Reviewerly/exHarmony huggingface dataset:
LexRank and PEGASUS Transformer for Summarization of Legal Documents

Legal documents are generally verbose and contain lots of dense legal text. Lawyers often must study prior cases, and reading such documents can be time-consuming. Such documents may also be incomprehensible to the ordinary public who lack legal understanding. Enormous amounts of legal data online have made access to case files and documents simple. Hence, automatic summarization and paraphrasing using machine learning (ML) of such documents has become an important area of research to make the documents comprehensible for lawyers and the ordinary public. In this paper, previous approaches to automatic summarization are reviewed by us and we compare their output on six different documents. We also provide a summarization and paraphrasing technique using the LexRank algorithm and PEGASUS transformer for abstractive summarization. The summary produced by the LexRank model outperforms the other models tested by obtaining a higher ROUGE-F1-score. The final summary retains the important information from the source document and is paraphrased to a simpler language.
Bitcoin’s Replacement Cost Valuation

Chapter 16 explained how electricity costs could be used as input for the valuation of a cryptoasset based on a proof-of-work consensus mechanism. To not break that chapter’s flow, the following computation-heavy example for Bitcoin’s valuation was moved to this appendix.
Frontend Technologies

With web applications situated on the interface between users and application functionality, and with the need to present well designed and highly usable frontends on various types of devices, frontend technologies have gained lots of attention in the developer community.
An Error Sensitive Fuzzy Clustering Technique for Mammogram Image Segmentation

Mammogram image segmentation plays a crucial role in detecting the lesion region in the breast masses. In this context, the key challenging issue is the false positive detection of pectoral muscles or fatty tissues as the lesion region. Further, the presence of noise and imaging errors degrade the segmentation accuracy. To address these problems, we suggest an Error Sensitive Fuzzy Clustering (ESFC) technique for delineating the different tissue regions in the mammogram images. The basic idea is to incorporate an error sensitive regulating factor in the objective function of the Fuzzy C-means (FCM) algorithm for enhancing the clustering performance in the noisy environment. The suggested technique is experimented with multiple volumes of mammogram images from standard databases. State-of-the-art methods are compared. Quantitative assessment is done using standard evaluation indices. The results indicate better quality with the proposed method.
Study on Random Generation of Virtual Avatars Based on Big Data

With big data growing rapidly in importance over the past few years, Virtual Reality (VR) display technology has gained widespread attention. Especially, virtual avatars with unique identification are becoming increasingly important. Traditional generation methods are complicated, time consuming and low image quality. To improve the quality and rendering speed of generated virtual avatars, we present a set of optimized parameters of GAN based on deep learning. The experimental results showed that the best image quality is achieved in the case of ADAM (Optimization Function), BCE (Loss Function), l-r = 0.00075 (Learning Rate), the epoch = 200. The questionnaire survey showed that the recognition accuracy could be up to 84.29%. The conclusion of the questionnaire survey is consistent with our experimental results.
Measuring Impact of Text Features and Image Features on Classifier’s Performance for Heterogeneous Data Formats

The social media has become increasingly popular for personal and professional use. Therefore, a significant amount of uncontrolled data has contributed by the users of social media. The uncontrolled information may be harmful for some individual or a community. Therefore, in this paper, we proposed to investigate the different text and image feature selection techniques that can be used for social media-based text and image data. The aim of this investigation is to demonstrate how we can get the optimal classification accuracy with limited and linguistic dynamic text and Web images. In this context, we design an experiment for demonstrating the effectiveness of different feature selection models for text and images. The experiment involves three text feature selection techniques, three image feature selection technique, and two classifiers for measuring the influence of feature selection on the classifier performance. The experiments on Twitter data for text and image datasets found the following consequences: (1) Small size of text classification is complex due to limited amount of features and variation of vocabulary, (2) individual features are less accurate as compared to their combinations in both the scenarios (i.e., text and images), (3) for text and image classification, SVM is more computationally expensive as compared to ANN, and (4) ANN-based classification provides higher accurate classification as compared to SVM-based combinations.
A Survey of Integrating Federated Learning with Smart Grids: Application Prospect, Privacy Preserving and Challenges Analysis

With the widespread promotion of smart grid, the power time series data collected by smart meters also increases rapidly. How to collect these data safely and effectively, analyze and utilize them, and provide better power supply service has become a hot topic of current research. The federated learning technology has attracted much attention from researchers in recent years and various federated learning-based applications have been utilized due to its characteristics of distributed, security, encryption, and reliability. In the development of smart grids, federated learning has been applied for data analytics, privacy preserving, energy management, and so on. This paper is aimed at exploring the feasibility of applying the federated learning framework to the area of smart grids. We conclude the analysis of power time series data, discussing the tribulations and solutions in the process of privacy preserving in the smart grid, and highlighting different challenges of federated learning with the smart grid. We present a summarization among federated learning-based methods with the smart grid for a variety of purposes, with the aim to draw a comparison among federated learning-based methods in the smart grid from different aspects.
Comparative Data Oversampling Techniques with Deep Learning Algorithms for Credit Card Fraud Detection

Financial businesses have a significant challenge with credit card fraud. Approximately more than one million individuals from different countries are losing money yearly due to credit card fraud. Due to privacy concerns, there is an absence of research studies that can be conducted on the analysis of real credit card data. In this study, various deep learning algorithms were applied for credit card fraud detection using a real credit card dataset. The methodology presented in this paper has also been focused on comparative of performance testing of different data resampling based on oversampling techniques like Random, SMOTE, BorderLine SMOTE, SVMSMOTE and ADASYN along with deep learning neural networks inculuded LSTM, MLP, and ANN models. For verifying the performance of the proposed models to detect fraud, the used dataset was splitted into 10% validation, 20% testing and 70% training. Furthermore, standard performance metrics such as accuracy, sensitivity, precision, and f1-score metrics were employed to assess and selects the best model. After comparing the results of the experiments performed for fraud detection, it was found that the LSTM model provided the best results for credit card fraud detection with achieving 99.9% accuracy, compared to the other models.
KMetaTagger: A Knowledge Centric Metadata Driven Hybrid Tag Recommendation Model Encompassing Machine Intelligence

The emergence of Web 3.0 has left very few tag recommendation structures compliant with its complex structure. There is a critical need for newer novel methods with improved accuracy and reduced complexity for tag recommendation, which complies with the Web 3.0 standard. In this paper, we propose KMetaTagger, a knowledge-centric metadata-driven hybrid tag recommendation framework. We consider the CISI dataset as the input, from which we identify the most informative terms by applying the Term Frequency - Inverse Document Frequency (TF-IDF) model. Topic modeling is done by Latent Semantic Indexing (LSI). A heterogeneous information network is formalized. Apart from this, the Metadata generation quantifies the exponential aggregation of real-world knowledge and is classified using Gated recurrent units(GRU). The Color Harmony algorithm filters out the initial feasible solutions into optimal solutions. This advanced solution set is finalized into the tag space. These tags are recommended along with the document keywords. When the suggested KMetaTagger's performance is compared to that of baseline techniques and models, it is found to be far superior.
Defending Wireless Receivers Against Adversarial Attacks on Modulation Classifiers

Deep learning has been adopted for a wide range of wireless communication tasks, including modulation classification, because of its great classification capability. However, deep learning models have been shown to also introduce risks and vulnerabilities. For instance, adversarial attacks craft and introduce imperceptible perturbations that compromise the accuracy of deep learning-based modulation classifiers on wireless receivers. Therefore, in this article, we propose a novel wireless receiver architecture that enhances deep learning-based modulation classifiers to defend them against adversarial attacks. Our experimental results show that our defense technique significantly diminishes the accuracy reduction that is caused by adversarial attacks by protecting modulation classifiers at least 18% more than existing defense techniques.
An Improved Multi-Instance Learning Model for Postoperative Early Recurrence Prediction of Hepatocellular Carcinoma Using Histopathological Images

HCC(Hepatocellular carcinoma) is one of the primary liver cancer around the world, and it produces a high mortality rate in clinical. Surgical resection is the first-line treatment choice for patients with HCC, but the problem is patients will still have a great chance of recurrence after surgery. Therefore postoperative early recurrence prediction of HCC patients is necessary, which can guide physicians to manage the individualized follow-up so that we could increase the survival time of patients. MIL(multi-instance learning) model is mostly utilized in histopathological image analysis, and it has achieved promising results in most tasks, but it has one major limitation: the conventional MIL model loses the histopathological spatial structure information when generating the slide-level representation on flattened instance embeddings. In this light, we revisit the conventional MIL model and propose an improved MIL model to tackle the limitation. Our proposed method consists of three stages, the first stage is tissue patching, which is the same as the conventional MIL; the second stage is early recurrence probability heatmaps(ER-ProbMaps) generation, which is conducted by a well-trained patch-level prediction model on the tissue patches of the same slide; the final stage is to produce the slide-level recurrence prediction by a slide-level prediction model. We train the slide-level prediction model on obtained heatmaps to complete our postoperative early recurrence prediction task. Our experimental results show that our proposed improved MIL model can achieve superior performance than the conventional MIL model in our task.
Valuation, Risk and Hedging of Bitcoin Mining Operations

The recent volatility in crypto-currency prices caught many Bitcoin miners, lenders, and financial institutions off guard, highlighting their lack of risk management practices for financing Bitcoin Mining ASICs.The lack of understanding of the sensitivity of ASIC machine pricing to BTC prices, coupled with the absence of practical tools for managing that price exposure, has contributed to borrower defaults borne by miners and in some cases the lenders in the space.While over-the-counter (OTC) derivatives and hash rate contracts \cite{Bitooda2020}, \cite{Luxor2022} have emerged as hedging tools for miners, they are still not widely used due to their illiquidity, high costs, and associated counterparty risks.This study aims to provide practical tools for pricing and hedging BTC mining operations by the following contributions.(1) We introduce the option bundle methodology in \cite{Yaish2020}, \cite{Hashimoto2019}, and show how it can be used to practically price BTC mining ASIC machines.(2) We conduct an empirical analysis using historical OTC machine price data spanning three years and propose the effective value and the implied payback time as a key variables in analysing machine price economics.(3) Using these tools, we demonstrate the outsized riskiness of unhedged financing arrangements then, miners are fundamentally levering long call option positions.(4) Finally we propose a \textit{Machine Replication} methodology for hedging ASIC machine sensitivity to BTC price risk without using or incurring the high costs of OTC derivative contracts.All code for this work can be found in \cite{Serenity2023}.
Robust Image Watermarking Using Arnold Map and Adaptive Threshold Value in LWT Domain

Digital data is growing day by day, and there is a need for an algorithm that can provide better security against intentional and non-intentional attacks. In this paper, blind image watermarking scheme, that uses lifting wavelet transform (LWT) and Arnold map is proposed using haar wavelets. LWT is preferred over conventional wavelet because of its energy compaction property and its ability to tolerate more distortion than any other conventional transform. In the proposed scheme, keys have been applied on various levels for enhancing security. The adaptive threshold is used for embedding binary watermarking bit 0 or 1. No visual deprivation is shown by the watermarked image as the PSNR value approx to 39 dB in case of no attack and more than 31 dB in case of various attack. The dependability of the watermarking scheme is tested and compared against the existing schemes that demonstrate that the proposed watermarking scheme offers adequate image quality and outperforms other watermarking scheme in terms of robustness.
Sparsity Preserved Pareto Optimization for Subset Selection

Subset selection that selects a limited number of variables optimizing many given criteria, is a fundamental problem with various applications such as sparse regression and unsupervised feature selection. Among the existing algorithms for subset selection, evolutionary algorithms (EAs) have achieved great performance in obtaining the subsets with high quality. However, as the selected subsets are sparse, i.e., most variables of these solutions are zero, most existing EAs for subset selection problems pose great challenges to find the optimal solutions effectively and efficiently, especially when the size of original set is large. To this end, we propose the SPESS approach which is a sparsity preserved evolutionary algorithm for subset selection. To be specific, we design two sparsity preserved operators (i.e., sparsity preserved crossover operator and sparsity preserved mutation operator), which can be used in SPESS to ensure the sparsity of the generated solutions in the process of Pareto optimization. The main advantages of the proposed sparsity preserved operators are that they can obtain high quality solutions and improve the search efficiency of the algorithm. According to the experimental results on 12 sparse regression problems and 12 unsupervised feature selection problems, the proposed SPESS is superior over the state-of-the-arts in solving subset selection tasks and the effectiveness of the proposed two sparsity preserved operators is also verified.
A Distributed Data Fusion Approach to Mitigate Node Redundancies in an IoT Network

Data redundancy is one of the prominent causes of excess energy expenditure and network lifetime degradation in the Internet of Things (IoT)-based sensor networks. Generally, the wireless sensor network (WSN) is equipped with low computational capability nodes which are vulnerable to the data redundancy issue when dealing with the raw data. We introduce a model in which the data sensed by the sensors containing redundancies are analyzed using Kernel density estimation (KDE) in combination with Pearson’s divergence with the distributed approach to calculate the global probability density function between the data points of two consecutive periods. The final data without redundancies are fused to the sink node using bacterial foraging optimized extreme learning machine (BFO-ELM) which also classifies the redundant nodes. The simulation performed on three real-world datasets shows that the proposed technique records the accuracy of about 95% and the energy consumption reduction by approximately 30% and promises a high network lifetime when compared with other distributed and centralized methods.
Research on Data Watermark Tracing System in Hadoop Environment

The application of big data requires effective tracing of data transmission and flow processes, so as to achieve effective determination of data authenticity and security. If network data is not effectively supervised, unexpected events such as data loss, leakage or tampering will occur, resulting in network data threats and risks that cannot be traced and responded to. The traditional data tracing methods are found difficult to meet the processing needs of massive data. Hence, data tracing in Hadoop environment is considered to better deal with the risk of data loss, tampering and leakage in the process of multiple data distribution. In this paper, the Hadoop environment system and its application in the field of data watermark tracing is explored. By analyzing the data tracing model and its implementation, a data watermark tracing system in Hadoop environment is established and the tracing process is examined. The experiments are designed and the efficiency of the proposed system is validated.
Explainable AI Model to Minimize AI Risk and Maximize Trust in Malignancy Detection of the Pulmonary Nodules

In the last decade, many AI-based CAD models were developed which hardly could convince the high-stake decision makers to accept the machine-specified decisions. In this research work, it was attempted to unbox a supervised AI model built using XGBoost on lung cancer detection by using XAI tools—two post-hoc methods (LIME and SHAP) and one ante-hoc method, to provide satisfactory explanations to medical practitioners, thereby minimize the AI risk factor in implementation of the model and reinforce the trust to the medical experts and patients in accepting such model. In this paper, the results of all three XAI tools were illustrated using heat maps to select important input biomarkers that contributed more in detection of the benign or malignancy state of the pulmonary nodules. Finally, the supervised AI model was rebuilt using only those important input features and it was found out that the metrics like specificity, precision and the AUC of the newer model under the ROC curve were giving better results in prediction of the detection of lung cancer. It is a study to explore how XAI tools highlight the contributions of input features in a CAD AI model for the prediction of lung cancer detection and how that AI model’s performance can be fine-tuned based on the outputs of XAI methods.
Design of an Active Data Watermark Detection System

Nowadays, with the rapid development of network technology and the rapid increase of information, the importance of data is becoming more and more obvious, and the requirements of data security are also improving. Using the detection and tracking technology based on data watermarking can quickly and effectively obtain the information of data watermarking, and trace the source of possible attacks, so as to improve the security of data. The traditional passive data watermarking detection and tracking technology has some limitations in the real-time tracking of possible attacks. Therefore, this paper proposed an active data watermark detection system. The related watermark embedding and detection technologies are firstly studied. The system architecture is proposed where primary modules and submodules are described. The proposed system is designed to be integrated into the network nodes and servers in order to embed and detect the watermark information in real time. In addition, the watermarking parameters can be adjusted according to the sensed network environment. The proposed system can be implemented in the industrial departments that own a large amount of sensitive data so that the data transmission can be monitored and traced.
Object Classification Using ECOC Multi-class SVM and HOG Characteristics

Nowadays classification of images into labeled multi-classes is one of the major research problems. In the field of artificial intelligence, the Histogram of Oriented Gradients (HOG) is employed for extracting features to identify which class a particular image belongs to. HOG counts the occurrence of gradient orientations in localized sections of an image. HOG features contain information about both edge and its direction other than edge detection which only contains edge information. Based on HOG features we classified images in the given dataset using Error-Correcting Output Codes (ECOC) based multi-class Support Vector Machine (SVM) classifier. The performance of ECOC-based multi-class SVM is compared with Selection based on Accuracy Intuition and Diversity algorithm (SAID) to find out the outperforming classifier over the given dataset. Based on confusion metrics, it is observed that ECOC-based multi-class SVM performs better than the SAID algorithm.
Implementation of Legal Documents Text Summarization and Classification by Applying Neural Network Techniques

Rhetorical role labelling of legal judgements and text summarization of legal documents help in various downstream tasks like computing document similarity, legal documents search, etc. So, it is important to work on these important problems. In these problems of our project, the corpus contains judgements from the Supreme Court of India that are provided by the Artificial Intelligence for Legal Assistance (AILA). These problems are challenging as legal case documents are usually not well-structured and variation of opinions exists between legal experts in rhetorical roles labelling and assigning relevance to legal judgements. Many prior works in these domains used conditional random fields (CRF) over manually hand-crafted features. Our research objective is to analyse the performance of deep learning models, namely BERT, RoBERTa, and XLNet over the CRF method as well as observe the nature of the corpus. We used the BERT-extractive-summarizer to generate a summary from the relevant legal judgements. Our best-performing model for legal judgement classification achieves a macro-averaged F1-score of 0.58 and for legal document summarization achieves an accuracy of 0.64 in finding the relevance of judgements and an average F1-score of 0.62 in summary evaluation’s ROUGE-1 scores. This paper presents various methodologies for rhetorical role labelling multi-class classification and finding relevant sentences using binary classification.