Reviewerly/exHarmony huggingface dataset:
Evaluation of an Enhanced Secure Quantum Communication Approach

The gathering and sharing of information is the foundation of our society. A message must be transmitted safely in a number of situations. QKD is a modern security innovation that helps to solve security problems and defend against hackers. QKD is already inseparable from the BB84 procedure; hence, academics are continually working on it. The purpose of this research article is to enhance existing security using polynomial key generation. As a result, this study will analyse the BB84 protocol’s key generation process with the polynomial-based key. Further, researchers will get insight into the various challenges involved in secure communication.
A Conceptual Prototype for Transparent and Traceable Supply Chain Using Blockchain

Over the past several years, blockchain technology has gained prominence and acceptance. However, there hasn't been much empirical study on the scope of blockchain technology in the supply chain. As a result, data traceability and transparency are also restricted. To illustrate the development of supply chain management using blockchain in the real world, various real-time use cases are shown. To compare our findings to earlier findings and determine what is the impact of blockchain on different sectors, a research direction has been considered. Creating a conceptual framework for supply chain management utilizing blockchain technology is the goal of this research. The framework suggests an idea to use cryptographic hash function to generate a unique ID which will help us to uniquely identify any product throughout the supply chain by enhancing supply chain traceability with the application of machine learning and internet of things. This study takes into account ongoing blockchain developments and also suggests that in comparison with permissionless blockchain, permissioned blockchain is a better match.
Use of Machine Learning Algorithms to Analyze the Digit Recognizer Problem in an Effective Manner

A remarkable and significant problem is Digit Recognition. The digit recognizer problem refers to the task of correctly identifying handwritten digits from images. The problem of handwritten digit recognition must be understood in the context of a variety of challenges since the manually written digits do not have uniform sizes, thicknesses, positions, or directions. The individuality and variety of compositional approaches of different people also have an impact on the example and presence of the digits. This paper looks at how machine learning (ML) methods can be used to solve the “digit recognizer problem” in an effective way and compares the performance of several machine learning algorithms, including support vector machine (SVM), convolutional neural network (CNN), multilayer perceptron (MLP), random forest (RF), and logistic regression (LR), on the MNIST dataset of handwritten digits. The results show that neural networks, specifically CNN, achieve the highest accuracy for the digit recognizer problem. Furthermore, this paper discusses the advantages and limitations of each approach and provides insights on how to improve their performance.
TableSF: A Structural Bias Framework for Table-To-Text Generation

Table-to-text generation is to generate a description from the tabular data. Existing methods typically encoded table content in a fixed order and relied heavily on the table row or column sequence. They generated error text descriptions when the row or column sequence changed. To solve the above problems, we proposed a novel structural bias framework that encodes tables using a modified self-attention mechanism. The framework captures the connectivity of cells in the same row or column through structural bias attention, distinguishing important cells from unimportant cells from a structural perspective. The structural bias attention will be added on top of the full self-attention, which can obtain the full structural information of the table. Experimental results show that this method generates better text descriptions on the public dataset and accomplishes a better understanding of the structured tables. This method not only obtains the relationship between cells but also improves the robustness of the pre-trained model.
PO-DARTS: Post-optimizing the Architectures Searched by Differentiable Architecture Search Algorithms

In neural architecture search, differentiable architecture search algorithm has become one of the mainstream methods. However, no matter in the search or evaluation stage, the architecture is repeatedly stacked by two kinds of Cells, namely Normal Cell and Reduction Cell, respectively. This undoubtedly limits the performance of the evaluation architecture to a large extent due to the architecture restriction, resulting in sub-optimal performance. In order to alleviate the impact of architecture restriction on network performance, this paper proposes to post-optimize the architecture searched by differentiable architecture search algorithms by freezing the architecture parameters of partial Cells and further searching other Cells to bring more diversity into the stacked Cells. The proposed post-optimizing methods consist of the global post-optimizing search method and the local post-optimizing search method, respectively. The performance of the evaluation architecture can benefit from the diverse stacked Cells with less architecture restriction. In the experiments, the proposed post-optimizing method is applied to the mainstream differentiable architecture search algorithms such as DARTS and P-DARTS, and superior results have been achieved on CIFAR-10 and CIFAR-100 datasets. Moreover, the proposed method can obtain the post-optimized architecture with limited computing resources.
Semantic Information Mining and Fusion Method for Bot Detection

Twitter bot detection is a crucial yet challenging task. The existing bot detection methods have limited semantic information mining ability for a large number of tweets posted by social media users. To address this challenge, we propose SIMF, which stands for Semantic Information Mining and Fusion. SIMF leverages a pre-training scorer to rank a large number of a user’s tweets, and filters them based on specific rules. Additionally, SIMF preprocesses tweets and integrates multiple information encodings to obtain user representation, which enhances its ability to capture a diverse array of fake bots. Our comprehensive experiments on the benchmark TwiBot-20 demonstrate that SIMF outperforms other competitive algorithms.
AuraCodes: Barcodes in the Aural Band

Barcodes are well-known tools initially developed in the twentieth century to tackle issues of inventory management in supermarkets, by using optical scanners to turn visible black-and-white lines into machine-readable binary code. This evolved over the years into many forms, like Codabar, EAN-8, and UPC-A. A major advancement in this technology was the representation of these codes two-dimensionally, giving rise to Data Matrixes, MaxiCodes, and the popular QR Codes. All these variants use visible surface area as the mode of transmission. However, these forms require line of sight, close physical proximity, and adequate lighting. This work proposes a novel way to generate and detect barcodes using audio frequencies spread across time, instead of surface area.
Data Prevention Protocol for Cloud Computing Security Using Blockchain Technology

Cloud computing is a new developing paradigm which provides many platforms to researchers. It is a promising technology which has continuously revolutionized different sectors in every possible factor. It is a promising technology which provides which provides new dimension, growth and many tremendous factors to the users as well as researchers. In that particular synergy fusion of cloud and block-chain technology helps the service providers and users to provide and utilize the resources which can be authenticate and meanwhile privacy also preserved. This joining method is unique, eco-friendly. This joint method enlightens the various path or dimensions of computing which will able to solve even more than security related issues. The major concern now in cloud is security. It is very much essential to service providers to sustain and maintain customer’s privacy and data loss. The present review paper focused on data security and block-chain using customer’s data privacy solutions. Also an in-depth review is made for improving the enhancement of ability or functionality of cloud systems and security improvement. Block-chain technology in different will help the cloud systems in different aspects and it shows the potential in performance and privacy improvement.
Light-Weight High-Performance HRNet for Human Pose Estimation

HRNet has demonstrated its effectiveness in feature representation with high-resolution network in human pose estimation. However, given that HRNet is still the state-of-the-art human pose estimation method, its storage and computation are expensive. In this article, we aim to reducing its storage and computation by introducing several new techniques into different parts of HRNet including the depth-wise convolution, the self-attention, the PixelShuffle, the swish activation function, the H-Random erasing etc. Based on the new planning of the HRNet, the proposed enhanced HRNet maintains the high-resolution information and at the same time reduces the computation burden largely. Comprehensive experiments are designed on the well-known benchmark datasets of MPII and COCO. The state-of-the-art results on both benchmark datasets are achieved while at a 62 $$\%$$ of the parameters and a 40 $$\%$$ of the computation comparing with HRNet.
Using Blockchain Smart Contracts to Regulate Forest Policies

This paper explores the implementation of blockchain algorithms, specifically using the Ethereum platform and Solidity programming language, for the management of forest policies, including the Clean Water Act (CWA) and the Sandalwood Act 2022. The use of smart contracts can help automate the compliance checks for these policies and ensure that all parties adhere to the terms outlined in the acts. The deployment of blockchain technology helps improve the implementation and monitoring of these policies by creating a decentralized system of record-keeping, reducing the complexity of the current system, and providing a secure and transparent record of all transactions. The adoption of blockchain, implemented through Solidity, can increase transparency and accountability, reduce administrative overhead, and improve data security in the management of forest policies.
Monitoring Algorithm for Datafication and Information Control for Data Optimization

Datafication is a technique of collecting, storing and analyzing the data in digital format. This process has become progressively predominant as advances in technology has made it easier to collect and store large amount of data, in order to identify pattern trends, trends and anomalies. These algorithms are also sued to ensure the integrity and quality of the data, and to detect and prevent the fraudulent activities. Information control is the process of managing and regulating the access to data. This study emphasizes that monitoring algorithm are used to restrict the access to sensitive data based on the identity of the user, their role and the context of the request. These algorithms can also encrypt or obscure sensitive data to protect it from unauthorized access. In total, monitoring algorithms has an important part in datafication and information control, as they help organizations to make sense of the vast amounts of the data they collect, and to protect sensitive information from unauthorized access. However, it is important to note that these algorithms are not full proof and should be part of a larger security strategy.
A Comparison of the Key Size and Security Level of the ECC and RSA Algorithms with a Focus on Cloud/Fog Computing

The rapid development of data generation and demand for real-time processing and analysis in the Internet of Things (IoT) has led to the emergence of new issues. In response, an extension of cloud computing, known as fog computing, aims to overcome these problems. It aims to bring computation, storage, and network services closer to the end-user by using a distributed computing infrastructure. With the ever-increasing need for secure communication in the digital age, the importance of encryption algorithms cannot be overstated. This study provides an analysis of two commonly used encryption techniques, Elliptic Curve Cryptography (ECC) and Rivest-Shamir-Adleman (RSA), with a specific focus on cloud/fog computing. The research compares the key size and security level of the ECC and RSA algorithms and evaluates their suitability for usage in resource-constrained fog computing environments. ECC, a newer technique, offers the same level of security as RSA but uses smaller key sizes, making it more resource-efficient. The research highlights that the choice of encryption technique for cloud/fog computing depends on the system's specific requirements. The study concludes that ECC could be recommended for improved security and faster performance without putting unnecessary strain on the computing resources. RSA, on the other hand, has been widely accepted and proven its security over the years. The comparison shows that the choice of encryption algorithm for cloud/fog computing depends on the specific requirements of the system.
Gesture Controlled Iterative Radio Controlled Car for Rehabilitation

When children sustain serious muscular or bone injuries its treatment includes confining the bone which leads to prolonged immobilization of muscles which further causes muscular weakness. Physical therapies can be used to deal with muscular weakness, however, they can be quite monotonous and difficult to engage children in such activities. So to make this tedious task entertaining for children we have devised a technology based Radio Controlled Car. This car works on the data based on hand gesture, which are received from the accelerometer. These data are then transmitted via transceiver module to the car to control the motors. Here the exercises will be considered as gestures to control the car movement. The gestures used for controlling the car are part of the exercises prescribed to the patients. As per these exercises, the hand movements were executed by selecting random participants and found the angle mapping with hand gestures to be accurate with the expected outcome. The deviation of the angle values from the expected values were found to be almost negligible thus making a robust and effective system for standard therapy.
Automatic Extraction of Software Requirements Using Machine Learning

System requirement specification (SRS) documents specify the client’s requirements and specifications in the software systems. Requirement engineering is a mandatory phase of the Software Development Life Cycle (SDLC) that includes defining, documenting, and maintaining system requirements. As the complexity increases it becomes difficult to categorize and prioritize the various Software Requirements (SR). Different natural language processing methods such as tokenization, lemmatization is used in the text pre-processing phase followed by Term frequency-inverse document frequency (TF-IDF). The aim of this research is to compare existing Machine Learning algorithms to evaluate which algorithm is able to efficiently classify the system requirements. The algorithms are assessed on two parameters precision and accuracy. The results obtained showed Decision Tree (DT) could identify all types of requirements except portability. But the accuracy of Support Vector Machine (SVM) is highest with 78.57% for the publicly available dataset than that of DT which has an accuracy of 61.42%
Using Ensemble of Different Classifiers for Defect Prediction

One of the most important characteristics of a software is its quality. Software defects are more likely to occur when software designs become more complicated in response to rising demand. Software quality is increased by testers by repairing defects. Consequently, the study of defects greatly raises the quality of software. Due to the increased amount of defects brought on by software complexity, manual defect detection can become an extremely time-consuming procedure. This encouraged researchers to create methods for the automatic detection of software defects. Due to the under/over fitting issues, existing methods for predicting software defects often have low accuracy (Matloob in IEEE Access 9:98754–98771, 2021). The use of machine learning is widespread in the realm of software defect prediction. The defect prediction effect of the single ML model isn’t optimal, according to the results of the available research. Hence, we suggest an ensemble learning method to resolve the issue, in which different machine learning algorithms are combined to produce an accurate defect prediction.
Automated Spurious Product Review Auditing System

As the technology mounts, people tend to adopt themselves to the changing technology. Technology growth comes in the form of development in electronic goods and services, improvement in the life style of the people, research fields and more. Considering about the ways of purchasing of things people need and want, the blooming technology led people switch from physical markets to online shopping sites. But spurious reviews are the main problem which is identified using signed interface and genetic algorithm in this paper. The result is compared with mechanisms which identify fake reviews using machine learning and collective hyping approach. This method proves to have elevated accuracy compared with other methods.
Refining Object Localization from Dialogues

The ability to locate objects accurately is crucial for robots’ application in large-scale environments. However, objects’ location changes constantly in practical scenes, so the robot’s knowledge of objects’ distribution needs to update correspondingly. We propose a method to refine objects’ location from dialogues between humans and robots. When a robot is searching for an object, if the information about the object’s location is insufficient, it constantly raises questions to a human informer until it acquires adequate location information. Furthermore, every possible target object found during the searching process is photographed and sent back to the human informer for judgment. If all possible locations have been searched and the target is not found, the robot requests more specific location information and starts a new search attempt after attaining the information. The interaction process repeats iteratively until the robot finally finds the target. The interactions with humans ensure the success and accuracy of searching. We deploy our method on a mobile robot and conduct language parsing experiment and locating objects experiment to evaluate the performance of our method. Moreover, we compare our method with a one-time message method from a previous work to demonstrate our advantage.
An Improved Method for Text Classification Using Contrastive Learning

Quality objection texts provided by customers are crucial for steel e-commerce platforms. How to classify these texts and quickly provide corresponding solutions is very important. This article presents an approach based on deep learning for classifying steel quality objection text. With the help of deep learning, the proposed model can automatically extract semantic features from unstructured text without manual intervention. Different from traditional methods, to further improve the model’s performance, this article employs contrastive learning to learn more robust representations. The evaluation of this approach on a large dataset of steel quality objection text demonstrates that it outperforms traditional methods and achieves excellent results. The proposed approach enables the identification of quality issues quickly, which is of utmost importance for the steel industrial practical implications. By reducing the time and resources required to identify quality objections, the proposed approach can help to improve the efficiency of the steel industry.
AHEAD: A Triple Attention Based Heterogeneous Graph Anomaly Detection Approach

Graph anomaly detection on attributed networks has become a prevalent research topic due to its broad applications in many influential domains. In real-world scenarios, nodes and edges in attributed networks usually display distinct heterogeneity, i.e. attributes of different types of nodes show great variety, different types of relations represent diverse meanings. Anomalies usually perform differently from the majority in various perspectives of heterogeneity in these networks. However, existing graph anomaly detection approaches do not leverage heterogeneity in attributed networks, which is highly related to anomaly detection. In light of this problem, we propose AHEAD: a heterogeneity-aware unsupervised graph anomaly detection approach based on the encoder-decoder framework. Specifically, for the encoder, we design three levels of attention, i.e. attribute level, node type level, and edge level attentions to capture the heterogeneity of network structure, node properties and information of a single node, respectively. In the decoder, we exploit structure, attribute, and node type reconstruction terms to obtain an anomaly score for each node. Extensive experiments show the superiority of AHEAD on several real-world heterogeneous information networks compared with the state-of-arts in the unsupervised setting. Further experiments verify the effectiveness and robustness of our triple attention, model backbone, and decoder in general.
An Analysis of Resource-Oriented Algorithms for Cloud Computing

This review paper provides the reader with an overview of some of the many resource scheduling algorithms. The paper also describes the characteristics of these algorithms and highlights their strengths and weaknesses. The main focus is on comparing and evaluating different resource scheduling algorithms so that one can incorporate them as required. The paper also discusses potential directions for future study in the field of resource scheduling in virtual environments. A conclusion is drawn upon careful analysis, comparison, and assessment of various algorithms and their applicability for use in practical applications.
An Analysis of Task-Based Algorithms for Cloud Computing

This review paper provides a comprehensive analysis of the most widely used task scheduling algorithms for load balancing in cloud computing environments. Task scheduling is one major scheduling where we have to allocate the processes by scheduler. Fact major load balancing scheduling in cloud computing are based on this scheduling. Hence with reducing overall execution time the task scheduling algorithm are implemented as part of application or as whole in the application’s environment. Task based algorithms will help us to get the best result according to its requirement which will make machine faster. The paper aims to evaluate the performance of these algorithms in terms of key metrics such as resource utilization, task completion time, and scalability.