Search results for: text embedding

Authors: Wadzani Dauda Palnam, Alao S. Emmanuel Laykay, Afiniki Bawa Zarafi, Olufunmilola Alabi, Dora N. Iortsuun

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Onion production is affected by several diseases including fusariosis. Study was conducted to investigate the histopathological features of different onion tissues infected with Fusarium equiseti by inoculation with soil drench, root dip and mycelia paste methods. This was carried out by fixation, dehydration, clearing, wax embedding, sectioning, staining and mounting of leaf and root sections for microscopical examination at 400x. Once infection occurred in the roots, the pathogen moved through the vascular system to colonize the whole plant. At first, it grew in the intercellular spaces of the root cortex but soon invaded the cells, followed by colonization of the cells by its hyphae and microconidia. At later stages of infection, the cortex tissue became completely disorganized and decomposed as the pathogen advance to the shoot system via the vessel elements; this may be responsible for the early wilting symptom of infected plants arising from the severe water stress due to blockage of the xylem tissues.

Keywords: onion, histopathology, infection, fusaria, inoculation

Procedia PDF Downloads 267

Authors: Jia-Jane Shuai

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Purpose of this research aims to improve understanding of the nature of online religion by examining the religious social websites. What motivates individual users to use the online religious social websites, and which factors affect those motivations. We survey various online religious social websites provided by different religions, especially the Taiwanese folk religion. Based on the theory of the Content Analysis and Social Network Analysis, religious social websites and religious web activities are examined. This research examined the folk religion websites’ presentation and contents that promote the religious use of the Internet in Taiwan. The difference among different religions and religious websites also be compared. First, this study used keywords to examine what types of messages gained the most clicks of “Like”, “Share” and comments on Facebook. Dividing the messages into four media types, namely, text, link, video, and photo, reveal which category receive more likes and comments than the others. Meanwhile, this study analyzed the five dialogic principles of religious websites accessed from mobile phones and also assessed their mobile readiness. Using the five principles of dialogic theory as a basis, do a general survey on the websites with elements of online religion. Second, the project analyzed the characteristics of Taiwanese participants for online religious activities. Grounded by social network analysis and text mining, this study comparatively explores the network structure, interaction pattern, and geographic distribution of users involved in communication networks of the folk religion in social websites and mobile sites. We studied the linkage preference of different religious groups. The difference among different religions and religious websites also be compared. We examined the reasons for the success of these websites, as well as reasons why young users accept new religious media. The outcome of the research will be useful for online religious service providers and non-profit organizations to manage social websites and internet marketing.

Keywords: content analysis, online religion, social network analysis, social websites

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Authors: Mehrnaz Mostafavi

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The assessment and categorization of incidental lung nodules present a considerable challenge in healthcare, often necessitating resource-intensive multiple computed tomography (CT) scans for growth confirmation. This research addresses this issue by introducing a distinct computational approach leveraging radiomics and deep-learning methods. However, understanding local services is essential before implementing these advancements. With diverse tracking methods in place, there is a need for efficient and accurate identification approaches, especially in the context of managing lung nodules alongside pre-existing cancer scenarios. This study explores the integration of text-based algorithms in medical data curation, indicating their efficacy in conjunction with machine learning and deep-learning models for identifying lung nodules. Combining medical images with text data has demonstrated superior data retrieval compared to using each modality independently. While deep learning and text analysis show potential in detecting previously missed nodules, challenges persist, such as increased false positives. The presented research introduces a Structured-Query-Language (SQL) algorithm designed for identifying pulmonary nodules in a tertiary cancer center, externally validated at another hospital. Leveraging natural language processing (NLP) and machine learning, the algorithm categorizes lung nodule reports based on sentence features, aiming to facilitate research and assess clinical pathways. The hypothesis posits that the algorithm can accurately identify lung nodule CT scans and predict concerning nodule features using machine-learning classifiers. Through a retrospective observational study spanning a decade, CT scan reports were collected, and an algorithm was developed to extract and classify data. Results underscore the complexity of lung nodule cohorts in cancer centers, emphasizing the importance of careful evaluation before assuming a metastatic origin. The SQL and NLP algorithms demonstrated high accuracy in identifying lung nodule sentences, indicating potential for local service evaluation and research dataset creation. Machine-learning models exhibited strong accuracy in predicting concerning changes in lung nodule scan reports. While limitations include variability in disease group attribution, the potential for correlation rather than causality in clinical findings, and the need for further external validation, the algorithm's accuracy and potential to support clinical decision-making and healthcare automation represent a significant stride in lung nodule management and research.

Keywords: lung cancer diagnosis, structured-query-language (SQL), natural language processing (NLP), machine learning, CT scans

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Authors: Julia Ive, Lucia Specia

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Understanding and detecting toxicity is an important problem to support safer human interactions online. Our work focuses on the important problem of contextual toxicity detection, where automated classifiers are tasked with determining whether a short textual segment (usually a sentence) is toxic within its conversational context. We use “toxicity” as an umbrella term to denote a number of variants commonly named in the literature, including hate, abuse, offence, among others. Detecting toxicity in context is a non-trivial problem and has been addressed by very few previous studies. These previous studies have analysed the influence of conversational context in human perception of toxicity in controlled experiments and concluded that humans rarely change their judgements in the presence of context. They have also evaluated contextual detection models based on state-of-the-art Deep Learning and Natural Language Processing (NLP) techniques. Counterintuitively, they reached the general conclusion that computational models tend to suffer performance degradation in the presence of context. We challenge these empirical observations by devising better contextual predictive models that also rely on NLP data augmentation techniques to create larger and better data. In our study, we start by further analysing the human perception of toxicity in conversational data (i.e., tweets), in the absence versus presence of context, in this case, previous tweets in the same conversational thread. We observed that the conclusions of previous work on human perception are mainly due to data issues: The contextual data available does not provide sufficient evidence that context is indeed important (even for humans). The data problem is common in current toxicity datasets: cases labelled as toxic are either obviously toxic (i.e., overt toxicity with swear, racist, etc. words), and thus context does is not needed for a decision, or are ambiguous, vague or unclear even in the presence of context; in addition, the data contains labeling inconsistencies. To address this problem, we propose to automatically generate contextual samples where toxicity is not obvious (i.e., covert cases) without context or where different contexts can lead to different toxicity judgements for the same tweet. We generate toxic and non-toxic utterances conditioned on the context or on target tweets using a range of techniques for controlled text generation(e.g., Generative Adversarial Networks and steering techniques). On the contextual detection models, we posit that their poor performance is due to limitations on both of the data they are trained on (same problems stated above) and the architectures they use, which are not able to leverage context in effective ways. To improve on that, we propose text classification architectures that take the hierarchy of conversational utterances into account. In experiments benchmarking ours against previous models on existing and automatically generated data, we show that both data and architectural choices are very important. Our model achieves substantial performance improvements as compared to the baselines that are non-contextual or contextual but agnostic of the conversation structure.

Keywords: contextual toxicity detection, data augmentation, hierarchical text classification models, natural language processing

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Authors: Steve Haberlin

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United States college students are experiencing record-high stress and anxiety rates, and due to technological advances, there are more distractions in the classroom. With these challenges comes the need to explore additional, non-traditional pedagogical strategies that can help students de-stress, become centered, and feel more deeply connected to content. In addition, embedding contemplative practices, such as mindfulness meditation, in the higher education classroom could assist faculty in presenting a more holistic education that encourages students to develop self-awareness, emotional intelligence, compassion, interconnectedness, and other “non-academic” qualities. Brief meditation may help students de-stress, focus, and connect. A facilitation guide could also help faculty implement classroom meditation practices; however, additional research is needed to determine how to best train faculty, what meditation techniques work best with students, and how to handle resistance. In this paper, a two-phase study is presented that involves a mindfulness meditation intervention with 180 undergraduate students at a private college in the southeastern United States. Data were collected through qualitative surveys and journaling and analyzed for themes. Findings included a majority of students reporting improved calm, reduced stress, and increased focus and ability to transition to classroom instruction.

Keywords: college students, higher education, mindfulness meditation, stress

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Authors: Finbarr Murphy, Wei Xu, Xian Xu

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The embedding of digital technologies in the global economy has attracted increasing attention from economists. With a large and detailed dataset, this study examines the specific case where consumers have a choice between offline and digital channels in the context of insurance purchases. We find that digital channels screen consumers with lower unobserved risk. For the term life, endowment, and disease insurance products, the average risk of the policies purchased through digital channels was 75%, 21%, and 31%, respectively, lower than those purchased offline. As a consequence, the lower unobserved risk leads to weaker information asymmetry and higher profitability of digital channels. We highlight three mechanisms of the risk screening effect: heterogeneous marginal influence of channel features on insurance demand, the channel features directly related to risk control, and the link between the digital divide and risk. We also find that the risk screening effect mainly comes from the extensive margin, i.e., from new consumers. This paper contributes to three connected areas in the insurance context: the heterogeneous economic impacts of digital technology adoption, insurer-side risk selection, and insurance marketing.

Keywords: digital economy, information asymmetry, insurance, mobile application, risk screening

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Authors: Vahid Ebrahimipour

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Word representation and context meaning of text-based documents play an essential role in knowledge modeling. Business procedures written in natural language are meant to store technical and engineering information, management decision and operation experience during the production system life cycle. Context meaning representation is highly dependent upon word sense, lexical relativity, and sematic features of the argument. This paper proposes a method for lexical semantic analysis and context meaning representation of maintenance activity in a mass production system. Our approach constructs a straightforward lexical semantic approach to analyze facilitates semantic and syntactic features of context structure of maintenance report to facilitate translation, interpretation, and conversion of human-readable interpretation into computer-readable representation and understandable with less heterogeneity and ambiguity. The methodology will enable users to obtain a representation format that maximizes shareability and accessibility for multi-purpose usage. It provides a contextualized structure to obtain a generic context model that can be utilized during the system life cycle. At first, it employs a co-occurrence-based clustering framework to recognize a group of highly frequent contextual features that correspond to a maintenance report text. Then the keywords are identified for syntactic and semantic extraction analysis. The analysis exercises causality-driven logic of keywords’ senses to divulge the structural and meaning dependency relationships between the words in a context. The output is a word contextualized representation of maintenance activity accommodating computer-based representation and inference using OWL/RDF.

Keywords: lexical semantic analysis, metadata modeling, contextual meaning extraction, ontology modeling, knowledge representation

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Authors: Edemealem Desalegn Kingawa, Kafte Tasew Timkete, Mekashaw Girmaw Abebe, Terefe Feyisa, Abiyot Bitew Mihretie, Senait Teklemarkos Haile

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Currently, hate speech is a growing challenge for society, individuals, policymakers, and researchers, as social media platforms make it easy to anonymously create and grow online friends and followers and provide an online forum for debate about specific issues of community life, culture, politics, and others. Despite this, research on identifying and detecting hate speech is not satisfactory performance, and this is why future research on this issue is constantly called for. This paper provides a systematic review of the literature in this field, with a focus on approaches like word embedding techniques, machine learning, deep learning technologies, hate speech terminology, and other state-of-the-art technologies with challenges. In this paper, we have made a systematic review of the last six years of literature from Research Gate and Google Scholar. Furthermore, limitations, along with algorithm selection and use challenges, data collection, and cleaning challenges, and future research directions, are discussed in detail.

Keywords: Amharic hate speech, deep learning approach, hate speech detection review, Afaan Oromo hate speech detection

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Authors: Mingyang Sun, Guozheng Yan, Dasheng Liu, Lei Yang

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Driven by the demand of intelligent monitoring in rehabilitation centers or hospitals, a high accuracy real-time location system based on UWB (ultra-wideband) technology was proposed. The system measures precise location of a specific person, traces his movement and visualizes his trajectory on the screen for doctors or administrators. Therefore, doctors could view the position of the patient at any time and find them immediately and exactly when something emergent happens. In our design process, different algorithms were discussed, and their errors were analyzed. In addition, we discussed about a , simple but effective way of correcting the antenna delay error, which turned out to be effective. By choosing the best algorithm and correcting errors with corresponding methods, the system attained a good accuracy. Experiments indicated that the ranging error of the system is lower than 7 cm, the locating error is lower than 20 cm, and the refresh rate exceeds 5 times per second. In future works, by embedding the system in wearable IoT (Internet of Things) devices, it could provide not only physical parameters, but also the activity status of the patient, which would help doctors a lot in performing healthcare.

Keywords: intelligent monitoring, ultra-wideband technology, real-time location, IoT devices, smart healthcare

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Authors: Mahmoud B. Rokaya

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The study of opinion mining and sentiment analysis includes analysis of opinions, sentiments, evaluations, attitudes, and emotions. The rapid growth of social media, social networks, reviews, forum discussions, microblogs, and Twitter, leads to a parallel growth in the field of sentiment analysis. The field of sentiment analysis tries to develop effective tools to make it possible to capture the trends of people. There are two approaches in the field, lexicon-based and corpus-based methods. A lexicon-based method uses a sentiment lexicon which includes sentiment words and phrases with assigned numeric scores. These scores reveal if sentiment phrases are positive or negative, their intensity, and/or their emotional orientations. Creation of manual lexicons is hard. This brings the need for adaptive automated methods for generating a lexicon. The proposed method generates dynamic lexicons based on the corpus and then classifies text using these lexicons. In the proposed method, different approaches are combined to generate lexicons from text. The proposed method classifies the tweets into 5 classes instead of +ve or –ve classes. The sentiment classification problem is written as an optimization problem, finding optimum sentiment lexicons are the goal of the optimization process. The solution was produced based on mathematical programming approaches to find the best lexicon to classify texts. A genetic algorithm was written to find the optimal lexicon. Then, extraction of a meta-level feature was done based on the optimal lexicon. The experiments were conducted on several datasets. Results, in terms of accuracy, recall and F measure, outperformed the state-of-the-art methods proposed in the literature in some of the datasets. A better understanding of the Arabic language and culture of Arab Twitter users and sentiment orientation of words in different contexts can be achieved based on the sentiment lexicons proposed by the algorithm.

Keywords: social media, Twitter sentiment, sentiment analysis, lexicon, genetic algorithm, evolutionary computation

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Authors: Fu Jinyu, Lin Jinguan

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This paper introduces a novel approach that unifies two types of models: one is the continuous-time jump-diffusion used to model high-frequency data, and the other is discrete-time GQARCH employed to model low-frequency financial data by embedding the discrete GQARCH structure with jumps in the instantaneous volatility process. This model is named “GQARCH-It\^{o} -Jumps mode.” We adopt the realized range-based threshold estimation for high-frequency financial data rather than the realized return-based volatility estimators, which entail the loss of intra-day information of the price movement. Meanwhile, a quasi-likelihood function for the low-frequency GQARCH structure with jumps is developed for the parametric estimate. The asymptotic theories are mainly established for the proposed estimators in the case of finite activity jumps. Moreover, simulation studies are implemented to check the finite sample performance of the proposed methodology. Specifically, it is demonstrated that how our proposed approaches can be practically used on some financial data.

Keywords: It\^{o} process, GQARCH, leverage effects, threshold, realized range-based volatility estimator, quasi-maximum likelihood estimate

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Authors: Sadeq Al Yaari, Muhammad Alkhunayn, Adham Al Yaari, Aayah Al Yaari, Montaha Al Yaari, Ayman Al Yaari, Sajedah Al Yaari, Fatehi Eissa

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The recent rise of smart educational aids and the move away from traditional listening aids are leading to a fundamental shift in the way in which individuals with attention and listening problems (ALP) manipulate listening inputs and/or act appropriately to the spoken information presented to them. A total sample of twenty-six ALP pupils (m=20 and f=6) between 7-12 years old was selected from different strata based on gender, region and school. In the sample size, thirteen (10 males and 3 females) received the treatment in terms of smart classes provided with smart educational aids in a listening course that lasted for four months, while others did not (they studied the same course by the same instructor but in ordinary class). A pretest was administered to assess participants’ levels, and a posttest was given to evaluate their attention and listening comprehension performance, namely in phonetic and phonological tests with sociolinguistic themes that have been designed for this purpose. Test results were analyzed both psychoneurolinguistically and statistically. Results reveal a remarkable change in pupils’ behavioral listening where scores witnessed a significant difference in the performance of the experimental ALP group in the pretest compared to the posttest (Pupils performed better at the pretest-posttest on phonetics than at the two tests on phonology). It is concluded that smart educational aids designed for listening skills help not only increase the listening command of pupils with ALP to understand what they listen to but also develop their interactive listening capability and, at the same rate, are responsible for increasing concentrated and in-depth listening capacity. Plus, ALP pupils become able to grasp the audio content of text recordings, including educational audio recordings, news, oral stories and tales, views, spiritual/religious text and general knowledge. However, the pupils have not experienced individual smart audio-visual aids that connect listening to other language receptive and productive skills, which could be the future area of research.

Keywords: smart aids, attention, listening, problems

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Authors: Ahmed Mamdouh

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Introduction: The era of Thutmosis III represents a transitional period between the art of the Thutmoside art and the Amarna period, so we intend to declare that it serves as the cradle of Amarna art. The study will examine the Statues of king Thutmose III that was discovered in Luxor by an Egyptian mission. These Statues have been transferred to the Conservation Center of the Grand Egyptian Museum (GEM) to be conserved and made ready to bedisplayed at the new museum (the project of the century). We focus upon three Statues (GEM numbers 45863, 45864, 45865), chosen because they relate to different years of the king's reign. These Statues were all made of granite. The first one is a Kneeling statue representing the god Amun showing king Thutmose III offering to the goddess Hathor. The second is decorated with king Thutmose III with the red crown, between the goddess Hathor and the royal wife, Nefertari. The third shows the king offering NW vessels and bread to the god Seker. Each Statue is divided into registers containing a description and decorated with scenes of the king presenting offerings to gods. Methodology: The proposed study will focus on the development which happened sequentially according to differences that occur in each Statue. We will use comparative research to determine the workshops of these statues, whether one or several, and what are the distinguishing features of each one. We will examine what innovations the artisans added to royal art. The description and the texts will be translated with linguistic comments. This research focuses on text analyses and technology. Paleographic information found on these objects includes the names and titles of the king. Conclusion: This research focuses on text analyses and technology. The study aims to create a manual that may help in dating the artwork of Thutmosis III. This research will be beneficial and useful for heritage and ancient civilizations, particularly when we talk about opening museums like the Grand Egyptian museum, which will exhibit a collection of statues. Indeed this kind of study will open a new destination in order to know how to identify these collections and how to exhibit them commensurate with the nature of ancient Egyptian history and heritage.

Keywords: archaeological study, Giza, new kingdom, statues, royal art

Procedia PDF Downloads 56

Authors: Sadeq Al Yaari, Muhammad Alkhunayn, Adham Al Yaari, Ayah Al Yaari, Ayman Al Yaari, Montaha Al Yaari, Sajedah Al Yaari, Fatehi Eissa

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The recent rise of smart educational aids and the move away from traditional listening aids are leading to a fundamental shift in the way in which individuals with attention and listening problems (ALP) manipulate listening inputs and/or act appropriately to the spoken information presented to them. A total sample of twenty-six ALP pupils (m=20 and f=6) between 7-12 years old was selected from different strata based on gender, region and school. In the sample size, thirteen (10 males and 3 females) received the treatment in terms of smart classes provided with smart educational aids in a listening course that lasted for four months, while others did not (they studied the same course by the same instructor but in ordinary class). A pretest was administered to assess participants’ levels, and a posttest was given to evaluate their attention and listening comprehension performance, namely in phonetic and phonological tests with sociolinguistic themes that have been designed for this purpose. Test results were analyzed both psychoneurolinguistically and statistically. Results reveal a remarkable change in pupils’ behavioral listening where scores witnessed a significant difference in the performance of the experimental ALP group in the pretest compared to the posttest (Pupils performed better at the pretest-posttest on phonetics than at the two tests on phonology). It is concluded that smart educational aids designed for listening skills help not only increase the listening command of pupils with ALP to understand what they listen to but also develop their interactive listening capability and, at the same rate, are responsible for increasing concentrated and in-depth listening capacity. Plus, ALP pupils become able to grasp the audio content of text recordings, including educational audio recordings, news, oral stories and tales, views, spiritual/religious text and general knowledge. However, the pupils have not experienced individual smart audio-visual aids that connect listening to other language receptive and productive skills, which could be the future area of research.

Keywords: smart educational aids, listening attention, pupils, problems

Procedia PDF Downloads 38

Authors: Giovana Perini-Loureiro

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The purpose of this research was to identify whether the nominalizations terminating in -TION in the academic discourse of native English speakers contain the arguments required by their input verbs. In the perspective of functional linguistics, ideational metaphors, with nominalization as their most pervasive realization, are lexically dense, and therefore frequent in formal texts. Ideational metaphors allow the academic genre to instantiate objectification, de-personalization, and the ability to construct a chain of arguments. The valence of those nouns present in nominalizations tends to maintain the same elements of the valence from its original verbs, but these arguments are not always expressed. The initial hypothesis was that these arguments would also be present alongside the nominalizations, through anaphora or cataphora. In this study, a qualitative analysis of the occurrences of the five more frequent nominalized terminations in -TION in academic texts was accomplished, and thus a verification of the occurrences of the arguments required by the original verbs. The assembling of the concordance lines was done through COCA (Corpus of Contemporary American English). After identifying the five most frequent nominalizations (attention, action, participation, instruction, intervention), the concordance lines were selected at random to be analyzed, assuring the representativeness and reliability of the sample. It was possible to verify, in all the analyzed instances, the presence of arguments. In most instances, the arguments were not expressed, but recoverable, either in the context or in the shared knowledge among the interactants. It was concluded that the realizations of the arguments which were not expressed alongside the nominalizations are part of a continuum, starting from the immediate context with anaphora and cataphora; up to a knowledge shared outside the text, such as specific area knowledge. The study also has implications for the teaching of academic writing, especially with regards to the impact of nominalizations on the thematic and informational flow of the text. Grammatical metaphors are essential to academic writing, hence acknowledging the occurrence of its arguments is paramount to achieve linguistic awareness and the writing prestige required by the academy.

Keywords: corpus, functional linguistics, grammatical metaphors, nominalizations, academic English

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Authors: Ling Qi, Matloob Khushi, Josiah Poon

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This paper introduces a pre-trained Masked Candlestick Model (MCM) for trading time-series data. The pre-trained model is based on three core designs. First, we convert trading price data at each data point as a set of normalized elements and produce embeddings of each element. Second, we generate a masked sequence of such embedded elements as inputs for self-supervised learning. Third, we use the encoder mechanism from the transformer to train the inputs. The masked model learns the contextual relations among the sequence of embedded elements, which can aid downstream classification tasks. To evaluate the performance of the pre-trained model, we fine-tune MCM for three different downstream classification tasks to predict future price trends. The fine-tuned models achieved better accuracy rates for all three tasks than the baseline models. To better analyze the effectiveness of MCM, we test the same architecture for three currency pairs, namely EUR/GBP, AUD/USD, and EUR/JPY. The experimentation results demonstrate MCM’s effectiveness on all three currency pairs and indicate the MCM’s capability for signal extraction from trading data.

Keywords: masked language model, transformer, time series prediction, trading prediction, embedding, transfer learning, self-supervised learning

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Authors: Afaque Manzoor Soomro, Faheem Ahmed, Fida Hussain Memon, Kyung Hyun Choi

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This paper proposes the bioinspired soft frog robot. All printing technology was used for the fabrication of the robot. Polyjet printing was used to print the front and back limbs, while ultrathin filament was used to print the body of the robot, which makes it a complete soft swimming robot. The dual thrust generation approach has been proposed by embedding the main muscle and antagonistic muscle in all the limbs, which enables it to attain high speed (18 mm/s), and significant control of swimming in dual modes (synchronous and asynchronous modes). To achieve the swimming motion of the frog, the design, motivated by the rigorous modelling and real frog dynamics analysis, enabled the as-developed frog robot (FROBOT) to swim at a significant level of consistency with the real frog. The FROBOT (weighing 65 g) can swim at different controllable frequencies (0.5–2Hz) and can turn in any direction by following custom-made LabVIEW software’s commands which enables it to swim at speed up to 18 mm/s on the surface of deep water (100 cm) with excellent weight balance.

Keywords: soft robotics, soft actuator, frog robot, 3D printing

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Authors: Kalyani Gandhi

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The eighteenth century in Britain saw a great burst of activity in writing (letters, journals, newspapers, essays); often these modes of writing had a public-spirited bent in-step with the prevailing intellectual atmosphere. Mary Wollstonecraft was one of the leading intellectuals of that period who utilized letter writing to convey her thoughts on the exciting political developments of the late eighteenth century. Fusing together her anxieties and concerns about humanity in general and herself in particular, Wollstonecraft’s views of the world around her are filtered through the lens of her subjectivity. Thus, Wollstonecraft’s letters covered a wide range of topics on both the personal and political level (for the two are often entwined in Wollstonecraft’s characteristic style of analysis) such as sentiment, gender, nature, peasantry, the class system, the legal system, political duties and rights of both rulers and subjects, death, immortality, religion, family and education. Therefore, this paper intends to examine the manner in which Wollstonecraft utilizes letter-writing to constitute and develop Romantic self-hood, understand the world around her and illustrate her ideas on the political and social happenings in Europe. The primary text analyzed will be Mary Wollstonecraft's Letters Written During a Short Stay in Sweden, Denmark and Norway (1796) and the analysis of this text will be supplemented by researching 18th-century British letter writing culture, with a special emphasis on the epistolary habits of women. Within this larger framework, this paper intends to examine the manner in which this hybrid of travel and epistolary writing aided Mary Wollstonecraft's expression on Romantic selfhood and how it was complicated by ideas of gender. This paper reveals Wollstonecraft's text to be wrought with anxiety about the world around her and within her; thus, the personal-public nature of the epistolary format particularly suits her characteristic point of view that looks within and without. That is to say, Wollstonecraft’s anxieties about gender and self, are as much about the women she sees in the world around her as much as they are about her young daughter and herself. Wollstonecraft constantly explores and examines this anxiety within the different but interconnected realms of politics, economics, history and society. In fact, it is her complex technique of entwining these aforementioned concerns with a closer look at interpersonal relationships among men and women (she often mentions specific anecdotes and instances) that make Wollstonecraft's Letters so engaging and insightful. Thus, Wollstonecraft’s Letters is an exemplar of British Romantic writing due to the manner in which it explores the bond between the individual and society. Mary Wollstonecraft's nuances this exploration by incorporating her concerns about women and the playing out of gender in society. Thus, Wollstonecraft’s Letters is an invaluable contribution to the field of British Romanticism, particularly as it offers crucial insight on female Romantic writing that can broaden and enrich the current academic understanding of the field.

Keywords: British romanticism, letters, feminism, travel writing

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Authors: Heichia Wang, Yalan Chao

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Social inequality is a persistent problem. One of the ways to solve this problem is through education. At present, vulnerable groups are often less geographically accessible to educational resources. However, compared with educational resources, communication equipment is easier for vulnerable groups. Now that information and communication technology (ICT) has entered the field of education, today we can accept the convenience that ICT provides in education, and the mobility that it brings makes learning independent of time and place. With mobile learning, teachers and students can start discussions in an online chat room without the limitations of time or place. However, because liquidity learning is quite convenient, people tend to solve problems in short online texts with lack of detailed information in a lack of convenient online environment to express ideas. Therefore, the ICT education environment may cause misunderstanding between teachers and students. Therefore, in order to better understand each other's views between teachers and students, this study aims to clarify the essays of the analysts and classify the students into several types of learning questions to clarify the views of teachers and students. In addition, this study attempts to extend the description of possible omissions in short texts by using external resources prior to classification. In short, by applying a short text classification, this study can point out each student's learning problems and inform the instructor where the main focus of the future course is, thus improving the ICT education environment. In order to achieve the goals, this research uses convolutional neural network (CNN) method to analyze short discussion content between teachers and students in an ICT education environment. Divide students into several main types of learning problem groups to facilitate answering student problems. In addition, this study will further cluster sub-categories of each major learning type to indicate specific problems for each student. Unlike most neural network programs, this study attempts to extend short texts with external resources before classifying them to improve classification performance. In short, by applying the classification of short texts, we can point out the learning problems of each student and inform the instructors where the main focus of future courses will improve the ICT education environment. The data of the empirical process will be used to pre-process the chat records between teachers and students and the course materials. An action system will be set up to compare the most similar parts of the teaching material with each student's chat history to improve future classification performance. Later, the function of short text classification uses CNN to classify rich chat records into several major learning problems based on theory-driven titles. By applying these modules, this research hopes to clarify the main learning problems of students and inform teachers that they should focus on future teaching.

Keywords: ICT education improvement, social equality, short text analysis, convolutional neural network

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Authors: Eleanor K. P. Kwan

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English academic writing, it has been argued, is an acquired language even for English speakers. For research students whose English is not their first language, however, the acquisition process is often more challenging. Instead of hoping that students would acquire the conventions themselves through extensive reading, there is a need for the explicit teaching of linguistic conventions in academic writing, as explicit teaching could help students to be more aware of the different generic conventions in different disciplines in science. This paper presents an interuniversity effort to develop an online academic writing resource for research students in five subdisciplines in engineering, upon the completion of the needs analysis which indicates that students and faculty members are more concerned about students’ ability to organize an extended text than about grammatical accuracy per se. In particular, this paper focuses on the materials developed for thesis writing (also called dissertation writing in some tertiary institutions), as theses form an essential graduation requirement for all research students and this genre is also expected to demonstrate the writer’s competence in research and contributions to the research community. Drawing on Swalesian move analysis of research articles, this online resource includes authentic materials written by students and faculty members from the participating institutes. Highlight will be given to several aspects and challenges of developing this online resource. First, as the online resource aims at moving beyond providing instructions on academic writing, a range of interactive activities need to be designed to engage the users, which is one feature which differentiates this online resource from other equally informative websites on academic writing. Second, it will also include discussion on divergent textual practices in different subdisciplines, which help to illustrate different practices among these subdisciplines. Third, since theses, probably one of the most extended texts a research student will complete, require effective use of signposting devices to facility readers’ understanding, this online resource will also provide both explanation and activities on different components that contribute to text coherence. Finally results from piloting will also be included to shed light on the effectiveness of the materials, which could be useful for future development.

Keywords: academic writing, English for academic purposes, online language learning materials, scientific writing

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Authors: Anne N. Heirman, Lisette van der Molen, Richard Dirven, Gyorgi B. Halmos, Michiel W.M. van den Brekel

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Background: Due to the rising incidence of oropharyngeal squamous cell cancer (OPSCC), many patients are challenged with choosing between transoral(robotic) surgery and radiotherapy, with equal survival and oncological outcomes. Also, functional outcomes are of little difference over the years. With this study, the wants and needs of patients and caregivers are identified to develop a comprehensible patient decision aid (PDA). Methods: The development of this PDA is based on the International Patient Decision Aid Standards criteria. In phase 1, relevant literature was reviewed and compared to current counseling papers. We interviewed ten post-treatment patients and ten doctors from four head and neck centers in the Netherlands, which were transcribed verbatim and analyzed. With these results, the first draft of the PDA was developed. Phase 2 beholds testing the first draft for comprehensibility and usability. Phase 3 beholds testing for feasibility. After this phase, the final version of the PDA was developed. Results: All doctors and patients agreed a PDA was needed. Phase 1 showed that 50% of patients felt well-informed after standard care and 35% missed information about treatment possibilities. Side effects and functional outcomes were rated as the most important for decision-making. With this information, the first version was developed. Doctors and patients stated (phase 2) that they were satisfied with the comprehensibility and usability, but there was too much text. The PDA underwent text reduction revisions and got more graphics. After revisions, all doctors found the PDA feasible and would contribute to regular counseling. Patients were satisfied with the results and wished they would have seen it before their treatment. Conclusion: Decision-making for OPSCC should focus on differences in side-effects and functional outcomes. Patients and doctors found the PDA to be of great value. Future research will explore the benefits of the PDA in clinical practice.

Keywords: head-and-neck oncology, oropharyngeal cancer, patient decision aid, development, shared decision making

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Authors: Abhisek Dash

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This study had three neural networks, one for number segmentation, one for number detection and one for number recognition all of which are coupled to one another. All networks were trained on the MNIST dataset and were convolutional. It was assumed that the images had lighter background and darker foreground. The segmentation network took 28x28 images as input and had sixteen outputs. Segmentation training starts when a dark pixel is encountered. Taking a window(7x7) over that pixel as focus, the eight neighborhood of the focus was checked for further dark pixels. The segmentation network was then trained to move in those directions which had dark pixels. To this end the segmentation network had 16 outputs. They were arranged as “go east”, ”don’t go east ”, “go south east”, “don’t go south east”, “go south”, “don’t go south” and so on w.r.t focus window. The focus window was resized into a 28x28 image and the network was trained to consider those neighborhoods which had dark pixels. The neighborhoods which had dark pixels were pushed into a queue in a particular order. The neighborhoods were then popped one at a time stitched to the existing partial image of the number one at a time and trained on which neighborhoods to consider when the new partial image was presented. The above process was repeated until the image was fully covered by the 7x7 neighborhoods and there were no more uncovered black pixels. During testing the network scans and looks for the first dark pixel. From here on the network predicts which neighborhoods to consider and segments the image. After this step the group of neighborhoods are passed into the detection network. The detection network took 28x28 images as input and had two outputs denoting whether a number was detected or not. Since the ground truth of the bounds of a number was known during training the detection network outputted in favor of number not found until the bounds were not met and vice versa. The recognition network was a standard CNN that also took 28x28 images and had 10 outputs for recognition of numbers from 0 to 9. This network was activated only when the detection network votes in favor of number detected. The above methodology could segment connected and overlapping numbers. Additionally the recognition unit was only invoked when a number was detected which minimized false positives. It also eliminated the need for rules of thumb as segmentation is learned. The strategy can also be extended to other characters as well.

Keywords: convolutional neural networks, OCR, text detection, text segmentation

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Authors: R. Rama Kishore, Sunesh

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This paper presents an optimized, robust, and secured watermarking technique. The methodology used in this work is the combination of entropy and chaotic grid map. The proposed methodology incorporates Discrete Cosine Transform (DCT) on the host image. To improve the imperceptibility of the method, the host image DCT blocks, where the watermark is to be embedded, are further optimized by considering the entropy of the blocks. Chaotic grid is used as a key to reorder the DCT blocks so that it will further increase security while selecting the watermark embedding locations and its sequence. Without a key, one cannot reveal the exact watermark from the watermarked image. The proposed method is implemented on four different images. It is concluded that the proposed method is giving better results in terms of imperceptibility measured through PSNR and found to be above 50. In order to prove the effectiveness of the method, the performance analysis is done after implementing different attacks on the watermarked images. It is found that the methodology is very strong against JPEG compression attack even with the quality parameter up to 15. The experimental results are confirming that the combination of entropy and chaotic grid map method is strong and secured to different image processing attacks.

Keywords: digital watermarking, discreate cosine transform, chaotic grid map, entropy

Procedia PDF Downloads 238

Authors: Eunjung Lee

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This research investigates the intricate connection between cultural differences and online consumer behaviors by integrating Hofstede's Cultural Dimensions theory with analysis methodologies such as text mining, data mining, and topic analysis. Our aim is to provide a comprehensive understanding of how national cultural differences influence individuals' behaviors when engaging with online reviews. To ensure the relevance of our investigation, we systematically analyze and interpret the cultural nuances influencing online consumer behaviors, especially in the context of online reviews. By anchoring our research in Hofstede's Cultural Dimensions theory, we seek to offer valuable insights for marketers to tailor their strategies based on the cultural preferences of diverse global consumer bases. In our methodology, we employ advanced text mining techniques to extract insights from a diverse range of online reviews gathered globally for a specific product or service like Netflix. This approach allows us to reveal hidden cultural cues in the language used by consumers from various backgrounds. Complementing text mining, data mining techniques are applied to extract meaningful patterns from online review datasets collected from different countries, aiming to unveil underlying structures and gain a deeper understanding of the impact of cultural differences on online consumer behaviors. The study also integrates topic analysis to identify recurring subjects, sentiments, and opinions within online reviews. Marketers can leverage these insights to inform the development of culturally sensitive strategies, enhance target audience segmentation, and refine messaging approaches aligned with cultural preferences. Anchored in Hofstede's Cultural Dimensions theory, our research employs sophisticated methodologies to delve into the intricate relationship between cultural differences and online consumer behaviors. Applied to specific cultural dimensions, such as individualism vs. collectivism, masculinity vs. femininity, uncertainty avoidance, and long-term vs. short-term orientation, the study uncovers nuanced insights. For example, in exploring individualism vs. collectivism, we examine how reviewers from individualistic cultures prioritize personal experiences while those from collectivistic cultures emphasize communal opinions. Similarly, within masculinity vs. femininity, we investigate whether distinct topics align with cultural notions, such as robust features in masculine cultures and user-friendliness in feminine cultures. Examining information-seeking behaviors under uncertainty avoidance reveals how cultures differ in seeking detailed information or providing succinct reviews based on their comfort with ambiguity. Additionally, in assessing long-term vs. short-term orientation, the research explores how cultural focus on enduring benefits or immediate gratification influences reviews. These concrete examples contribute to the theoretical enhancement of Hofstede's Cultural Dimensions theory, providing a detailed understanding of cultural impacts on online consumer behaviors. As online reviews become increasingly crucial in decision-making, this research not only contributes to the academic understanding of cultural influences but also proposes practical recommendations for enhancing online review systems. Marketers can leverage these findings to design targeted and culturally relevant strategies, ultimately enhancing their global marketing effectiveness and optimizing online review systems for maximum impact.

Keywords: comparative analysis, cultural dimensions, marketing intelligence, national culture, online consumer behavior, text mining

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Authors: Sadeq Al Yaari, Muhammad Alkhunayn, Aayah Al Yaari, Ayman Al Yaari, Montaha Al Yaari, Sajedah Al Yaari, Fatehi Eissa

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The recent rise of smart educational aids and the move away from traditional listening aids are leading to a fundamental shift in the way in which individuals with attention and listening problems (ALP) manipulate listening inputs and/or act appropriately to the spoken information presented to them. A total sample of twenty-six ALP pupils (m=20 and f=6) between 7-12 years old was selected from different strata based on gender, region and school. In the sample size, thirteen (10 males and 3 females) received the treatment in terms of smart classes provided with smart educational aids in a listening course that lasted for four month-semester while others did not (they studied the same course by the same instructor but in ordinary class). A pretest was administered to assess participants’ levels, and a posttest was given to evaluate their attention and listening comprehension performance, namely in phonetic and phonological tests with sociolinguistic themes that have been designed for this purpose. Test results were analyzed both psychoneurolinguistically and statistically. Results reveal a remarkable change in pupils’ behavioral listening where scores witnessed a significant difference in the performance of the experimental ALP group in the pretest compared to the posttest (Pupils performed better at the pretest-posttest on phonetics than at the two tests on phonology). It is concluded that smart educational aids designed for listening skills help not only increase the listening command of pupils with ALP to understand what they listen to but also develop their interactive listening capability and, at the same rate, are responsible for increasing concentrated and in-depth listening capacity. Plus, ALP pupils become able to grasp the audio content of text recordings, including educational audio recordings, news, oral stories and tales, views, spiritual/religious text and general knowledge. However, the pupils have not experienced individual smart audio-visual aids that connect listening to other language receptive and productive skills, which could be the future area of research.

Keywords: language skills, implementing, listening skill, attention, smart aids

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Authors: Yuntao Liu, Lei Wang, Haoran Xia

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Machine learning has shown extensive applications in the development of classification models for autism spectrum disorder (ASD) using neural image data. This paper proposes a fusion multi-modal classification network based on a graph neural network. First, the brain is segmented into 116 regions of interest using a medical segmentation template (AAL, Anatomical Automatic Labeling). The image features of sMRI and the signal features of fMRI are extracted, which build the node and edge embedding representations of the brain map. Then, we construct a dynamically updated brain map neural network and propose a method based on a dynamic brain map adjacency matrix update mechanism and learnable graph to further improve the accuracy of autism diagnosis and recognition results. Based on the Autism Brain Imaging Data Exchange I dataset(ABIDE I), we reached a prediction accuracy of 74% between ASD and TD subjects. Besides, to study the biomarkers that can help doctors analyze diseases and interpretability, we used the features by extracting the top five maximum and minimum ROI weights. This work provides a meaningful way for brain disorder identification.

Keywords: autism spectrum disorder, brain map, supervised machine learning, graph network, multimodal data, model interpretability

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Authors: Ganna Zakharova

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The persuasive influence of tourism promotional materials is very much experienced nowadays. In order to attract the attention of viewers, marketers choose various techniques in their digital texts. Video is an essential element for attraction and seduction; it is a trigger element for tourists. This solution for web marketing engages and convinces potential tourists to book a tourism product. Embedding video materials into a website provides useful information, create different feelings in viewers, and help them finalize their decisions. The present article discusses video solutions for health tourism websites used to allure potential tourists. The paper reviews the influential elements of persuasive tourism marketing videos. The article highlights how these components as persuasive strategies of tourism promotional materials can influence the decisions of tourism websites’ users. The result section provides the real examples of the deployment of the mentioned technique to convince the audience by the website of 'Karpaty' resort (Ukraine). This technique is worth attention as it plays an important role in the promotion of tourism services. The data collection of this study will provide updated information in relation to the rhetoric of tourism.

Keywords: tourism discourse, persuasive video, influential videos in marketing, persuasive discourse, tourism promotion

Procedia PDF Downloads 111

Authors: Samiullah Paracha, Sania Jehanzeb, M. H. Gharanai, A. R. Ahmadi, H.Sokout, Toshiro Takahara

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The purpose of this study is to examine how health consumers utilize pictures when developing an understanding of multimedia health documents, and whether attentional processes, measured by eye-tracking, relate to differences in health-related cognitive resources and passage comprehension. To investigate these issues, we will present health-related text-picture passages to elders and collect eye movement data to measure readers’ looking behaviors.

Keywords: multimedia, eye-tracking, consumer health informatics, human-computer interaction

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Authors: Georgios Alexandropoulos

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The purpose of this study is to examine the historical text of Alexiad by Anna Comnena using computational tools for the extraction of lexical bundles containing the name of her father, Alexius Comnenus. For this reason, in this research we apply corpus linguistics techniques for the automatic extraction of lexical bundles and through them we will draw conclusions about how these lexical bundles serve her support provided to her father.

Keywords: lexical bundles, computational literature, critical discourse analysis, Alexiad

Procedia PDF Downloads 607

Authors: Mai Hussein

Abstract:

'Littoral' is an original voice in Québécois theatre, spanning the cultural gaps that can exist between the playwrights’ native Lebanon, North America, Quebec, and Europe. Littoral is a 'crossroad' of cultures and themes, a 'bridge' connecting cultures and languages. It represents a new form of theatrical writing that combines the verbal, the vocal and the pantomimic, calling upon the stage to question the real, to engage characters in a quest, in a journey of mourning, of reconstructing identity and a collective memory despite ruins and wars. A theatre of witness, a theatre denouncing irrationality of racism and war, a theatre 'performing' the symptoms of the stress disorders of characters passing from resistance and anger to reconciliation and giving voice to the silenced victims, these are some of the pillars that this play has to offer. In this corrida between life and death, the identity seems like a work-in-progress that is shaped in the presence of the Self and the Other. This trajectory will lead to re-open widely the door to questions, interrogations, and reflections to show how this play is at the nexus of contemporary preoccupations of the 21st century: the importance of memory, the search for meaning, the pursuit of the infinite. It also shows how a play can create bridges between languages, cultures, societies, and movements. To what extent does it mediate between the words and the silence, and how does it burn the bridges or the gaps between the textual and the performative while investigating the power of intermediality to confront racism and segregation. It also underlines the centrality of confrontation between cultures, languages, writing and representation techniques to challenge the characters in their quest to restructure their shattered, but yet intertwined identities. The goal of this theatre would then be to invite everyone involved in the process of a journey of self-discovery away from their comfort zone. Everyone will have to explore the liminal space, to read in between the lines of the written text as well as in between the text and the performance to explore the gaps and the tensions that exist between what is said, and what is played, between the 'parole' and the performative body.

Keywords: identity, memory, performance, testimony, trauma

Procedia PDF Downloads 106