Search results for: Semantic textual similarity

Authors: Wang Yunhong

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The present paper is preoccupied with the different styles of three translators in their translating a Chinese classical novel Shuihu Zhuan. Based on a parallel corpus, it adopts a target-oriented approach to look into whether and what stylistic differences and shifts the three translations have revealed. The findings show that the three translators demonstrate different styles concerning their word choices and sentence preferences, which implies that identification of recurrent textual patterns may be a basic step for investigating the style of a translator.

Keywords: Corpus, lexical choices, sentence characteristics, style.

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Authors: Bunakiye R. Japheth, Ogude U. Cyril

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Since the process of transforming user requirements to modeling constructs are not very well supported by domain-specific frameworks, it became necessary to integrate domain requirements with the specific architectures to achieve an integrated customizable solutions space via artifact orientation. Domain-specific modeling language specifications of model-driven engineering technologies focus more on requirements within a particular domain, which can be tailored to aid the domain expert in expressing domain concepts effectively. Modeling processes through domain-specific language formalisms are highly volatile due to dependencies on domain concepts or used process models. A capable solution is given by artifact orientation that stresses on the results rather than expressing a strict dependence on complicated platforms for model creation and development. Based on this premise, domain-specific methods for producing artifacts without having to take into account the complexity and variability of platforms for model definitions can be integrated to support customizable development. In this paper, we discuss methods for the integration capabilities and necessities within a common structure and semantics that contribute a metamodel for artifact-orientation, which leads to a reusable software layer with concrete syntax capable of determining design intents from domain expert. These concepts forming the language formalism are established from models explained within the oil and gas pipelines industry.

Keywords: Control process, metrics of engineering, structured abstraction, semantic model.

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Authors: Mingxin Zhang, Zhaogan Lu, Junyi Shen

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In current common research reports, salient regions are usually defined as those regions that could present the main meaningful or semantic contents. However, there are no uniform saliency metrics that could describe the saliency of implicit image regions. Most common metrics take those regions as salient regions, which have many abrupt changes or some unpredictable characteristics. But, this metric will fail to detect those salient useful regions with flat textures. In fact, according to human semantic perceptions, color and texture distinctions are the main characteristics that could distinct different regions. Thus, we present a novel saliency metric coupled with color and texture features, and its corresponding salient region extraction methods. In order to evaluate the corresponding saliency values of implicit regions in one image, three main colors and multi-resolution Gabor features are respectively used for color and texture features. For each region, its saliency value is actually to evaluate the total sum of its Euclidean distances for other regions in the color and texture spaces. A special synthesized image and several practical images with main salient regions are used to evaluate the performance of the proposed saliency metric and other several common metrics, i.e., scale saliency, wavelet transform modulus maxima point density, and important index based metrics. Experiment results verified that the proposed saliency metric could achieve more robust performance than those common saliency metrics.

Keywords: salient regions, color and texture features, image segmentation, saliency metric

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Authors: Vishal Raj, Noorhan Abbas

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Ambiguity in NLP (Natural Language Processing) refers to the ability of a word, phrase, sentence, or text to have multiple meanings. This results in various kinds of ambiguities such as lexical, syntactic, semantic, anaphoric and referential. This study is focused mainly on solving the issue of Lexical ambiguity. Word Sense Disambiguation (WSD) is an NLP technique that aims to resolve lexical ambiguity by determining the correct meaning of a word within a given context. Most WSD solutions rely on words for training and testing, but we have used lemma and Part of Speech (POS) tokens of words for training and testing. Lemma adds generality and POS adds properties of word into token. We have designed a method to create an affinity matrix to calculate the affinity between any pair of lemma_POS (a token where lemma and POS of word are joined by underscore) of given training set. Additionally, we have devised an algorithm to create the sense clusters of tokens using affinity matrix under hierarchy of POS of lemma. Furthermore, three different mechanisms to predict the sense of target word using the affinity/similarity value are devised. Each contextual token contributes to the sense of target word with some value and whichever sense gets higher value becomes the sense of target word. So, contextual tokens play a key role in creating sense clusters and predicting the sense of target word, hence, the model is named Contextual SenSe Model (CSM). CSM exhibits a noteworthy simplicity and explication lucidity in contrast to contemporary deep learning models characterized by intricacy, time-intensive processes, and challenging explication. CSM is trained on SemCor training data and evaluated on SemEval test dataset. The results indicate that despite the naivety of the method, it achieves promising results when compared to the Most Frequent Sense (MFS) model.

Keywords: Word Sense Disambiguation, WSD, Contextual SenSe Model, Most Frequent Sense, part of speech, POS, Natural Language Processing, NLP, OOV, out of vocabulary, ELMo, Embeddings from Language Model, BERT, Bidirectional Encoder Representations from Transformers, Word2Vec, lemma_POS, Algorithm.

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Authors: Mohamed Badeche, Mohamed Benmohammed

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augmented reality is a technique used to insert virtual objects in real scenes. One of the most used libraries in the area is the ARToolkit library. It is based on the recognition of the markers that are in the form of squares with a pattern inside. This pattern which is mostly textual is source of confusing. In this paper, we present the results of a classification of Latin characters as a pattern on the ARToolkit markers to know the most distinguishable among them.

Keywords: ARToolkit library, augmented reality, K-means, patterns

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Authors: Mawloud Mosbah, Bachir Boucheham

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We present here the results for a comparative study of some techniques, available in the literature, related to the relevance feedback mechanism in the case of a short-term learning. Only one method among those considered here is belonging to the data mining field which is the K-nearest neighbors algorithm (KNN) while the rest of the methods is related purely to the information retrieval field and they fall under the purview of the following three major axes: Shifting query, Feature Weighting and the optimization of the parameters of similarity metric. As a contribution, and in addition to the comparative purpose, we propose a new version of the KNN algorithm referred to as an incremental KNN which is distinct from the original version in the sense that besides the influence of the seeds, the rate of the actual target image is influenced also by the images already rated. The results presented here have been obtained after experiments conducted on the Wang database for one iteration and utilizing color moments on the RGB space. This compact descriptor, Color Moments, is adequate for the efficiency purposes needed in the case of interactive systems. The results obtained allow us to claim that the proposed algorithm proves good results; it even outperforms a wide range of techniques available in the literature.

Keywords: CBIR, Category Search, Relevance Feedback (RFB), Query Point Movement, Standard Rocchio’s Formula, Adaptive Shifting Query, Feature Weighting, Optimization of the Parameters of Similarity Metric, Original KNN, Incremental KNN.

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Authors: Marijana M. Ačanski, Kristian A. Pastor, Djura N. Vujić

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The identification of lipid and soluble sugar components in flour samples of different cultivars belonging to common oat species (Avena sativa L.) was performed: spring oat, winter oat and hulless oat. Fatty acids were extracted from flour samples with n-hexane, and derivatized into volatile methyl esters, using TMSH (trimethylsulfonium hydroxide in methanol). Soluble sugars were then extracted from defatted and dried samples of oat flour with 96% ethanol, and further derivatized into corresponding TMS-oximes, using hydroxylamine hydrochloride solution and BSTFA (N,O-bis-(trimethylsilyl)-trifluoroacetamide). The hexane and ethanol extracts of each oat cultivar were analyzed using GC-MS system. Lipid and simple sugar compositions are very similar in all samples of investigated cultivars. Chemometric tool was applied to numeric values of automatically integrated surface areas of detected lipid and simple sugar components in their corresponding derivatized forms. Hierarchical cluster analysis shows a very high similarity between the investigated flour samples of oat cultivars, according to the fatty acid content (0.9955). Moderate similarity was observed according to the content of soluble sugars (0.50). These preliminary results support the idea of establishing methods for oat flour authentication, and provide the means for distinguishing oat flour samples, regardless of the variety, from flour samples made of other cereal species, just by lipid and simple sugar profile analysis.

Keywords: Authentication, chemometrics, GC-MS, lipid and soluble sugar composition, oat cultivars.

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Authors: Sylvie Ratté, Wilfried Njomgue, Pierre-André Ménard

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In this paper, we present symbolic recognition models to extract knowledge characterized by document structures. Focussing on the extraction and the meticulous exploitation of the semantic structure of documents, we obtain a meaningful contextual tagging corresponding to different unit types (title, chapter, section, enumeration, etc.).

Keywords: Information retrieval, document structures, symbolic grammars.

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Authors: Tibor Kulcsar, Gabor Sarossy, Gabor Bereznai, Robert Auer, Janos Abonyi

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On-line (near infrared) spectroscopy is widely used to support the operation of complex process systems. Information extracted from spectral database can be used to estimate unmeasured product properties and monitor the operation of the process. These techniques are based on looking for similar spectra by nearest neighborhood algorithms and distance based searching methods. Search for nearest neighbors in the spectral space is an NP-hard problem, the computational complexity increases by the number of points in the discrete spectrum and the number of samples in the database. To reduce the calculation time some kind of indexing could be used. The main idea presented in this paper is to combine indexing and visualization techniques to reduce the computational requirement of estimation algorithms by providing a two dimensional indexing that can also be used to visualize the structure of the spectral database. This 2D visualization of spectral database does not only support application of distance and similarity based techniques but enables the utilization of advanced clustering and prediction algorithms based on the Delaunay tessellation of the mapped spectral space. This means the prediction has not to use the high dimension space but can be based on the mapped space too. The results illustrate that the proposed method is able to segment (cluster) spectral databases and detect outliers that are not suitable for instance based learning algorithms.

Keywords: indexing high dimensional databases, dimensional reduction, clustering, similarity, k-nn algorithm.

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Authors: Javad Razjouyan, Shahriar Gharibzadeh, Ali Fallah, Mehdi Moghaddasi, Mohsen Seyfi, Amir Kasaeian

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Paced Auditory Serial Addition Test (PASAT) has been used as a common research tool for different neurological disorders like Multiple Sclerosis. Recently, technology let researchers to introduce a new versions of the visual test, the paced visual serial addition test (PVSAT). In this paper, the computerized version of these two tests is introduced. Beside the number of true responses are interpreted, the reaction time of subjects are calculated by the software. We hypothesize that paying attention to the reaction time may be valuable. For this purpose, sixty eight female normal subjects and fifty eight male normal subjects are enrolled in the study. We investigate the similarity between the PASAT3 and PVSAT3 in number of true responses and the new criterion (the average reaction time of each subject). The similarity between two tests were rejected (p-value = 0.000) which means that these two test differ. The effect of sex in the tests were not approved since the pvalues of different between PASAT3 and PVSAT3 in both sex is the same (p-value = 0.000) which means that male and female subjects performed the tests at no different level of performance. The new criterion shows a negative correlation with the age which offers aged normal subjects may have the same number of true responses as the young subjects but they have latent responses. This will give prove for the importance of reaction time.

Keywords: Paced Auditory Serial Addition Test, Pace Visual Serial Addition Test, reaction time.

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Authors: Waralak Vongdoiwang Siricharoen

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One of object oriented software developing problem is the difficulty of searching the appropriate and suitable objects for starting the system. In this work, ontologies appear in the part of supporting the object discovering in the initial of object oriented software developing. There are many researches try to demonstrate that there is a great potential between object model and ontologies. Constructing ontology from object model is called ontology engineering can be done; On the other hand, this research is aiming to support the idea of building object model from ontology is also promising and practical. Ontology classes are available online in any specific areas, which can be searched by semantic search engine. There are also many helping tools to do so; one of them which are used in this research is Protégé ontology editor and Visual Paradigm. To put them together give a great outcome. This research will be shown how it works efficiently with the real case study by using ontology classes in travel/tourism domain area. It needs to combine classes, properties, and relationships from more than two ontologies in order to generate the object model. In this paper presents a simple methodology framework which explains the process of discovering objects. The results show that this framework has great value while there is possible for expansion. Reusing of existing ontologies offers a much cheaper alternative than building new ones from scratch. More ontologies are becoming available on the web, and online ontologies libraries for storing and indexing ontologies are increasing in number and demand. Semantic and Ontologies search engines have also started to appear, to facilitate search and retrieval of online ontologies.

Keywords: Software Developing, Ontology, Ontology Library, Artificial Intelligent, Protégé, Object Model.

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Authors: Hebberly Ahatlan

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The process of digitizing energy systems involves transforming traditional energy infrastructure into interconnected, data-driven systems that enhance efficiency, sustainability, and responsiveness. As smart grids become increasingly integral to the efficient distribution and management of electricity from both fossil and renewable energy sources, the energy industry faces strategic challenges associated with digitalization and interoperability — particularly in the context of modern energy business models, such as virtual power plants (VPPs). The critical challenge in modern smart grids is to seamlessly integrate diverse technologies and systems, including virtualization, grid computing and service-oriented architecture (SOA), across the entire energy ecosystem. Achieving this requires addressing issues like semantic interoperability, Information Technology (IT) and Operational Technology (OT) convergence, and digital asset scalability, all while ensuring security and risk management. This paper proposes a four-layer digitalization framework to tackle these challenges, encompassing persistent data protection, trusted key management, secure messaging, and authentication of IoT resources. Data assets generated through this framework enable AI systems to derive insights for improving smart grid operations, security, and revenue generation. Furthermore, this paper also proposes a Trusted Energy Interoperability Alliance as a universal guiding standard in the development of this digitalization framework to support more dynamic and interoperable energy markets.

Keywords: Digitalization, IT/OT convergence, semantic interoperability, TEIA alliance, VPP.

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Authors: Chia-Chen Wu, Chih-Fu Wu, Pey-Weng Lien, Kai-Chieh Lin

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In the past, the most comprehensively adopted light source was incandescent light bulbs, but with the appearance of LED light sources, traditional light sources have been gradually replaced by LEDs because of its numerous superior characteristics. However, many of the standards do not apply to LEDs as the two light sources are characterized differently. This also intensifies the significance of studies on LEDs. As a Kansei design study investigating the visual glare produced by traffic arrows implemented with LEDs, this study conducted a semantic analysis on the styles of traffic arrows used in domestic and international occasions. The results will be able to reduce drivers’ misrecognition that results in the unsuccessful arrival at the destination, or in traffic accidents. This study started with a literature review and surveyed the status quo before conducting experiments that were divided in two parts. The first part involved a screening experiment of arrow samples, where cluster analysis was conducted to choose five representative samples of LED displays. The second part was a semantic experiment on the display of arrows using LEDs, where the five representative samples and the selected ten adjectives were incorporated. Analyzing the results with Quantification Theory Type I, it was found that among the composition of arrows, fletching was the most significant factor that influenced the adjectives. In contrast, a “no fletching” design was more abstract and vague. It lacked the ability to convey the intended message and might bear psychological negative connotation including “dangerous,” “forbidden,” and “unreliable.” The arrow design consisting of “> shaped fletching” was found to be more concrete and definite, showing positive connotation including “safe,” “cautious,” and “reliable.” When a stimulus was placed at a farther distance, the glare could be significantly reduced; moreover, the visual evaluation scores would be higher. On the contrary, if the fletching and the shaft had a similar proportion, looking at the stimuli caused higher evaluation at a closer distance. The above results will be able to be applied to the design of traffic arrows by conveying information definitely and rapidly. In addition, drivers’ safety could be enhanced by understanding the cause of glare and improving visual recognizability.

Keywords: LED, arrow, Kansei research, preferred imagery.

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Authors: Shaun Cox, Michael Oakes, Stefan Wermter, Maurice Hawthorne

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We report on the results of a pilot study in which a data-mining tool was developed for mining audiology records. The records were heterogeneous in that they contained numeric, category and textual data. The tools developed are designed to observe associations between any field in the records and any other field. The techniques employed were the statistical chi-squared test, and the use of self-organizing maps, an unsupervised neural learning approach.

Keywords: Audiology, data mining, chi-squared, self-organizing maps

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Authors: Yanto Santosa, Catharina Yudea

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The rapid growth of oil palm industry in Indonesia raised many negative accusations from various parties, who said that oil palm plantation is damaging the environment and biodiversity, including birds. Since research on oil palm plantation impacts on bird diversity is still limited, this study needs to be developed in order to gain further learning and understanding. Data on bird diversity were collected in March 2018 in KJNP Estate, Riau Province using strip transect method on five different land cover types (young, intermediate, and old growth of oil palm plantation, high conservation value area, and crops field or the baseline). The observations were conducted simultaneously, with three repetitions. The result shows that the baseline has 19 species of birds and land cover after the oil palm plantation has 39 species. HCV (high conservation value) area has the highest increase in diversity value. Oil palm plantation has changed the composition of bird species. The highest similarity index is shown by young growth oil palm land cover with total score 0.65, meanwhile the lowest similarity index with total score 0.43 is shown by HCV area. Overall, the existence of oil palm plantation made a positive impact by increasing bird species diversity, with total 23 species gained and 3 species lost.

Keywords: Bird diversity, crops field, impact of oil palm plantation, KJNP estate.

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Authors: M. Mohsenzadeh, F. Shams, M. Teshnehlab

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Current research on semantic web aims at making intelligent web pages meaningful for machines. In this way, ontology plays a primary role. We believe that logic can help ontology languages (such as OWL) to be more fluent and efficient. In this paper we try to combine logic with OWL to reduce some disadvantages of this language. Therefore we extend OWL by logic and also show how logic can satisfy our future expectations of an ontology language.

Keywords: Logical Programming, OWL, Language Extension.

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Authors: Eusebio A. Jiménez-Arévalo, Deifilia Ahuatzi-Chacón, Juvencio Galíndez-Mayer, Cleotilde Juárez-Ramírez, Nora Ruiz-Ordaz

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Bendiocarb is a known toxic xenobiotic that presents acute and chronic risks for freshwater invertebrates and estuarine and marine biota; thus, the treatment of water contaminated with the insecticide is of concern. In this paper, a bacterial community with the capacity to grow in bendiocarb as its sole carbon and nitrogen source was isolated by enrichment techniques in batch culture, from samples of a composting plant located in the northeast of Mexico City. Eight cultivable bacteria were isolated from the microbial community, by PCR amplification of 16 rDNA; Pseudoxanthomonas spadix (NC_016147.2, 98%), Ochrobacterium anthropi (NC_009668.1, 97%), Staphylococcus capitis (NZ_CP007601.1, 99%), Bosea thiooxidans. (NZ_LMAR01000067.1, 99%), Pseudomonas denitrificans. (NC_020829.1, 99%), Agromyces sp. (NZ_LMKQ01000001.1, 98%), Bacillus thuringiensis. (NC_022873.1, 97%), Pseudomonas alkylphenolia (NZ_CP009048.1, 98%). NCBI accession numbers and percentage of similarity are indicated in parentheses. These bacteria were regarded as the isolated species for having the best similarity matches. The ability to degrade bendiocarb by the immobilized bacterial community in a packed bed biofilm reactor, using as support volcanic stone fragments (tezontle), was evaluated. The reactor system was operated in batch using mineral salts medium and 30 mg/L of bendiocarb as carbon and nitrogen source. With this system, an overall removal efficiency (η_bend) rounding 90%, was reached.

Keywords: Bendiocarb, biodegradation, biofilm reactor, carbamate insecticide.

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Authors: Nurcihan Hacioglu, Cigdem Gul, Murat Tosunoglu

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In this article, the antibiogram and heavy metal resistance profile of the bacteria isolated from total 34 studied animals (Pelophylax ridibundus = 12; Mauremys rivulata = 14; Natrix natrix = 8) captured around the Biga Stream, are described. There was no database information on antibiogram and heavy metal resistance profile of bacteria from these area’s amphibians and reptiles. A total of 200 bacteria were successfully isolated from cloaca and oral samples of the aquatic amphibians and reptiles as well as from the water sample. According to Jaccard’s similarity index, the degree of similarity in the bacterial flora was quite high among the amphibian and reptile species under examination, whereas it was different from the bacterial diversity in the water sample. The most frequent isolates were A. hydrophila (31.5%), B. pseudomallei (8.5%), and C. freundii (7%). The total numbers of bacteria obtained were as follows: 45 in P. ridibundus, 45 in N. natrix 30 in M. rivulata, and 80 in the water sample. The result showed that cefmetazole was the most effective antibiotic to control the bacteria isolated in this study and that approximately 93.33% of the bacterial isolates were sensitive to this antibiotic. The multiple antibiotic resistances (MAR) index indicated that P. ridibundus (0.95) > N. natrix (0.89) > M. rivulata (0.39). Furthermore, all the tested heavy metals (Pb+2, Cu+2, Cr+3, and Mn+2) inhibit the growth of the bacterial isolates at different rates. Therefore, it indicated that the water source of the animals was contaminated with both antibiotic residues and heavy metals.

Keywords: Amphibian, Bacteriological Quality, Reptile, Antibiotic & Heavy Metal Resistance.

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Authors: Meysam Naeimi, Zili Li, Roumen Petrov, Rolf Dollevoet, Jilt Sietsma, Jun Wu

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The substantial similarity of fatigue mechanism in a new test rig for rolling contact fatigue (RCF) has been investigated. A new reduced-scale test rig is designed to perform controlled RCF tests in wheel-rail materials. The fatigue mechanism of the rig is evaluated in this study using a combined finite element-fatigue prediction approach. The influences of loading conditions on fatigue crack initiation have been studied. Furthermore, the effects of some artificial defects (squat-shape) on fatigue lives are examined. To simulate the vehicle-track interaction by means of the test rig, a threedimensional finite element (FE) model is built up. The nonlinear material behaviour of the rail steel is modelled in the contact interface. The results of FE simulations are combined with the critical plane concept to determine the material points with the greatest possibility of fatigue failure. Based on the stress-strain responses, by employing of previously postulated criteria for fatigue crack initiation (plastic shakedown and ratchetting), fatigue life analysis is carried out. The results are reported for various loading conditions and different defect sizes. Afterward, the cyclic mechanism of the test rig is evaluated from the operational viewpoint. The results of fatigue life predictions are compared with the expected number of cycles of the test rig by its cyclic nature. Finally, the estimative duration of the experiments until fatigue crack initiation is roughly determined.

Keywords: Fatigue, test rig, crack initiation, life, rail, squats.

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Authors: Mohd Sidek Ahmad, Zainon Mohd Noor, Zaidah Zainal Ariffin

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Fibrin degradation is an important part in prevention or treatment of intravascular thrombosis and cardiovascular diseases. Plasmin like fibrinolytic enzymes has given new hope to patient with cardiovascular diseases by treating fibrin aggregation related diseases with traditional plasminogen activator which have many side effects. Various researches involving wide range of sources for production of fibrinolytic proteases, from bacteria, fungi, insects and fermented foods. But few have looked into endophytic fungi as a potential source. Sixteen (16) endophytic fungi were isolated from Hibiscus sp. leaves from six different locations in Shah Alam, Selangor. Only two endophytic fungi, FH3 and S13 showed positive fibrinolytic protease activities. FH3 produced 5.78cm and S13 produced 4.48cm on Skim Milk Agar after 4 days of incubation at 27°C. Fibrinolytic activity was observed; 3.87cm and 1.82cm diameter clear zone on fibrin plate of FH3 and S13 respectively. 18srRNA was done for identification of the isolated fungi with positive fibrinolytic protease. S13 had the highest similarity (100%) to that of Penicillium citrinum strain TG2 and FH3 had the highest similarity (99%) to that of Fusarium sp. FW2PhC1, Fusarium sp. 13002, Fusarium sp. 08006, Fusarium equiseti strain Salicorn 8 and Fungal sp. FCASAn-2. Media composition variation showed the effects of carbon nitrogen on protein concentration, where the decrement of 50% of media composition caused drastic decrease in protease of FH3 from 1.081 to 0.056 and also S13 from 2.946 to 0.198.

Keywords: Isolation, identification, fibrinolytic protease, endophytic fungi, Hibiscus leaves.

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Authors: Yesubai Rubavathi Charles, Ravi Ramraj

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In order to retrieve images efficiently from a large database, a unique method integrating color and texture features using genetic programming has been proposed. Opponent color histogram which gives shadow, shade, and light intensity invariant property is employed in the proposed framework for extracting color features. For texture feature extraction, fast discrete curvelet transform which captures more orientation information at different scales is incorporated to represent curved like edges. The recent scenario in the issues of image retrieval is to reduce the semantic gap between user’s preference and low level features. To address this concern, genetic algorithm combined with relevance feedback is embedded to reduce semantic gap and retrieve user’s preference images. Extensive and comparative experiments have been conducted to evaluate proposed framework for content based image retrieval on two databases, i.e., COIL-100 and Corel-1000. Experimental results clearly show that the proposed system surpassed other existing systems in terms of precision and recall. The proposed work achieves highest performance with average precision of 88.2% on COIL-100 and 76.3% on Corel, the average recall of 69.9% on COIL and 76.3% on Corel. Thus, the experimental results confirm that the proposed content based image retrieval system architecture attains better solution for image retrieval.

Keywords: Content based image retrieval, Curvelet transform, Genetic algorithm, Opponent color histogram, Relevance feedback.

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Authors: Kamal K.Bharadwaj, Rekha Kandwal

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An important structuring mechanism for knowledge bases is building clusters based on the content of their knowledge objects. The objects are clustered based on the principle of maximizing the intraclass similarity and minimizing the interclass similarity. Clustering can also facilitate taxonomy formation, that is, the organization of observations into a hierarchy of classes that group similar events together. Hierarchical representation allows us to easily manage the complexity of knowledge, to view the knowledge at different levels of details, and to focus our attention on the interesting aspects only. One of such efficient and easy to understand systems is Hierarchical Production rule (HPRs) system. A HPR, a standard production rule augmented with generality and specificity information, is of the following form Decision If < condition> Generality Specificity . HPRs systems are capable of handling taxonomical structures inherent in the knowledge about the real world. In this paper, a set of related HPRs is called a cluster and is represented by a HPR-tree. This paper discusses an algorithm based on cumulative learning scenario for dynamic structuring of clusters. The proposed scheme incrementally incorporates new knowledge into the set of clusters from the previous episodes and also maintains summary of clusters as Synopsis to be used in the future episodes. Examples are given to demonstrate the behaviour of the proposed scheme. The suggested incremental structuring of clusters would be useful in mining data streams.

Keywords: Cumulative learning, clustering, data mining, hierarchical production rules.

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Authors: Martin H¨ausl, Maximilian Auch, Johannes Forster, Peter Mandl, Alexander Schill

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In order to survive on the market, companies must constantly develop improved and new products. These products are designed to serve the needs of their customers in the best possible way. The creation of new products is also called innovation and is primarily driven by a company’s internal research and development department. However, a new approach has been taking place for some years now, involving external knowledge in the innovation process. This approach is called open innovation and identifies customer knowledge as the most important source in the innovation process. This paper presents a concept of using social media posts as an external source to support the open innovation approach in its initial phase, the Ideation phase. For this purpose, the social media posts are semantically structured with the help of an ontology and the authors are evaluated using graph-theoretical metrics such as density. For the structuring and evaluation of relevant social media posts, we also use the findings of Natural Language Processing, e. g. Named Entity Recognition, specific dictionaries, Triple Tagger and Part-of-Speech-Tagger. The selection and evaluation of the tools used are discussed in this paper. Using our ontology and metrics to structure social media posts enables users to semantically search these posts for new product ideas and thus gain an improved insight into the external sources such as customer needs.

Keywords: Idea ontology, innovation management, open innovation, semantic search.

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Authors: Amir Moslemi, Amir Movafeghi, Shahab Moradi

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One of the most important challenging factors in medical images is nominated as noise. Image denoising refers to the improvement of a digital medical image that has been infected by Additive White Gaussian Noise (AWGN). The digital medical image or video can be affected by different types of noises. They are impulse noise, Poisson noise and AWGN. Computed tomography (CT) images are subjects to low quality due to the noise. Quality of CT images is dependent on absorbed dose to patients directly in such a way that increase in absorbed radiation, consequently absorbed dose to patients (ADP), enhances the CT images quality. In this manner, noise reduction techniques on purpose of images quality enhancement exposing no excess radiation to patients is one the challenging problems for CT images processing. In this work, noise reduction in CT images was performed using two different directional 2 dimensional (2D) transformations; i.e., Curvelet and Contourlet and Discrete Wavelet Transform (DWT) thresholding methods of BayesShrink and AdaptShrink, compared to each other and we proposed a new threshold in wavelet domain for not only noise reduction but also edge retaining, consequently the proposed method retains the modified coefficients significantly that result good visual quality. Data evaluations were accomplished by using two criterions; namely, peak signal to noise ratio (PSNR) and Structure similarity (Ssim).

Keywords: Computed Tomography (CT), noise reduction, curve-let, contour-let, Signal to Noise Peak-Peak Ratio (PSNR), Structure Similarity (Ssim), Absorbed Dose to Patient (ADP).

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Authors: Mohammad Yasrebi, Somayeh Khosravi

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Machine-understandable data when strongly interlinked constitutes the basis for the SemanticWeb. Annotating web documents is one of the major techniques for creating metadata on the Web. Annotating websitexs defines the containing data in a form which is suitable for interpretation by machines. In this paper, we present a better and improved approach than previous [1] to annotate the texts of the websites depends on the knowledge base.

Keywords: Knowledge base, ontology, semantic annotation, XML.

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Authors: Bainian Li, Kongsheng Zhang, Jian Xu

Abstract:

In this paper we study the fuzzy c-mean clustering algorithm combined with principal components method. Demonstratively analysis indicate that the new clustering method is well rather than some clustering algorithms. We also consider the validity of clustering method.

Keywords: FCM algorithm, Principal Components Analysis, Clustervalidity

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Authors: Arishai Akraudom

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The objective of this research was to study the themes of alcoholic beverage advertisements in Thailand after the enactment of the 2008 Alcoholic Beverage Control Act. Data was collected through textual analysis of 35 television and cinema advertisements for alcoholic beverage products broadcast in Thailand. Nine themes were identified, seven of which were themes that had previously been used before the new law (i.e. power, competition, friendship, Thainess, success, romance and safety) and two of which were new themes (volunteerism and conservation) that were introduced as a form of adaptation and negotiation in response to the new law.

Keywords: Alcoholic Beverage Control Act 2008, Alcohol Advertising, Analysis of Themes, Narrative Structure, Health Communication.

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Authors: Nadeem Iftikhar, Zouhaib Zafar, Shaukat Ali

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Efficient retrieval of multimedia objects has gained enormous focus in recent years. A number of techniques have been suggested for retrieval of textual information; however, relatively little has been suggested for efficient retrieval of multimedia objects. In this paper we have proposed a generic architecture for contextaware retrieval of multimedia objects. The proposed framework combines the well-known approaches of text-based retrieval and context-aware retrieval to formulate architecture for accurate retrieval of multimedia data.

Keywords: Context-aware retrieval, information retrieval, multimedia databases, multimedia data.

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Authors: Dr. H. B. Kekre, Sudeep D. Thepade

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Panoramic view generation has always offered novel and distinct challenges in the field of image processing. Panoramic view generation is nothing but construction of bigger view mosaic image from set of partial images of the desired view. The paper presents a solution to one of the problems of image seascape formation where some of the partial images are color and others are grayscale. The simplest solution could be to convert all image parts into grayscale images and fusing them to get grayscale image panorama. But in the multihued world, obtaining the colored seascape will always be preferred. This could be achieved by picking colors from the color parts and squirting them in grayscale parts of the seascape. So firstly the grayscale image parts should be colored with help of color image parts and then these parts should be fused to construct the seascape image. The problem of coloring grayscale images has no exact solution. In the proposed technique of panoramic view generation, the job of transferring color traits from reference color image to grayscale image is done by palette based method. In this technique, the color palette is prepared using pixel windows of some degrees taken from color image parts. Then the grayscale image part is divided into pixel windows with same degrees. For every window of grayscale image part the palette is searched and equivalent color values are found, which could be used to color grayscale window. For palette preparation we have used RGB color space and Kekre-s LUV color space. Kekre-s LUV color space gives better quality of coloring. The searching time through color palette is improved over the exhaustive search using Kekre-s fast search technique. After coloring the grayscale image pieces the next job is fusion of all these pieces to obtain panoramic view. For similarity estimation between partial images correlation coefficient is used.

Keywords: Panoramic View, Similarity Estimate, Color Transfer, Color Palette, Kekre's Fast Search, Kekre's LUV

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Authors: Jingling Li, Yi Zhang, Wei Liang, Tao Cui, Jun Li

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Compared with terrestrial network, the traffic of spatial information network has both self-similarity and short correlation characteristics. By studying its traffic prediction method, the resource utilization of spatial information network can be improved, and the method can provide an important basis for traffic planning of a spatial information network. In this paper, considering the accuracy and complexity of the algorithm, the spatial information network traffic is decomposed into approximate component with long correlation and detail component with short correlation, and a time series hybrid prediction model based on wavelet decomposition is proposed to predict the spatial network traffic. Firstly, the original traffic data are decomposed to approximate components and detail components by using wavelet decomposition algorithm. According to the autocorrelation and partial correlation smearing and truncation characteristics of each component, the corresponding model (AR/MA/ARMA) of each detail component can be directly established, while the type of approximate component modeling can be established by ARIMA model after smoothing. Finally, the prediction results of the multiple models are fitted to obtain the prediction results of the original data. The method not only considers the self-similarity of a spatial information network, but also takes into account the short correlation caused by network burst information, which is verified by using the measured data of a certain back bone network released by the MAWI working group in 2018. Compared with the typical time series model, the predicted data of hybrid model is closer to the real traffic data and has a smaller relative root means square error, which is more suitable for a spatial information network.

Keywords: Spatial Information Network, Traffic prediction, Wavelet decomposition, Time series model.

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