Reviewerly/exHarmony huggingface dataset:
International Law Shaping and Digital Eco-Systems: Between Sensationalism and Liberation

Traditional media of academic expression in international law have been increasingly ceding space to social media and blogs. Legal practice communities are, similarly, gradually abandoning press conferences and releases in favour of tweets and digital newsletters. This trend toward the digitalization of the eco-systems within which international law shaping takes place has a “liberating” potential for academic discourse communities; that is, it can increase the pool of eligible participants, while favouring progress through contestation and encouraging legal pluralism. At the same time, this trend provides fertile ground for academic over-contentiousness and sensationalism, at the expense of genuine dialogue and problem-solving. It can encourage “celebritism” and “click-hunting”, reduce the substance of legal argumentation to one-liners and lead to the proliferation of academic “battles of impressions”. As regards legal practice communities, the configuration and spatial particularities of social media platforms, and especially their immediateness and illusion of safe space they provide, may encourage rapid exchanges and reposts with minimal prior reflection. To wit, digital eco-systems make it possible for one politician to blow up a trade deal with a click. At all events, for better or worse, the internet is now the predominant conduit of social expression, and international law, as a form of social ordering, could constitute no exception within this context. The trend toward the digitalization of the ecosystems within which international law shaping occurs appears neither reversible, nor even necessarily desirable, given its various benefits. What is desirable and feasible, instead, is the mitigation of this trend’s various risks through an internal process of increased awareness. The contribution prefaced in this abstract represents a step in this direction.
Detection of Fraudulent Credit Card Transactions Using Advanced LightGBM Approach

Due to the rapid growth of e-commerce businesses, the use of credit cards for online purchases has increased exponentially and so has fraud. In recent years, it has gotten increasingly difficult for banks to detect fraud in credit card systems. Hence, it is important to have effective and reliable methods for identifying fraud in credit card transactions. Machine learning is critical for the detection of fraudulent credit card transactions. Banks utilize various machine learning approaches to forecast these transactions; prior data are collected, and new features are applied to improve prediction capability. Classical algorithms such as Logistic Regression (LR) and Random Forest (RF) are proven useful for detection of fraudulent credit card transactions. However, their performance is not satisfactory. Hence, we proposed an approach to improve the detection accuracy. This paper introduces an Advanced Light Gradient Boosting Machine technique to detect credit card frauds. In this method, a Bayesian-based Hyperparameter Optimization technique is incorporated to Light Gradient Boosting Machine (LightGBM) in modifying its hyperparameters. To validate its efficacy in identifying fraudulent credit card transactions, the proposed Advanced LightGBM technique was evaluated on two credit card transaction datasets that comprised both fraudulent and genuine transactions. The method outperformed current techniques in terms of performance.
AI-Based Real Time Object Measurement Using YOLO V5 Algorithm

In our paper various object's size will be measured from an input image and also by means of camera. We had chosen open computer vision algorithm and Yolo v5 algorithm for faster and simpler process. Open cv has large open-source library which will be useful for the machine learning and image processing users. It can be able to process the images and videos to identify the face of a person, objects and etc. Main aim of this project is to measure the dimension (width and height) of an object from a given image and the distance between the objects will be automatically computed and stored in the memory. It has a necessary ways to obtain the object dimension, first we need to find a pixel value, to find the pixel value we need to select one reference object from an image by considering the width & height of an image from distance and also the measuring parameter units such as inches /mm/cm.
Deep Learning-Based Assessment of Histological Grading in Breast Cancer Whole Slide Images

Histological grading of breast cancer samples is critical for determining a patient’s prognosis. Automatic grading of pathological cancer images promotes early diagnosis of the disease, as it is a long and tedious task for health professionals. In this paper, we propose an algorithm capable of predicting each component of the histological grade in Hematoxylin and eosin (H &E)-stained Whole-Slide Images (WSIs) of breast cancer. First, the WSI is split into tiles, and a classifier predicts the grade of both tubular formation and nuclear pleomorphism. Experiments are carried out with a proprietary database of 1,374 breast biopsy DICOM WSIs and evaluated on an independent test set of 120 images. Our model allows us to accurately classify the constitutive components of the histological grade both for all tumour samples and only-invasive samples.
Gestural Interpretation of Harmonic Dynamics in Tonal Modulation and Future Developments

We give a gestural interpretation of tonal modulations. We discuss some music software which uses classification results, especially the RUBATO® components for rhythmical and harmonic analysis. This chapter gives some perspectives relating to future directions in theory and software, especially regarding classification in performance.
Security Risk Management of the Internet of Things Based on 5G Technology

The Internet of Things brings security issues as well as high connectivity. This paper proposes a security risk decision based on the bounded rationality of users, aiming at the security problems of power Internet of things. First of all, a sparse node cognitive network is constructed for each user. Based on this simplified cognitive network, each user establishes his own security decision by minimizing his own security cost in the real world. These two stages constitute a game-to-game framework. Then the concept of a structured Nash equilibrium (GNE) solution is proposed to solve the game decisions of users in security management under this bounded rationality. At the same time, an iterative algorithm based on the nearest point is designed to calculate GNE. Finally, we analyze the case of intelligent power station in the Internet of Things, and the results show that this algorithm can successfully identify key users. Other users need to consider the decisions of these key users in the security decision-making process, and their own security decisions also reduce each other’s security management costs.
Research on 5G-Based Zero Trust Network Security Platform

The number and types of network access devices and functions in the cloud computing and big data environments are continuously increasing, so the ability to actively expand network services is needed to ensure that the security and privacy protection needs of network practices are met on a continuous basis. The traditional physical isolation network security protection architecture is facing challenges in new technology scenarios such as big data and mobile Internet, and urgently needs to be changed. Based on this, this paper proposes a 5G-based zero-trust network security platform to better adapt and serve the dynamic network application environment. The security architecture and model are firstly studied to provide security principles of the proposed platform. The implementation scheme of proposed platform is then further analyzed where security algorithms are suggested. The results show that the proposed platform demonstrates better performance in identity, program, configuration and behavior detection. Therefor, it is expected to better deal with the network risks under the new technology scenarios.
New Sequence Alignment Algorithm Using AI Rules and Dynamic Seeds

DNA sequence alignment is important today as it is usually the first step in finding gene mutation, evolutionary similarities, protein structure, drug development and cancer treatment. Covid-19 is one recent example. There are many sequencing algorithms developed over the past decades but the sequence alignment using expert systems is quite new. To find DNA sequence alignment, dynamic programming was used initially. Later faster algorithms used small DNA sequence length of fixed size to find regions of similarity, and then build the final alignment using these regions. Such systems were not sensitive but were fast. To improve the sensitivity, we propose a new algorithm which is based on finding maximal matches between two sequences, find seeds between them, employ rules to find more seeds of varying length, and then employ a new stitching algorithm, and weighted seeds to solve the problem.
A Brief History of Money

The history of cryptoassets, like this book, begins with money. In particular, with the established monetary system. Understanding what makes money and how it has evolved is essential to understand the value proposition of cryptoassets—Bitcoin in particular. Therefore, let us start with the basics by going back several millennia to identify the fundamentals of money. As you will see, they naturally make a case for what is coming next.
Approach of Different Classification Algorithms to Compare in N-gram Feature Between Bangla Good and Bad Text Discourses

Bangla Natural Language Processing (BNLP) is a newish challenge in Artificial Intelligence. With the rapid expansion of the Bangla language, it is now adopted on a variety of platforms, including social media, communication platforms, news media, and so on. The classification of text documents becomes an important factor in resolving the challenge of information organization and knowledge management. This study uses five supervised classification methods to explore the categorization of Bangla text discourse using N-gram (unigram, bigram, and trigram) features. Bangla text discourse is collected from different platforms such as social media, personal Bangla blogs, and people's utterances in order to accomplish the research goal. After collecting data, the most difficult part of the Bangla language preprocessing is completed, which includes adding contractions, removing punctuations, encoding, and a variety of other operations. For this study, 1499 text documents were initially used, with 1459 Bangla text discourses being used after preprocessing. To convert the text into a token, N-gram feature methods utilizing TF-IDF-Vectorizer are used. During the experiment phase, unigram, bigram, and trigram feature techniques are used to apply Logistic Regression (LR), Decision Tree Classifier (DTC), Random Forest (RF), Multinomial Naive Bayes (MNB), and K-Nearest Neighbors (KNN) models to the dataset. In the unigram and bigram features, Multinomial Naive Bayes (MNB) outperformed all other classifiers, with the highest accuracy of 89.31% and 86.94%, respectively. The trigram feature of K-Nearest Neighbors (KNN) achieves a maximum accuracy of 84.25%, and the proposed model can classify the Bangla text document as Good or Bad Discourse.
Kernel-Level Pruning for CNN

Deep learning solves many real-life problems with excellent accuracy, designing a model from scratch face two challenges. The first one is that the dataset size for many applications is relatively small, which leads to overfitting. The second challenge is that the computational cost for training will be very high when we have a huge dataset. Most developer prefers transfer learning where we choose a standard pre-train model like VGGNet, ResNet, GoogLeNet. These pre-train models are trained on a similar problem with a huge dataset. For example, for the Image Classification problem, most developers choose a model trained on the ImageNet dataset. ImageNet dataset has 1000 images of each 1000 different classes, so the total number of images is 1000 $$\,\times \,$$ 1000 of size 224 $$\,\times \,$$ 224 each. Pre-train models are extensive and computationally expensive during inference time, making it challenging to deploy in real-life applications. The recent trend in research is to compress deep neural networks to reduce computational cost and memory requirements. In this paper, we focus on kernel-level pruning. We have reduced pruning sparsity to 30 to 40% with a nominal drop in the accuracy to 7%.
Strategic Risk Management for Low-Code Development Platforms with Enterprise Architecture Approach: Case of Global Pharmaceutical Enterprise

Global Pharmaceutical Enterprises (GPEs) have increasingly adopted and promoted Low-Code Development Platforms (LCDPs) to aim for higher agility and resilience to the rapidly changing market demands. While LCDPs facilitate rapid application development by democratising application development platforms to non-professional developers (“citizen developers”), these organisations are challenged to establish strategic conformance with Low-Code applications regarding interoperability, scalability, security, and vendor lock-in. Since these LCDP challenges relate to the organisation’s enterprise strategy, the organisations should implement mitigations at the strategic level where Enterprise Architecture frameworks play an important role. This paper investigates how the LCDP challenges can be managed with the “Adaptive Integrated Digital Architecture Framework (AIDAF) with Design Thinking Approach.” A case project in a GPE verified that the AIDAF with Design Thinking Approach successfully supported managing a Low-Code application project and achieved strategic conformance with the developed Low-Code application. In addition, challenges and future issues for this research area are presented.
Precursors of Cryptoassets

Despite being sometimes presented as such, Bitcoin did not emerge in isolation. The first cryptoasset had many precursors that either tried to accomplish the same goal or developed similar technology for other purposes. None of them achieved mainstream adoption like Bitcoin, but they directly influenced how cryptoassets developed, even beyond Bitcoin’s conception.
Optimization of Accuracy of Tweets for Coronavirus Pandemic Using BERT-Based CNN Model

Optimization accuracy of tweets for coronavirus pandemic is the analysis from the primary collection of data. In this context, it assures descriptive form of analysis of the collected primary data. It also assures summarizing the collected data and information with descriptive form of statistics. It interprets the results without any bias regarding the primary data collected from survey due to its quantitative nature. In this paper, coronavirus pandemic using hybrid bidirectional encoder representations from transformers (BERT) with convolution neural network (CNN) is present. At the start of training of CNN, filters are initialized with random values, and as the model gets trained by back-propagation algorithm using the errors in estimating the actual output, the weights of the filters are modified to identify specific patterns in inputs. We show that combining CNN with BERT is better than using BERT and CNN on its own, and we emphasize the importance of utilizing pre-trained coronavirus models for downstream tasks. The hybrid model is implemented Python platform and calculates accuracy, precision, recall, and F1-score.
A Stable Model for Learningand Memorization in Over-Parameterized Networks

Highly over-parameterized neural networks have the ability to completely shatter the entire dataset. Despite their huge capacity, over-parameterized networks show strong algorithmic stability. In cases where semantic and localized information is completely lost, over-parameterized networks show learning. In this paper, the learning and memorizing abilities of over-parameterized neural networks on CIFAR data have been tested. Four architectures are used in this proposed work: VGG (modified), tNet, smallNet, and convNet. On binary classification, all models achieved approx. 100% training accuracy and approx. 96% test accuracy or higher. On a ten-class classification, all models achieved approximately 100% training accuracy except smallNet (80% training accuracy). VGG (modified) achieved 75% test accuracy, smallNet 78%, tNet 69%, and convNet 85%. When semantic and localized meanings are distorted by shuffling the bytes, all models achieve approximately 100% training accuracy, except smallNet (93% and 28% training accuracy), on binary and ten-class classification. On binary classification, all models achieved test accuracy of approximately 74–75% and on ten-class classification, 18–21% test accuracy, which is better than random guessing.
Hausa Character Recognition Using Logistic Regression

Optical character recognition systems enable human–machine interaction by using technology to discern published or handwritten letterings inside digital representations of physical documents; for instance, a scanned paper document, and these are widely utilized in a variety of applications. Other categorization techniques have been used to study Yoruba, English, Arabic, and other Latin characters. In light of this, it is worth noting that only a few articles have directly tackled the subject of Hausa character recognition using logistic regression (LR). The work develops a technique for the recognition of Hausa characters using LR. The system’s user interface was developed with C-sharp (C#) programming language. Data were gathered from many authors and scanned photographs were treated to some level of preprocessing to improve the quality of the digitized images, including text segmentation into individual characters, feature extraction, and recognition using LR. It was deduced that the recognition accuracy of the LR indicated 89% which outperformed other classifiers used on Hausa characters.
Fault Detection Method for Power Distribution Network Based on Ensemble Learning

The State Grid Shanghai Institute of Electrical Science and Technology fault statistics system requires implementation of data analysis and index control capability improvement. This requires the purchase of corresponding hardware and ongoing algorithm and rule optimization in accordance with the application’s actual needs. By enhancing the loss function of the unsupervised LSTM prediction model based on the enhanced experience of the semi-supervised time series anomaly detection algorithm based on LSTM autoencoder and attention mechanism, a semi-supervised LSTM prediction model is proposed in this paper to address the issue that the prediction model is affected by the abnormal data in the training set. This paper further proposes a semi-supervised anomaly detection algorithm based on ensemble learning after verifying the enhanced semi-supervised LSTM prediction model. The algorithm consists of two parts: an anomaly detection model based on LSTM autoencoder and attention mechanism, and a semi-supervised LSTM prediction model. In order to create a semi-supervised model with superior performance, we combined the semi-supervised LSTM prediction model with the semi-supervised LSTM autoencoder model after applying the improved experience of the LSTM autoencoder to the LSTM prediction model. Both algorithms can successfully address the issues of high marking costs and significant time series data reliance in order to produce improved time series anomaly detection results.
Improved Diagnostic Accuracy of Rheumatoid Arthritic by Image Generation Using StyleGAN2

By generating images using StyleGAN2, we aimed to increase the number of test and training images. We also aimed to further improve diagnostic accuracy by generating targeted types of joint images through image manipulation. Experimental results show that image generation with StyleGAN2 is effective and improves the diagnostic accuracy of the system.
JPEG Decoding With Nonlinear Inverse Transform Network and Progressive Recurrent Residual Network

JPEG is the most widely used image compression format, especially with the popularity of mobile or portable devices. However, the quality of a decoded JPEG image is usually degraded by compression artifacts such as blocking effect and ringing effect, especially at low bit rates. Recently, some convolutional neural network (CNN) based methods have been designed to solve the above problem. These methods take the problem as post-processing and only add a CNN-based post-processing network after a JPEG decoder to improve the image quality. In this paper, the JPEG decoding with nonlinear inverse transform network and progressive recurrent residual network (dubbed as JDNet) is proposed. JDNet can reconstruct JPEG images of different quality factors (QF) with only one model. In JDNet, first, the CNN-based inverse transform network (iTNet) is proposed to learn the nonlinear mapping from DCT coefficients to their corresponding original pixels, which is against the linear mapping in previous DCT networks. iTNet can reduce error propagation during inverse DCT and obtain more accurate reconstruction. Furthermore, iTNet can be combined with any JPEG post-processing method to improve its performance. Second, the progressive recurrent residual network (PRRN) is proposed for local feature extraction in the designed post-processing network which utilizes local and nonlocal similarities in multi-scale space (LNLMS) to further enhance the decoded image quality. The experimental results show that compared with JPEG, ARCNN, DnCNN and STRRN, the average gains of JDNet are 1.88dB, 0.63dB, 0.35dB, 0.24dB on Live1 dataset, 2.61dB, 1.11dB, 0.69dB and 0.29dB on Urban100 dataset, and 1.91dB, 0.69dB, 0.37dB and 0.13dB on BSD500 dataset, respectively.
RePI: Research Paper Impact Analysis

The purpose of this study is to analyze the impact a Research Paper has on other Research Papers which cite it. It can be used by students and researchers to understand a domain and the reach of their work. The method of studying the relevance by reading all citing documents is time-consuming. RePI provides a web-based tool to visualize the key aspects of a paper. It makes use of NLP to extract keywords and APIs to analyze metadata such as the number of cited papers, influential citation count, year of publication, etc. This data can be obtained by a unique identifier for the Research Paper such as a DOI number, etc. RePI also introduces the concept of Impact Metric Ratio which quantifies the Paper’s relevance. It is extrapolated using the influential citation count and makes it easy to compare the papers efficiently. Current methodologies provide techniques to measure journal impact very well, but Research Paper Impact Analysis has scope for further research.