Search results for: recognizing textual entailment (RTE)

Authors: Isaac K. E. Ampomah, Seong-Bae Park, Sang-Jo Lee

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Over the past decade, there have been promising developments in Natural Language Processing (NLP) with several investigations of approaches focusing on Recognizing Textual Entailment (RTE). These models include models based on lexical similarities, models based on formal reasoning, and most recently deep neural models. In this paper, we present a sentence encoding model that exploits the sentence-to-sentence relation information for RTE. In terms of sentence modeling, Convolutional neural network (CNN) and recurrent neural networks (RNNs) adopt different approaches. RNNs are known to be well suited for sequence modeling, whilst CNN is suited for the extraction of n-gram features through the filters and can learn ranges of relations via the pooling mechanism. We combine the strength of RNN and CNN as stated above to present a unified model for the RTE task. Our model basically combines relation vectors computed from the phrasal representation of each sentence and final encoded sentence representations. Firstly, we pass each sentence through a convolutional layer to extract a sequence of higher-level phrase representation for each sentence from which the first relation vector is computed. Secondly, the phrasal representation of each sentence from the convolutional layer is fed into a Bidirectional Long Short Term Memory (Bi-LSTM) to obtain the final sentence representations from which a second relation vector is computed. The relations vectors are combined and then used in then used in the same fashion as attention mechanism over the Bi-LSTM outputs to yield the final sentence representations for the classification. Experiment on the Stanford Natural Language Inference (SNLI) corpus suggests that this is a promising technique for RTE.

Keywords: deep neural models, natural language inference, recognizing textual entailment (RTE), sentence-to-sentence relation

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Authors: Wided Bakari, Patrice Bellot, Omar Trigui, Mahmoud Neji

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This article constitutes an opening to think of the modeling and analysis of Arabic texts in the context of a question-answer system. It is a question of exceeding the traditional approaches focused on morphosyntactic approaches. Furthermore, we present a new approach that analyze a text in order to extract correct answers then transform it to logical predicates. In addition, we would like to represent different levels of information within a text to answer a question and choose an answer among several proposed. To do so, we transform both the question and the text into logical forms. Then, we try to recognize all entailment between them. The results of recognizing the entailment are a set of text sentences that can implicate the user’s question. Our work is now concentrated on an implementation step in order to develop a system of question-answering in Arabic using techniques to recognize textual implications. In this context, the extraction of text features (keywords, named entities, and relationships that link them) is actually considered the first step in our process of text modeling. The second one is the use of techniques of textual implication that relies on the notion of inference and logic representation to extract candidate answers. The last step is the extraction and selection of the desired answer.

Keywords: NLP, Arabic language, question-answering, recognition text entailment, logic forms

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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: Szymon Rutkowski, Tomasz Korbak

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We present a reinforcement-learning-based method of training neural machine translation models without parallel corpora. The standard encoder-decoder approach to machine translation suffers from two problems we aim to address. First, it needs parallel corpora, which are scarce, especially for low-resource languages. Second, it lacks psychological plausibility of learning procedure: learning a foreign language is about learning to communicate useful information, not merely learning to transduce from one language’s 'encoding' to another. We instead pose the problem of learning to translate as learning a policy in a communication game between two agents: the translator and the classifier. The classifier is trained beforehand on a natural language inference task (determining the entailment relation between a premise and a hypothesis) in the target language. The translator produces a sequence of actions that correspond to generating translations of both the hypothesis and premise, which are then passed to the classifier. The translator is rewarded for classifier’s performance on determining entailment between sentences translated by the translator to disciple’s native language. Translator’s performance thus reflects its ability to communicate useful information to the classifier. In effect, we train a machine translation model without the need for parallel corpora altogether. While similar reinforcement learning formulations for zero-shot translation were proposed before, there is a number of improvements we introduce. While prior research aimed at grounding the translation task in the physical world by evaluating agents on an image captioning task, we found that using a linguistic task is more sample-efficient. Natural language inference (also known as recognizing textual entailment) captures semantic properties of sentence pairs that are poorly correlated with semantic similarity, thus enforcing basic understanding of the role played by compositionality. It has been shown that models trained recognizing textual entailment produce high-quality general-purpose sentence embeddings transferrable to other tasks. We use stanford natural language inference (SNLI) dataset as well as its analogous datasets for French (XNLI) and Polish (CDSCorpus). Textual entailment corpora can be obtained relatively easily for any language, which makes our approach more extensible to low-resource languages than traditional approaches based on parallel corpora. We evaluated a number of reinforcement learning algorithms (including policy gradients and actor-critic) to solve the problem of translator’s policy optimization and found that our attempts yield some promising improvements over previous approaches to reinforcement-learning based zero-shot machine translation.

Keywords: agent-based language learning, low-resource translation, natural language inference, neural machine translation, reinforcement learning

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Authors: Habiba Ben Abdessalem

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The emergence of giant set of textual data was the push that has encouraged researchers to invest in this field. The purpose of textual data analysis methods is to facilitate access to such type of data by providing various graphic visualizations. Applying these methods requires a corpus pretreatment step, whose standards are set according to the objective of the problem studied. This step determines the forms list contained in contingency table by keeping only those information carriers. This step may, however, lead to noisy contingency tables, so the use of wavelet denoising function. The validity of the proposed approach is tested on a text database that offers economic and political events in Tunisia for a well definite period.

Keywords: textual data, wavelet, denoising, contingency table

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Authors: Vishwanath Pethri Kamath, Jayantha Gowda Sarapanahalli, Vishal Mishra, Siddhesh Balwant Bandgar

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Recognition of emotional information is essential in any form of communication. Growing HCI (Human-Computer Interaction) in recent times indicates the importance of understanding of emotions expressed and becomes crucial for improving the system or the interaction itself. In this research work, textual data for emotion recognition is used. The text being the least expressive amongst the multimodal resources poses various challenges such as contextual information and also sequential nature of the language construction. In this research work, the proposal is made for a neural architecture to resolve not less than 8 emotions from textual data sources derived from multiple datasets using google pre-trained word2vec word embeddings and a Multi-head attention-based bidirectional LSTM model with a one-vs-all Multi-Level Classification. The emotions targeted in this research are Anger, Disgust, Fear, Guilt, Joy, Sadness, Shame, and Surprise. Textual data from multiple datasets were used for this research work such as ISEAR, Go Emotions, Affect datasets for creating the emotions’ dataset. Data samples overlap or conflicts were considered with careful preprocessing. Our results show a significant improvement with the modeling architecture and as good as 10 points improvement in recognizing some emotions.

Keywords: text emotion recognition, bidirectional LSTM, multi-head attention, multi-level classification, google word2vec word embeddings

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Authors: Kriuk Boris, Kriuk Fedor

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This paper presents a comparative study of fundamental similarity functions for Siamese networks in semantic textual similarity (STS) tasks. We evaluate various similarity functions using the STS Benchmark dataset, analyzing their performance and stability. Additionally, we introduce a multi-objective approach for optimal threshold selection. Our findings provide insights into the effectiveness of different similarity functions and offer a straightforward method for threshold selection optimization, contributing to the advancement of Siamese network architectures in STS applications.

Keywords: siamese networks, semantic textual similarity, similarity functions, STS benchmark dataset, threshold selection

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Authors: Sadiq Aminu, Ahmed Lamido

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This research studies Barack Obama’s second inaugural speech using Halliday’s Systemic Functional Grammar (SFG). SFG is a text grammar which describes how language is used, so that the meaning of the text can be better understood. The primary source of data in this research work is Barack Obama’s second inaugural speech which was obtained from the internet. The analysis of the speech was based on the ideational and textual metafunctions of Systemic Functional Grammar. Specifically, the researcher analyses the Process Types and Participants (ideational) and the Theme/Rheme (textual). It was found that material process (process of doing) was the most frequently used ‘Process type’ and ‘We’ which refers to the people of America was the frequently used ‘Theme’. Application of the SFG theory, therefore, gives a better meaning to Barack Obama’s speech.

Keywords: ideational, metafunction, rheme, textual, theme

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Authors: Ricardo Avila, Gabriel Lopes, Vania Vidal, Jose Macedo

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This article offers a approach to the automatic discovery of semantic concepts and links in the domain of Oil Exploration and Production (E&P). Machine learning methods combined with textual pre-processing techniques were used to detect local patterns in texts and, thus, generate new concepts and new semantic links. Even using more specific vocabularies within the oil domain, our approach has achieved satisfactory results, suggesting that the proposal can be applied in other domains and languages, requiring only minor adjustments.

Keywords: semantic links, data mining, linked data, SKOS

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Authors: Bency Jacob, S.B. Waykar

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Degradations in historical document images appear due to aging of the documents. It is very difficult to understand and retrieve text from badly degraded documents as there is variation between the document foreground and background. Thresholding of such document images either result in broken characters or detection of false texts. Numerous algorithms exist that can separate text and background efficiently in the textual regions of the document; but portions of background are mistaken as text in areas that hardly contain any text. This paper presents a way to overcome these problems by a robust binarization technique that recovers the text from a severely degraded document images and thereby increases the accuracy of optical character recognition systems. The proposed document recovery algorithm efficiently removes degradations from document images. Here we are using the ostus method ,local thresholding and global thresholding and after the binarization training and recognizing the characters in the degraded documents.

Keywords: binarization, denoising, global thresholding, local thresholding, thresholding

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Authors: Ruijuan Bi

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Discrimination in insurance pricing is a topic of increasing concern, particularly in the context of the rapid development of big data and artificial intelligence. There is a need to explore the various forms of discrimination, such as direct and indirect discrimination, proxy discrimination, algorithmic discrimination, and unfair discrimination, and understand their implications in insurance pricing models. This paper aims to analyze and interpret the definitions of discrimination in insurance pricing and explore measures to reduce discrimination. It utilizes a textual analysis methodology, which involves gathering qualitative data from relevant literature on definitions of discrimination. The research methodology focuses on exploring the various forms of discrimination and their implications in insurance pricing models. Through textual analysis, this paper identifies the specific characteristics and implications of each form of discrimination in the general insurance industry. This research contributes to the theoretical understanding of discrimination in insurance pricing. By analyzing and interpreting relevant literature, this paper provides insights into the definitions of discrimination and the laws and regulations surrounding it. This theoretical foundation can inform future empirical research on discrimination in insurance pricing using relevant theories of probability theory.

Keywords: algorithmic discrimination, direct and indirect discrimination, proxy discrimination, unfair discrimination, insurance pricing

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Authors: Elham Shirvani–Ghadikolaei, Seyed Mahdi Sajjadi

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State of the art technology has the tremendous impact on our life, in this situation education system have been influenced as well as. In this paper, tried to compare two space of learning text and hypertext with each other, and some challenges of using hypertext in religious education. Regarding the fact that, hypertext is an undeniable part of learning in this world and it has highly beneficial for the education process from class to office and home. In this paper tried to solve this question: the consequences and challenges of applying hypertext in religious education. Also, the consequences of this survey demonstrate the role of curriculum designer and planner of education to solve this problem.

Keywords: Hyper-textual, learning, religious education, learning text

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Authors: Robert Saputra, Tomas Havlicek

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This study discusses the design of collaborative rural governance strategies to enhance the rural economy through Village-owned Enterprises (VOE) in Riau Province, Indonesia. Using Soft Systems Methodology (SSM) combined with Textual Network Analysis (TNA) in the Rich Picture stage of SSM, we investigated the current state of VOE management. Significant obstacles identified include insufficient business feasibility analyses, lack of managerial skills, misalignment between strategy and practice, and inadequate oversight. To address these challenges, we propose a collaborative strategy involving regional governments, academic institutions, NGOs, and the private sector. This strategy emphasizes community needs assessments, efficient resource mobilization, and targeted training programs. A dedicated working group will ensure continuous monitoring and iterative improvements. Our research highlights the novel integration of SSM with TNA, providing a robust framework for improving VOE management and demonstrating the potential of collaborative efforts in driving rural economic development.

Keywords: village-owned enterprises (VOE), rural economic development, soft system methodology (SSM), textual network analysis (TNA), collaborative governance

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Authors: Luisa Mesquita Oliveira Ribeiro, Alexei Manso Correa Machado

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Handwritten signature is a unique form for recognizing an individual, used to discern documents, carry out investigations in the criminal, legal, banking areas and other applications. Signature verification is based on large amounts of biometric data, as they are simple and easy to acquire, among other characteristics. Given this scenario, signature forgery is a worldwide recurring problem and fast and precise techniques are needed to prevent crimes of this nature from occurring. This article carried out a study on the efficiency of the Capsule Network in analyzing and recognizing signatures. The chosen architecture achieved an accuracy of 98.11% and 80.15% for the CEDAR and GPDS databases, respectively.

Keywords: biometrics, deep learning, handwriting, signature forgery

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Authors: Habibollah Mashhady, Moslem Fatollahi

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This study tries to contrast the use of metadiscoursal markers in English and Persian Newspaper Editorials as persuasive text types. These markers are linguistic elements in the text which do not add to the propositional content of it, rather they serve to realize the Halliday’s (1985) textual and interpersonal functions of language. At first, some of the most common markers from five subcategories of Text Connectives, Illocution Markers, Hedges, Emphatics, and Attitude Markers were identified in both English and Persian newspapers. Then, the frequency of occurrence of these markers in both English and Persian corpus consisting of 44 randomly selected editorials (18,000 words in each) from several English and Persian newspapers was recorded. After that, using a two-way chi square analysis, the overall x2 obs was found to be highly significant. So, the null hypothesis of no difference was confidently rejected. Finally, in order to determine the contribution of each subcategory to the overall x 2 value, one-way chi square analyses were applied to the individual subcategories. The results indicated that only two of the five subcategories of markers were statistically significant. This difference is then attributed to the differing spirits prevailing in the linguistic communities involved. Regarding the minor research question it was found that, in contrast to English writers, Persian writers are more writer-oriented in their writings.

Keywords: metadiscoursal markers, textual meta-function, interpersonal meta-function, persuasive texts, English and Persian newspaper editorials

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Authors: Wen Chen

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To investigate the interleaving effect of subject matter (mathematics vs. history) and perceptual modality (visual vs. auditory materials) on student’s attention and learning outcomes, the present study collected self-reported data on subjective cognitive load (SCL) and attention level, EEG data, and learning outcomes from micro-lectures. Eighty-one 7th grade students were randomly assigned to four learning conditions: blocked (by subject matter) micro-lectures with auditory textual information (B-A condition), blocked (by subject matter) micro-lectures with visual textual information (B-V condition), interleaved (by subject matter) micro-lectures with auditory textual information (I-A condition), and interleaved micro-lectures by both perceptual modality and subject matter (I-all condition). The results showed that although interleaved conditions may show advantages in certain indices, the I-all condition showed the best overall outcomes (best performance, low SCL, and high attention). This study suggests that interleaving by both subject matter and perceptual modality should be preferred in scheduling and planning classes.

Keywords: cognitive load, interleaving effect, micro-lectures, sustained attention

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Authors: Ma. Gracia Corazon Cayanan, Kai Yuen Cheong, Li Sha

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Training a language model for a minority language has been a challenging task. The lack of available corpora to train and fine-tune state-of-the-art language models is still a challenge in the area of Natural Language Processing (NLP). Moreover, the need for high computational resources and bulk data limit the attainment of this task. In this paper, we presented the following contributions: (1) we introduce and used a translation pair set of Tagalog and English (TL-EN) in pre-training a language model to a minority language resource; (2) we fine-tuned and evaluated top-ranking and pre-trained semantic textual similarity binary task (STSB) models, to both TL-EN and STS dataset pairs. (3) then, we reduced the size of the model to offset the need for high computational resources. Based on our results, the models that were pre-trained to translation pairs and STS pairs can perform well for STSB task. Also, having it reduced to a smaller dimension has no negative effect on the performance but rather has a notable increase on the similarity scores. Moreover, models that were pre-trained to a similar dataset have a tremendous effect on the model’s performance scores.

Keywords: semantic matching, semantic textual similarity binary task, low resource minority language, fine-tuning, dimension reduction, transformer models

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Authors: Shatha Khuzaee

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The study develops multimodal stylistic approach to analyse a number of BBC online news articles reporting some key events from the so called ‘Arab Uprisings’. Critical stylistics (CS) and visual grammar (VG) provide insightful arguments to the ways ideology is projected through different verbal and visual modes, yet they are mode specific because they examine how each mode projects its meaning separately and do not attempt to clarify what happens intersemiotically when the two modes co-occur. Therefore, it is the task undertaken in this research to propose multimodal stylistic approach that addresses the issue of ideology construction when the two modes co-occur. Informed by functional grammar and social semiotics, the analysis attempts to integrate three linguistic models developed in critical stylistics, namely, transitivity choices, prioritizing and hypothesizing along with their visual equivalents adopted from visual grammar to investigate the way ideology is constructed, in multimodal text, when text/image participate and interrelate in the process of meaning making on the textual level of analysis. The analysis provides comprehensive theoretical and analytical elaborations on the different points of integration between CS linguistic models and VG equivalents which operate on the textual level of analysis to better account for ideology construction in news as non-literary multimodal texts. It is argued that the analysis well thought out a plan that would remark the first step towards the integration between the well-established linguistic models of critical stylistics and that of visual analysis to analyse multimodal texts on the textual level. Both approaches are compatible to produce multimodal stylistic approach because they intend to analyse text and image depending on whatever textual evidence is available. This supports the analysis maintain the rigor and replicability needed for a stylistic analysis like the one undertaken in this study.

Keywords: multimodality, stylistics, visual grammar, social semiotics, functional grammar

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Authors: Lemac Tin

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This paper deals with the lyrical subject of the confessional poetry which is the main part of her stylistic strucuture. We concluded two types of this subject in the classical confessional poetic discourse; reflexive and authentic subject. We offer the model of their genesis, textual features and appeareance realisations. Genesis is related to the theories of deriving poetry from emotion and magic and their similar position in the primitive lyrics and lyrics of the ancient civilizations. Textual features are related to the emotive and semiotic analysis of each type. Appearance realisations of these two types are I-subject, We-subject, transvocal and objectified subject. We check this approaches on some of the poems from World literature.

Keywords: confessional poetry, confessional lyrical subject, magic, emotion, emotive analysis, semiotic analysis

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Authors: Sana Ammad

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The tradition of linguistic analysis in Islam and Christianity has developed independently of each other in lieu of the social developments specific to their historical context. However, recently increasing number of Muslim academics educated in the West have tried to apply the Western tradition of linguistic interpretation to the Qur’anic text while completely disregarding the Islamic linguistic tradition used and developed by the traditional scholars over the centuries. The aim of the paper is to outline the linguistic tools and methods used by the traditional Islamic scholars for the purpose of interpretating the Holy Qur’an and shed light on how they contribute towards a better understanding of the text compared to their Western counterparts. This paper carries out a descriptive-comparative study of the linguistic tools developed and perfected by the traditional scholars in Islam for the purpose of textual analysis of the Qur’an as they have been described in the authentic works of Usul Al Fiqh (Jurisprudence) and the principles of textual analysis employed by the Western hermeneutical tradition for the study of the Bible. First, it briefly outlines the independent historical development of the two traditions emphasizing the final normative shape that they have taken. Then it draws a comparison of the two traditions highlighting the similarities and the differences existing between them. In the end, the paper demonstrates the level of academic excellence achieved by the traditional linguistic scholars in their efforts to develop appropriate tools of textual interpretation and how these tools are more suitable for interpreting the Qur’an compared to the Western principles. Since the aim of interpreters of both the traditions is to try and attain an objective understanding of the Scriptures, the emphasis of the paper shall be to highlight how well the Islamic method of linguistic interpretation contributes to an objective understanding of the Qur’anic text. The paper concludes with the following findings: The Western hermeneutical tradition of linguistic analysis developed within the Western historical context. However, the Islamic method of linguistic analysis is much more highly developed and complex and serves better the purpose of objective understanding of the Holy text.

Keywords: Islamic jurisprudence, linguistic analysis, textual interpretation, western hermeneutics

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Authors: Yun-Ju Chiu, Feng-Yi Chen

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Kinematics is the beginning to learn mechanics in physics course. The concept of acceleration plays an important role in learning kinematics. Teachers usually instruct the conception through the formulas and graphs of kinematics and the well-known law F = ma. However, over the past few decades, a lot of researchers reveal numerous students’ difficulties in learning acceleration. One of these difficulties is that students frequently confuse acceleration with velocity and force. Why is the concept of acceleration so difficult to learn? The aim of this study is to understand the conceptual evolution of acceleration through the historical textual analysis. Text analysis and one-to-one interviews with high school students and teachers are used in this study. This study finds the history of science constructed from textbooks is usually quite different from the real evolution of history. For example, most teachers and students believe that the best-known law F = ma was written down by Newton. The expression of the second law is not F = ma in Newton’s best-known book Principia in 1687. Even after more than one hundred years, a famous Cambridge textbook titled An Elementary Treatise on Mechanics by Whewell of Trinity College did not express this law as F = ma. At that time of Whewell, the early mid-nineteenth century Britain, the concept of acceleration was not only ambiguous but also confused with the concept of force. The process of learning the concept of acceleration is analogous to its conceptual development in history. The study from the perspective of historical textual analysis will promote the understanding of the concept learning difficulties, the development of professional physics teaching, and the improvement of the context of physics textbooks.

Keywords: acceleration, textbooks, mechanics, misconception, history of science

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Authors: Yehudit Dror

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Parallelism¬, or as it is called in Arabic, al-muqābala, occupies a central position in the rhetorical discipline of ʻilm al-bayān. Parallelism is used as a figure of textual ornamentation or embellishment and can be divided into several types that are based on the semantics of parallelism and its formative structure. Parallelism in Arabic has received a considerable amount of attention from the Arab rhetorician, which enables understanding the essence of parallelism in Arabic – its types, structure and meaning. However, there are some lacunae in their descriptions concerning the function and thematic restrictions of parallelism in the Qur’ān. In my presentation, which focuses on grammatical parallelism where the two stichos of the parallelism are the same with respect to syntax and morphology, I will show that parallelism has some important roles in the textual arrangement; it may, for example, conclude a thematic section, indicate a turning point in the text or to clarify what has been said previously. In addition, it will be shown that parallelism is not used randomly in the Qurʼān but rather is restricted to repeated themes which carry the most important messages of the Qurʼān, such as God's Might or behavioral patterns of the believers and the non-believers; or it can be used as a stylistic device.

Keywords: grammatical parallelism, half-line, symmetry, Koran

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Authors: Samaneh Eshraghi Ivaria, Torkild Thellefsenb

Abstract:

The article discusses the significance of understanding the semiotics of future cities and recognizing the signs of cultural identity in contributing to the preservation of citizens' memories. The identities of citizens are conveyed through memories in urban planning, and with the rapid advancements in technology, cities are constantly changing. Therefore, preserving memories in the design of future cities is essential in maintaining a quality environment that reflects the citizens' identities. The article focuses on the semiotics of the movement pattern morphology in Mashhad city's historical area, using the historical interpretation method. The practice of walking to the shrine of Imam Reza as a religious building has been a historical and religious custom among Shiites from the past until now. By recognizing the signs that result from this religious and cultural approach on the morphology of the city, the aim of the research is to preserve the place of memories in future cities. Overall, the article highlights the importance of recognizing the cultural and historical significance of cities in designing future urban spaces. By doing so, it is possible to preserve the memories and identities of citizens, ensuring that the urban environment reflects the unique cultural heritage of a place.

Keywords: memories, future cities, movement pattern, mashhad, semiotics

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Authors: Sanda-Maria Ardeleanu, Ana Crăciunescu

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This paper is sustained by our previous research on discursive strategies, whichdemonstrated that tourismhas developed and employed apromotional languageper se. We have studied this concept within the framework of audio-visual advertising by analyzing its discursive structures at the level of three main strategies (textual, visual, and both textual and visual) and confirmed the applicability of the promotional language per se within the field. Tourism, at large, represents a largely potential interdisplinary field, which allowed us to use qualitative methods of research such as Discourse Analysis (DA). Due to further research which showed that in the third phase of qualitative research methodologies, scholars in tourism recognized semiotics and DA as potential paths to follow, but which were insufficiently explored at the time, we soon realized that the natural next step to take is to bring together common qualitative methodologies for both fields, such as the method of observation, the triangulation, Discourse Analysis, etc. Therefore and in the light of fast transformations of the medium that intermediates the message, in this paper, we are going to focus on the manifestations of the promotional language in social media texts, which advertise for the urban industry of accommodation in Romania. We shall constitute a corpus of study as the basis for our research methodology and, through the empirical method of observation and DA, we propose to recognize or discover new patterns developed at textual (mainly) and visual level or the mix of the two, known as strategies of the promotional language of tourism.

Keywords: discourse analysis, promotional language of tourism, social media, urban accommodation industry, tourism

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Authors: Muhammad Ilham Agus Salim

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The liberalization of Islamic thought is not only an impact on the views of Muslim community regarding worldview, but has touched the stage reconstruction of contemporary general science. It can be seen from the emergence of Western and Eastern intellectual movements that try to reconstruct contemporary science arguing that scientific culture is not currently able to deliver audiences to change the order of the better society. Such Islamic thought liberalization has a huge influence on the multidimensional crisis in various sectors such as the economic, culture, politic, ecology, and other sectors. Therefore, this paper examines the effects of the liberalization of contemporary Islamic thought towards on the development of modern science. The method used in this paper is based on textual study of Al -Qur'an, Hadith (prophetic tradition), and the history of contemporary Islamic thought and comparing it with the reality of the development of science today. So the influence of Islamic thought liberalization has created a crisis and stagnation of the development of scientific disciplines can be found.

Keywords: liberalization, science, Islam, al-Qur’an textual studies

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Authors: Oke Oktavianty, Tadayuki Kyoutani, Shigeyuki Haruyama, Junji Kaneko, Ken Kaminishi

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Modelica has many advantages and it is very useful in modeling and simulation especially for the multi-domain with a complex technical system. However, the big obstacle for a beginner is to understand the basic concept and to build a new system model for a real system. In order to understand how to solve the simple circuit model by hand translation and to get a better understanding of how modelica works, we provide a detailed explanation about solver ordering system in horizontal and vertical sorting and make some proposals for improvement. In this study, some difficulties in using modelica software with the original concept and the comparison with Finite Element Method (FEM) approach is discussed. We also present our textual modeling approach using FEM concept for acausal and causal model construction. Furthermore, simulation results are provided that demonstrate the comparison between using textual modeling with original coding in modelica and FEM concept.

Keywords: FEM, a causal model, modelica, horizontal and vertical sorting

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Authors: Oshin Anand, Atanu Rakshit

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The vast increment in the e-commerce ventures makes this area a prominent research stream. Besides several quantified parameters, the textual content of reviews is a storehouse of many information that can educate companies and help them earn profit. This study is an attempt in this direction. The article attempts to categorize data based on a computed metric that quantifies the influencing capacity of reviews rendering two categories of high and low influential reviews. Further, each of these document is studied to conclude several product feature categories. Each of these categories along with the computed metric is converted to linguistic identifiers and are used in an association mining model. The article makes a novel attempt to combine feature attraction with quantified metric to categorize review text and finally provide frequent patterns that depict customer preferences. Frequent mentions in a highly influential score depict customer likes or preferred features in the product whereas prominent pattern in low influencing reviews highlights what is not important for customers. This is achieved using a hybrid approach of text mining for feature and term extraction, sentiment analysis, multicriteria decision-making technique and association mining model.

Keywords: association mining, customer preference, frequent pattern, online reviews, text mining

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Authors: F. L. Bernardo, R. A. S. Zacarias

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The ways to translate are manifold since textual genres undergoing translations are diverse. In this essay, our goal is to give special attention to the literary genre and to the online translation tool Google Translate (GT), widely used either by nonprofessionals or by scholars, in order to show evidence of the indispensability of human wit in a good translation. Our study has its basis on a literary review of prominent authors, with emphasis on translation categories. Also highlighting the issue of polysemous literary translation, we aim to shed light on the translator’s craft and the fallible nature of online translation. To better illustrate these principles, the methodology consisted on performing a comparative analysis involving the original text Moll Flanders by Daniel Defoe in English to its online translation given by GT and to a translation into Brazilian Portuguese performed by a human. We proceeded to identifying and analyzing the degrees of textual equivalence according to the following categories: volume, levels and order. The results have attested the unsuitability in a translation done by a computer connected to the World Wide Web.

Keywords: Google Translator, human translation, literary translation, Moll Flanders

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Authors: Masood Varshosaz, Kamyar Hasanpour

Abstract:

In recent years, we have seen growing interest in recognizing humans in drone images for post-disaster search and rescue operations. Deep learning algorithms have shown great promise in this area, but they often require large amounts of labeled data to train the models. To keep the data acquisition cost low, augmentation techniques can be used to create additional data from existing images. There are many techniques of such that can help generate variations of an original image to improve the performance of deep learning algorithms. While data augmentation is potentially assumed to improve the accuracy and robustness of the models, it is important to ensure that the performance gains are not outweighed by the additional computational cost or complexity of implementing the techniques. To this end, it is important to evaluate the impact of data augmentation on the performance of the deep learning models. In this paper, we evaluated the most currently available 2D data augmentation techniques on a standard convolutional network which was trained for recognizing humans in drone images. The techniques include rotation, scaling, random cropping, flipping, shifting, and their combination. The results showed that the augmented models perform 1-3% better compared to a base network. However, as the augmented images only contain the human parts already visible in the original images, a new data augmentation approach is needed to include the invisible parts of the human body. Thus, we suggest a new method that employs simulated 3D human models to generate new data for training the network.

Keywords: human recognition, deep learning, drones, disaster mitigation

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Authors: Ashwani Kumar

Abstract:

In human beings, eyes play a vital role. A very less research has been done for rehabilitation of blindness for the blind people. This paper discusses the work that helps blind people for recognizing the basic shapes of the objects like circle, square, triangle, horizontal lines, vertical lines, diagonal lines and the wave forms like sinusoidal, square, triangular etc. This is largely achieved by using a digital camera, which is used to capture the visual information present in front of the blind person and a software program, which achieves the image processing operations, and finally the processed image is converted into sound. After the sound generation process, the generated sound is fed to the blind person through headphones for visualizing the imaginary image of the object. For visualizing the imaginary image of the object, it needs to train the blind person. Various training process methods had been applied for recognizing the object.

Keywords: image processing, pixel, pitch, loudness, sound generation, edge detection, brightness

Procedia PDF Downloads 388