Search results for: text preprocessing

Authors: Roxana D. Maiorescu-Murphy

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In the US, corporations have recently embraced political stances in an attempt to respond to the external pressure exerted by activist groups. To date, research in this area remains in its infancy, and few studies have been conducted on the way stakeholder groups respond to corporate political advocacy in general and in the immediacy of such a corporate announcement in particular. The current study aims to fill in this research void. In addition, the study contributes to an emerging trajectory in the field of crisis management by focusing on the delineation between crises (unexpected events related to products and services) and scandals (crises that spur moral outrage). The present study looked at online reactions in the aftermath of Elon Musk’s endorsement of the Republican party on Twitter. Two data sets were collected from Twitter following two political endorsements made by Elon Musk on May 18, 2022, and June 15, 2022, respectively. The total sample of analysis stemming from the data two sets consisted of N=1,374 user comments written as a response to Musk’s initial tweets. Given the paucity of studies in the preceding research areas, the analysis employed a case study methodology, used in circumstances in which the phenomena to be studied had not been researched before. According to the case study methodology, which answers the questions of how and why a phenomenon occurs, this study responded to the research questions of how online users perceived Tesla and why they did so. The data were analyzed in NVivo by the use of the grounded theory methodology, which implied multiple exposures to the text and the undertaking of an inductive-deductive approach. Through multiple exposures to the data, the researcher ascertained the common themes and subthemes in the online discussion. Each theme and subtheme were later defined and labeled. Additional exposures to the text ensured that these were exhaustive. The results revealed that the CEO’s political endorsements triggered moral outrage, leading to Tesla’s facing a scandal as opposed to a crisis. The moral outrage revolved around the stakeholders’ predominant rejection of a perceived intrusion of an influential figure on a domain reserved for voters. As expected, Musk’s political endorsements led to polarizing opinions, and those who opposed his views engaged in online activism aimed to boycott the Tesla brand. These findings reveal that the moral outrage that characterizes a scandal requires communication practices that differ from those that practitioners currently borrow from the field of crisis management. Specifically, because scandals flourish in online settings, practitioners should regularly monitor stakeholder perceptions and address them in real-time. While promptness is essential when managing crises, it becomes crucial to respond immediately as a scandal is flourishing online. Finally, attempts should be made to distance a brand, its products, and its CEO from the latter’s political views.

Keywords: crisis management, communication management, Tesla, corporate political activism, Elon Musk

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Authors: Shuo Liu, Dan Liu

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In the field of named entity recognition, most of the research has been conducted around simple entities. However, for nested named entities, which still contain entities within entities, it has been difficult to identify them accurately due to their boundary ambiguity. In this paper, a hierarchical recognition model is constructed based on the grammatical structure and semantic features of Chinese text for boundary calculation based on lexical features. The analysis is carried out at different levels in terms of granularity, semantics, and lexicality, respectively, avoiding repetitive work to reduce computational effort and using the semantic features of words to calculate the boundaries of entities to improve the accuracy of the recognition work. The results of the experiments carried out on web-based microblogging data show that the model achieves an accuracy of 86.33% and an F1 value of 89.27% in recognizing nested named entities, making up for the shortcomings of some previous recognition models and improving the efficiency of recognition of nested named entities.

Keywords: coarse-grained, nested named entity, Chinese natural language processing, word embedding, T-SNE dimensionality reduction algorithm

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Authors: Faraji Sepideh

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Nowadays, purchase rate of the smart device is increasing and user authentication is one of the important issues in information security. Alphanumeric strong passwords are difficult to memorize and also owners write them down on papers or save them in a computer file. In addition, text password has its own flaws and is vulnerable to attacks. Graphical password can be used as an alternative to alphanumeric password that users choose images as a password. This type of password is easier to use and memorize and also more secure from pervious password types. In this paper we have designed a more secure graphical password system to prevent shoulder surfing, smudge and brute force attack. This scheme is a combination of two types of graphical passwords recognition based and Cued recall based. Evaluation the usability and security of our proposed scheme have been explained in conclusion part.

Keywords: brute force attack, graphical password, shoulder surfing attack, smudge attack

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Authors: Usha Lokala, Srinivas Nowduri, Prabhakar K. Sharma

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Useful information has been extracted from the road accident data in United Kingdom (UK), using data analytics method, for avoiding possible accidents in rural and urban areas. This analysis make use of several methodologies such as data integration, support vector machines (SVM), correlation machines and multinomial goodness. The entire datasets have been imported from the traffic department of UK with due permission. The information extracted from these huge datasets forms a basis for several predictions, which in turn avoid unnecessary memory lapses. Since data is expected to grow continuously over a period of time, this work primarily proposes a new framework model which can be trained and adapt itself to new data and make accurate predictions. This work also throws some light on use of SVM’s methodology for text classifiers from the obtained traffic data. Finally, it emphasizes the uniqueness and adaptability of SVMs methodology appropriate for this kind of research work.

Keywords: support vector mechanism (SVM), machine learning (ML), support vector machines (SVM), department of transportation (DFT)

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Authors: Pau Rodriguez, Jordi Gonzalez, Josep M. Gonfaus, Xavier Roca

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Image sharing in social networks has increased exponentially in the past years. Officially, there are 600 million Instagrammers uploading around 100 million photos and videos per day. Consequently, there is a need for developing new tools to understand the content expressed in shared images, which will greatly benefit social media communication and will enable broad and promising applications in education, advertisement, entertainment, and also psychology. Following these trends, our work aims to take advantage of the existing relationship between text and personality, already demonstrated by multiple researchers, so that we can prove that there exists a relationship between images and personality as well. To achieve this goal, we consider that images posted on social networks are typically conditioned on specific words, or hashtags, therefore any relationship between text and personality can also be observed with those posted images. Our proposal makes use of the most recent image understanding models based on neural networks to process the vast amount of data generated by social users to determine those images most correlated with personality traits. The final aim is to train a weakly-supervised image-based model for personality assessment that can be used even when textual data is not available, which is an increasing trend. The procedure is described next: we explore the images directly publicly shared by users based on those accompanying texts or hashtags most strongly related to personality traits as described by the OCEAN model. These images will be used for personality prediction since they have the potential to convey more complex ideas, concepts, and emotions. As a result, the use of images in personality questionnaires will provide a deeper understanding of respondents than through words alone. In other words, from the images posted with specific tags, we train a deep learning model based on neural networks, that learns to extract a personality representation from a picture and use it to automatically find the personality that best explains such a picture. Subsequently, a deep neural network model is learned from thousands of images associated with hashtags correlated to OCEAN traits. We then analyze the network activations to identify those pictures that maximally activate the neurons: the most characteristic visual features per personality trait will thus emerge since the filters of the convolutional layers of the neural model are learned to be optimally activated depending on each personality trait. For example, among the pictures that maximally activate the high Openness trait, we can see pictures of books, the moon, and the sky. For high Conscientiousness, most of the images are photographs of food, especially healthy food. The high Extraversion output is mostly activated by pictures of a lot of people. In high Agreeableness images, we mostly see flower pictures. Lastly, in the Neuroticism trait, we observe that the high score is maximally activated by animal pets like cats or dogs. In summary, despite the huge intra-class and inter-class variabilities of the images associated to each OCEAN traits, we found that there are consistencies between visual patterns of those images whose hashtags are most correlated to each trait.

Keywords: emotions and effects of mood, social impact theory in social psychology, social influence, social structure and social networks

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Authors: William Ho, Agus Wicaksana

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Anecdotal evidence in the last decade shows that the occurrence of disruptive events and uncertainties in the supply chain is increasing. The coupling of these events with the nature of an increasingly complex and interdependent business environment leads to devastating impacts that quickly propagate within and across organizations. For example, the recent COVID-19 pandemic increased the global supply chain disruption frequency by at least 20% in 2020 and is projected to have an accumulative cost of $13.8 trillion by 2024. This crisis raises attention to organizational resilience to weather business uncertainty. However, the concept has been criticized for being vague and lacking a consistent definition, thus reducing the significance of the concept for practice and research. This study is intended to solve that issue by providing a comprehensive review of the conceptualization, measurement, and antecedents of operational resilience that have been discussed in the supply chain risk management literature (SCRM). We performed a Scholarly Network Analysis, combining citation-based and text-based approaches, on 252 articles published from 2000 to 2021 in top-tier journals based on three parameters: AJG ranking and ABS ranking, UT Dallas and FT50 list, and editorial board review. We utilized a hybrid scholarly network analysis by combining citation-based and text-based approaches to understand the conceptualization, measurement, and antecedents of operational resilience in the SCRM literature. Specifically, we employed a Bibliographic Coupling Analysis in the research cluster formation stage and a Co-words Analysis in the research cluster interpretation and analysis stage. Our analysis reveals three major research clusters of resilience research in the SCRM literature, namely (1) supply chain network design and optimization, (2) organizational capabilities, and (3) digital technologies. We portray the research process in the last two decades in terms of the exemplar studies, problems studied, commonly used approaches and theories, and solutions provided in each cluster. We then provide a conceptual framework on the conceptualization and antecedents of resilience based on studies in these clusters and highlight potential areas that need to be studied further. Finally, we leverage the concept of abnormal operating performance to propose a new measurement strategy for resilience. This measurement overcomes the limitation of most current measurements that are event-dependent and focus on the resistance or recovery stage - without capturing the growth stage. In conclusion, this study provides a robust literature review through a scholarly network analysis that increases the completeness and accuracy of research cluster identification and analysis to understand conceptualization, antecedents, and measurement of resilience. It also enables us to perform a comprehensive review of resilience research in SCRM literature by including research articles published during the pandemic and connects this development with a plethora of articles published in the last two decades. From the managerial perspective, this study provides practitioners with clarity on the conceptualization and critical success factors of firm resilience from the SCRM perspective.

Keywords: supply chain risk management, organizational resilience, scholarly network analysis, systematic literature review

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Authors: Dominika Collett

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AI applications, such as machine learning, require access to a vast amount of data in the training phase, which can often be the subject of copyright protection. During later usage, the various content with which the application works can be recorded or made available on the basis of which it produces the resulting output. The EU has recently adopted new legislation to secure machine access to protected works under the DSM Directive; but, the issue of machine use of copyright works is not clearly addressed. However, such clarity is needed regarding the increasing importance of AI and its development. Therefore, this paper provides a basic background of the technology used in the development of applications in the field of computer creativity. The second part of the paper then will focus on a legal analysis of machine use of the authors' works from the perspective of existing European and Czech legislation. The main results of the paper discuss the potential collision of existing legislation in regards to machine use of works with special focus on exceptions and limitations. The legal regulation of machine use of copyright work will impact the development of AI technology.

Keywords: copyright, artificial intelligence, legal use, infringement, Czech law, EU law, text and data mining

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Authors: Ali Hamza

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Accurate segmentation of breast ultrasound images is of paramount importance in enhancing the diagnostic capabilities of breast cancer detection. This study presents an approach utilizing the U-Net architecture for segmenting breast ultrasound images aimed at improving the accuracy and reliability of mass identification within the breast tissue. The proposed method encompasses a multi-stage process. Initially, preprocessing techniques are employed to refine image quality and diminish noise interference. Subsequently, the U-Net architecture, a deep learning convolutional neural network (CNN), is employed for pixel-wise segmentation of regions of interest corresponding to potential breast masses. The U-Net's distinctive architecture, characterized by a contracting and expansive pathway, enables accurate boundary delineation and detailed feature extraction. To evaluate the effectiveness of the proposed approach, an extensive dataset of breast ultrasound images is employed, encompassing diverse cases. Quantitative performance metrics such as the Dice coefficient, Jaccard index, sensitivity, specificity, and Hausdorff distance are employed to comprehensively assess the segmentation accuracy. Comparative analyses against traditional segmentation methods showcase the superiority of the U-Net architecture in capturing intricate details and accurately segmenting breast masses. The outcomes of this study emphasize the potential of the U-Net-based segmentation approach in bolstering breast ultrasound image analysis. The method's ability to reliably pinpoint mass boundaries holds promise for aiding radiologists in precise diagnosis and treatment planning. However, further validation and integration within clinical workflows are necessary to ascertain their practical clinical utility and facilitate seamless adoption by healthcare professionals. In conclusion, leveraging the U-Net architecture for breast ultrasound image segmentation showcases a robust framework that can significantly enhance diagnostic accuracy and advance the field of breast cancer detection. This approach represents a pivotal step towards empowering medical professionals with a more potent tool for early and accurate breast cancer diagnosis.

Keywords: mage segmentation, U-Net, deep learning, breast cancer detection, diagnostic accuracy, mass identification, convolutional neural network

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Authors: Duncan Wallace, M-Tahar Kechadi

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In recent years, Machine Learning (ML) approaches have been successfully applied to an analysis of patient symptom data in the context of disease diagnosis, at least where such data is well codified. However, much of the data present in Electronic Health Records (EHR) are unlikely to prove suitable for classic ML approaches. Furthermore, as scores of data are widely spread across both hospitals and individuals, a decentralized, computationally scalable methodology is a priority. The focus of this paper is to develop a method to predict outliers in an out-of-hours healthcare provision center (OOHC). In particular, our research is based upon the early identification of patients who have underlying conditions which will cause them to repeatedly require medical attention. OOHC act as an ad-hoc delivery of triage and treatment, where interactions occur without recourse to a full medical history of the patient in question. Medical histories, relating to patients contacting an OOHC, may reside in several distinct EHR systems in multiple hospitals or surgeries, which are unavailable to the OOHC in question. As such, although a local solution is optimal for this problem, it follows that the data under investigation is incomplete, heterogeneous, and comprised mostly of noisy textual notes compiled during routine OOHC activities. Through the use of Deep Learning methodologies, the aim of this paper is to provide the means to identify patient cases, upon initial contact, which are likely to relate to such outliers. To this end, we compare the performance of Long Short-Term Memory, Gated Recurrent Units, and combinations of both with Convolutional Neural Networks. A further aim of this paper is to elucidate the discovery of such outliers by examining the exact terms which provide a strong indication of positive and negative case entries. While free-text is the principal data extracted from EHRs for classification, EHRs also contain normalized features. Although the specific demographical features treated within our corpus are relatively limited in scope, we examine whether it is beneficial to include such features among the inputs to our neural network, or whether these features are more successfully exploited in conjunction with a different form of a classifier. In this section, we compare the performance of randomly generated regression trees and support vector machines and determine the extent to which our classification program can be improved upon by using either of these machine learning approaches in conjunction with the output of our Recurrent Neural Network application. The output of our neural network is also used to help determine the most significant lexemes present within the corpus for determining high-risk patients. By combining the confidence of our classification program in relation to lexemes within true positive and true negative cases, with an inverse document frequency of the lexemes related to these cases, we can determine what features act as the primary indicators of frequent-attender and non-frequent-attender cases, providing a human interpretable appreciation of how our program classifies cases.

Keywords: artificial neural networks, data-mining, machine learning, medical informatics

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Authors: Ali Dulla

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This paper introduces a new dataset (and the methodology used to generate it) based on a wide range of historical Arabic documents containing clean data simple and homogeneous-page layouts. The experiments are implemented on printed and handwritten documents obtained respectively from some important libraries such as Qatar Digital Library, the British Library and the Library of Congress. We have gathered and commented on 150 archival document images from different locations and time periods. It is based on different documents from the 17th-19th century. The dataset comprises differing page layouts and degradations that challenge text line segmentation methods. Ground truth is produced using the Aletheia tool by PRImA and stored in an XML representation, in the PAGE (Page Analysis and Ground truth Elements) format. The dataset presented will be easily available to researchers world-wide for research into the obstacles facing various historical Arabic documents such as geometric correction of historical Arabic documents.

Keywords: dataset production, ground truth production, historical documents, arbitrary warping, geometric correction

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Authors: Ichihan Tai, Bill Olson, Paul Blessner

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Traditional early warning systems that alarm against crisis are generally based on structured or numerical data; therefore, a system that can make predictions based on unstructured textual data, an uncorrelated data source, is a great complement to the traditional early warning systems. The Chicago Board Options Exchange (CBOE) Volatility Index (VIX), commonly referred to as the fear index, measures the cost of insurance against market crash, and spikes in the event of crisis. In this study, news data is consumed for prediction of whether there will be a market-wide crisis by predicting the movement of the fear index, and the historical references to similar events are presented in an unsupervised manner. Topic modeling-based prediction and representation are made based on daily news data between 1990 and 2015 from The Wall Street Journal against VIX index data from CBOE.

Keywords: early warning system, knowledge management, market prediction, topic modeling.

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Authors: Maria-Agori Gravvani

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The part of communication holds a significant place in human life. From the early beginning of human history, humans tried to communicate orally with other people in order to survive and to communicate their needs. The level of education that the majority of the Athenean citizens had the opportunity to acquire in the Classic period was very low. Only the wealthy ones had the opportunity of the upper form of education that led them to a career in politics, while the other ones struggled for their daily survival. In the corpus of Euripides' and Sophocles' tragedies, the type of communication is written, too. Not only in the Iphigenia's tragedies of Euripides but also in the Sophocles' Trachiniae, the use of δέλτος bonds significant messages with people. Those written means of private communication play an important role in the plot of the tragedy and have hidden private messages from their owners. The main aim of this paper is to analyze the power of the deltos' written text in the tragedies of Euripides Ifigenia Taurica and Ifigenia Aulidensis and Sophocles' Trachiniae.

Keywords: deltos, ancient greek tragedy, sophocles, euripides

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Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

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Digitalisation in production technology is a driver for the application of machine learning methods. Through the application of predictive quality, the great potential for saving necessary quality control can be exploited through the data-based prediction of product quality and states. However, the serial use of machine learning applications is often prevented by various problems. Fluctuations occur in real production data sets, which are reflected in trends and systematic shifts over time. To counteract these problems, data preprocessing includes rule-based data cleaning, the application of dimensionality reduction techniques, and the identification of comparable data subsets to extract stable features. Successful process control of the target variables aims to centre the measured values around a mean and minimise variance. Competitive leaders claim to have mastered their processes. As a result, much of the real data has a relatively low variance. For the training of prediction models, the highest possible generalisability is required, which is at least made more difficult by this data availability. The implementation of a machine learning application can be interpreted as a production process. The CRoss Industry Standard Process for Data Mining (CRISP-DM) is a process model with six phases that describes the life cycle of data science. As in any process, the costs to eliminate errors increase significantly with each advancing process phase. For the quality prediction of hydraulic test steps of directional control valves, the question arises in the initial phase whether a regression or a classification is more suitable. In the context of this work, the initial phase of the CRISP-DM, the business understanding, is critically compared for the use case at Bosch Rexroth with regard to regression and classification. The use of cross-process production data along the value chain of hydraulic valves is a promising approach to predict the quality characteristics of workpieces. Suitable methods for leakage volume flow regression and classification for inspection decision are applied. Impressively, classification is clearly superior to regression and achieves promising accuracies.

Keywords: classification, CRISP-DM, machine learning, predictive quality, regression

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Authors: Rodrigo S. Fonseca, Antônio C. P. Veiga

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Aiming at helping people with some movement limitation that makes typing and communication difficult, there is a need to customize an assistive tool with a learning environment that helps the user in order to optimize text input, identifying the error and providing the correction and possibilities of choice in the Portuguese language. The work presents an Orthographic and Grammatical System that can be incorporated into writing environments, improving and facilitating the use of an alphanumeric keyboard, using a prototype built using a genetic algorithm in addition to carrying out the prediction, which can occur based on the quantity and position of the inserted letters and even placement in the sentence, ensuring the sequence of ideas using a Long Short Term Memory (LSTM) neural network. The prototype optimizes data entry, being a component of assistive technology for the textual formulation, detecting errors, seeking solutions and informing the user of accurate predictions quickly and effectively through machine learning.

Keywords: genetic algorithm, neural networks, word prediction, machine learning

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Authors: Ruth Mandujano Lopez

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During the second half of the 19th century, in the transition from sail to steam navigation, the transpacific space underwent major transformation. This paper examines the role that the steamship companies between Mexico, the rest of North America and Asia played in that process. Based on primary sources found in Mexico, California, London and Hong Kong, it argues that these companies actively participated in the redefining of the Pacific space as they opened new routes, transported thousands of people and had an impact on regional geopolitics. In order to prove this, the text will present the cases of a handful of companies that emerged between 1867 and 1910 and of some of their passengers. By looking at the way the Mexican ports incorporated to the transpacific steam maritime network, this work contributes to have a better understanding of the role that Latin American ports have played in the formation of a global order. From a theoretical point of view, it proposes the conceptualization of space in the form of transnational networks as a point of departure to conceive a history that is truly global.

Keywords: mexico, steamships, transpacific, maritime companies

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Authors: Jungho Moon, Lae-Jeong Park

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This paper introduces a signal monitoring program developed with a view to helping electrical engineering students get familiar with sensors with digital output. Because the output of digital sensors cannot be simply monitored by a measuring instrument such as an oscilloscope, students tend to have a hard time dealing with digital sensors. The monitoring program runs on a PC and communicates with an MCU that reads the output of digital sensors via an asynchronous communication interface. Receiving the sensor data from the MCU, the monitoring program shows time and/or frequency domain plots of the data in real time. In addition, the monitoring program provides a serial terminal that enables the user to exchange text information with the MCU while the received data is plotted. The user can easily observe the output of digital sensors and configure the digital sensors in real time, which helps students who do not have enough experiences with digital sensors. Though the monitoring program was programmed in the Matlab programming language, it runs without the Matlab since it was compiled as a standalone executable.

Keywords: digital sensor, MATLAB, MCU, signal monitoring program

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Authors: Farrukh Shahzad

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The rich Islamic resources related to religious text, Islamic sciences, and history are widely available in print and in electronic format online. However, most of these works are only available in Arabic language. In this research, an attempt is made to utilize these resources to create interactive web applications in Arabic, English and other languages. The system utilizes the Pattern Recognition, Knowledge Management, Data Mining, Information Retrieval and Management, Indexing, storage and data-analysis techniques to parse, store, convert and manage the information from authentic Arabic resources. These interactive web Apps provide smart multi-lingual search, tree based search, on-demand information matching and linking. In this paper, we provide details of application architecture, design, implementation and technologies employed. We also presented the summary of web applications already developed. We have also included some screen shots from the corresponding web sites. These web applications provide an Innovative On-line Learning Systems (eLearning and computer based education).

Keywords: Islamic resources, Muslim scholars, hadith, narrators, history, fiqh

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Authors: Rin Li Si Samantha

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The arts and cultures of Southeast Asia, particularly ancient or precolonial Southeast Asia, are commonly understood via two distinct theories: Indianisation and localisation. Indianisation takes Southeast Asia as a region to be cultural satellites or even colonies of a great Indian civilisation; Philip Rawson, in his 1967 book The Art of Southeast Asia, is to a large degree a proponent of this perspective. Localisation, a theory which has gained much traction in contemporaneous discourse, chooses instead to privilege local continuities and agencies in selectively accepting and adapting foreign influences to give form to new, syncretised traditions. The art historian Fiona Kerlogue’ similarly-named Arts of Southeast Asia, published in 2004, takes this perspective as its bedrock. This essay compares the many opposing ideological commitments of Rawson and Kerlogue: Indianisation versus localisation, evaluation versus explanation, and antiquity versus entirety. In the end, it reconciles the two as hallmarks of their time periods and is complementary in the pursuit of a holistic study of the art history of Southeast Asia.

Keywords: art history, Southeast Asia, Indianisation, localisation, precolonial, orientalism, comparative analysis, text

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Authors: A. K. O'Loughlin

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In Simone de Beauvoir’s climatic 1959 meditation, The Second Sex, she avows that 'On ne naît pas femme; on le devient,' translated most recently in the unabridged text (2010) as 'One is not born, but rather becomes, woman.' The signifier ‘woman’ in this context, signifies Beauvoir’s contemplation of the institution, the concept of woman(ness) defined in relation to the binary and hegemonic man(ness.) She is 'the other.' This paper is a theoretical contemplation of (1) how we actively teach 'othering' in the institution of schooling and (2) new considerations of pluralism for self-reflection and subversion that teachers, in particular, are faced with. How, in schooling, do we learn one’s options for racialized, classed and sexualized gender identification and the hierarchical signification that define these signifiers? Just like the myth of apolitical schooling, we cannot escape teaching social organization in the classroom. Yet, we do have a choice. How do we as educators learn about our own embodied intersectionalities? How do we unlearn our own binaries? How do we teach about intersectional gender? How do we teach 'the other'? We posit the processes of these reflections by educators may move our classrooms beyond binaries, engage pluralism and queer alterity itself.

Keywords: othering, alterity, education, schooling, identity, racialization, gender, intersectionality, pluralism

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Authors: P. Wlodarczak, J. Soar, M. Ally

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The dramatic rise in the use of Social Media (SM) platforms such as Facebook and Twitter provide access to an unprecedented amount of user data. Users may post reviews on products and services they bought, write about their interests, share ideas or give their opinions and views on political issues. There is a growing interest in the analysis of SM data from organisations for detecting new trends, obtaining user opinions on their products and services or finding out about their online reputations. A recent research trend in SM analysis is making predictions based on sentiment analysis of SM. Often indicators of historic SM data are represented as time series and correlated with a variety of real world phenomena like the outcome of elections, the development of financial indicators, box office revenue and disease outbreaks. This paper examines the current state of research in the area of SM mining and predictive analysis and gives an overview of the analysis methods using opinion mining and machine learning techniques.

Keywords: social media, text mining, knowledge discovery, predictive analysis, machine learning

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Authors: Armineh Nourbakhsh, Sameena Shah, Carolyn Rose

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Layout-aware language models have been used to create multimodal representations for documents that are in image form, achieving relatively high accuracy in document understanding tasks. However, the large number of parameters in the resulting models makes building and using them prohibitive without access to high-performing processing units with large memory capacity. We propose an alternative approach that can create efficient representations without the need for a neural visual backbone. This leads to an 80% reduction in the number of parameters compared to the smallest SOTA model, widely expanding applicability. In addition, our layout embeddings are pre-trained on spatial and visual cues alone and only fused with text embeddings in downstream tasks, which can facilitate applicability to low-resource of multi-lingual domains. Despite using 2.5% of training data, we show competitive performance on two form understanding tasks: semantic labeling and link prediction.

Keywords: layout understanding, form understanding, multimodal document understanding, bias-augmented attention

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Authors: Hilya Mudrika Arini, Nur Aini Masruroh, Budi Hartono

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There are more than 20 terrorist threats from 2002 to 2012 in Indonesia. Despite of this fact, preventive solution through studies in the field of national security in Indonesia has not been conducted comprehensively. This study aims to provide a preventive solution by developing prediction model of the terrorist threat in Indonesia by using Bayesian network. There are eight stages to build the model, started from literature review, build and verify Bayesian belief network to what-if scenario. In order to build the model, four experts from different perspectives are utilized. This study finds several significant findings. First, news and the readiness of terrorist group are the most influent factor. Second, according to several scenarios of the news portion, it can be concluded that the higher positive news proportion, the higher probability of terrorist threat will occur. Therefore, the preventive solution to reduce the terrorist threat in Indonesia based on the model is by keeping the positive news portion to a maximum of 38%.

Keywords: Bayesian network, decision analysis, national security system, text mining

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Authors: Mohua Dutta

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The primary aim of this research paper is to investigate the forced urban migration of Dalits in India who are fleeing caste persecution in rural areas. This paper examines the relationship between caste and rural-to-urban internal migration in India using a literary text, Rohinton Mistry’s A Fine Balance, highlighting the challenges faced by Dalits in rural areas that force them to migrate to urban areas. Despite the prevalence of such discussions in Dalit autobiographies written in vernacular languages, there is a lack of discussion regarding caste migration in Indian English Literature, including this present text, as evidenced by the existing critical interpretations of the novel, which this paper seeks to rectify. The primary research question is how urban migration affects caste system in India and why rural-to-urban caste migration occurs. The purpose of this paper is to better understand the reasons for Dalit migration, the challenges they face in rural and urban areas, and the lingering influence of caste in both rural and urban areas. The study reveals that the promise of mobility and emancipation provided by class operations drives rural-to-urban caste migration in India, but it also reveals that caste marginalization in rural areas is closely linked to class marginalization and other forms of subalternity in urban areas. Moreover, the caste system persists in urban areas as well, making Dalit migrants more vulnerable to social, political, and economic discrimination. The reason for this is that, despite changes in profession and urban migration, the trapped structure of caste capital and family networks exposes migrants to caste and class oppressions. To reach its conclusion, this study employs a variety of methodologies. Discourse analysis is used to investigate the current debates and narratives surrounding caste migration. Critical race theory, specifically intersectional theory and social constructivism, aids in comprehending the complexities of caste, class, and migration. Mistry's novel is subjected to textual analysis in order to identify and interpret references to caste migration. Secondary data, such as theoretical understanding of the caste system in operation and scholarly works on caste migration, are also used to support and strengthen the findings and arguments presented in the paper. The study concludes that rural-to-urban caste migration in India is primarily motivated by the promise of socioeconomic mobility and emancipation offered by urban spaces. However, the caste system persists in urban areas, resulting in the continued marginalisation and discrimination of Dalit migrants. The study also highlights the limitations of urban migration in providing true emancipation for Dalit migrants, as they remain trapped within caste and family network structures. Overall, the study raises awareness of the complexities surrounding caste migration and its impact on the lives of India's marginalised communities. This study contributes to the field of Migration Studies by shedding light on an often-overlooked issue: Dalit migration. It challenges existing literary critical interpretations by emphasising the significance of caste migration in Indian English Literature. The study also emphasises the interconnectedness of caste and class, broadening understanding of how these systems function in both rural and urban areas.

Keywords: rural-to-urban caste migration in india, internal migration in india, caste system in india, dalit movement in india, rooster coop of caste and class, urban poor as subalterns

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Authors: Sijia Guo

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The development of new technologies and the falling cost of high-speed Internet access have made it easier for institutes and language teachers to opt different ways to communicate with students at distance. The emergence of web-conferencing applications, which integrate text, chat, audio / video and graphic facilities, offers great opportunities for language learning to through the multimodal environment. This paper reports on data elicited from a Ph.D. study of using web-conferencing in the teaching of first-year Chinese class in order to promote learners’ collaborative learning. Firstly, a comparison of four desktop videoconferencing (DVC) tools was conducted to determine the pedagogical value of the videoconferencing tool-Blackboard Collaborate. Secondly, the evaluation of 14 campus-based Chinese learners who conducted five one-hour online sessions via the multimodal environment reveals the users’ choice of modes and their learning preference. The findings show that the tasks designed for the web-conferencing environment contributed to the learners’ collaborative learning and second language acquisition.

Keywords: computer-mediated communication (CMC), CALL evaluation, TBLT, web-conferencing, online Chinese teaching

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Authors: Theng Theng Ong

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This study adopts a multidisciplinary method by combining the corpus-based discourse analysis study and language attitude study to explore the construction of Malaysia airline tragedies: MH370, MH17 and QZ8501 in the selected Malaysian and United Kingdom (UK) online news. The study aims to determine the ways in which Malaysian Airline tragedies MH370, MH17 and QZ8501 are linguistically defined and constructed in terms of keyword and collocation. The study also seeks to identify the types of discourse that are presented in the new articles. The differences or similarities in terms of keywords, topics or issues covered by the selected Malaysian and UK news media will also be examined. Finally, the language attitude study will be carried out to examine the Malaysia and UK university students’ attitudes toward the keywords, topics or issues covered by the selected Malaysian and UK news media pertaining to Malaysian Airline tragedies MH370, MH17 and QZ8501. The analysis is divided into two parts with the first part focusing on corpus-based discourse analysis on the media text. The second part of the study is to investigate Malaysians and UK news readers’ attitudes towards the online news being reported by the Malaysian and UK news media pertaining to the Airline tragedies. The main findings of corpus-based discourse analysis are essential in designing the questions in the questionnaires and interview and therefore led to the identification of the attitudes among Malaysian and UK news. This study adopts a multidisciplinary method by combining the corpus-based discourse analysis study and language attitude study to explore the construction of Malaysia airline tragedies: MH370, MH17 and QZ8501 in the selected Malaysian and United Kingdom (UK) online news. The study aims to determine the ways in which Malaysian Airline tragedies MH370, MH17 and QZ8501 are linguistically defined and constructed in terms of keyword and collocation. The study also seeks to identify the types of discourse that are presented in the new articles. The differences or similarities in terms of keywords, topics or issues covered by the selected Malaysian and UK news media will also be examined. Finally, the language attitude study will be carried out to examine the Malaysia and UK university students’ attitudes toward the keywords, topics or issues covered by the selected Malaysian and UK news media pertaining to Malaysian Airline tragedies MH370, MH17 and QZ8501. The analysis is divided into two parts with the first part focusing on corpus-based discourse analysis on the media text. The second part of the study is to investigate Malaysians and UK news readers’ attitudes towards the online news being reported by the Malaysian and UK news media pertaining to the Airline tragedies. The main findings of corpus-based discourse analysis are essential in designing the questions in the questionnaires and interview and therefore led to the identification of the attitudes among Malaysian and UK news.

Keywords: corpus linguistics, critical discourse analysis, news media, tragedies study

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Authors: O. F. Elkommos

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“Humour studies” is an interdisciplinary research area that is relatively recent. It interests researchers from the disciplines of psychology, sociology, medicine, nursing, in the work place, gender studies, among others, and certainly teaching, language learning, linguistics, and literature. Linguistic theories of humour research are numerous; some of which are of interest to the present study. In spite of the fact that humour courses are now taught in universities around the world in the Egyptian context it is not included. The purpose of the present study is two-fold: to review the state of arts and to show how linguistic theories of humour can be possibly used as an art and craft of teaching and of learning in EFL literature classes. In the present study linguistic theories of humour were applied to selected literary texts to interpret humour as an intrinsic artistic communicative competence challenge. Humour in the area of linguistics was seen as a fifth component of communicative competence of the second language leaner. In literature it was studied as satire, irony, wit, or comedy. Linguistic theories of humour now describe its linguistic structure, mechanism, function, and linguistic deviance. Semantic Script Theory of Verbal Humor (SSTH), General Theory of Verbal Humor (GTVH), Audience Based Theory of Humor (ABTH), and their extensions and subcategories as well as the pragmatic perspective were employed in the analyses. This research analysed the linguistic semantic structure of humour, its mechanism, and how the audience reader (teacher or learner) becomes an interactive interpreter of the humour. This promotes humour competence together with the linguistic, social, cultural, and discourse communicative competence. Studying humour as part of the literary texts and the perception of its function in the work also brings its positive association in class for educational purposes. Humour is by default a provoking/laughter-generated device. Incongruity recognition, perception and resolving it, is a cognitive mastery. This cognitive process involves a humour experience that lightens up the classroom and the mind. It establishes connections necessary for the learning process. In this context the study examined selected narratives to exemplify the application of the theories. It is, therefore, recommended that the theories would be taught and applied to literary texts for a better understanding of the language. Students will then develop their language competence. Teachers in EFL/ESL classes will teach the theories, assist students apply them and interpret text and in the process will also use humour. This is thus easing students' acquisition of the second language, making the classroom an enjoyable, cheerful, self-assuring, and self-illuminating experience for both themselves and their students. It is further recommended that courses of humour research studies should become an integral part of higher education curricula in Egypt.

Keywords: ABTH, deviance, disjuncture, episodic, GTVH, humour competence, humour comprehension, humour in the classroom, humour in the literary texts, humour research linguistic theories, incongruity-resolution, isotopy-disjunction, jab line, longer text joke, narrative story line (macro-micro), punch line, six knowledge resource, SSTH, stacks, strands, teaching linguistics, teaching literature, TEFL, TESL

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Authors: Atje Setiawan Abdullah, Budi Nurani Ruchjana, I. Gede Nyoman Mindra Jaya, Eddy Hermawan

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Ethnoscience is used to see the culture with a scientific perspective, which may help to understand how people develop various forms of knowledge and belief, initially focusing on the ecology and history of the contributions that have been there. One of the areas studied in ethnoscience is etno-informatics, is the application of informatics in the culture. In this study the science of informatics used is data mining, a process to automatically extract knowledge from large databases, to obtain interesting patterns in order to obtain a knowledge. While the application of culture described by naming database village on the island of Java were obtained from Geographic Indonesia Information Agency (BIG), 2014. The purpose of this study is; first, to classify the naming of the village on the island of Java based on the structure of the word naming the village, including the prefix of the word, syllable contained, and complete word. Second to classify the meaning of naming the village based on specific categories, as well as its role in the community behavioral characteristics. Third, how to visualize the naming of the village to a map location, to see the similarity of naming villages in each province. In this research we have developed two theorems, i.e theorems area as a result of research studies have collected intersection naming villages in each province on the island of Java, and the composition of the wedge theorem sets the provinces in Java is used to view the peculiarities of a location study. The methodology in this study base on the method of Knowledge Discovery in Database (KDD) on data mining, the process includes preprocessing, data mining and post processing. The results showed that the Java community prioritizes merit in running his life, always working hard to achieve a more prosperous life, and love as well as water and environmental sustainment. Naming villages in each location adjacent province has a high degree of similarity, and influence each other. Cultural similarities in the province of Central Java, East Java and West Java-Banten have a high similarity, whereas in Jakarta-Yogyakarta has a low similarity. This research resulted in the cultural character of communities within the meaning of the naming of the village on the island of Java, this character is expected to serve as a guide in the behavior of people's daily life on the island of Java.

Keywords: ethnoscience, ethno-informatics, data mining, clustering, Java island culture

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Authors: Elcin Timur Cakmak, Ayse Oguzlar

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This study presents the results of the tweets sent by Twitter users on social media about the Russia-Ukraine war by bigram and trigram methods. On February 24, 2022, Russian President Vladimir Putin declared a military operation against Ukraine, and all eyes were turned to this war. Many people living in Russia and Ukraine reacted to this war and protested and also expressed their deep concern about this war as they felt the safety of their families and their futures were at stake. Most people, especially those living in Russia and Ukraine, express their views on the war in different ways. The most popular way to do this is through social media. Many people prefer to convey their feelings using Twitter, one of the most frequently used social media tools. Since the beginning of the war, it is seen that there have been thousands of tweets about the war from many countries of the world on Twitter. These tweets accumulated in data sources are extracted using various codes for analysis through Twitter API and analysed by Python programming language. The aim of the study is to find the word sequences in these tweets by the n-gram method, which is known for its widespread use in computational linguistics and natural language processing. The tweet language used in the study is English. The data set consists of the data obtained from Twitter between February 24, 2022, and April 24, 2022. The tweets obtained from Twitter using the #ukraine, #russia, #war, #putin, #zelensky hashtags together were captured as raw data, and the remaining tweets were included in the analysis stage after they were cleaned through the preprocessing stage. In the data analysis part, the sentiments are found to present what people send as a message about the war on Twitter. Regarding this, negative messages make up the majority of all the tweets as a ratio of %63,6. Furthermore, the most frequently used bigram and trigram word groups are found. Regarding the results, the most frequently used word groups are “he, is”, “I, do”, “I, am” for bigrams. Also, the most frequently used word groups are “I, do, not”, “I, am, not”, “I, can, not” for trigrams. In the machine learning phase, the accuracy of classifications is measured by Classification and Regression Trees (CART) and Naïve Bayes (NB) algorithms. The algorithms are used separately for bigrams and trigrams. We gained the highest accuracy and F-measure values by the NB algorithm and the highest precision and recall values by the CART algorithm for bigrams. On the other hand, the highest values for accuracy, precision, and F-measure values are achieved by the CART algorithm, and the highest value for the recall is gained by NB for trigrams.

Keywords: classification algorithms, machine learning, sentiment analysis, Twitter

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Authors: Attahir Shehu Mainiyo, Ahmad Ibrahim Karfe

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The Glorious Qur’an, as the central religious text of Islam, addresses various aspects of human life and provides guidance for personal and societal development. It also outlines the moral excellence of individuals and communities, focusing on spiritual, moral, and social dimensions. Da’wah is the act of inviting others to Islam, emphasizing the significance of conveying the message with kindness, patience, and understanding. Qur’anic moral excellence, as evinced in the Qur’an encompasses virtues such as compassion, honesty, humility, patience, and generosity. The Glorious Qur’an, therefore, harps on the importance of embodying these values in daily life, serving as a guide for individuals engaged in Da’wah activities to exemplify moral excellence through their actions and characters. It is in line with this backdrop that this article intends to assess the Da’wah and Qur’anic Moral Excellence. However, to achieve the objectives of the research, the article attempts to answer some basic questions. Emphasizes were laid in the Glorious on the need to invite others to the true path of Islam and the qualities of Da’i necessary for his Da’wah activities. The paper also discussed the impact of Qur’anic moral excellence on the Da’i and those invited to Islam. The paper adopts an analytical methodology and utilizes secondary data for the research.

Keywords: Da'wah, Qur'an, moral, excellence

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Authors: Mustafa Ekici, Sacide Güzin Mazman

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Programming is the one of ability in computer science fields which is generally perceived difficult by students and various individual differences have been implicated in that ability success. Although several factors that affect programming ability have been identified over the years, there is not still a full understanding of why some students learn to program easily and quickly while others find it complex and difficult. Programming self-efficacy and mathematic success are two of those essential individual differences which are handled as having important effect on the programming success. This study aimed to identify the relationship between programming performance, programming self efficacy and mathematics success. The study group is consisted of 96 undergraduates from Department of Econometrics of Uşak University. 38 (39,58%) of the participants are female while 58 (60,41%) of them are male. Study was conducted in the programming-I course during 2014-2015 fall term. Data collection tools are comprised of programming course final grades, programming self efficacy scale and a mathematics achievement test. Data was analyzed through correlation analysis. The result of study will be reported in the full text of the study.

Keywords: programming performance, self efficacy, mathematic success, computer science

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