Reviewerly/exHarmony huggingface dataset:
Inspired Symbol-based Authentication against Dictionary attacks Using ML Algorithm

Verifying an individual's identity before granting them access to a linked device, an internet service, or another resource is known as user authentication. The significance of authentication lies in its ability to safeguard data, apps, and networks for companies by limiting access to their protected resources to only authorized individuals or processes. This paper used the popular Big data technology Apache Spark for storing, and processing large data and proposed a novel authentication framework. A viable replacement for conventional alphanumeric passwords, bio-metric and facial authentications with dynamic symbol selection as an authentication. This authentication is tested in the Apache spark cluster which is the most distributed system. In these methods, SHA512 cryptography is used in several ways and comparison is done using existing authentication and machine learning algorithms. The straightforward authentication scheme and applied Apache Spark distributed system with 10 nodes produced the best results.
Cross-Domain Aspect Extraction using Adversarial Domain Adaptation

Aspect extraction, the task of identifying and categorizing aspects or features in text, plays a crucial role in sentiment analysis. However, aspect extraction models often struggle to generalize well across different domains due to domain-specific language patterns and variations. In order to tackle this challenge, we propose an approach called "Cross-Domain Aspect Extraction using Adversarial-Based Domain Adaptation". Our model combines the power of pre-trained language models, such as BERT, with adversarial training techniques to enable effective aspect extraction in diverse domains. The model learns to extract domain-invariant aspects by incorporating a domain discriminator, making it adaptable to different domains. We evaluate our model on datasets from multiple domains and demonstrate its effectiveness in achieving cross-domain aspect extraction. The results of our experiments reveal that our model outperforms baseline techniques, resulting in significant gains in aspect extraction across various domains. Our approach opens new possibilities for domain adaptation in aspect extraction tasks, providing valuable insights for sentiment analysis in diverse domains.
Facial Expression Recognition in the Wild Using FAM, RRDB and Vision Transformer Based Convolutional Neural Networks

Facial expression recognition in the wild is a quite challenge task because facial images captured in natural settings are often affected by various factors such as irregular occlusions, inconsistent face angles and varying light levels. To overcome these factors, we presented a new triple-branch deep neural network model containing facial attention module, residual in residual dense block and vision transformer to deal with facial expression recognition problem. The facial attention module can help backbone network extract more useful features. Residual in residual dense block and vision transformer can further get more detailed features. In addition to the image problems caused by above factors, the ratio of number of images for different expressions in the dataset is also very different. We proposed a new loss function to tackle this uneven ration problem. Experimental results on two benchmark in-the-wild datasets show that our model is indeed helpful for images in the wild. We also created a new expressions dataset called Fairfaceplus, which is built by adding expression categories to the original label on FairFace dataset. The code of our proposed method will be made available on GitHub https://github.com/dreampledge/fairfaceplus.
Pyramid Pooling-based Local Profiles for Graph Classification

Many natural and engineering systems can be modeled and represented in the forms of graph data, and then studied using graph theory and network analysis tools. Graph representation learning aims at generating lower-dimensional representations from higher-dimensional graph data, which is a crucial step that facilitates the follow-up tasks, such as node and graph classifications. In this paper, we present a simple but effective graph representation learning method, namely the pyramid pooling-based local profile (PPLP), which enables local nodal profiles to be transformed into a graph representation, with multi-scale features extracted. PPLP can be either embedded into a graph neural network as the readout layer, or perform independently as a graph embedding algorithm. The resultant representations of PPLP are for graph-level tasks. PPLP is experimentally tested by performing graph classification tasks on ten representative datasets, either as the readout layer of different graph neural networks, or as an independent graph embedding algorithm. Experimental results demonstrate that: 1) when embedded into graph neural networks, PPLP outperforms the widely-used global pooling-based readout methods; 2) as an independent graph embedding algorithm, PPLP performs fairly good, especially on the social network datasets. The investigation confirms PPLP as a simple but promising method for graph-level tasks.
Stepwise Search Transition-Based Hybrid Optimization for 3D Pose Estimation

We aim to develop a simple motion capture system for home environments. As users need to install their own cameras, a calibration-free system is required. Therefore, we propose a 3D pose estimation method based on 3D joint angles of humans using multiple smart devices with a hybrid optimization method that combines Particle Swarm Optimization and steepest descent method. We also estimate the relative angles between humans and cameras to facilitate camera calibration. In this paper, we discuss the impact of the combination of global and local search capabilities of the optimization method on the system's performance. Specifically, we propose an optimization method that gradually changes the number of iterations of Particle Swarm Optimization and Steepest Descent Method and compare it with a simple sequential combination.
Improving Data Security in Public Cloud Storage with the Implementation of Data Obfuscation and Steganography Techniques

Cloud computing is a widely used distribution paradigm for delivering secure information services over the internet. The advantages of cloud computing include the capacity to remotely access one's data from any location, eliminating concerns over data backups, as well as the establishment of disaster recovery and business continuity facilities. Nevertheless, cloud computing gives rise to apprehensions over the appropriate management of information and interactions by cloud service providers, user organisations, and governments. Cloud computing has become an increasingly popular choice for both big organisations and individuals seeking cost-effective access to a wide range of network services. Typically, individuals' information is kept on a public Cloud, which is accessible to everybody. This basic gives rise to several concerns that are contrary to the adaptable services offered by cloud providers, such as Confidentiality, Integrity, Availability, Authorization, and others. Currently, there are several choices available for safeguarding data, with encryption being the most favoured one. Encryption alone is insufficient for adequately safeguarding the sensitive information of many users. Additionally, the encryption and decryption procedure for each every query requires a greater amount of time. Furthermore, it is not advisable to just prioritise user-centric thinking, since users relinquish direct control over their data once it is uploaded to Cloud premises. Given this reality, it is important to contemplate the security of users' vital information on the Cloud server. This may be achieved by the use of the crucial method known as obfuscation. In order to alleviate the load on the Cloud server and provide sufficient security for user data, we suggest an approach that combines both strategies, namely... The thesis explores the concepts of obfuscation and encryption. If the files or documents need security, the user data may be encrypted. The Cloud's DaaS service is protected utilising obfuscation methods. By using a dual-pronged strategy, the suggested technique provides enough protection for anonymous access and ensures the preservation of privacy, even while dealing with information stored on Cloud servers. The objective is to provide a robust integrity checking method, an enhanced access control mechanism, and a group sharing mechanism. These improvements seek to reduce the workload and foster a higher degree of confidence between clients and service providers.
Study on the Recent Cyber Security-Attacks and the Economic Loss Due to the Growing of Cyber-Attacks

Economic damages caused by cyberattacks have been on the rise recently, on a global scale. However, there has not been enough research on the economic damage caused by cyberattacks to other industries; most studies have concentrated on the attacked enterprises. As a means of better damage prediction and other national measures, this study examined the economic damages inflicted on Japan by cyberattacks by employing the production function and the input-output model. We begin by outlining a production function–based approach to estimating the yearly direct harm by industry. Working hours lost due to cyber events are the major input dataset. Second, we used the input-output model to create a model that could estimate the national spillover damage. Thirdly, we explained how to estimate direct and spillover harm in all Japanese industries, even if the data on cyber damage was confined to interview data from the JNSA and the IPA. Therefore, we think our approach of estimating is workable and efficient on a nationwide scale.
Study of Clustering Data Mining Techniques

Data mining's primary purpose is to take a massive records series and wreck it down right into a more plausible form for evaluation and alertness. Exploratory facts evaluation and information mining applications frequently center on clustering. The time period "clustering" refers back to the method of categorizing facts factors into groupings wherein the objects within every cluster have more similarities than differences (clusters). Each approach serves a completely unique motive, determined by using the nature of the records at hand and the needs of the software. Nonetheless, our research has led us to the realization that the K-way approach outperforms the options in a huge type of settings. In this look at, senior undergraduate and master's degree college students from the Faculty of Economics and Business Administration at Babe?-Bolyai University of Cluj-Napoca participated via the usage of questionnaires in a collaborative effort, with the gathered data being processed through information mining clustering techniques, graphical and percent representations, the use of algorithms applied in the software program Weka
An Empirical Paradigm on Cybersecurity Vulnerability Mitigation Framework

Current cybersecurity vulnerability assessment tools were developed in accordance with guidelines established by entities like the National Institute of Standards and Technology (NIST) and the United States Department of Energy. When assessing their facility's cybersecurity maturity, owners and operators of critical infrastructure frequently use frameworks like the NIST Cybersecurity Framework (CSF) and the cybersecurity capability maturity model (C2M2). These frameworks are great at finding vulnerabilities and doing qualitative cybersecurity analysis, but they don't help you get to the level of cybersecurity maturity you want by letting you prioritise how you fix those flaws. Cyber dangers pose a significant risk to businesses and are becoming more pervasive in our everyday lives. In this way, businesses may devise a strategy and set of guidelines by simulating a breach attack. But these strategies are based on experts' tacit knowledge. In response to this problem, the authors of this study suggest an automated and formal process for creating prioritised action plans to enhance environmental transparency. An experiment proving the validity of the proposed method was conducted, yielding consistent and applicable results to the tested scenario. Through testing against a real-world cyberattack that targeted industrial control systems at a critical infrastructure facility, this article presents a thorough architecture of CyFEr and demonstrates its application to CSF.
A Proposed Secure Framework for Supply-Chain Management using Blockchain Technology

Decentralization increases the performance of the Supply Chain by distributing financial choices more evenly. The technology that is causing the biggest upheaval is the aforementioned challenges that are hampering today's supply chain are addressed by block chain, which is a cost-effective and realistic solution. Many autonomous networks, self-sufficient persons and institutions can emerge and operate in a distributed, dispersed operating environment thanks to block chain. Theblock chain’s data is unchangeable, transparent, and recorded in a distributed ledger that adheres to consensus mechanisms throughout.Hence, the data stored in it cannot be edited and forged i.e., it is trusted if the writer is trusted. Block chain also has its own advantages and setbacks: decentralized network operating in centralized computing devices. Web pages are used to captured changes in original data to malicious data, network synchronization, service outages, and other challenges are some of the issues that could arise in block chain.Thereare several entities and modules involved in the model of this Supply Chain which are tested using an efficient top-down manner in this article. The proposed supply chain management model can be adaptive to various supply chains like food, textile, healthcare and also educational institutions. A comprehensive analysis of the major-cause of numerous threats, susceptibility, and hazards is assisted on the system of supply chain management as imitated in different scenarios.
SOME ISSUES OF EDUCATION OF PUPILS IN NATIONAL VALUES AND GETTING EDUCATION

This paper discusses the upbringing problems of the current education system. The paper also will touch the issues related to the role of the family, school, relationship of the society and children, meaning and substans of the folk value, the role of the parents in upbringing the children, mother tounge and how to increase the impact of the upbringing. Upbringing is the set of the consistent activities desiygned to the certain result. It is indivisible, comlex and long process. It consists of moral, aesthetic, social, labour and other components. It is necessary that the meaning is consistent with the goals. There is no use of the upbringing if the ways of the achieving the goals are formal. So, there will be no quality education, unless there is decent upbringing.
Chatbots: history, technology and application in higher education

Over the past year, the high interest in ChatGPT has encouraged researchers to look for more opportunities to implement conversational AI. The use of chatbot technology in education is one of the new challenges for both educators and students. The purpose of this study is to present the most recent and widely used applications of chatbots in education.
Improving Reliability of State Estimation by Particle Filter with Cauchy Likelihood Function

Particle filtering, a sequential Monte Carlo method, is a powerful framework that the posterior probability density function (PDF) of the state can be approximated by a set of random particles and their associated weights. Particle filter (PF) is composed of these steps: particle initialization, prediction, updating, and resampling. For updating step, the framework needs to calculate the likelihood function which is based on the measurement model of system. When the measurement noise is considered additive and Gaussian, the conventional Gaussian error model is utilized. However, when the measurement noise level is low, Gaussian noise model provides large prediction error. We therefore aim to remedy the problem when the measurement noise level is low, instead of using standard likelihood calculation, we propose to implement Cauchy likelihood function (CLF) for particle weight calculation to resolve the problem. Via the root mean squared error (RMSE) and mean absolute error (MAE), simulation results show that it can provide comparatively better estimation performance than Gaussian likelihood function.
Photovoltaic Power Prediction from Medium-Range Weather Forecasts: a Real Case Study

The aim of this work is to utilize weather forecasts with a lead time from 6 h to 30 h as input data of a photovoltaic (PV) model to predict the AC power production. In order to always use the last forecasts, the inputs are updated every time there are new data, e.g., every 6 h. The ability of the model is tested on a residential PV plant for which global irradiance and electrical power are measured. The typical indicators of forecast accuracy in the PV applications are used: mean bias error and mean absolute error for both irradiance and power. However, they are normalized with respect to the standard irradiance and the PV rated power. Their values are generally adequate in clear sky and overcast conditions, remaining around the 10% limit.
AgBIS: A Blockchain-enabled Crop Insurance Platform Against Adverse Selection, Moral Hazard, and Insurance Frauds

Crop insurance is widely adopted to help farmers stabilize incomes and mitigate uncertainties. However, current crop insurance systems face significant challenges, including adverse selection, moral hazard, and insurance frauds, further exacerbating the difficulties associated with cost, program sustainability, and market failures. This paper dives into the subsidized multi-peril crop insurance programs and proposes AgBIS, a blockchain-enabled crop insurance platform, to mitigate the problems of adverse selection, moral hazard, and insurance frauds within crop insurance ecosystems. We conduct a systematic study on risk factors and the negative effects of those problems and identify information asymmetry as the main cause of the above problems. AgBIS aims to reconstruct the crop insurance system by facilitating transparency, efficiency, and interoperability among stakeholders. Additionally, AgBIS integrates a trustworthy hardware oracle to provide participants with highly reliable and authentic crop-related data, thus reducing information asymmetry and enhancing program sustainability.
Disjunctive Edge Map based Image Sterilization for Destruction of Steganograms in Spatial Domain

Image sterilization is a form of steganalysis where the suspected carrier image is actively manipulated, filtered or modified to mutilate the steganograms likely to be concealed in it. This paper proposes an image sterilization method for mutilating the secret messages hidden in a cover image by adaptive pixel modification on the basis of an edge map. The edge map is obtained from the disjunctive combination of binary thresholded edge matrices of Canny, Weighted Sobel and Laplacian filtering on the suspected stego image. The sterilization scheme injects high impact multiplicative noise to the edge pixels while the non-edge pixels are modified using low impact additive noise. Eight benchmark steganography algorithms including LSB, PVD, EMD, WOW, HUGO and UNIWARD are applied to sample images taken from USC-SIPI database to prepare the experimental dataset. Analysis of experimental results reveals the superiority of the proposed technique in comparison with the state-of-the-art image sterilization methods.
Advancements in Sentiment Analysis: A Deep Learning Approach

Sentiment analysis, a pivotal discipline in the digital era, revolves around the nuanced task of categorizing user sentiments within textual data. This research embarks on an exhaustive exploration of diverse sentiment analysis models, comprising Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), Support Vector Machines (SVMs), and a Baseline Model. Through a rigorous comparative analysis of their performance across varied datasets, this study illuminates the unique strengths and limitations inherent to each model. Furthermore, the research extends beyond the realm of academic inquiry to unveil the practical applications of sentiment analysis. It underscores the profound impact of sentiment analysis in contemporary datadriven decision-making, illustrating its significance across multifaceted domains such as marketing, social media monitoring, finance, customer service, and public sentiment analysis. This investigation seeks to empower stakeholders with invaluable insights, thereby facilitating informed choices and strategies in the ever-evolving digital landscape.
TEACHING MODELING TO FUTURE PRIMARY SCHOOL TEACHERS WHEN SOLVING TEXT PROBLEMS

The article deals with the problems of teaching the solution of text problems for younger schoolchildren in the preparation of future primary school teachers. The results of a survey of students, which was conducted in order to find out about the problem of modeling when solving text problems, are presented. Based on the results, we saw that students are able to solve problems associated with processes, but it was noted that they have difficulties in choosing an auxiliary model and solving it. The results were analyzed and reviewed using methodological techniques with auxiliary models of the work of solving text problems. Also, in the preparation of future primary school teachers, diagnostic work was carried out among students of different groups and the results were derived. Examples were given of the use of auxiliary models with the help of which solutions to text problems were shown. Thus, summing up, it was revealed that auxiliary models allow you to correctly find ways to solve text problems and develop general methodological logical thinking skills.
Information Visualization for a Comprehensive Cybersecurity Risk Quantification and Measurement

This paper presents an innovative dashboard design for cybersecurity risk management, addressing the urgent need for effective visualization and quantification of cyber threats in organizations. Through semi-structured interviews with security executives, we identify key challenges in cybersecurity operations and propose a user-centric dashboard. This tool integrates an overall risk score, trend analysis, global threat mapping, and a three-step workflow from risk insights to remediation, all aimed at enhancing decision-making for Chief Information Security Officers (CISOs) and their teams. Usability testing underscores its effectiveness and user-friendliness, signaling a significant step forward in cybersecurity risk management by providing a comprehensive, intuitive, and actionable overview of an organization’s security posture
Interface Design for Cybersecurity Risk Quantification with Monte Carlo Simulation

This paper explores Zscaler Risk360, an innovative cybersecurity risk management platform that quantifies cyber risks financially, using Monte Carlo Simulation. Risk360 stands out by converting complex cybersecurity data into financial risk metrics, offering organizations a clear understanding of their cyber vulnerabilities in monetary terms. This approach facilitates informed decision-making, resource optimization, and effective stakeholder communication. The paper briefly discusses the platform’s methodology, including risk score calculation and financial impact analysis, and notes its reliance on data quality and adaptability to new threats as challenges. It concludes with potential future enhancements, such as AI and ML integration, underscoring Risk360’s role in evolving cybersecurity risk management