Reviewerly/exHarmony huggingface dataset:
Early-Stage Prediction of Breast Cancer Using Suggested Machine Learning Techniques

In this research, we investigate the severity level of breast cancer and generate alerts to mitigate the problem at an early stage. Based on the previous cases, many machine learning algorithms were used, and the frequent encountered problem was that of finding which genes are most informative. We had seen an evaluation in approaches to cancer treatment and their choices available. Hence, there is an utmost need for accurate prediction which can be improved at considerable level using machine learning approaches which are still under research. In the proposed work, few algorithms were used, and among them, the machine learning approach-based algorithm shows better results in comparison with other algorithms for predicting breast cancer. Proposed work shows an accuracy up to 97% using machine learning approaches. In this research, three algorithms are presented, one of which is a machine learning algorithm, which has the highest accuracy of the three algorithms analyzed. Using these algorithms, we can detect breast cancer at an early stage.
Steganography: The Art of Data Hiding

In the past year, the sender could send messages to the recipient directly. In the present scenario, different ways are available for information exchange. It is mandatory to maintain information security. There were so many methods available for this purpose. The most common way to security is steganography by embedding data into the cover image and creating a stego-image. Data (information bits) is buried in the pixel values of an image, making it impossible for an attacker to discover where the data is stored. Reading this paper will provide you with recently used steganography applications developed on the desktop or web-based. Today, technology is changing and easily accessible in the Android environment, so steganography technology still presents challenges for different image types, especially JPEG image formats, through this operating system. Here, a comparison of steganography approaches reveals patterns in recently developed techniques and the degree to which the system is deficient.
Token Economy Dynamics Toward Achieving a Sustainable Token Economy for Carbon Credit Exchange

Blockchain technology has introduced a range of cryptocurrency tokens linked to various commercial and non-profit endeavors. With the rise of cryptocurrencies, several token economies have emerged to allow stakeholders to profit from the tokens they earn. This has prompted numerous researches to determine the market trends in this arena. This paper first conducts two studies to understand the cause of fluctuations in crypto-token prices and attempts to understand the underlying causes before proposing a sustainable solution to a token reward-based carbon reduction initiative in which the stakeholders will be awarded crypto-tokens in accordance with the underlying algorithm. In this article, we put forward two hypotheses and tried to come to a conclusion by putting out a fitment factor for stabilizing our cryptocurrency from the influence of the supply and demand curve for a long-term token economy. This paper describes the methodology, findings, and interpretation of the two investigation remedies for the same.
Optimization of t-SNE by Tuning Perplexity for Dimensionality Reduction in NLP

The widespread availability and access of ever-growing datasets from different domains has resulted in creation of demand for automatic processes for extracting information from them. Knowledge Discovery in Databases (KDD) is the extraction of implicit and potentially useful information from the data stored in databases. In case of high dimensional datasets, having hundreds and thousands of dimensions, the analysts require tools for its correct interpretation and clear visualization (Arora et al. An Analysis of the t-SNE Algorithm for Data Visualization.”). Only with the correct visualization, the hidden patterns in the huge datasets can be inferred and cross-referenced what and what if scenarios can be examined by the domain experts. Also, it is evident from the ever-growing big data scenario that a tool is the need of the hour for faithful dimensionality reduction so as to clearly understand and comprehend the big data. In this paper, we have explored one such tool t-distributed Stochastic Neighbor Embedding (t-SNE) and covered the various aspects with respect to smaller datasets for understanding this magical dimensionality reduction algorithm. The objective of the current research paper is to get insights in how dimensionality reduction works and affects the data visualization in order to arrive at clear cut decisions. We have optimized t-SNE by specifically tuning perplexity for faithful dimensionality reduction targeting Natural language Processing (NLP) applications. Results with different perplexity values are presented and discussed.
A Study on the Data Securing Algorithms: The State of Art

Today’s world is predominantly dependent on the data that the user provides in the Internet platform for getting different services and for entertainment purposes. Irrespective of the kind of data such as financial data, personal information, etc., of an individual or others, it is the self or organization’s responsibility to secure the data that is stored or shared in the public platforms and in the private sectors. Hitherto the data securing techniques has a screaming evolution in cryptography, the hacking techniques is also originating comparatively. This monograph accounts about the threats that affect the data, different algorithms, and their metrics that are available for reinforcing the data, which plays an imperative part in every aspect of life.
A Brief Overview of E-Government Security

E-government is a complex phenomenon that involves e-data and e-exchange. The security of e-government functions is one of the fundamental priorities of a country or region, and the highest importance is given to securing e-government sites because they contain sensitive, private, and confidential information. This paper provides a general overview of the most critical aspects of e-government security. It defines the term “e-government” from various perspectives and discusses the most important issues affecting security. We begin by defining the concept and goals of e-government security and review the literature on e-government security. We then present the issues that affect e-government security and the security requirements needed to counter these issues. The presentation includes a literature review that discusses the risks identified by global research and shows how modern technology has been used to increase security. Finally, we identify the main challenges threatening e-government security today and summarize the discussion of the most important axes in the security of electronic government transactions.
A Survey on Image and Text Encryption Using ECC and Steganography

In contrast to well-known cryptosystems, image encryption that depends on elliptic curves offers high protection with a relatively smaller key size. This is why it is becoming a new trend in cryptography. Due to the computational expense of addressing the discrete logarithm problem and the factorization challenge, the recent demonstration shows that systems based on elliptic curves with finite rings may offer higher security. As ECs over finite rings require the computation cost to solve both the factorization and discrete logarithm problems, these cryptosystems may provide better security. This method consists of three main steps. In the first step, to mask the plain image, points of the elliptic curve are used with finite rings. EC over the finite rings-based diffusion matrix is created in step two which maps the elliptical curve of finite fields to elliptical curve of finite rings. A substitution box depending on the requested EC is created to generate high confusion in plaintext, which is then utilized to rearrange the pixels to generate the cipher image. The capability of encryption strategy that uses ECC will depend on how well the procedure of mapping pixels on an elliptical curve is done. Before developing any of these mapping techniques, factors like cost completeness, effectiveness, bandwidth, and invertibility used must be taken into account.
A Novel Deep Multi-head Attentive Vulnerable Line Detector

Detecting and fixing vulnerabilities in software programs before production is crucial in software engineering. Manual vulnerability detection is labor-intensive, especially for large programs, leading to the proposal of machine learning-based methods for automation. However, existing approaches primarily detect vulnerabilities at the function level, providing non-specific results that require additional developer effort to locate vulnerabilities. Detection at the line-of-code level is an underexplored area. In this paper, we propose a novel deep learning method for line-of-code vulnerability detection. Our hybrid neural network combines a memory network and multi-head attention mechanism. Through comprehensive experiments, we analyze the impact of each modification, demonstrating significant improvements in performance. Our approach outperforms existing methods for comparison, showcasing its effectiveness in vulnerability detection.
Evaluation of Attention-Based LSTM and Bi-LSTM Networks For Abstract Text Classification in Systematic Literature Review Automation

Systematic Review (SR) presents the highest form of evidence in research for decision and policy-making. Nonetheless, the structured steps involved in carrying out SRs make it demanding for reviewers. Many studies have projected the abstract screening stage in the SR process to be the most burdensome for reviewers, thus automating this stage with artificial intelligence (AI). However, majority of these studies focus on using traditional machine learning classifiers for the abstract classification. Thus, there remain a gap to explore the potential of deep learning techniques for this task. This study seeks to bridge the gap by exploring how LSTM and Bi-LSTM models together with GloVe for vectorisation can accelerate this stage. As a further aim to increase precision while sustaining a recall >= 95% due to precision-recall trade-off, attention mechanics is added to these classifiers. The final experimental results obtained showed that Bi-LSTM with attention has the capacity to expedite citation screening.
Meta-Statistical Deep Learning for Stochastic Time-Series Prediction in Agricultural Applications

Certain variables of interest in agriculture may preferably be mapped to distributions rather than to real numbers, e.g. the variable “apple (fruit) weight” on an apple tree at sampling times; hence, a stochastic time-series emerges. Since a cumulative probability distribution can be represented by a Type-1 Intervals’ Number (IN) in the mathematical lattice (F1,⪯), this work considers a stochastic time-series F: R → F1 and interprets it as a parametric Type-2 IN, symbolically F(x; t). Given samples F(x; ti), i ≤ k, the problem here is to predict F(x, ti) for i > k, assuming a deterministic relation among samples F(x; ti), i ε {1,...,n}. Prediction is pursued by a novel neural architecture, namely meta-Statistical IN Neural Network, or metaStatINNN for short, consisting of two neural modules in series: The first one, namely lowerNN, induces INs from data using deep learning; whereas, the second one, namely upperNN, is a well known INNN (IN Neural Network) that implements a function f: F1N → F1. This preliminary work considers a benchmark time-series of temperature distributions as well as artificial data sets of increasing cardinality. Computational experiments are demonstrated comparatively to a conventional neural network. The results by metaStatINNN are superior not only in terms of computational speed as well as prediction accuracy but also because a conventional neural network typically predicts only first-order data statistics, whereas the metaStatINNN may predict all-order data statistics; in addition, the metaStatINNN can explain its answers; moreover, an IN can represent big data.
Efficient Multi-hop Question Generation

Question generation is the parallel task of question answering, where given an input context and optionally, an answer, the goal is to generate a relevant and fluent natural language question. Although recent works on question generation have experienced success by utilizing sequence-to-sequence models, there is a need for question generation models to handle increasingly complex input contexts with the goal of producing increasingly elaborate questions. Multi-hop question generation is a more challenging task that aims to generate questions by connecting multiple facts from multiple input contexts. In this work we apply a transformer model to the task of multi-hop question generation, without utilizing any sentence-level supporting fact information. We utilize concepts that have proven effective in single-hop question generation, including a copy mechanism and placeholder tokens. We evaluate our model's performance on the HotpotQA dataset using automated evaluation metrics and human evaluation, and show an improvement over the previous works.
Distributions-free Martingales Test Distributions-shift

A standard assumption of the theory of machine learning is the data are generated from a fixed but unknown probability distribution. Although this assumption is based on the foundations of the theory of probability, however, for most learning problems we usually technically random shuffle the original datasets, such as random split into training and test datasets before the training model, to satisfy the assumption, and then we use the shuffled training dataset to train a machine learning model. However, honestly, for real-life learning applications, the data pairs are observed batch by batch under their own original order and it is not necessary to randomly shuffle the original order in advance. From a mathematical point of view, we test if the random shuffling will play a non-negligible influence on the generalization of learning machines. We reduce the problem of random shuffling into the problem of distribution-shift detection. This paper is devoted to testing the null hypothesis that random shuffling does not affect the generalization of learning machines and introduces a distributions-free martingales method against the null hypothesis. We report the five real-life benchmarks of experimental performance with the help of Support Vector Machines and a multi-layer perceptron model. The results show a bonafide fact that the distribution shift in itself of the data is an inescapable reality when we build machine learning algorithms as the original order.
BiLSTM-ANN Based Employee Job Satisfaction Analysis from Glassdoor Data Using Web Scraping

Job satisfaction among employees is a crucial factor in a burgeoning organization. Satisfied current employees mean more skilled employees will be interested in joining the organization in the future, which will reinforce the prosperity of the organization. Hence, employee job satisfaction is something that should be looked for both by the employees, and the employers. However, the job sector of a country often undergoes major changes, which affect the satisfaction of the employees. Analyzing anonymous employee job satisfaction may lead to an understanding of an organization's major pros and cons, which can help the employer determine the strategies they should adopt in the future. It can also be helpful for potential future employees to decide whether they would like to be a part of such an organization. Taking all of these into account, we attempted to analyze the job satisfaction of employees in this research. A deep hybrid learning-based architecture, BiLSTM-ANN is proposed to understand employee job satisfaction in a shorter period of time. To do so, we have accumulated employee reviews of 12 renowned IT and software companies in Bangladesh from Glassdoor.com. A sophisticated dataset is built to feed into the proposed architecture. Five polarities, namely super positive, positive, neutral, negative, and super negative, are assigned based on the rating given on Glassdoor.com. The evaluation shows that the proposed BiLSTM-ANN model outperformed the state-of-the-art architectures in different performance metrics. The model exhibits an F1 Score of 88.64% with a significantly less number of trainable parameters than other architectures.
DGC-CRL: Dependency Graph Convolution based Contrastive Representation Learning for Chinese Medical Question Matching

As one kind of domain-specific question answering (QA) systems, the medical QA systems require much more stability, fast system speed and response accuracy. Therefore, the retrieval based QA systems are more suitable, among which the deep semantic matching models become prevalent to be studied and they are playing the very important role on the quality of retrieval based medical QA systems. In this paper, we propose a two-stage solution (named with Dependency Graph Convolution based Contrastive Representation Learning) which includes a dependency graph convolution module to explicitly capture the semantic similarity between Chinese questions. At the first stage, we adopt the contrastive learning to further distinguish the similarity within the domain-specific corpus itself and learn discriminative textual representations. At the second stage, the down-streaming question matching task is benefited by using the newly-learned representations. In our experiments, we collect two Chinese medical datasets (CBLUE-STS and COVID-19) and the results can demonstrate that our proposed method is effective and general to different medical QA corpora. Also the ablation experiments indicate the proposed Dependency Graph Convolution module and contrastive learning method are both efficient.
Prototypical Quadruplet for Few-Shot Class Incremental Learning

The scarcity of data and incremental learning of new tasks pose two major bottlenecks for many modern computer vision algorithms.The phenomenon of catastrophic forgetting, i.e., the model's inability to classify previously learned data after training with new batches of data, is a major challenge.Conventional methods address catastrophic forgetting while compromising the current session's training.Generative replay-based approaches, such as generative adversarial networks (GANs), have been proposed to mitigate catastrophic forgetting, but training GANs with few samples may lead to instability.To address these challenges, we propose a novel method that improves classification robustness by identifying a better embedding space using an improved contrasting loss.Our approach retains previously acquired knowledge in the embedding space, even when trained with new classes, by updating previous session class prototypes to represent the true class mean, which is crucial for our nearest class mean classification strategy.We demonstrate the effectiveness of our method by showing that the embedding space remains intact after training the model with new classes and outperforms existing state-of-the-art algorithms in terms of accuracy across different sessions.
On Adversarial Vulnerability of Activation Functions in Automatic Speaker verification System

Automatic Speaker verification (ASV) is the task of authenticating the claimed identity of a speaker from his/her voice characteristics. Even though deep neural network (DNN) based ASV systems achieved improved performance, recent investigations revealed that they are vulnerable to adversarial attacks. Several defense strategies have been proposed in the literature, which operates either at data augmentation or network architecture levels. Activation functions with local competition and stochasticity were proposed to improve the adversarial robustness of the DNN models. This work critically analyzes the adversarial vulnerability of the winner takes all (WTA), and its stochastic counterpart stochastic local winner takes all (SLWTA) activation functions. It is observed that while the SLWTA activation function offers improved robustness to the white box attacks, it is equally vulnerable to transferable black box attacks. We show that the adversarial robustness of SLWTA activation to white-box attacks can be attributed to its inability to generate effective adversarial examples rather than built-in robustness against adversarial attacks.
HScore: A Target Attention-based Filter Pruning with Visualizing the Area of Feature Map

Neural network pruning plays an important role in the deployment on resource-constrained devices by reducing the scale of the network and the computational complexity. Different from existing pruning methods that only consider the amount of information filters contain, we think the distribution of information is more important to the model accuracy. Then we use visualization of feature map to consider the impact of the attention area on the network decision. In order to directly relate the attention area with confidence score, we propose HScore to use interpolation and normalization to visualize the attention area of feature map and calculate the confidence score by putting only the attention area into the model. The principle behind is that if the feature map focuses on an area which can achieve a higher classification confidence, then the corresponding channel is important to keep the accuracy of model. The confidence score of attention area represents the importance of the channel to the model and filters with smaller confidences would be pruned. The experiment results prove that HScore can prune more redundant filters while maintaining better accuracy. Notably, our method can reduce 68.6% parameters and 73.0% FLOPs of ResNet-110 even with 0.14% top-1 accuracy increase on CIFAR-10. With ResNet-50, we achieve a 55% FLOPs reduction with only a loss of 1.03% in the top-1 accuracy on LSVRC-2012, which has advanced the state-of-the-art.
An End-to-End Network for Upright Adjustment of Panoramic Images

Nowadays, panoramic images can be easily obtained by panoramic cameras. However, when the panoramic camera orientation is tilted, a non-upright panoramic image will be captured. Existing upright adjustment models focus on how to estimate more accurate camera orientation, and attribute image reconstruction to offline or post-processing tasks. To this end, we propose an online end-to-end network for upright adjustment. Our network is designed to reconstruct the image while finding the angle. Our network consists of three modules: orientation estimation, LUT online generation, and upright reconstruction. Direction estimation estimates the tilt angle of the panoramic image. Then, a converter block with upsampling function is designed to generate angle to LUT. This module can output corresponding online LUT for different input angles. Finally, a lightweight generative adversarial network (GAN) aims to generate upright images from shallow features. The experimental results show that in terms of angles, we have improved the accuracy of small angle errors. In terms of image reconstruction, In image reconstruction, we have achieved the first real-time online upright reconstruction of panoramic images using deep learning networks.