Reviewerly/exHarmony huggingface dataset:
Automatic Financial and Economic Information Processing System Based on RPA and Big data Algorithms

In the practice of financial management, the main links of financial and economic information processing include data collection, data integration, data analysis, and report preparation. Manually processing this type of information is not only inefficient but also error-prone. This article proposes an automatic financial and economic information processing system based on RPA (Robotic Process Automation) and big data algorithms, uses RPA technology to build the overall framework of the system, and conducts in-depth analysis of financial and economic information based on big data algorithms. This system includes a data collection module, a data analysis module and a report generation module, which can realize the functions of automatic collection, processing, analysis and report preparation of financial and economic information, and solve the problems of low efficiency and high error probability in traditional manual processing of financial and economic information, and provide effective data support for enterprise decision-making. The experimental results show that the work efficiency of the financial and economic information automatic processing system based on RPA and big data algorithms can reach 92.3%. The development and application of this system can improve financial work efficiency, reduce human cost input, and provide effective data support for enterprise decision-making.
Research on Key Information Extraction Algorithm in Image based on Visual Communication Technology

With the development of image processing and artificial intelligence technology, image key information extraction algorithm has become an indispensable tool and technology in many fields. By comparing traditional algorithms with algorithms based on deep learning, such as convolutional neural network (CNN), it is found that the key information extraction algorithm based on visual communication technology has higher accuracy and stability. When this algorithm is used to process multiple different types of images, its highest accuracy can reach 0.95, and its highest recall rate can reach 0.93, which performs well in various algorithms. Therefore, this paper reveals the advantages of image key information extraction algorithm based on visual communication technology in various image processing applications, which has good promotion and application value. This algorithm can improve the accuracy and stability of image processing and analysis, and provide more effective solutions for image processing and analysis tasks in different fields.
Attention U-Net for Low Light Image Enhancement

Image enhancement has been a very significant part of computer vision. The image captured in low light has a poor quality and includes a lot of noise which has a negative impact on various learning models. In the past decade, Artificial Intelligence technology has advanced more rapidly than ever, and a wide range of its applications, including autonomous vehicles, AR, VR, speech recognition, picture identification demands enhancement of images. Currently, the available methods give promising results with optimally lit images but have a poor performance against low-light images. The image captured in low light has a poor quality and includes a lot of noise which degrades the performance of vision-based algorithms. To make the details buried in the image more prominent, reducing the noise and blur from the image is crucial. The advancements in deep learning have introduced numerous techniques to improve the image quality under dim light. A great deal of research has been devoted to this in the past to boost the quality of images clicked in low light conditions. Numerous deep learning-based approaches are proposed to solve the issue. This paper presents a thorough survey of deep learning-based models and improves an attention-based convolution network - Attention U-Net aimed at improving image perception and interpretability in an environment with poor illumination.
Forgetting User Preference in Recommendation Systems with Label-Flipping

Recommendation systems play a crucial role in identifying users’ preferences based on their historical interaction records and those of other users. However, the ability to “forget” certain users’ preferences is indispensable for ensuring user privacy and maintaining recommendation accuracy. It is essential to accommodate a user’s request to exclude their behavioral data from the recommendation system when necessary. Likewise, if certain data corrupts the system, its impact should be removed to restore system performance. In this paper, we propose FlipRec, a general and efficient framework for recommendation models to “forget” the preferences of specific users while retaining the model’s performance for all other users. Our concept of forgetting user preferences is inspired by the label-flipping attack, a technique where the labels of some training samples are inverted to adversarially manipulate the weights of the trained model. FlipRec adjusts the recommendation model weights to forget the targeted users by flipping their interaction records $y \in \{ 0,1\}$. To preserve the model’s performance for the remaining users, we augment the fine-tuning data with samples from users who have interacted with the same items as the targeted users. This ensures minimal impact on these users during the “forgetting” process. FlipRec has been validated on both contentbased recommendation models and collaborative filtering models. The experimental results show that FlipRec outperforms stateof-the-art unlearning methods in terms of efficiency, the ability to forget targeted users, and the preservation of performance for the remaining users.
Flood Detection from Satellite Images Using Self-Attention Empowered EfficientNetV2 Mechanism

Floods occur when water overflows onto normally dry land and are a destructive natural disaster. In recent times, deep learning models have demonstrated their remarkable capabilities in identifying objects and classifying images. As a result, the utilization of deep learning in remote sensing, especially for the crucial task of flood detection, has become an increasingly prominent and promising area of research and application. This study proposes a lightweight yet powerful feature extraction framework by leveraging a self-attention(SA) based EfficientNetV2. The EfficientNetV2 architecture serves as the backbone of the model and has exhibited effectiveness in image classification tasks. By incorporating self-attention mechanisms, the model can capture spatial relationships within remote sensing images, enhancing its accuracy and efficiency in flood detection. The model was trained and assessed using the SEN12-FLOOD dataset, and the outcomes demonstrate that the suggested framework exhibits enhanced flood detection performance, achieving an accuracy of 95.9%.
Optimizing CNN Model Performance for MNIST and CIFAR Classification Using Rectified Sigmoid and ReS Activation Functions

In this work, we implemented and evaluated the accuracy of two commonly used activation functions in convolutional neural networks (CNNs), Rectified Linear Unit (ReLU) or/and Sigmoid, as well as a newer activation function called Residual (Res) activation function. trained CNN models with each of these activation functions on MNIST and CIFAR datasets and evaluate their performance. Overall, this work provides insights into the performance of different activation functions in CNNs.in CIFAR dataset Res Activation function provides slightly better accuracy than the Leaky ReLU and PReLU activation function and rectify_sigmoid achieving a slightly higher accuracy of 89.82%, compared to the activation function of res.
Image Text-to-Speech Converter with Desired Language Translation

Pictorial data is the most expressive representation of an information using the graphics and designs. Mostly pictorial text data which is needed by the user are unable to access due to a language barrier (pictorial data may be in a different language) or because the image itself prevents to doing so. On platforms like the internet or social media, millions of photographs are shared with billions of written sentences. The issue arises when a person finds an image on the internet that contains text data but, in a language, they are unfamiliar with. Presentations are crucial in the corporate world for expressing concepts through visual information. When dealing with people from different cultures, a language barrier is likely to arise. This is significant on a worldwide scale. Image text translation is desperately needed there. It takes a lot of effort and human capacity to address this problem manually by utilizing books and dictionaries. There are numerous websites and businesses on the internet that are active and offering the service of image text translation in speech form as a solution to this issue. Not just for this, but also when there is account case of a user who is blind and speaks a foreign culture's language and is unable to translate any pictorial text data. There, the issue of speech conversion and translation also surfaces. Many social media platforms are working to implement the speech recognition and translation of pictorial data so that it is simple to share the information with every user of their platform.
Voice Stimulated Inclusive Multiplayer Game Development with Speaker Recognition

Gaming technologies have now become an inseparable part of audio processing applications. Several innovations have been made to improve gaming experience. Now the user does not necessarily need a joystick to interact with the computer, his gestures and vocal stimuli can act as controllers. This study provides a novel solution towards developing inclusive games for all with the use of natural language processing and human computer interaction. We discuss the development of a multiplayer game that uses speaker recognition to differentiate between multiple player’s vocal stimuli, thus allowing players to play voice-controlled games on the same device in proximity.
E-Learning Mobile Application Integrated with Augmented Reality

Over the years, the usage of e-learning platform has increased as students of all ages and background have been using the applications to support and assist their educational needs. The aim of this study is to help students with poor academic performance improve their performance. Questionnaires were distributed to 30 participants in order to gain their perspective, interpretation and opinion on the current system trends, e-learning and augmented reality. The findings of the questionnaire were used to improve or emphasize on from the current market applications. The analysis result shows that students are interested with e-learning platforms due to its efficiency and effectiveness. However, it has to be taken into consideration that the participants prefer to use a free e-learning platforms rather than the paid one.
An improved local mean-based pseudo nearest neighbor classification using different distance metrics

Local mean-based pseudo nearest neighbor (LMPNN) is an efficient classification with accurate prediction, novel methods, and broad application prospects. This paper proposes an improved LMPNN classification using Euclidean, Cosine, Correlation, Spearman, and Jaccard distance. This classifier tests and compares which of the five effective distance metrics can achieve the lowest average error rate, and studies the universal best distance metric of the LMPNN classification to improve the performance of the improved LMPNN. In order to achieve the above goals, this paper conducted extensive classification error experiments on ten representative datasets from the UCI repository. Experimental results show that Cosine can achieve higher prediction accuracy than the Euclidean often used in standard LMPNN.
Breast Cancer Prediction in Mammogram Images using EfficientNet Based Hybrid Deep Learning Model

Scientists are interested in this area since many women with breast cancer do not seek medical attention until the disease has progressed. Genetics, hereditary factors, and inactivity are all contributors to the development of breast cancer. Breast cancer is death in women in India, and its incidence is rising across the country. Women over the age of 45 have an increased risk of contracting this illness. When compared to prose, pictures tell a thousand words. As computing power has increased, a number of methods have emerged for automatically gleaning previously undetectable details from photographs. Among the many fields where the processed photos have been put to use is medicine. The presence of breast lumps in the breast area is a necessary and sufficient factor in the progression of breast cancer in women. Therefore, a more trustworthy method is required to lessen the amount of pointless biopsies performed while diagnosing breast cancer. Doctors can make better judgements and reduce the number of needless biopsies with the use of computer-aided diagnostic technologies. For this purpose, the study applied the deep learning-based model for classification. Initially, three various steps such as normalization, contrast enhancement and noise removal during pre-processing. K-means clustering algorithm is used for segmentation, then the features are extracted by using pre-trained model called EfficientNet. The hybrid model called sparse auto-encoder based Long-short Term Memory (SAE-LSTM) is used. The proposed model efficiency is tested on mammographic image analysis society (MIAS) in terms of various metrics. The results indicated that the projected model is achieved a better performance than other counterparts.
Data Extraction and Analysis of Generative NFT Art Collections

Non-Fungible Tokens (NFTs) have emerged as a popular form of digital asset ownership and trading on Web 3.0. In particular, generative NFT art collections such as Cryp-toPunks, Bored Ape Yacht Club, Mutant Ape Yacht Club, and others have seen significant hype and monetary value, with some individual NFTs selling for millions of dollars. With such high stakes involved, it is critical for NFT collectors and those interested in starting their own collections to have access to reliable and comprehensive data analysis tools that can help them make informed decisions. This research paper presents a data extraction and analysis tool for generative NFT art collections that aims to address this need. The tool leverages publicly available data from blockchain ledgers to scrape and extract information on various collections, including details such as ownership, transaction history, rarity, and more. The extracted data is then transformed from an unstructured to a structured format and analyzed using various statistical and machine-learning techniques to identify patterns, trends, and insights. Through the use of this tool, NFT collectors and individuals interested in starting their own collections can gain valuable insights into the performance and value of different generative NFT art collections over time. For example, the tool can help users identify which collections have experienced the highest growth in value, which NFTs within a collection are the most valuable, and which collections are most likely to increase in value in the future. Overall, this research paper presents a comprehensive data extraction and analysis tool for generative NFT art collections. By enabling users to conduct better research and make more informed decisions, the tool has the potential to increase the success and profitability of NFT collectors and those interested in starting their own collections in rapidly evolving NFT markets.
Cancer Classification Revolution: Employing Advanced Deep CNNs for Multi-Class Detection of Breast Irregularities

early detection plays a pivotal role in reducing breast cancer mortality rates significantly. Detecting breast cancer at an early stage can increase positive outcomes by up to 8%. While radiologists analyze breast images using techniques such as mammograms, X-rays, and MRIs, accurately identifying features like micro calcifications, lumps, and masses remains challenging, leading to high false positives and false negatives. Recent advancements in deep learning and image processing offer hope for more advanced tools in early breast cancer diagnosis. This study focuses on constructing a Deep Convolutional Neural Network (CNN) capable of recognizing and categorizing various breast abnormalities, including lumps, asymmetries, calcifications, and carcinomas. Unlike previous research that primarily differentiated between benign and malignant cancers, this approach allows for more specialized disease treatment. The methodology involves transfer learning using a pre-trained model (ResNet50), fine-tuning it with the available dataset, and developing an enhanced deep learning model emphasizing learning speed. The proposed deep learning model achieved an impressive 88% accuracy in classifying masses, calcifications, carcinomas, and asymmetrical mammograms. This progress holds significant promise for improving the overall accuracy of breast cancer diagnosis and subsequent treatment decisions. The study aligns with ongoing efforts to enhance early detection and treatment, potentially saving lives through more efficient medical procedures.
Vision Paper: Uncovering Illegal Firearm Transactions in Cryptocurrency Networks

This research delves into the dual nature of cryptocurrencies, offering financial opportunities while addressing the surge in digital criminal activities, especially in illegal firearms trafficking. The decentralized nature of blockchain technology presents unique challenges for law enforcement, necessitating innovative approaches to uncover and prevent criminal transactions. The study utilizes advanced data mining, analytics techniques, and machine learning models to analyze transactional graphs of prominent cryptocurrencies, aiming to identify and thwart transactions linked to illegal firearms trafficking.Additionally, the paper provides an overview of the current state of blockchain technology research and introduces the ambitious Ceasefire project. This initiative outlines a systematic approach to combat illegal firearms trading within the cryptocurrency domain, leveraging cutting-edge techniques and strategic partnerships with leading blockchain analysis platforms.By proposing a novel method, this paper enhances the ability to detect illicit firearms trading in cryptocurrencies, specifically focusing on Bitcoin and Ethereum networks. The approach combines predictive modeling with rule-based matching to identify potentially suspicious addresses in both ecosystems. This empowers authorities to track individuals attempting to conceal their transactional activities by transitioning between Bitcoin and Ethereum, thus bolstering efforts to maintain the integrity of decentralized financial systems.
Exploring the Value of Pre-trained Language Models for Clinical Named Entity Recognition

The practice of fine-tuning Pre-trained Language Models (PLMs) from general or domain-specific data to a specific task with limited resources, has gained popularity within the field of natural language processing (NLP). In this work, we re-visit this assumption and carry out an investigation in clinical NLP, specifically Named Entity Recognition (NER) on drugs and their related attributes. We compare Transformer models that are trained from scratch to fine-tuned BERT-based Large Language Models (LLMs) namely BERT, BioBERT, and ClinicalBERT. Furthermore, we examine the impact of an additional Conditional Random Field (CRF) layer on such models to encourage contextual learning. We use n2c2-2018 shared task data for model development and evaluations. The experimental outcomes show that 1) CRF layers improved all language models; 2) referring to BIO-strict span level evaluation using macro-average F1 score, although the fine-tuned LLMs achieved 0.83+ scores, the TransformerCRF model trained from scratch achieved 0.78+, demonstrating comparable performances with much lower cost, e.g. with 39.80% less training parameters; 3) referring to BIO-strict span-level evaluation using weighted-average F1 score, ClinicalBERT-CRF, BERT-CRF, and TransformerCRF exhibited lower score differences, with 97.59%/97.44%/96.84% respectively. 4) applying efficient training by down-sampling for better data distribution further reduced the training cost and need for data, while maintaining similar scores -i.e. around 0.02 points lower compared to using the full dataset. This This TRANSFORMERCRF project is hosted at https://github.com/HECTA-UoM/TransformerCRF
Hate speech and hate crimes: a data-driven study of evolving discourse around marginalized groups

This study explores the dynamic relationship between online discourse, as observed in tweets, and physical hate crimes, focusing on marginalized groups. Leveraging natural language processing techniques, including keyword extraction and topic modeling, we analyze the evolution of online discourse after events affecting these groups. Examining sentiment and polarizing tweets, we establish correlations with hate crimes in Black and LGBTQ+ communities. Using a knowledge graph, we connect tweets, users, topics, and hate crimes, enabling network analyses. Our findings reveal divergent patterns in the evolution of user communities for Black and LGBTQ+ groups, with notable differences in sentiment among influential users. This analysis sheds light on distinctive online discourse patterns and emphasizes the need to monitor hate speech to prevent hate crimes, especially following significant events impacting marginalized communities.
Continuous Soccer Pass Detection: A Comparison between Traditional and Streaming Machine Learning Methods

Sports analytics has grown significantly through continuous data streams from wearable tracking devices. This article explores the performance of traditional Machine Learning methods (ML) compared to Streaming Machine Learning approaches (SML) in the context of sports analytics, specifically in identifying passes during a soccer match. The study utilizes leg movement data from wearable sensors on players’ shoes to differentiate between pass and nonpass actions. Balanced, imbalanced and rebalanced datasets are created and analyzed. Several traditional and streaming algorithms are tested, and statistical analyses are performed to assess their performance. The findings indicate that Streaming Machine Learning can achieve comparable or better performance than traditional methods, especially on larger rebalanced datasets. This research highlights the potential of Streaming Machine Learning for online sports analytics and suggests future directions for exploring player tendencies and edge device adaptability.
Enhancing Cyber Threat Hunting: A Visual Approach with the Forensic Visualization Toolkit

In today’s dynamic cyber threat landscape, organizations must take proactive steps to bolster their cybersecurity defenses. Cyber threat hunting is a proactive and iterative process aimed at identifying and mitigating advanced threats that may go undetected by traditional security measures. Rather than waiting for automated security systems to flag potential threats, threat hunting involves actively searching for signs of malicious activity within an organization’s network. In this paper, we present the Forensic Visualization Toolkit, a powerful tool designed for digital forensics investigations, analysis of digital evidence, and advanced visualizations to enhance cybersecurity situational awareness and risk management and empower security analysts with an intuitive and interactive tool. Through practical, real-world scenarios, we demonstrate how FVT significantly amplifies the capabilities of cybersecurity professionals, enabling them to effectively identify, analyze, and respond to threats. Furthermore, it is important to highlight that FVT has been integrated into, utilized, and continually enhanced within various EU-funded research projects over recent years.