Reviewerly/exHarmony huggingface dataset:
A hybrid combination of CNN Attention with optimized random forest with grey wolf optimizer to discriminate between Arabic hateful, abusive tweets

Authors:
raw_author_name: Abeer Aljohani
raw_affiliation_string: Applied College, Taibah University, Kingdom of Saudi Arabia
raw_author_name: Nawaf Alharbe
raw_affiliation_string: Applied College, Taibah University, Kingdom of Saudi Arabia
raw_author_name: Rabia Emhamed Al Mamlook
raw_affiliation_string: Department I Engineering Management, Trine University, United States of America
raw_author_name: Mashael M. Khayyat
raw_affiliation_string: Department of Information Systems and Technology, Faculty of Computer Science and Engineering, University of jeddah, Jeddah, Kingdom of Saudi Arabia

Abstract: Arabic hateful speech recognition has long been a major area of focus in Natural Language Processing (NLP) research. In light of recent advancements in transformer models and deep learning, researchers are now turning to transfer learning techniques based on existing models such as BERT for Arabic hateful speech recognition. To detect Arabic hateful contexts, using advanced machine learning algorithms and NLP techniques is essential. These techniques can help to detect different forms of hateful contexts in Arabic by analyzing the text for lexical, semantic, and syntactic features. In this research, we proposed a new hybrid approach that combines deep and machine learning models to detect hateful and abusive content in Arabic. The proposed model consists of a combination of convolutional neural networks and attention layers that are trained to differentiate between normal, abusive, and hateful contexts in Arabic. In the first step, we used a pre-trained model to extract features from the hateful Arabic context. After that, we used an optimized random forest combined with particle swarm optimization and wolf optimizer to classify the extracted features. Finally, we evaluated the performance of the model to detect hateful Arabic contexts. To evaluate the proposed method we used 5846 and 6023 tweets with 3 categories of hateful, abusive, and normal Arabic contexts. The experimental result indicates sensitivity have been reported CNN Attention + optimized random forest by the wolf optimizer on 6023 tweets.
Exploring emergent syllables in end-to-end automatic speech recognizers through model explainability technique

Authors:
raw_author_name: Vincenzo Norman Vitale
raw_affiliation_string: UrbanECO, Università degli Studi di Napoli Federico II, Corso Umberto I, 40, 80138, Naples, Italy
raw_author_name: Francesco Cutugno
raw_affiliation_string: UrbanECO, Università degli Studi di Napoli Federico II, Corso Umberto I, 40, 80138, Naples, Italy
raw_author_name: Antonio Origlia
raw_affiliation_string: UrbanECO, Università degli Studi di Napoli Federico II, Corso Umberto I, 40, 80138, Naples, Italy
raw_author_name: Gianpaolo Coro
raw_affiliation_string: Istituto di Scienza e Tecnologie dell’Informazione “Alessandro Faedo”, CNR, Via Moruzzi, 1, 56124, Pisa, Italy

Abstract: Abstract Automatic speech recognition systems based on end-to-end models (E2E-ASRs) can achieve comparable performance to conventional ASR systems while reproducing all their essential parts automatically, from speech units to the language model. However, they hide the underlying perceptual processes modelled, if any, and they have lower adaptability to multiple application contexts, and, furthermore, they require powerful hardware and an extensive amount of training data. Model-explainability techniques can explore the internal dynamics of these ASR systems and possibly understand and explain the processes conducting to their decisions and outputs. Understanding these processes can help enhance ASR performance and reduce the required training data and hardware significantly. In this paper, we probe the internal dynamics of three E2E-ASRs pre-trained for English by building an acoustic-syllable boundary detector for Italian and Spanish based on the E2E-ASRs’ internal encoding layer outputs. We demonstrate that the shallower E2E-ASR layers spontaneously form a rhythmic component correlated with prominent syllables, central in human speech processing. This finding highlights a parallel between the analysed E2E-ASRs and human speech recognition. Our results contribute to the body of knowledge by providing a human-explainable insight into behaviours encoded in popular E2E-ASR systems.
Generation Z’s Trust in The Chatbot of Mental Health Service

Authors:
raw_author_name: Bayu Kelana
raw_affiliation_string: Sekolah Ilmu Manajemen dan Ilmu Komputer ESQ

Abstract: Generation Z is the age group that has experienced the most mental health-related issues due to the COVID-19 pandemic in Indonesia. Mental health information system services have become increasingly popular, so patient services are overwhelmed. Chatbot has emerged as one of the solutions to address this problem. However, conversations with them have led to social issues alongside the growing use of chatbots. This study aims to identify the factors influencing Generation Z's trust in patient service chatbots within mental health applications in Indonesia. Using the Interpretative Phenomenological Analysis method, this research analyses qualitative data from observations and interviews with five undergraduate students. Based on the analysis, the study identified seven factors that influence Generation Z's trust in chatbot customer service within mental health applications, with three being novel findings.
Medical Image Description Based on Multimodal Auxiliary Signals and Transformer

Authors:
raw_author_name: Yun Tan
raw_affiliation_string: Central South University of Forestry and Technology, Changsha, China
raw_author_name: Jiaohua Qin
raw_affiliation_string: Central South University of Forestry and Technology, Changsha, China
raw_author_name: Youyuan Xue
raw_affiliation_string: Central South University of Forestry and Technology, Changsha, China
raw_author_name: Xuyu Xiang
raw_affiliation_string: Central South University of Forestry and Technology, Changsha, China

Abstract: Medical image description can be applied to clinical medical diagnosis, but the field still faces serious challenges. There is a serious problem of visual and textual data bias in medical datasets, which are the imbalanced distribution of health and disease data. This can greatly affect the learning performance of data-driven neural networks and finally lead to errors in the generated medical image descriptions. To address this problem, we propose a new medical image description network architecture named multimodal data-assisted knowledge fusion network (MDAKF), which introduces multimodal auxiliary signals to guide the Transformer network to generate more accurate medical reports. In detail, audio auxiliary signals provide clear abnormal visual regions to alleviate the visual data bias problem. However, the audio modality signals with similar pronunciation lack recognizability, which may lead to incorrect mapping of audio labels to medical image regions. Therefore, we further fuse the audio with text features as the auxiliary signal to improve the overall performance of the model. Through the experiments on two medical image description datasets, IU-X-ray and COV-CTR, it is found that the proposed model is superior to the previous models in terms of language generation evaluation indicators.
Network Algorithms for Intelligent Evaluation of Composition in Middle School English Cloud Classrooms

Authors:
raw_author_name: Chengbo Zhang
raw_affiliation_string: Suqian University, Suqian, China

Abstract: This study combines link grammar (LG) detector with N-grammar model to analyze and evaluate grammar in compositions. And then the composition level is judged through information entropy. Finally, the composition score is calculated based on the overall composition level and grammar weight. The experimental results show that the combined weight of recall and accuracy of the proposed method in this study is 89.9%, which is 26.6% higher than LG and 9.7% higher than Grammarly. In the performance test of scoring the entire essay, the proportion of error between the proposed method and manual evaluation is 87.29%, with a lower overall mean square error of only 3.08 and a shorter average running time of only 22.69 seconds. The method proposed in this study has a high accuracy and strong applicability in the evaluation of English compositions for middle school students, providing a new approach for teaching English writing for middle school students.
Mapping Ni-Cu-Platinum Group Element-Hosting, Small-Sized, Mafic-Ultramafic Rocks Using WorldView-3 Images and a Spatial-Spectral Transformer Deep Learning Method

Authors:
raw_author_name: Chuntao Yin
raw_affiliation_string: 1 School of Earth Science and Resources, Chang’an University, Xi’an 710054, China

Abstract: Abstract Mafic-ultramafic intrusive complexes possess a considerable capacity for hosting Ni-Cu-platinum group element (PGE) sulfide deposits. However, the mapping of small outcrops over large areas by field surveys is time-consuming. In this study, WorldView-3 (WV-3) data with moderate spectral and very high spatial resolution were employed for mapping mafic-ultramafic units using spectral indices and the spatial-spectral transformer (SSTF) deep learning method in the Luotuoshan area of Beishan, Gansu Province, China. Based on representative reflectance signatures extracted from imagery of known locations, false-color composites of three-band ratios and a newly proposed shortwave infrared (SWIR) spectral index provided reasonable delineation of mafic-ultramafic rocks. The SSTF method facilitated mapping the occurrence of small mafic-ultramafic outcrops and defining much clearer boundaries, particularly for tiny units at meter scale. Moreover, the SSTF method is not sensitive to the occurrence of carbonate lenses that may affect the reflectance of outcrops. Field investigation and laboratory sample analyses confirmed the occurrence of mafic and ultramafic rocks with substantial metallic mineral potential in this area. Seven prospects were confirmed to be related to mafic-ultramafic intrusions during field validation, four of which contained metallic minerals such as chalcopyrite, pentlandite, pyrite, and chromite in the samples observed by scanning electron microscopy and energy dispersive spectrometry. This study proves that the spectral indices and SSTF deep learning method applied on WV-3 multispectral data are useful for discriminating small-sized mafic-ultramafic intrusive rocks (&amp;lt;100 m) for prospecting of local mineralization.
Deep learning-based privacy-preserving recommendations in federated learning

Authors:
raw_author_name: Chandra Sekhar Kolli
raw_affiliation_string: Department of Computer Science, Gandhi Institute of Technology and Management, Visakhapatnam, India
raw_author_name: Tatireddy Subba Reddy
raw_affiliation_string: Department of Computer Science and Engineering, B V Raju Institute of Technology, Narsapur, India
raw_author_name: Mohan Kumar Chandol
raw_affiliation_string: Department of CSE, Koneru Lakshmaiah Education Foundation, Guntur, India
raw_author_name: Durga Bhavani Dasari
raw_affiliation_string: Department of Computer Science and Engineering, Institute of Aeronautical Engineering, Hyderabad, India

Abstract: Privacy preservation in recommendations has been increasingly garnering huge interest from the research community owing to the rapid rise in data security and privacy concerns among users. The computation overhead and attaining high recommendation accuracy remain the key issues in the existing methods. In this research, a course recommendation method using Federated Learning (FL) based on Deep Learning is presented. The course recommendation technique is carried out in the local nodes using multiple phases, like agglomerative matrix generation, course grouping, bi-level matching, retrieval of learner-preferred courses, and course recommendation. Here, course grouping is accomplished using Deep Fuzzy Clustering (DFC), and Deep Convolutional Neural Networks (DCNN) performs recommendation. The DFC-DCNN-FL is examined based on accuracy, False Positive Rate (FPR), loss function, Mean Square Error (MSE), Root MSE (RMSE), and Mean Average Precision (MAP) and is found to have attained values of 0.909, 0.116, 0.126, 0.291, 0.539, and 0.925.
Exploiting multi-scale hierarchical feature representation for visual tracking

Authors:
raw_author_name: Jun Wang
raw_affiliation_string: School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330029, China
raw_author_name: Peng Yin
raw_affiliation_string: School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330029, China
raw_author_name: Wenhui Yang
raw_affiliation_string: School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330029, China
raw_author_name: Yuanyun Wang
raw_affiliation_string: School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330029, China
raw_author_name: Shengqian Wang
raw_affiliation_string: School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330029, China

Abstract: Abstract Convolutional neural networks (CNNs) have been the dominant architectures for feature extraction tasks, but CNNs do not look for and focus on some specific image features. Correlation operations play an important role in visual tracking. However, the correlation operation reserves a large amount of unfavorable background information. In this paper, we propose an effective feature recognizer including channel and spatial attention modules to focus on important object feature information. Thus, the representation power of the feature extraction network is improved. Further, we design a multi-scale feature fusion network. The fusion network performs feature fusion on template feature and encoded feature branches to establish connections between features at different scales. Experiments on six benchmarks demonstrate that the proposed tracker outperforms the state-of-the-art trackers. In particular, the proposed tracker achieves an 80.4% AUC on TrackingNet and a 68.4% AUC on GOT-10k while running at a real-time speed.
ReLiCADA: Reservoir Computing Using Linear Cellular Automata design algorithm

Authors:
raw_author_name: Walter Stechele
raw_affiliation_string: TUM School of Computation, Information and Technology, Department of Computer Engineering, Arcisstr. 21, 80333, Munich, Germany

Abstract: Abstract In this paper, we present a novel algorithm to optimize the design of Reservoir Computing using Cellular Automata models for time series applications. Besides selecting the models’ hyperparameters, the proposed algorithm particularly solves the open problem of Linear Cellular Automaton rule selection. The selection method pre-selects only a few promising candidate rules out of an exponentially growing rule space. When applied to relevant benchmark datasets, the selected rules achieve low errors, with the best rules being among the top 5% of the overall rule space. The algorithm was developed based on mathematical analysis of Linear Cellular Automaton properties and is backed by almost one million experiments, adding up to a computational runtime of nearly one year. Comparisons to other state-of-the-art time series models show that the proposed Reservoir Computing using Cellular Automata models have lower computational complexity and, at the same time, achieve lower errors. Hence, our approach reduces the time needed for training and hyperparameter optimization by up to several orders of magnitude.
A lexicon-based diachronic comparison of emotions and sentiments in literary translation: A case study of five Chinese versions of David Copperfield

Authors:
raw_author_name: Muhammad Afzaal
raw_affiliation_string: Institute of Corpus Studies and Applications, Shanghai International Studies University, China
raw_author_name: Yixin Yin
raw_affiliation_string: Press Institute, Beijing Institution of Graphic Communication, Beijing City, China

Abstract: In the Chinese context, translations have served as a useful conduit for providing access to wider literature authored in other languages. A prominent question has been whether translators' linguistic choices are influenced by factors such as translators' social and cultural background and emotions towards the texts they are translating. When multiple translations of the same text over a span of time are produced, another layer of complexity is introduced, and research such as the present study, must examine how or whether variation in the expression of emotions within translations produced over a period of time is discernible. To this end, the present study made use of Lexicon-based Sentiment Analysis (LBSA), a common natural language processing (NLP) approach, to study people's attitudes, opinions or emotions towards a certain person or thing. LBSA has attracted much attention in the literary works or translated works for analyzing reader response and appraisal of the works themselves. The present study undertook a diachronic comparison of emotions and sentiments in five translations of David Copperfield based on the emotion lexicons. The corpus of the study comprised translations of five books and 3,084,599 tokens. We applied the computational method of emotion and sentiment analysis to the emotion words in the five translations. In addition, we used python and R package to analyze the positive and negative words in five versions. The study revealed that translators as social beings in the target world express unique reactions towards the same emotion in the original text as well as in literary translations. Yet, the modern vernacular Chinese versions also showcase a similarity in the expression of emotions thus demonstrating the decisive role of the overall flow of emotion in the original plays and in translation. The contribution of the study is significant as it is a pioneering investigation given that it undertakes a sentiment and emotion analysis of literary translations in Chinese.
Synthesis of Hybrid Data Consisting of Chest Radiographs and Tabular Clinical Records Using Dual Generative Models for COVID-19 Positive Cases

Authors:
raw_author_name: Tomohiro Kikuchi
raw_affiliation_string: Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
raw_author_name: Shouhei Hanaoka
raw_affiliation_string: Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan
raw_author_name: Takahiro Nakao
raw_affiliation_string: Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
raw_author_name: Tomomi Takenaga
raw_affiliation_string: Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan
raw_author_name: Yukihiro Nomura
raw_affiliation_string: Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
raw_author_name: Takeharu Yoshikawa
raw_affiliation_string: Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan

Abstract: To generate synthetic medical data incorporating image-tabular hybrid data by merging an image encoding/decoding model with a table-compatible generative model and assess their utility. We used 1342 cases from the Stony Brook University Covid-19-positive cases, comprising chest X-ray radiographs (CXRs) and tabular clinical data as a private dataset (pDS). We generated a synthetic dataset (sDS) through the following steps: (I) dimensionally reducing CXRs in the pDS using a pretrained encoder of the auto-encoding generative adversarial networks (αGAN) and integrating them with the correspondent tabular clinical data; (II) training the conditional tabular GAN (CTGAN) on this combined data to generate synthetic records, encompassing encoded image features and clinical data; and (III) reconstructing synthetic images from these encoded image features in the sDS using a pretrained decoder of the αGAN. The utility of sDS was assessed by the performance of the prediction models for patient outcomes (deceased or discharged). For the pDS test set, the area under the receiver operating characteristic (AUC) curve was calculated to compare the performance of prediction models trained separately with pDS, sDS, or a combination of both. We created an sDS comprising CXRs with a resolution of 256 × 256 pixels and tabular data containing 13 variables. The AUC for the outcome was 0.83 when the model was trained with the pDS, 0.74 with the sDS, and 0.87 when combining pDS and sDS for training. Our method is effective for generating synthetic records consisting of both images and tabular clinical data.
Automobile Insurance Fraud Detection Using Data Mining: A Systematic Literature Review

Authors:
raw_author_name: Dipti K. Sarmah
raw_affiliation_string: SCS, EEMCS, University of Twente, Drienerlolaan 5, 7522 NB, Enschede, the Netherlands
raw_author_name: Mohammed El-hajj
raw_affiliation_string: SCS, EEMCS, University of Twente, Drienerlolaan 5, 7522 NB, Enschede, the Netherlands

Abstract: Insurance is a pivotal element in modern society, but insurers face a persistent challenge from fraudulent behaviour performed by policyholders. This behaviour could be detrimental to both insurance companies and their honest customers, but the intricate nature of insurance fraud severely complicates its efficient, automated detection. This study surveys fifty recent publications on automobile insurance fraud detection, published between January 2019 and March 2023, and presents both the most commonly used data sets and methods for resampling and detection, as well as interesting, novel approaches. The study adopts the highly-cited Systematic Literature Review (SLR) methodology for software engineering research proposed by Kitchenham and Charters and collected studies from four online databases. The findings indicate limited public availability of automobile insurance fraud data sets. In terms of detection methods, the prevailing approach involves supervised machine learning methods that utilise structured, intrinsic features of claims or policies and that lack consideration of an example-dependent cost of misclassification. However, alternative techniques are also explored, including the use of graph-based methods, unstructured textual data, and cost-sensitive classifiers. The most common resampling approach was found to be oversampling. This SLR has identified commonly used methods in recent automobile insurance fraud detection research, and interesting directions for future research. It adds value over a related review by also including studies published from 2021 onward, and by detailing the used methodology. Limitations of this SLR include its restriction to a small number of considered publication years and limited validation of choices made during the process.