Natural Language Understanding and Multimodal Discourse Analysis for Interpreting Extremist Communications and the Re-Use of These Materials Online

This paper reports on a study that is part of a project which aims to develop a multimodal analytical approach for big data analytics, initially in the context of violent extremism. The findings reported here tested the application of natural language processing models to the text of a sample of articles from the online magazines Dabiq and Rumiyah, produced by the Islamic extremist organisation ISIS. For comparison, text of articles found by reverse image search software which re-used the lead images from the original articles in text which either reported on or opposed extremist activities was also analysed. The aim was to explore what insights the natural language processing models could provide to distinguish between texts produced as propaganda to incite violent extremism and texts which either reported on or opposed violent extremism. The results showed that some valuable insights can be gained from such an approach and that these results could be improved through integrating automated analyses with a theoretical approach with analysed language and images in their immediate and social contexts. Such an approach will inform the interpretation of results and will be used in training software so that stronger results can be achieved in the future

Designing the interface between research, learning and teaching.

Abstract: This paper’s central argument is that teaching and research need to be reshaped so that they connect in a productive way. This will require actions at a whole range of levels, from the individual teacher to the national system and include the international communities of design scholars. To do this, we need to start at the level of the individual teacher and course team. This paper cites some examples of strategies that focus on what students do as learners and how teachers teach and design courses to enhance research-led teaching. The paper commences with an examination of the departmental context of (art and) design education. This is followed by an exploration of what is understood by research-led teaching and a further discussion of the dimensions of research-led teaching. It questions whether these dimensions are evident, and if so to what degree in design departments, programmes and courses. The discussion examines the features of research-led departments and asks if a department is not research-led in its approach to teaching, why it should consider changing strategies

LEXICAL METAPHOR IN SURA' AL WAQI'AH

This study deals with lexical metaphor in Sura’ Al Waqi’ah by applying Systemic Functional Linguistics (SFL). The objectives of the study are to describe the types of lexical metaphor used in Sura’ Al Waqi’ah, how are the metaphors used in Sura’ Al Waqi’ah, and the reason for the use of lexical metaphor in Sura’ Al Waqi’ah. This study was conducted by using descriptive research. The sources of data were Sura’ Al Waqi’ah. The findings indicated that firstly, there are three concepts of lexical metaphor in Sura’ Al Waqi’ah such as 13 verses (23.21%) for noun–noun concept, 23 verses (41.07%) for noun-verb concept or verb-noun concept and 20 verses (35.71%) for noun- adjective concept or adjective-noun concept. Secondly, linguistics realization of Lexical Metaphor in Sura’ Al Waqi’ah are through comparing noun–noun, noun-verb or verb-noun and noun–adjective or adjective-noun. Finally, the reasons of lexical metaphors used in Sura’ Al Waqi’ah are explaining the idea which is out of human life experience, explaining an abstract thing concretely and explaining something unknown yet with something familiar. Sura’ Al Waqi’ah tells us about the resurrection day. This Sura’ explains about what will happen before and after resurrection day. Resurrection day is an abstract thing. No one in this world has experience about the resurrection day. So, this Sura’ mostly discusses about it. That’s why, the most dominant reason of using lexical metaphor in Sura’ Al Waqi’ah is explaining an abstract thing concretely.&nbsp

Word2Vec model for sentiment analysis of product reviews in Indonesian language

Online product reviews have become a source of greatly valuable information for consumers in making purchase decisions and producers to improve their product and marketing strategies. However, it becomes more and more difficult for people to understand and evaluate what the general opinion about a particular product in manual way since the number of reviews available increases. Hence, the automatic way is preferred. One of the most popular techniques is using machine learning approach such as Support Vector Machine (SVM). In this study, we explore the use of Word2Vec model as features in the SVM based sentiment analysis of product reviews in Indonesian language. The experiment result show that SVM can performs well on the sentiment classification task using any model used. However, the Word2vec model has the lowest accuracy (only 0.70), compared to other baseline method including Bag of Words model using Binary TF, Raw TF, and TF.IDF. This is because only small dataset used to train the Word2Vec model. Word2Vec need large examples to learn the word representation and place similar words into closer position

Artificial Intelligence Chatbots: A Survey of Classical versus Deep Machine Learning Techniques

Artificial Intelligence (AI) enables machines to be intelligent, most importantly using Machine Learning (ML) in which machines are trained to be able to make better decisions and predictions. In particular, ML-based chatbot systems have been developed to simulate chats with people using Natural Language Processing (NLP) techniques. The adoption of chatbots has increased rapidly in many sectors, including, Education, Health Care, Cultural Heritage, Supporting Systems and Marketing, and Entertainment. Chatbots have the potential to improve human interaction with machines, and NLP helps them understand human language more clearly and thus create proper and intelligent responses. In addition to classical ML techniques, Deep Learning (DL) has attracted many researchers to develop chatbots using more sophisticated and accurate techniques. However, research has paid chatbots have widely been developed for English, there is relatively less research on Arabic, which is mainly due to its complexity and lack of proper corpora compared to English. Though there have been several survey studies that reviewed the state-of-the-art of chatbot systems, these studies (a) did not give a comprehensive overview of how different the techniques used for Arabic chatbots in comparison with English chatbots; and (b) paid little attention to the application of ANN for developing chatbots. Therefore, in this paper, we conduct a literature survey of chatbot studies to highlight differences between (1) classical and deep ML techniques for chatbots; and (2) techniques employed for Arabic chatbots versus those for other languages. To this end, we propose various comparison criteria of the techniques, extract data from collected studies accordingly, and provide insights on the progress of chatbot development for Arabic and what still needs to be done in the future

A new hybrid convolutional neural network and eXtreme gradient boosting classifier for recognizing handwritten Ethiopian characters

Handwritten character recognition has been profoundly studied for many years in the field of pattern recognition. Due to its vast practical applications and financial implications, handwritten character recognition is still an important research area. In this research, the Handwritten Ethiopian Character Recognition (HECR) dataset has been prepared to train the model. The images in the HECR dataset were organized with more than one color pen RGB main spaces that have been size normalized to 28 × 28 pixels. The dataset is a combination of scripts (Fidel in Ethiopia), numerical representations, punctuations, tonal symbols, combining symbols, and special characters. These scripts have been used to write ancient histories, science, and arts of Ethiopia and Eritrea. In this study, a hybrid model of two super classifiers: Convolutional Neural Network (CNN) and eXtreme Gradient Boosting (XGBoost) is proposed for classification. In this integrated model, CNN works as a trainable automatic feature extractor from the raw images and XGBoost takes the extracted features as an input for recognition and classification. The output error rates of the hybrid model and CNN with a fully connected layer are compared. A 0.4630 and 0.1612 error rates are achieved in classifying the handwritten testing dataset images, respectively. Thus XGBoost as a classifier performs a better result than the traditional fully connected layer

Alleged case of blasphemy on podcast: Forensic linguistic analysis

This study aimed to analyze the linguistic features that allegedly contained elements of blasphemy in Jenderal Dudung Abdurrachman's (JDA) speech. The analysis was carried out using a forensic linguistic perspective. This research data is in the form of JDA utterances delivered in a broadcast conducted with Deddy Corbuzier (DC). Based on a forensic linguistic analysis of the text of JDA's conversation with DC on Deddy Corbuzier's podcast, it can be concluded that JDA's linguistic evidence does not support blasphemy but a lack of knowledge in religion, especially in matters of faith. Due to the lack of knowledge of religion, JDA interprets religion and personifies God according to his understanding. In JDA's speech, there is also no intention to tarnish religion because the controversial JDA's speech cannot be interpreted partially but can be interpreted with other speeches. If it is related to other utterances, it can be concluded that there is no intention of JDA to tarnish religion. However, there are efforts by JDA to invite audiences to follow JDA's understanding and interpretation of religion.Dugaan kasus penodaan agama di podcast: Analisis linguistik forensikTujuan penelitian ini adalah menganalisis fitur kebahasaan yang diduga memuat unsur penodaan agama dalam tuturan Jenderal Dudung Abdurrachman (JDA). Analisis dilakukan dengan menggunakan perspektif linguistik forensik. Data penelitian ini berupa tuturan JDA yang disampaikan dalam siniar yang dilakukan bersama Deddy Corbuzier. Berdasarkan analisis linguistik forensik atas teks perbincangan JDA dengan DC di siniar DC, dapat disimpulkan bahwa tuturan JDA bukti-bukti kebahasaan tidak mendukung adanya penodaan agama, melainkan kurangnya pengetahuan dalam beragama, terutama dalam hal akidah. Akibat kurangnya pengetahuan dalam beragama tersebut, JDA menafsirkan agama dan mempersonifikasi Tuhan sesuai dengan pemahamannya. Dalam tuturan JDA juga tidak terdapat niat untuk menodai agama karena tuturan JDA yang kontroversial tersebut tidak dapat dimaknai secara parsial, melainkan dimaknai secara menyeluruh dengan tuturan-tuturan lain. Jika dikaitkan dengan tuturan yang lain, dapat disimpulkan bahwa tidak ada niat JDA untuk menodai agama. Meski demikian, terdapat upaya JDA untuk mengajak audiens agar mengikuti pemahaman dan penafsiran JDA tentang agama

People’s Initiatives to Use IT for Development

human development, technology