Reviewerly/exHarmony huggingface dataset:
A Brain Function Connection Method for Tone Processing of Deaf Children Based on One-dimensional Convolutional Network

The purpose of this study is to investigate the differences in brain functional connectivity between deaf and typically developing children when processing three different tones (tone 1, tone 2, tone 3) using deep learning methods. Additionally, the goal is to identify variations in activated brain regions during tone processing between these two groups. Five deaf children and two typically developing children were selected as participants. Resting-state functional magnetic resonance imaging (fMRI) scans were conducted using an fMRI scanner. Subsequently, data preprocessing and fMRI data analysis were performed. A one-dimensional convolutional network was employed to extract features and classify the input brain functional connectivity. Through a series of theoretical and comparative analyses, it was determined that the one-dimensional convolutional network is capable of capturing local patterns within sequential data. This network can extract information related to connectivity patterns from the one-dimensional sequence of connection weights, making it more suitable for processing brain functional connectivity data in deaf children.
Clustering Based Unsupervised Hashing Framework for Image Retrieval

Abstract-This work presents a novel clustering-based unsupervised deep hashing framework for image retrieval that can incrementally train for clustering network and output hash codes while keeping the architecture of the input data distributions, solving the aforementioned problems. The study's deep unsupervised hashing framework retains binary codes' spatial structure without pre-computation of pseudo labels, unlike k-means-based hashing. Clustering-based unsupervised hashing, a deep end-to-end network, may tackle big datasets by optimizing all objective functions concurrently using stochastic gradient descent and mini-batches. Our solution beats state-of-the-art algorithms in extensive CIFAR-10 and NUS-WIDE dataset experiments.
Re-parameterizable Residual Multiple Convolutions Network for Efficient Single Image Super-Resolution

The performance of single image super-resolution (SISR) has improved significantly in recent years. However, most existing methods suffer from large model parameters, high computational costs, and slow inference times, making them unsuitable for resource-constrained devices. To optimize these limitations, we propose a new re-parameterizable residual multiple convolutions network (RepRMCN) that strikes a better balance between model performance, scale, and inference time. Our approach introduces a convolutional reparameterization strategy that combines multiple convolutionals into a single convolution after model training, improving model performance during training and reducing the parameters and inference time when deployed. We also designed a simple grouped structure with different convolutions that inspired by grouped convolution, Inception, and heterogeneous kernel-based convolution. Our proposed structure splits features into two groups and processes them using re-parameterizable multiple convolutions module and dilation convolution, respectively. Experimental results show that RepRMCN outperforms existing high-efficiency SR models in terms of parameter reduction and inference time while maintaining good performance. Specifically, when tested on the Urban100 dataset with a 4× scale factor, our RepRMCN has 495K parameters, which is 48K less than the current state-of-the-art model RLFN. The inference time on the GeForce GTX 1080Ti is 16.47ms, which is comparable to RLFN.
Cantonese to Written Chinese Translation via HuggingFace Translation Pipeline

Cantonese, a low-resource language [5] that has been used in Southeastern China for hundreds of years, with over 85 million native speakers worldwide, is poorly supported in the mainstream language model for existing translation platforms such as Baidu, Google and Bing. This paper presents a large parallel corpus of 130 thousand Cantonese and Written Chinese pairs. The data are used to train a translation model using the translation pipeline of the Hugging Face Transformers architecture, a dominant architecture for natural language processing nowadays [18]. The BLEU score and manual assessment evaluate the performance. The translation results achieve a BLEU score of 41.35and chrF++ score of 44.88on the entire validation set. The model also works reasonably well with long sentences of over 20 Chinese characters. It achieves a BLEU score of 48.61and chrF++ score of 39.87on long sentences. Those results are comparable with the existing Baidu Fanyi and Bing Translate. We also establish a Cantonese sentence evaluation metric to classify the quality of the source Cantonese sentence by professional translators. We then compare the BLEU and chrF++ scores with the corresponding evaluation score and found that the better the quality of the source sentence, the higher the BLEU and chrF++ scores. Last, we proved that our corpus enabled the Cantonese translation capability of the Chinese BART pre-trained model.
Web Based Signature Authenticity Application

Our signatures are very important to our daily lives, as they anchor the agreement to a legally binding, enforceable, and actionable reality. The old-style purpose of a signature is to permanently affix to a document a person's uniquely personal, undeniable self-identification as physical evidence of that person's personal witness and certification of the content of all, or a specified part, of the document. The purpose of the proposed capstone project is to determine the authenticity of a signature in an important document, namely, certificates, checks, legal documents, letters, and receipts. Automatically identifies a signature without human intervention. The overall objective of the proposed capstone project is to have immediate and accurate results of an investigation whether the signature is legitimate or counterfeit. The conclusion of the study shows that image recognition is the core development of the project, with the use of Artificial Intelligence, feeding the database with images will have a higher accuracy rate in authenticating signatures.
Classifying User Experience (UX) Of The M-Commerce Application Using Multinomial Naive Bayes Algorithm

This research study uses the Multinomial Nave Bayes (MNB) algorithm to categorize and analyze the user experience (UX) of users of mobile commerce applications. The goal of the study is to give business owners insightful information on how well their mobile applications are performing. The study's goals are to establish evaluation standards for categorizing user experiences, use MNB to classify user experience reviews to their appropriate UX elements, analyze the results of the classification, and suggest areas for improvement to enhance the usability of m-commerce. The research plan consists of a number of sprints, including data extraction, data cleaning, classification system creation using the Multinomial Naive Bayes algorithm, and model accuracy rate evaluation. The proposed system integrates the algorithm and uses data from m-commerce applications. The results of the analysis provide insights into the different UX elements such as Value, Adoptability, Desirability, and Usability. The analysis's findings shed light on many UX components like Value, Adoptability, Desirability, and Usability. The classification model was evaluated for accuracy, achieving a result of 89.243%. This means that the model correctly classified 89.243% of the user experience reviews in the evaluation dataset, indicating a satisfactory level of accuracy. However, there were some misclassifications in the remaining 10.757% of the reviews. Therefore, the research successfully developed a system that analyzed and classifies user experiences from customer reviews using MNB. The classification model demonstrated a satisfactory level of accuracy. The findings provide valuable insights and recommendations for improving the mobile application browsing experience based on user feedback and experiences.
ChainDetector: Identifying Anonymous Blockchain Traffic

Blockchain is promised for a wide range of applications in fields such as the Internet of Things and cryptocurrencies. Properly utilized, blockchain technology can play a positive role in the social economy. However, there are many gray industries, such as gambling and money laundering, which also build an anonymous blockchain to start illegal acts, and cause negative effects on the economy and society. There has not been any research on detecting anonymous blockchains and discovering nodes that use anonymous blockchains. We propose ChainDetector, a technique for detecting anonymous blockchains using network traffic and discovering nodes that use anonymous blockchains. We exploit the traffic features of blockchain at runtime and use CNN models to train the identification and discovery of blockchain traffic in the network. The results show that we can achieve 96% identification accuracy for some types of blockchain traffic.
Automated Framework to Extract Software Requirements from Source Code

Software maintenance and innovation are constant challenges across industries, especially as programming languages evolve with technology. Similarly, poor lexicon quality degrades program comprehension, increasing the effort required by developers to improve existing software products. To address these challenges, we propose a novel automated framework that extracts software requirements directly from source code using a baseline AI language model applied to a Java code base. Leveraging natural language processing techniques, the framework validates programs and generates easily readable requirements by analyzing file contents. The framework enhances agility and flexibility by providing comprehensive documentation for existing software systems. It caters to both experienced and less-experienced developers, offering an intuitive graphical user interface and enabling efficient identification and resolution of errors. The resulting output facilitates natural interaction through language processing. By automating the extraction process, the framework allows developers to better understand software systems, make informed decisions, and adapt to evolving needs.
Status Quo and Prospects of Automotive Cyber Threat Intelligence

Abstract. With the continuous improvement of the degree of telematics, automobiles gradually evolve from traditional means of transportation to aggregations of new network products, and the continuous enrichment of product functions also makes its network risk exposure increasing. Unlike traditional network products such as cell phones and computers, automobiles have the characteristics of faster movement, more complex internal systems and longer industrial chains, resulting in higher security risks and greater hazards. To hedge automotive cyber security risks, the first step is the automotive cyber threat acquisition and identification. Therefore, this paper summarizes the current situation of automotive cyber threat intelligence and presents a prospect of the future development trend of threat intelligence in the automotive field.
One-D Convolution Neural Network Models for Human Activity Recognition using mHealth Datasets

The task of identifying a person's specific physical activity based on sensor data is a complex problem in time series classification. It falls under the field of research called "Human Activity Recognition (HAR)." Successfully training a machine learning model for this task often requires significant domain knowledge and the application of signal processing techniques to extract useful features from the raw data. Deep learning methods such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have proven to be effective, as they can automatically extract features from the raw sensor data and achieve state-of-the-art results. In this study, we propose a multi-label One-Dimensional Convolutional Neural Network (1D CNN) for detecting human physical activity in the mHealth dataset. Our Deep Learning (DL) model demonstrates promising results, achieving an accuracy of 99.58% in categorizing various physical activities. Among the different approaches for feature extraction and movement prediction from raw sensor data, Convolutional Neural Networks (CNNs) are found to be the most effective method.
Estimating Task Priority in Japanese Disaster Chronology Logs

At the time of a disaster, various information is collected at the Disaster Response Headquarters, and based on this information, the headquarters makes decisions and provides instructions to relevant organizations and medical teams. The information that contains medical demands as well as meetings reports is recorded in short documents called Chronology Logs with the timestamp, sender, and receiver. Since the wide variety of the contents in the information, the staffs at the Disaster Response Headquarters need to evaluate the priority of the chronology logs to decide where to use the medical resources. Thus, an automatic priority estimation system of chronology logs can reduce the burden on the staffs. This can be expected to increase the amount of time to disaster management and medical cares. In this study, first we construct manually annotated data of priorities for chronology logs and then we apply neural-based estimation models to the annotated chronology logs. We employ the different modelings of the text embedding methods, which are Bag-of-Words (BOW), BERT and GPT-NeoX model to compare the effect of embedding. Since the task of priority estimation is not a simple text classification task but the classes are ordered, we apply the soft label approach to the error function. The experimental results reveal that the GPT-NeoX model demonstrates the highest F-score in priority estimation.
Extracting structured information from the textual description of geometry word problems

AI (Artificial Intelligence) is playing its role in every field as automation is the way forward. Solving geometry mathematics word problems (MWPs) automatically can help in smart tutoring and has a plethora of applications in many fields. To solve a geometry MWPs, multi-modal methods are needed to parse the question text using Natural Language Processing (NLP) and diagram using Image Processing (IP) techniques and combine the information from both. The information is then to be structured and appropriate axioms and theorems need to be applied to transform the question into intermediate equations which could be solved by equation solvers. In this entire pipeline, text parsing of the question content is a crucial component which is precisely the main focus of this paper. There have been attempts in the literature and techniques to solve geometry MWPs have been proposed, however they rely on input predicates and do not generate them automatically. In cases where they are generated, regular expressions are used which lack generalisation and scalability. This paper models the text parsing problem as a Relation Extraction and Natural Language Generation problem where from the question text structured information is generated in the form of predicates. We basically generate appropriate geometric tags by using a Named Entity Recognition (NER) annotator and then utilize these tags to generate predicates that represent the question in a machine readable formal language. To test the proposed approach, a custom dataset is created containing about 500 questions from standard elementary school-level Indian text books. It has been demonstrated through experiments on the custom dataset that the suggested method is capable of extracting geometric relations. To evaluate the generated predicates, we create ground truth predicates for each of the questions through skilled domain experts. We also design a unique method to evaluate the generated predicates with the METEOR (Metric for Evaluation of Translation with Explicit ORdering) metric. The experimental findings on this dataset indicate precision (P) of 0.70, recall (R) of 0.60, harmonic mean (Fmean) of 0.60, average penalty (p) of 0.12 and final METEOR Score (M) of 0.54. Furthermore, we experiment the proposed technique on another dataset. When tested on Geometry3K data set, a precision of 0.67, recall of 0.64, harmonic mean (Fmean) of 0.64, penalty (p) of 0.27 and final METEOR Score (M) of 0.42 is obtained.
Action-Item-Driven Summarization of Long Meeting Transcripts

The increased prevalence of online meetings has significantly enhanced the practicality of a model that can automatically generate the summary of a given meeting. This paper introduces a novel and effective approach to automate the generation of meeting summaries. Current approaches to this problem generate general and basic summaries, considering the meeting simply as a long dialogue. However, our novel algorithms can generate abstractive meeting summaries that are driven by the action items contained in the meeting transcript. This is done by recursively generating summaries and employing our action-item extraction algorithm for each section of the meeting in parallel. All of these sectional summaries are then combined and summarized together to create a coherent and action-item-driven summary. In addition, this paper introduces three novel methods for dividing up long transcripts into topic-based sections to improve the time efficiency of our algorithm, as well as to resolve the issue of large language models (LLMs) forgetting long-term dependencies. Our pipeline achieved a BERTScore of 64.98 across the AMI corpus, which is an approximately 4.98% increase from the current state-of-the-art result produced by a fine-tuned BART (Bidirectional and Auto-Regressive Transformers) model.1
Arabic Sentiment Analysis with Noisy Deep Explainable Model

Sentiment Analysis (SA) is an essential task for numerous real-world applications. However, the majority of SA research focuses on high-resource languages such as English and Chinese, while limited-resource languages like Arabic and Bengali receive less attention. Additionally, existing Arabic sentiment analysis methods based on advanced artificial intelligence (AI) approaches tend to operate as black boxes, making it challenging to comprehend the reasoning behind their predictions. This paper proposes an explainable sentiment classification framework for the Arabic language. We introduce a noise layer to different deep learning (DL) models, including BiLSTM and CNN-BiLSTM, to address the issue of overfitting. The proposed framework enables the explanation of specific predictions by training a local surrogate explainable model, shedding light on the reasons behind each sentiment prediction (positive or negative). Experiments were conducted on publicly available benchmark Arabic SA datasets, and the results demonstrated that the inclusion of noise layers in the DL model improves performance for the Arabic language by mitigating overfitting. Our method also outperformed several state-of-the-art approaches. Moreover, the introduction of explainability with the noise layer enhances transparency and accountability, making the model suitable for practical adoption in AI-enabled systems.
Explainable Machine Learning Models for Swahili News Classification

Although Swahili is considered a well-resourced language, challenges persist in utilizing it for Natural Language Processing (NLP) tasks, primarily due to the limited data availability required for these systems. For instance, obtaining sufficient Swahili news data for classification remains a significant obstacle. This paper addresses the problem of accurate Swahili news classification by leveraging classical machine learning (ML) models and deep neural networks (DNN). Our proposed method involves data acquisition, Exploratory Data Analysis (EDA), and employing modelling techniques using classical ML models, such as Support Vector Machine (SVM), Logistic Regression, Multinomial Naive Bayes, Random Forest, Gradient Boosting, Hard Voting, and Bagging, as well as DNN models including Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and CNN-Bi-LSTM + Attention. The models were evaluated using Area Under the Curve (AUC) metrics and accuracy. Our results demonstrate commendable performance for classical ML classifiers and DNN models, with accuracies above 75%. Notably, the CNN-Bi-LSTM + Attention model achieved an impressive AUC score of 97%. Additionally, explainability using LIME (Local Interpretable Model-agnostic Explanations) provided valuable insights into model decisions. This research contributes to Swahili natural language processing and lays the foundation for further explorations into transformer-based models for improved classification.
Cross-lingual Text Clustering in a Large System

The multilingual world needs systems that can cluster text written in multiple languages into the same thread or topic. Clustering multilingual text can be accomplished by translating and then clustering text in a canonical language, using multilingual embeddings to cluster articles in a shared embedding space, and via other language-independent methods. The performance and pitfalls of these various methods have not been well studied in the context of real-time clustering across documents written in many languages. We address this problem by generating a large dataset of news articles using a reference architecture that continuously indexed and clustered articles spanning 17 languages over the last 15 years. Through the analysis of these documents and their clusters, the clustering quality is shown to be dependent on the normalization of proper nouns, the types of georeferences, and the overall geographic focus of the document.
Named Entities Based on the BERT-BILSTM-ACRF Model Recognition Research

As a crucial first step in the process of constructing knowledge graph, the accuracy of named entity recognition determines the construction effect of the final graph. However, at present, Chinese named entity recognition methods still have many problems, such as long training time and lower accuracy. Hence, we come up with a BERT-BILSTM-ACRF entity recognition method that combines the "self-attention" mechanism. To begin with, Bert model is selected as the embedding layer, the text is vectorized, and the character position in-formation is obtained through the bidirectional Long Short-Term Memory network. Secondly, the internal relationship of the character sequence is further searched through the self-attention mechanism, and finally the final optimal sequence is decoded by the conditional random field model. To check the effectiveness of the BERT-BILSTM-ACRF model, the model is applied to the data set of the university course textbook" Da-ta Structure", and the result reaches 98.97%F1 value and 98.14%accuracy, which has good experimental results and certain practical value.
Explaining Prediction Uncertainty in Text Classification: The DUX Approach

Overcoming the black-box behavior of text classification models is crucial in order to maintain trustful, understandable and accountable predictions. While visual explanation techniques provide meaningful insights on why certain predictions occur, they fall short in explaining why a prediction might be unreliable or difficult to make. In this work, we investigate the potential of explaining prediction uncertainties of text classification outcomes to human users. First, we introduce a novel type of uncertainty explanation. Second, we conduct a human evaluation studying the benefits of uncertainty explanations in text classification. We found that uncertainty explanations provide valuable insights for human users to support the understanding of artificial decisions. Further, we demonstrate that uncertainty explanations are a useful and desired extension to traditional local explanations that cover only word relevance.