Search results for: semantic features

Authors: Raquel Rossini, Edelvais Caldeira

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The aim of this work is to investigate the motivation for the metaphorical uses of two verb combinations: come out and take over. Drawing from cognitive semantics theories, image schemas and metaphors, it was attempted to demonstrate that: a) the metaphorical senses of both 'come out' and 'take over' extend from both the verbs and the particles central (spatial) senses in such verb combinations; and b) the particles 'out' and 'over' also contribute to the whole meaning of the verb combinations. In order to do so, a random selection of 579 concordance lines for come out and 1,412 for take over was obtained from the Corpus of Contemporary American English – COCA. One of the main procedures adopted in the present work was the establishment of verb and particle central senses. As per the research questions addressed in this study, they are as follows: a) how does the identification of trajector and landmark help reveal patterns that contribute for the identification of the semantic network of these two verb combinations?; b) what is the relationship between the schematic structures attributed to the particles and the metaphorical uses found in empirical data?; and c) what conceptual metaphors underlie the mappings from the source to the target domains? The results demonstrated that not only the lexical verbs come and take, but also the particles out and over play an important whole in the different meanings of come out and take over. Besides, image schemas and conceptual metaphors were found to be helpful in order to establish the motivations for the metaphorical uses of these linguistic structures.

Keywords: cognitive linguistics, English syntax, multi-word verbs, prepositions

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Authors: Muhammed Muvaffak Alhasan, Ali Alriyami, Ali Almanei

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Despite the extreme importance of etymological studies in documenting the linguistic heritage, and showing its roots and connections in the classical language; However, etymological dictionaries are still rare in the Arab library in general. Etymology is the science of etymology that investigates how vocabulary is reproduced and reproduced, by exploring the origin of words and the phonetic and semantic changes that occurred in them over time, trying to reconfigure an identity card for the word showing its origin and the path it took through time until it reached its current state. This research seeks to make an etymological study on the terminology used in the traditional dagger making in the Sultanate of Oman through the following steps: 1. Collecting the terms relating to traditional dagger making and recording them in order to document and preserve them. 2. Arranging them alphabetically in order to facilitate searching and dealing with them. 3. Setting up a historical identification card for each word by applying an etymological study that shows its source from which they descended its links with standard and the phonological and semantic changes it underwent until it reached its current form.

Keywords: cultural heritage, etymology, Omani dagger, Oman

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Authors: Dean Raiyasmi, Elvi Citraresmana, Sutiono Mahdi

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Language shapes the human mind and its concept toward things. Using image schemas, in nowadays technology, even AI (artificial intelligence) can concept things in response to their creator negativity or positivity. This is reflected inside one of the most selling game around the world in 2012 called Dragon Age Origins. The AI in form of NPC (Non-Playable Character) inside the game reflects on the creator of the game on negativity or positivity toward the lexical concept of mage. Through image schemas, shaping the lexical concept of mage deemed possible and proved the negativity or positivity creator of the game toward mage. This research analyses the cognitive-semantic process of image schema and shaping the concept of ‘mage’ by describing kinds of image schemas exist in the Dragon Age Origin Game. This research is also aimed to analyse kinds of image schemas and describing the image schemas which shaping the concept of ‘mage’ itself. The methodology used in this research is qualitative where participative observation is employed with five stages and documentation. The results shows that there are four image schemas exist in the game and those image schemas shaping the lexical concept of ‘mage’.

Keywords: cognitive semantic, image-schema, conceptual metaphor, video game

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Authors: Sufia Maroof

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This empirical study investigated the web-translations of five Pakistani public offices (FPSC, FIA, HEC, USB, and Ministry of Finance) offering Urdu tab as an option to access information on their official websites. Triangulation of quantitative and qualitative research design informed the researcher of the semantic, lexical and syntactic caveats in these translations. The study hypothesized that majority of the Pakistani population is oblivious of the Supreme Court’s amendments in language policy concerning national and official language; hence, Urdu web-translations of the public departments have not been accessed effectively. Firstly, the researcher conducted an online survey, comprising of two sections, close ended and short answer based questions. Secondly, the researcher compiled corpus of the five selected websites in a tabular form to compare the data. Thirdly, the administrators of the departments had been contacted regarding the methods of translation and the expertise of the personnel involved. The corpus was assessed for TQA after examining the lexical, semantic, syntactical and technical alignment inaccuracies and imperfections. The study suggests the public offices to invest in their Urdu webs by either hiring expert translators or engaging expertise of a translation agency for this project to offer quality translation to public.

Keywords: machine translations, public offices, Urdu translations, websites

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Authors: Michael Steve Lopez Bernabe

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Political news coverage of tabloid sheets as one of the print media molds or influences public opinions and perceptions. In this study, Critical Discourse Analysis was employed to 30 political news taken from major tabloid sheets in the Philippines in order to determine the linguistics features and other features characterizing the political news in tabloids such as discursive styles, news topics or contexts, journalistic roles and news sources. The political underpinnings through framing were also explored in the study. The results revealed that the linguistics features of the news coverage include moods and modalities (morphology), passivity and transitivity, nominalization, appositives and embedding (syntax), and pre-modifications, the use of verbs and omissions (grammatical features). The discursive features were direct or indirect speech; cohesion; endophora and classifications. In terms of news sources were politicians, experts, and journalists; and the tabloid perform the journalistic roles such as an intervention, watchdog, loyal-facilitator, service, infotainment and civic. The news was also evident of different political underpinnings such as game or strategic framing, conflict framing, human interest framing, attrition of responsibility framing, morality framing, economic consequences framing and issue framing.

Keywords: critical discourse analysis, political news, applied linguistics, Philippines, tabloid sheets

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Authors: Anitha Narasimhan, Aarthy Anandan, Madhan Karky, C. N. Subalalitha

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Games can be the excellent tools for teaching a language. There are few e-learning games in Indian languages like word scrabble, cross word, quiz games etc., which were developed mainly for educational purposes. This paper proposes a Tamil word game called, “Porul”, which focuses on education as well as on players’ thinking and decision-making skills. Porul is a multiple choice based quiz game, in which the players attempt to answer questions correctly from the given multiple options that are generated using a unique algorithm called the Option Selection algorithm which explores the semantics of the question in various dimensions namely, synonym, rhyme and Universal Networking Language semantic category. This kind of semantic exploration of the question not only increases the complexity of the game but also makes it more interesting. The paper also proposes a Scoring Algorithm which allots a score based on the popularity score of the question word. The proposed game has been tested using 20,000 Tamil words.

Keywords: Porul game, Tamil word game, option selection, flash card, scoring, algorithm

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Authors: Skiker Kaoutar, Mounir Maouene

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Neuroimaging studies have shown that animal and tool concepts rely on distinct networks of brain areas. Animal concepts depend predominantly on temporal areas while tool concepts rely on fronto-temporo-parietal areas. However, the origin of this neurofunctional distinction for processing animal and tool concepts remains still unclear. Here, we address this question from a network perspective suggesting that the neural distinction between animals and tools might reflect the differences in their structural semantic networks. We build semantic networks for animal and tool concepts derived from Mc Rae and colleagues’s behavioral study conducted on a large number of participants. These two networks are thus analyzed through a large number of graph theoretical measures for small-worldness: centrality, clustering coefficient, average shortest path length, as well as resistance to random and targeted attacks. The results indicate that both animal and tool networks have small-world properties. More importantly, the animal network is more vulnerable to targeted attacks compared to the tool network a result that correlates with brain lesions studies.

Keywords: animals, tools, network, semantics, small-world, resilience to damage

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Authors: Ghada E. Esheba, Ghadeer F. Alharthi, Duaa A. Alhejaili, Rawan E. Hudairy, Wafaa A. Altaezi, Raghad M. Alhejaili

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Background: Inflammatory bowel disease (IBD) consists of two specific gastrointestinal disorders: ulcerative colitis (UC) and Crohn's disease (CD). Despite their distinct natures, these two diseases share many similar etiologic, clinical and pathological features, as a result, their accurate differential diagnosis may sometimes be difficult. Correct diagnosis is important because surgical treatment and long-term prognosis differ from UC and CD. Aim: This study aims to study the characteristic clinicopathological features which help in the differential diagnosis between UC and CD, and assess the disease activity in ulcerative colitis. Materials and methods: This study was carried out on 50 selected cases. The cases included 27 cases of UC and 23 cases of CD. All the cases were examined using H& E and immunohistochemically for bcl-2 expression. Results: Characteristic features of UC include: decrease in mucous content, irregular or villous surface, crypt distortion, and cryptitis, whereas the main cardinal histopathological features seen in CD were: epitheloid granuloma, transmural chronic inflammation, absence of mucin depletion, irregular surface, or crypt distortion. 3 cases of UC were found to be associated with dysplasia. UC mucosa contains fewer Bcl-2+ cells compared with CD mucosa. Conclusion: This study using multiple parameters such clinicopathological features and Bcl-2 expression as studied by immunohistochemical stain, helped to gain an accurate differentiation between UC and CD. Furthermore, this work spotted the light on the activity and different grades of UC which could be important for the prediction of relapse.

Keywords: Crohn's disease, dysplasia, inflammatory bowel disease, ulcerative colitis

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Authors: Ayad Dalloo, Sulaf Dalloo

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Electrocardiogram (ECG) recording is prone to complications, on analysis by physicians, due to noise and artifacts, thus creating ambiguity leading to possible error of diagnosis. Such drawbacks may be overcome with the advent of high resolution Methods, such as Discrete Wavelet Analysis and Digital Signal Processing (DSP) techniques. This ECG signal analysis is implemented in three stages: ECG preprocessing, features extraction and classification with the aim of realizing high resolution ECG diagnosis and improved detection of abnormal conditions in the heart. The preprocessing stage involves removing spurious artifacts (noise), due to such factors as muscle contraction, motion, respiration, etc. ECG features are extracted by applying DSP and suggested sloping method techniques. These measured features represent peak amplitude values and intervals of P, Q, R, S, R’, and T waves on ECG, and other features such as ST elevation, QRS width, heart rate, electrical axis, QR and QT intervals. The classification is preformed using these extracted features and the criteria for cardiovascular diseases. The ECG diagnostic system is successfully applied to 12-lead ECG recordings for 12 cases. The system is provided with information to enable it diagnoses 15 different diseases. Physician’s and computer’s diagnoses are compared with 90% agreement, with respect to physician diagnosis, and the time taken for diagnosis is 2 seconds. All of these operations are programmed in Matlab environment.

Keywords: ECG diagnostic system, QRS detection, ECG baseline removal, cardiovascular diseases

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Authors: Sanpachai Huvanandana, Settapong Malisuwan, Soparwan Tongyuak, Prust Pannachet, Anong Phoepueak, Navneet Madan

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Many regions of Thailand are prone to flooding due to tropical climate. A commonly increasing precipitation in this continent results in risk of flooding. Many efforts have been implemented such as drainage control system, multiple dams, and irrigation canals. In order to decide where the drainages, dams, and canal should be appropriately located, the flooding risk area should be determined. This paper is aimed to identify the appropriate features that can be used to classify the flooding risk area in Thailand. Several features have been analyzed and used to classify the area. Non-supervised clustering techniques have been used and the results have been compared with ten years average actual flooding area.

Keywords: flood area clustering, geographical information system, flood features

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Authors: Raymon Maac, Michael Arthus Muega, Joyce Ann Calingasan, Elva Maureen Gorospe

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Critical pedagogy is known for its advocacy of humanistic and liberating education. Its far-reaching approach helps students to understand and analyze their own situations and the realities happening in their society. However, this pedagogy together with its promising features is not well-known in the Philippines. This paper determines the place of critical pedagogy in the new values education curriculum and analyzes its features in the K-12 Values Education curriculum and textbook. The study examines the position of critical pedagogy in the Philippine K-12 Values Education curriculum by closely studying and comparing their features; and scrutinizes the Grade 8 Values Education textbook specifically modules 4, 8, 10 and 13 which comprises 25% of the total 16 modules. The said modules are concerned with the role of the family in the preservation of social justice, which is one of the objectives of critical pedagogy. The findings in this research were based on the pieces of evidence gathered from the curriculum and textbook itself. Based on the evaluation done, the study found out that the ideas of critical pedagogy were the same with that of the objectives of K-12 Values Education Curriculum. Due to this, values education teachers can utilize critical pedagogy in their subject. In addition, the K-12 Values Education curriculum exhibits some of the features of critical pedagogy such as authentic student empowerment and critical thinking. Lastly, some features of critical pedagogy are also evident in some of the general parts and recommended activities in the K-12 Values Education textbook while other activities need to be fully developed by both teacher and students to reflect the genuine critical pedagogy.

Keywords: authentic student empowerment, critical pedagogy, critical thinking, liberating education

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Authors: Rita Darmayanti

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This study aims at investigating the discourse pattern and structure of research article abstracts. The characteristics of the language used in abstracts written by non-native English-speaking (NNES) novice researchers are mainly examined in terms of rhetorical moves and the degree of variability of the rhetorical features as indicated by the structure of clauses and the linguistic features of the text. To this end, 20 abstracts written by undergraduate students of the accounting department at the State Polytechnic of Malang in 2018-2019 were employed as the data of this study. Findings showed that the most frequently used pattern of the rhetorical move is I(Introduction)-P(Purpose)-M(Method)-Pr(Product or Result)-C(Conclusion) with the significant use of active sentence and present and past tense. The findings of the study are projected to be utilized for evaluating the quality of students’ abstracts and generating a pedagogical proposal of ESP writing course or at least providing a critical review of current practices in ESP program intended for non-native English students at tertiary level.

Keywords: rhetorical features, rhetorical moves, non-native English-speaking novice researchers, research abstract

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Authors: Nikola Tomasevic, Marko Batic, Sanja Vranes

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Emerging Semantic Web technologies can be seen as the next step in evolution of the intelligent facility management systems. Particularly, this considers increased usage of open source and/or standardized concepts for data classification and semantic interpretation. To deliver such facility management systems, providing the comprehensive integration and interoperability platform in from of the facility data model is a prerequisite. In this paper, one of the possible modelling approaches to provide such integrative facility data model which was based on the ontology modelling concept was presented. Complete ontology development process, starting from the input data acquisition, ontology concepts definition and finally ontology concepts population, was described. At the beginning, the core facility ontology was developed representing the generic facility infrastructure comprised of the common facility concepts relevant from the facility management perspective. To develop the data model of a specific facility infrastructure, first extension and then population of the core facility ontology was performed. For the development of the full-blown facility data models, Malpensa and Fiumicino airports in Italy, two major European air-traffic hubs, were chosen as a test-bed platform. Furthermore, the way how these ontology models supported the integration and interoperability of the overall airport energy management system was analyzed as well.

Keywords: airport ontology, energy management, facility data model, ontology modeling

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Authors: Alanod Alqasim, Yasmine Iskandarani, Sita Algethami, Jawaher alzughaiby

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This study focuses on the usage of AI (Artificial Intelligence) systems and features on the Instagram application and how it influences user experience and satisfaction. The aim is to evaluate the techniques and current capabilities, restrictions, and potential future directions of AI in an Instagram application. Following a concise explanation of the core concepts underlying AI usage on Instagram. To answer this question, 19 randomly selected users were asked to complete a 9-question survey on their experience and satisfaction with the app's features (Filters, user preferences, translation tool) and authenticity. The results revealed that there were three prevalent allegations. These declarations include that Instagram has an extremely attractive user interface; secondly, Instagram creates a strong sense of community; and lastly, Instagram has an important influence on mental health.

Keywords: AI (Artificial Intelligence), instagram, features, satisfaction, experience

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Authors: Anna Petr Safrankova, Karla Hrbackova

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The education of socially disadvantaged students is in the long term spotlight of many pedagogical researches in both Czech and foreign environment. These researches among others investigate this topic from the point of view of individual compensatory measure which tries to overcome or remove the social disadvantage. The focus of the study is to highlight the important role of teachers in the education of this specific group of students, among others in terms of their (teachers´) pre-graduate training. The aim of the study is to point out the importance of teachers´ self-efficacy. The study is based on the assumption that the teacher's self-efficacy may significantly affect the teacher's perception of a particular group of students and thereby affect the education of the students. The survey involved 245 teachers from the two regions in the Czech Republic. In the research were used TES questionnaire (with the dimensions personal teaching efficacy – PTE and general teaching efficacy – GTE) by Gibson and Dembo and the semantic differential (containing 12 scales with bipolar adjectives) which investigated the components of teachers' attitudes toward socially disadvantaged students. It was found that teachers’ self-efficacy significantly affects the teachers’ perception of the group of socially disadvantaged students. Based on this finding we believe that it is necessary to work with this concept (prepare teachers to educate this specific group of students) already during higher education and especially during the pre-graduate teachers training.

Keywords: teachers, socially disadvantaged students, semantic differential, teachers self-efficacy

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Authors: Dennis Okora Amima Ondieki

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Fertility transition has been identified to be affected by numerous factors. This research aimed to investigate the most real factors affecting fertility transition in Kenya. These factors were firstly extracted from the literature convened into demographic features, social, and economic features, social-cultural features, reproductive features and modernization features. All these factors had 23 factors identified for this study. The data for this study was from the Kenya Demographic and Health Surveys (KDHS) conducted in 1999-2003 and 2003-2008/9. The data was continuous, and it involved the mean birth order for the ten periods. Principal component analysis (PCA) was utilized using 23 factors. Principal component analysis conveyed religion, region, education and marital status as the real factors. PC scores were calculated for every point. The identified principal components were utilized as forecasters in the multiple regression model, with the fertility level as the response variable. The four components were found to be affecting fertility transition differently. It was found that fertility is affected positively by factors of region and marital and negatively by factors of religion and education. These four factors can be considered in the planning policy in Kenya and Africa at large.

Keywords: fertility transition, principal component analysis, Kenya demographic health survey, birth order

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Authors: P. S. Jagadeesh Kumar, Yang Yung, Wenli Hu

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Speech emotion classification is a dominant research field in finding a sturdy and profligate classifier appropriate for different real-life applications. This effort accentuates on classifying different emotions from speech signal quarried from the features related to pitch, formants, energy contours, jitter, shimmer, spectral, perceptual and temporal features. Tensor deep stacking neural networks were supported to examine the factors that influence the classification success rate. Facial electromyography signals were composed of several forms of focuses in a controlled atmosphere by means of audio-visual stimuli. Proficient facial electromyography signals were pre-processed using moving average filter, and a set of arithmetical features were excavated. Extracted features were mapped into consistent emotions using bilinear mapping. With facial electromyography signals, a database comprising diverse emotions will be exposed with a suitable fine-tuning of features and training data. A success rate of 92% can be attained deprived of increasing the system connivance and the computation time for sorting diverse emotional states.

Keywords: speech emotion classification, tensor deep stacking neural networks, facial electromyography, bilinear mapping, audio-visual stimuli

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Authors: Tatiana V. Raudina, Sergey P. Kulizhskiy

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A large part of Russia is located in permafrost areas. These areas are widely used because there are concentrated valuable natural resources. Therefore to explore of cryosols it is important due to the significant increase of anthropogenic stress as well as the problem of global climate change. In the north of Western Siberia permafrost phenomena is widespread. Permafrost as a factor of soil formation and cryogenesis as a process have a great impact on the soil formation of these areas. Based on the research results of permafrost-affected soils tundra landscapes formed in the central part of the Tazovskiy Peninsula in cryogenic conditions, data were obtained which characterize the morphological features of soils. The specificity of soil cover distribution and manifestation of soil-forming processes within the study area are noted. Permafrost features such as frost cracking, cryoturbation, thixotropy, movement of humus are formed. The formation of these features is increased with the development of the territory. As a consequence, there is a change in the components of the environment and the destruction of the soil cover.

Keywords: gleyed and nongleyed soils, permafrost, soil cryogenesis (pedocryogenesis), soil-forming macroprocesses

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Authors: Fatma Abdedayem

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We propose an extension to the famous method called Bag of words (BOW) which proved a successful role in the field of image categorization. Practically, this method based on representing image with visual words. In this work, firstly, we extract features from images using Spatial Pyramid Representation (SPR) and two dissimilar color descriptors which are opponent-SIFT and transformed-color-SIFT. Secondly, we fuse color local features by joining the two histograms coming from these descriptors. Thirdly, after collecting of all features, we generate multi-dictionaries coming from n random feature subsets that obtained by dividing all features into n random groups. Then, by using these dictionaries separately each image can be represented by n histograms which are lately concatenated horizontally and form the final histogram, that allows to combine Multiple Dictionaries (MDBoW). In the final step, in order to classify image we have applied Support Vector Machine (SVM) on the generated histograms. Experimentally, we have used two dissimilar image datasets in order to test our proposition: Caltech 256 and PASCAL VOC 2007.

Keywords: bag of words (BOW), color descriptors, multi-dictionaries, MDBoW

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Authors: Soyon Kim, Edward Kim

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In order to retrieve information from the massive stream of songs in the music industry, music search by title, lyrics, artist, mood, and genre has become more important. Despite the subjectivity and controversy over the definition of music genres across different nations and cultures, automatic genre classification systems that facilitate the process of music categorization have been developed. Manual genre selection by music producers is being provided as statistical data for designing automatic genre classification systems. In this paper, an automatic music genre classification system utilizing non-negative matrix factorization (NMF) is proposed. Short-term characteristics of the music signal can be captured based on the timbre features such as mel-frequency cepstral coefficient (MFCC), decorrelated filter bank (DFB), octave-based spectral contrast (OSC), and octave band sum (OBS). Long-term time-varying characteristics of the music signal can be summarized with (1) the statistical features such as mean, variance, minimum, and maximum of the timbre features and (2) the modulation spectrum features such as spectral flatness measure, spectral crest measure, spectral peak, spectral valley, and spectral contrast of the timbre features. Not only these conventional basic long-term feature vectors, but also NMF based feature vectors are proposed to be used together for genre classification. In the training stage, NMF basis vectors were extracted for each genre class. The NMF features were calculated in the log spectral magnitude domain (NMF-LSM) as well as in the basic feature vector domain (NMF-BFV). For NMF-LSM, an entire full band spectrum was used. However, for NMF-BFV, only low band spectrum was used since high frequency modulation spectrum of the basic feature vectors did not contain important information for genre classification. In the test stage, using the set of pre-trained NMF basis vectors, the genre classification system extracted the NMF weighting values of each genre as the NMF feature vectors. A support vector machine (SVM) was used as a classifier. The GTZAN multi-genre music database was used for training and testing. It is composed of 10 genres and 100 songs for each genre. To increase the reliability of the experiments, 10-fold cross validation was used. For a given input song, an extracted NMF-LSM feature vector was composed of 10 weighting values that corresponded to the classification probabilities for 10 genres. An NMF-BFV feature vector also had a dimensionality of 10. Combined with the basic long-term features such as statistical features and modulation spectrum features, the NMF features provided the increased accuracy with a slight increase in feature dimensionality. The conventional basic features by themselves yielded 84.0% accuracy, but the basic features with NMF-LSM and NMF-BFV provided 85.1% and 84.2% accuracy, respectively. The basic features required dimensionality of 460, but NMF-LSM and NMF-BFV required dimensionalities of 10 and 10, respectively. Combining the basic features, NMF-LSM and NMF-BFV together with the SVM with a radial basis function (RBF) kernel produced the significantly higher classification accuracy of 88.3% with a feature dimensionality of 480.

Keywords: mel-frequency cepstral coefficient (MFCC), music genre classification, non-negative matrix factorization (NMF), support vector machine (SVM)

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Authors: Anes Enakoa, Yawei Liang

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Question Answering (QA) system is one of the most important and demanding tasks in the field of Natural Language Processing (NLP). In QA systems, the answer generation task generates a list of candidate answers to the user's question, in which only one answer is correct. Answer selection is one of the main components of the QA, which is concerned with selecting the best answer choice from the candidate answers suggested by the system. However, the selection process can be very challenging especially in Arabic due to its particularities. To address this challenge, an approach is proposed to answer questions with multiple answer choices for Arabic QA systems based on Textual Entailment (TE) recognition. The developed approach employs a Support Vector Machine that considers lexical, semantic and syntactic features in order to recognize the entailment between the generated hypotheses (H) and the text (T). A set of experiments has been conducted for performance evaluation and the overall performance of the proposed method reached an accuracy of 67.5% with C@1 score of 80.46%. The obtained results are promising and demonstrate that the proposed method is effective for TE recognition task.

Keywords: information retrieval, machine learning, natural language processing, question answering, textual entailment

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Authors: Mohammad Sheikhalishahi, Vahid Ebrahimipour, Amir Hossein Radman-Kian

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This paper proposes an Ontology framework for knowledge modeling and representation of the equipment integrity process in a typical oil and gas production plant. Our aim is to construct a knowledge modeling that facilitates translation, interpretation, and conversion of human-readable integrity interpretation into computer-readable representation. The framework provides a function structure related to fault propagation using ISO 14224 and ISO 15926 OWL-Lite/ Resource Description Framework (RDF) to obtain a generic system-level model of asset integrity that can be utilized in the integrity engineering process during the equipment life cycle. It employs standard terminology developed by ISO 15926 and ISO 14224 to map textual descriptions of equipment failure and then convert it to a causality-driven logic by semantic interpretation and computer-based representation using Lite/RDF. The framework applied for an offshore gas riser. The result shows that the approach can cross-link the failure-related integrity words and domain-specific logic to obtain a representation structure of equipment integrity with causality inference based on semantic extraction of inspection report context.

Keywords: asset integrity modeling, interoperability, OWL, RDF/XML

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Authors: Imed Feki, Faouzi Msahli

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Selection of relevant parameters from a high dimensional process operation setting space is a problem frequently encountered in industrial process modelling. This paper presents a method for selecting the most relevant fabric physical parameters for each sensory quality feature. The proposed relevancy criterion has been developed using two approaches. The first utilizes a fuzzy sensitivity criterion by exploiting from experimental data the relationship between physical parameters and all the sensory quality features for each evaluator. Next an OWA aggregation procedure is applied to aggregate the ranking lists provided by different evaluators. In the second approach, another panel of experts provides their ranking lists of physical features according to their professional knowledge. Also by applying OWA and a fuzzy aggregation model, the data sensitivity-based ranking list and the knowledge-based ranking list are combined using our proposed percolation technique, to determine the final ranking list. The key issue of the proposed percolation technique is to filter automatically and objectively the relevant features by creating a gap between scores of relevant and irrelevant parameters. It permits to automatically generate threshold that can effectively reduce human subjectivity and arbitrariness when manually choosing thresholds. For a specific sensory descriptor, the threshold is defined systematically by iteratively aggregating (n times) the ranking lists generated by OWA and fuzzy models, according to a specific algorithm. Having applied the percolation technique on a real example, of a well known finished textile product especially the stonewashed denims, usually considered as the most important quality criteria in jeans’ evaluation, we separate the relevant physical features from irrelevant ones for each sensory descriptor. The originality and performance of the proposed relevant feature selection method can be shown by the variability in the number of physical features in the set of selected relevant parameters. Instead of selecting identical numbers of features with a predefined threshold, the proposed method can be adapted to the specific natures of the complex relations between sensory descriptors and physical features, in order to propose lists of relevant features of different sizes for different descriptors. In order to obtain more reliable results for selection of relevant physical features, the percolation technique has been applied for combining the fuzzy global relevancy and OWA global relevancy criteria in order to clearly distinguish scores of the relevant physical features from those of irrelevant ones.

Keywords: data sensitivity, feature selection, fuzzy logic, OWA operators, percolation technique

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Authors: Fatima Akhmedova, Simon Liao

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One of the most critical decision points in the design of a face recognition system is the choice of an appropriate face representation. Effective feature descriptors are expected to convey sufficient, invariant and non-redundant facial information. In this work, we propose a set of Hahn moments as a new approach for feature description. Hahn moments have been widely used in image analysis due to their invariance, non-redundancy and the ability to extract features either globally and locally. To assess the applicability of Hahn moments to Face Recognition we conduct two experiments on the Olivetti Research Laboratory (ORL) database and University of Notre-Dame (UND) X1 biometric collection. Fusion of the global features along with the features from local facial regions are used as an input for the conventional k-NN classifier. The method reaches an accuracy of 93% of correctly recognized subjects for the ORL database and 94% for the UND database.

Keywords: face recognition, Hahn moments, recognition-by-parts, time-lapse

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Authors: Shimaa Holail, Tamer Saleh, Xiongwu Xiao

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Building change detection (BCD) from satellite imagery is an essential topic in urbanization monitoring, agricultural land management, and updating geospatial databases. Recently, methods for detecting changes based on deep learning have made significant progress and impressive results. However, it has the problem of being insensitive to changes in buildings with complex spectral differences, and the features being extracted are not discriminatory enough, resulting in incomplete buildings and irregular boundaries. To overcome these problems, we propose a dual Siamese network based on the Unet model with the addition of a deep supervision strategy (DS) in this paper. This network consists of a backbone (encoder) based on ImageNet pre-training, a fusion block, and feature pyramid networks (FPN) to enhance the step-by-step information of the changing regions and obtain a more accurate BCD map. To train the proposed method, we created a new dataset (EGY-BCD) of high-resolution and multi-temporal aerial images captured over New Cairo in Egypt to detect building changes for this purpose. The experimental results showed that the proposed method is effective and performs well with the EGY-BCD dataset regarding the overall accuracy, F1-score, and mIoU, which were 91.6 %, 80.1 %, and 73.5 %, respectively.

Keywords: building change detection, deep supervision, semantic segmentation, EGY-BCD dataset

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Authors: Seyed Mehdi Ghezi, Hesam Hasanpoor

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This scientific article explores enhancement methods for ensemble learning with the aim of improving the performance of classifiers in the analysis and classification of brain signals. The research approach in this field consists of two main parts, each with its own strengths and weaknesses. The choice of approach depends on the specific research question and available resources. By combining these approaches and leveraging their respective strengths, researchers can enhance the accuracy and reliability of classification results, consequently advancing our understanding of the brain and its functions. The first approach focuses on utilizing machine learning methods to identify the best features among the vast array of features present in brain signals. The selection of features varies depending on the research objective, and different techniques have been employed for this purpose. For instance, the genetic algorithm has been used in some studies to identify the best features, while optimization methods have been utilized in others to identify the most influential features. Additionally, machine learning techniques have been applied to determine the influential electrodes in classification. Ensemble learning plays a crucial role in identifying the best features that contribute to learning, thereby improving the overall results. The second approach concentrates on designing and implementing methods for selecting the best classifier or utilizing meta-classifiers to enhance the final results in ensemble learning. In a different section of the research, a single classifier is used instead of multiple classifiers, employing different sets of features to improve the results. The article provides an in-depth examination of each technique, highlighting their advantages and limitations. By integrating these techniques, researchers can enhance the performance of classifiers in the analysis and classification of brain signals. This advancement in ensemble learning methodologies contributes to a better understanding of the brain and its functions, ultimately leading to improved accuracy and reliability in brain signal analysis and classification.

Keywords: ensemble learning, brain signals, classification, feature selection, machine learning, genetic algorithm, optimization methods, influential features, influential electrodes, meta-classifiers

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Authors: Khalid Salhi, El Miloud Jaara, Mohammed Talibi Alaoui

Abstract:

In this paper, we present a neural approach for unsupervised natural color-texture image segmentation, which is based on both Kohonen maps and mathematical morphology, using a combination of the texture and the image color information of the image, namely, the fractal features based on fractal dimension are selected to present the information texture, and the color features presented in RGB color space. These features are then used to train the network Kohonen, which will be represented by the underlying probability density function, the segmentation of this map is made by morphological watershed transformation. The performance of our color-texture segmentation approach is compared first, to color-based methods or texture-based methods only, and then to k-means method.

Keywords: segmentation, color-texture, neural networks, fractal, watershed

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Authors: Methawee Yuttapongtada

Abstract:

Intensifier is the linguistic term and device that is generally found in different languages in order to enhance and give additional quantity, quality or emotion to the words of each language. In fact, each language in the world has both of the similar and dissimilar intensifying device. More specially, the wide variety of intensifying device is used for Thai language and one of those is usage of the impolite word or the word that used to mean something negative as intensifier. The data collection in this study was done throughout the spoken language style by collecting from intensifiers regarded as impolite words because these words as employed in the other contexts will be held as the rude, swear words or the words with negative meaning. Then, backward study to the past was done in order to consider the historical change. Explanation of the original meaning and the contexts of words use from the past till the present time were done by use of both textual documents and dictionaries available in different periods. It was found that regarding the semantics and pragmatic aspects, subjectification also is the significant motivation that changed the impolite words to intensifiers. At last, it can explain pathway of the semantic change of these very words undoubtedly. Moreover, it is found that use tendency in the impolite word or the word that used to mean something negative will more be increased and this phenomenon is commonly found in many languages in the world and results of this research may support to the belief that human language in the world is universal and the same still reflected that human has the fundamental thought as the same to each other basically.

Keywords: impolite word, intensifier, Thai, semantic change

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Authors: Somaiyeh Dehghan, Mohammad Reza Kheirkhahan Haghighi

Abstract:

The rapid growth of domains that have data with a large number of features, while the number of samples is limited has caused difficulty in constructing strong classifiers. To reduce the dimensionality of the feature space becomes an essential step in classification task. Random subspace method (or attribute bagging) is an ensemble classifier that consists of several classifiers that each base learner in ensemble has subset of features. In the present paper, we introduce Random Subspace Ensemble of CMAC neural network (RSE-CMAC), each of which has training with subset of features. Then we use this model for classification task. For evaluation performance of our model, we compare it with bagging algorithm on 36 UCI datasets. The results reveal that the new model has better performance.

Keywords: classification, random subspace, ensemble, CMAC neural network

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Authors: Jarbas Lopes Cardoso Jr., Frederic Andres, Alexandre Guitton, Asanee Kawtrakul, Silvio E. Barbin

Abstract:

The important objective of the CyberBrain Mass Agriculture Alarm Acquisition and Analysis (CBMa4) project is to minimize the impacts of diseases and disasters on rice cultivation. For example, early detection of insects will reduce the volume of insecticides that is applied to the rice fields through the use of CBMa4 platform. In order to reach this goal, two major factors need to be considered: (1) the social network of smart farmers; and (2) the warning data alarm acquisition and analysis component. This paper outlines the process for collecting the warning and improving the decision-making result to the warning. It involves two sub-processes: the warning collection and the understanding enrichment. Human sensors combine basic suitable data processing techniques in order to extract warning related semantic according to collective intelligence. We identify each warning by a semantic content called 'warncons' with multimedia metaphors and metadata related to these metaphors. It is important to describe the metric to measuring the relation among warncons. With this knowledge, a collective intelligence-based decision-making approach determines the action(s) to be launched regarding one or a set of warncons.

Keywords: agricultural engineering, warning systems, social network services, context awareness

Procedia PDF Downloads 363