters, likewise the inverse kinematic model was developed using the same method, verifying that in the workload border the inverse kinematic presents considerable errors, therefore a genetic algorithm was implemented to optimize the model improving greatly the efficiency of the model.

Keywords: Direct Kinematic, Genetic Algorithm, InverseKinematic, Optimization, Robot Manipulator

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Authors: Jamuna Bhandari

Abstract:

String matching also known as pattern matching is one of primary concept for network security. In this area the effectiveness and efficiency of string matching algorithms is important for applications in network security such as network intrusion detection, virus detection, signature matching and web content filtering system. This paper presents brief review on some of string matching techniques used for network security.

Keywords: Filtering, honeypot, network telescope, pattern, string, signature.

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Authors: Essam Al-Daoud

Abstract:

A potentially serious problem with current payment systems is that their underlying hard problems from number theory may be solved by either a quantum computer or unanticipated future advances in algorithms and hardware. A new quantum payment system is proposed in this paper. The suggested system makes use of fundamental principles of quantum mechanics to ensure the unconditional security without prior arrangements between customers and vendors. More specifically, the new system uses Greenberger-Home-Zeilinger (GHZ) states and Quantum Key Distribution to authenticate the vendors and guarantee the transaction integrity.

Keywords: Bell state, GHZ state, Quantum key distribution, Quantum payment system.

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Authors: Razagh Hafezi, Ahmad Keshavarz, Vida Moshfegh

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This paper presents a novel algorithm of stereo correspondence with rank transform. In this algorithm we used the genetic algorithm to achieve the accurate disparity map. Genetic algorithms are efficient search methods based on principles of population genetic, i.e. mating, chromosome crossover, gene mutation, and natural selection. Finally morphology is employed to remove the errors and discontinuities.

Keywords: genetic algorithm, morphology, rank transform, stereo correspondence

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Authors: C. E. Nugraheni, L. Abednego

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This paper is concerned with minimization of mean tardiness and flow time in a real single machine production scheduling problem. Two variants of genetic algorithm as metaheuristic are combined with hyper-heuristic approach are proposed to solve this problem. These methods are used to solve instances generated with real world data from a company. Encouraging results are reported.

Keywords: Hyper-heuristics, evolutionary algorithms, production scheduling.

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Authors: Sotiris B. Kotsiantis

Abstract:

Bagging and boosting are among the most popular re-sampling ensemble methods that generate and combine a diversity of regression models using the same learning algorithm as base-learner. Boosting algorithms are considered stronger than bagging on noise-free data. However, there are strong empirical indications that bagging is much more robust than boosting in noisy settings. For this reason, in this work we built an ensemble using an averaging methodology of bagging and boosting ensembles with 10 sub-learners in each one. We performed a comparison with simple bagging and boosting ensembles with 25 sub-learners on standard benchmark datasets and the proposed ensemble gave better accuracy.

Keywords: Regressors, statistical learning.

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Authors: Chong-Yang Qiao, Teeravarunyou Sakol

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Current production-oriented factories need maintenance operators to work in shifts monitoring and inspecting complex systems and different equipment in the situation of mechanical breakdown. Augmented reality (AR) is an emerging technology that embeds data into the environment for situation awareness to help maintenance operators make decisions and solve problems. An application was designed to identify the problem of steam generators and inspection centrifugal pumps. The objective of this research was to find the best medium of AR and type of problem solving strategies among analogy, focal object method and mean-ends analysis. Two scenarios of inspecting leakage were temperature and vibration. Two experiments were used in usability evaluation and future innovation, which included decision-making process and problem-solving strategy. This study found that maintenance operators prefer build-in magnifier to zoom the components (55.6%), 3D exploded view to track the problem parts (50%), and line chart to find the alter data or information (61.1%). There is a significant difference in the use of analogy (44.4%), focal objects (38.9%) and mean-ends strategy (16.7%). The marked differences between maintainers and operators are of the application of a problem solving strategy. However, future work should explore multimedia information retrieval which supports maintenance operators for decision-making.

Keywords: Augmented reality, situation awareness, decision-making, problem-solving.

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Authors: Omer Farooq, Salman Ahmed Khan, Sadaf Khalid

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Modern day stock markets have almost entirely became automated. Even though it means increased profits for the investors by algorithms acting upon the slightest price change in order of microseconds, it also has given birth to many ethical dilemmas in the sense that slightest mistake can cause people to lose all of their livelihoods. This paper reviews one such event that happened on May 06, 2010 in which $1 trillion dollars disappeared from the Dow Jones Industrial Average. We are going to discuss its various aspects and the ethical dilemmas that have arisen due to it.

Keywords: Flash Crash, Market Crash, Stock Market, Stock Market Crash.

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