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Textbook question answering (TQA) is a challenging task in artificial intelligence due to the complex nature of context and multimodal data. Although previous research has significantly improved the task, there are still some limitations including the models' weak reasoning and inability to capture contextual information in the lengthy context. The introduction of large language models (LLMs) has revolutionized the field of AI, however, directly applying LLMs often leads to inaccurate answers. This paper proposes a methodology that handle the out-of-domain scenario in TQA where concepts are spread across different lessons by incorporating the retrieval augmented generation (RAG) technique and utilize transfer learning to handle the long context and enhance reasoning abilities. Through supervised fine-tuning of the LLM model Llama-2 and the incorporation of RAG, our architecture outperforms the baseline, achieving a 4.12% accuracy improvement on validation set and 9.84% on test set for non-diagram multiple-choice questions.

Speech acts are a speakers actions when performing an utterance within a conversation, such as asking, recommending, greeting, or thanking someone, expressing a thought, or making a suggestion. Understanding speech acts helps interpret the intended meaning and actions behind a speakers or writers words. This paper proposes a Twitter dialectal Arabic speech act classification approach based on a transformer deep learning neural network. Twitter and social media, are becoming more and more integrated into daily life. As a result, they have evolved into a vital source of information that represents the views and attitudes of their users. We proposed a BERT based weighted ensemble learning approach to integrate the advantages of various BERT models in dialectal Arabic speech acts classification. We compared the proposed model against several variants of Arabic BERT models and sequence-based models. We developed a dialectal Arabic tweet act dataset by annotating a subset of a large existing Arabic sentiment analysis dataset (ASAD) based on six speech act categories. We also evaluated the models on a previously developed Arabic Tweet Act dataset (ArSAS). To overcome the class imbalance issue commonly observed in speech act problems, a transformer-based data augmentation model was implemented to generate an equal proportion of speech act categories. The results show that the best BERT model is araBERTv2-Twitter models with a macro-averaged F1 score and an accuracy of 0.73 and 0.84, respectively. The performance improved using a BERT-based ensemble method with a 0.74 and 0.85 averaged F1 score and accuracy on our dataset, respectively., Comment: 16 pages, 6 figures

Social media platforms have revolutionized traditional communication techniques by enabling people globally to connect instantaneously, openly, and frequently. People use social media to share personal stories and express their opinion. Negative emotions such as thoughts of death, self-harm, and hardship are commonly expressed on social media, particularly among younger generations. As a result, using social media to detect suicidal thoughts will help provide proper intervention that will ultimately deter others from self-harm and committing suicide and stop the spread of suicidal ideation on social media. To investigate the ability to detect suicidal thoughts in Arabic tweets automatically, we developed a novel Arabic suicidal tweets dataset, examined several machine learning models, including Na\"ive Bayes, Support Vector Machine, K-Nearest Neighbor, Random Forest, and XGBoost, trained on word frequency and word embedding features, and investigated the ability of pre-trained deep learning models, AraBert, AraELECTRA, and AraGPT2, to identify suicidal thoughts in Arabic tweets. The results indicate that SVM and RF models trained on character n-gram features provided the best performance in the machine learning models, with 86% accuracy and an F1 score of 79%. The results of the deep learning models show that AraBert model outperforms other machine and deep learning models, achieving an accuracy of 91\% and an F1-score of 88%, which significantly improves the detection of suicidal ideation in the Arabic tweets dataset. To the best of our knowledge, this is the first study to develop an Arabic suicidality detection dataset from Twitter and to use deep-learning approaches in detecting suicidality in Arabic posts.

Legal Judgment Prediction (LJP) aims to predict judgment outcomes based on case description. Several researchers have developed techniques to assist potential clients by predicting the outcome in the legal profession. However, none of the proposed techniques were implemented in Arabic, and only a few attempts were implemented in English, Chinese, and Hindi. In this paper, we develop a system that utilizes deep learning (DL) and natural language processing (NLP) techniques to predict the judgment outcome from Arabic case scripts, especially in cases of custody and annulment of marriage. This system will assist judges and attorneys in improving their work and time efficiency while reducing sentencing disparity. In addition, it will help litigants, lawyers, and law students analyze the probable outcomes of any given case before trial. We use a different machine and deep learning models such as Support Vector Machine (SVM), Logistic regression (LR), Long Short Term Memory (LSTM), and Bidirectional Long Short-Term Memory (BiLSTM) using representation techniques such as TF-IDF and word2vec on the developed dataset. Experimental results demonstrate that compared with the five baseline methods, the SVM model with word2vec and LR with TF-IDF achieve the highest accuracy of 88% and 78% in predicting the judgment on custody cases and annulment of marriage, respectively. Furthermore, the LR and SVM with word2vec and BiLSTM model with TF-IDF achieved the highest accuracy of 88% and 69% in predicting the probability of outcomes on custody cases and annulment of marriage, respectively.

Individual abnormal behaviors vary depending on crowd sizes, contexts, and scenes. Challenges such as partial occlusions, blurring, large-number abnormal behavior, and camera viewing occur in large-scale crowds when detecting, tracking, and recognizing individuals with abnormal behaviors. In this paper, our contribution is twofold. First, we introduce an annotated and labeled large-scale crowd abnormal behaviors Hajj dataset (HAJJv2). Second, we propose two methods of hybrid Convolutional Neural Networks (CNNs) and Random Forests (RFs) to detect and recognize Spatio-temporal abnormal behaviors in small and large-scales crowd videos. In small-scale crowd videos, a ResNet-50 pre-trained CNN model is fine-tuned to verify whether every frame is normal or abnormal in the spatial domain. If anomalous behaviors are observed, a motion-based individuals detection method based on the magnitudes and orientations of Horn-Schunck optical flow is used to locate and track individuals with abnormal behaviors. A Kalman filter is employed in large-scale crowd videos to predict and track the detected individuals in the subsequent frames. Then, means, variances, and standard deviations statistical features are computed and fed to the RF to classify individuals with abnormal behaviors in the temporal domain. In large-scale crowds, we fine-tune the ResNet-50 model using YOLOv2 object detection technique to detect individuals with abnormal behaviors in the spatial domain.

Social media platforms have transformed traditional communication methods by allowing users worldwide to communicate instantly, openly, and frequently. People use social media to express their opinion and share their personal stories and struggles. Negative feelings that express hardship, thoughts of death, and self-harm are widespread in social media, especially among young generations. Therefore, using social media to detect and identify suicidal ideation will help provide proper intervention that will eventually dissuade others from self-harming and committing suicide and prevent the spread of suicidal ideations on social media. Many studies have been carried out to identify suicidal ideation and behaviors in social media. This paper presents a comprehensive summary of current research efforts to detect suicidal ideation using machine learning algorithms on social media. This review 24 studies investigating the feasibility of social media usage for suicidal ideation detection is intended to facilitate further research in the field and will be a beneficial resource for researchers engaged in suicidal text classification., Comment: 14 pages, 7 figures

Question answering(QA) is one of the most challenging yet widely investigated problems in Natural Language Processing (NLP). Question-answering (QA) systems try to produce answers for given questions. These answers can be generated from unstructured or structured text. Hence, QA is considered an important research area that can be used in evaluating text understanding systems. A large volume of QA studies was devoted to the English language, investigating the most advanced techniques and achieving state-of-the-art results. However, research efforts in the Arabic question-answering progress at a considerably slower pace due to the scarcity of research efforts in Arabic QA and the lack of large benchmark datasets. Recently many pre-trained language models provided high performance in many Arabic NLP problems. In this work, we evaluate the state-of-the-art pre-trained transformers models for Arabic QA using four reading comprehension datasets which are Arabic-SQuAD, ARCD, AQAD, and TyDiQA-GoldP datasets. We fine-tuned and compared the performance of the AraBERTv2-base model, AraBERTv0.2-large model, and AraELECTRA model. In the last, we provide an analysis to understand and interpret the low-performance results obtained by some models.

The enormous amount of data being generated on the web and social media has increased the demand for detecting online hate speech. Detecting hate speech will reduce their negative impact and influence on others. A lot of effort in the Natural Language Processing (NLP) domain aimed to detect hate speech in general or detect specific hate speech such as religion, race, gender, or sexual orientation. Hate communities tend to use abbreviations, intentional spelling mistakes, and coded words in their communication to evade detection, adding more challenges to hate speech detection tasks. Thus, word representation will play an increasingly pivotal role in detecting hate speech. This paper investigates the feasibility of leveraging domain-specific word embedding in Bidirectional LSTM based deep model to automatically detect/classify hate speech. Furthermore, we investigate the use of the transfer learning language model (BERT) on hate speech problem as a binary classification task. The experiments showed that domainspecific word embedding with the Bidirectional LSTM based deep model achieved a 93% f1-score while BERT achieved up to 96% f1-score on a combined balanced dataset from available hate speech datasets.

White supremacists embrace a radical ideology that considers white people superior to people of other races. The critical influence of these groups is no longer limited to social media; they also have a significant effect on society in many ways by promoting racial hatred and violence. White supremacist hate speech is one of the most recently observed harmful content on social media.Traditional channels of reporting hate speech have proved inadequate due to the tremendous explosion of information, and therefore, it is necessary to find an automatic way to detect such speech in a timely manner. This research investigates the viability of automatically detecting white supremacist hate speech on Twitter by using deep learning and natural language processing techniques. Through our experiments, we used two approaches, the first approach is by using domain-specific embeddings which are extracted from white supremacist corpus in order to catch the meaning of this white supremacist slang with bidirectional Long Short-Term Memory (LSTM) deep learning model, this approach reached a 0.74890 F1-score. The second approach is by using the one of the most recent language model which is BERT, BERT model provides the state of the art of most NLP tasks. It reached to a 0.79605 F1-score. Both approaches are tested on a balanced dataset given that our experiments were based on textual data only. The dataset was combined from dataset created from Twitter and a Stormfront dataset compiled from that white supremacist forum., Comment: 32 pages,2 figures

In this paper, we propose an extension to graph-based sentiment lexicon induction methods by incorporating distributed and semantic word representations in building the similarity graph to expand a three-dimensional sentiment lexicon. We also implemented and evaluated the label propagation using four different word representations and similarity metrics. Our comprehensive evaluation of the four approaches was performed on a single data set, demonstrating that all four methods can generate a significant number of new sentiment assignments with high accuracy. The highest correlations (tau=0.51) and the lowest error (mean absolute error < 1.1%), obtained by combining both the semantic and the distributional features, outperformed the distributional-based and semantic-based label-propagation models and approached a supervised algorithm.

This paper describes a novel method for building affectively intelligent human-interactive agents. The method is based on a key sociological insight that has been developed and extensively verified over the last twenty years, but has yet to make an impact in artificial intelligence. The insight is that resource bounded humans will, by default, act to maintain affective consistency. Humans have culturally shared fundamental affective sentiments about identities, behaviours, and objects, and they act so that the transient affective sentiments created during interactions confirm the fundamental sentiments. Humans seek and create situations that confirm or are consistent with, and avoid and supress situations that disconfirm or are inconsistent with, their culturally shared affective sentiments. This "affect control principle" has been shown to be a powerful predictor of human behaviour. In this paper, we present a probabilistic and decision-theoretic generalisation of this principle, and we demonstrate how it can be leveraged to build affectively intelligent artificial agents. The new model, called BayesAct, can maintain multiple hypotheses about sentiments simultaneously as a probability distribution, and can make use of an explicit utility function to make value-directed action choices. This allows the model to generate affectively intelligent interactions with people by learning about their identity, predicting their behaviours using the affect control principle, and taking actions that are simultaneously goal-directed and affect-sensitive. We demonstrate this generalisation with a set of simulations. We then show how our model can be used as an emotional "plug-in" for artificially intelligent systems that interact with humans in two different settings: an exam practice assistant (tutor) and an assistive device for persons with a cognitive disability.