Reviewerly/exHarmony huggingface dataset:
Improving Cybersecurity Awareness: Tweet Classification using Multilingual Sentence Embeddings and Contextual Features

The presence of accounts managed by cybersecurity experts, professionals, and organizations makes social media a valuable source for computer security awareness. By regularly capturing and analyzing the posts on emerging cyber threats, individuals and organizations can understand potential dangers in a timely manner and effectively implement mitigation strategies. However, retrieving relevant and informative posts from a social network is challenging due to the high percentage of posts containing uninformative content. This paper proposes a novel approach based on supervised classifiers for selecting relevant social media posts and categorizing them according to different types of vulnerabilities. To accomplish this task, we designed a pipeline combining text classifiers in cascade, training them on manually labelled data. We analyzed various neural network techniques, leveraging language-agnostic sentence-level embeddings and past user activity, validating these techniques in a cross-validation setup. With an achieved accuracy of 87%, our approach offers effective filtering and classification of social media posts, empowering cybersecurity professionals to stay informed and take appropriate measures.
Abstractive Text Summarization Models Using Machine Learning Algorithms

In today's era of cyberspace and intermedia, the number of e-documents have been enlarged enormously. The speedy growth of large data and documents in the field of Data Mining and emerging domain such as Information Retrieval (IR) and the demanding area of Natural Language Processing (NLP) needs Automated Text Summarization. Text Summarization is a process that decreases size of the source document while keeping its important information with actual meaning. It is burdensome and laborious for Homo sapiens to physically do the summarization of huge documents of text. Thus it is necessary to revise old methods and techniques of abstractive text summarization models and modernize them to be efficient and fulfill the requirements of users as per the need by resolving issues of continuously increased data. Now this paper presents a survey of the current futuristic models and the various algorithms and techniques used in Abstractive Text Summarization systems liable to be subjected to an Encoder-Decoder framework. This research can help to gain the overall concepts of recent machine learning algorithms and encoder-decoder architecture resting on abstractive text summarization prototypes with a review of the challenges, issues, and future scope of these models. This research can help solve the research community's problems, newsletters, email overload, and media monitoring.
A Store Evaluation System using Automatic Scoring of Retail Stores Based on Product Review Analysis

When users engage in online shopping, they often rely on product reviews as a reference. However, efficiently determining the overall evaluation of each product from a large number of reviews is not easy. Previous studies have addressed this issue by automatically scoring each product based on text analysis of product reviews. On the other hand, evaluating the performance of the retail store itself poses challenges. There are usually fewer reviews specifically targeting the store, and evaluations of the store are often embedded within product reviews. As a result, automatically scoring the performance of retail stores is not a straightforward task. Therefore, in this study, we propose a method to extract evaluations of retail stores from product reviews and automatically score them based on store-specific criteria. Additionally, we developed a system that utilizes the calculated store scores, allowing users engaged in online shopping to search for the evaluations of the retail stores that sell the products they are interested in. We present the details of our proposed method and the development of the search system, along with the results of the evaluation experiments conducted using the developed system.
Enhanced Mobility Assistive And Tracking Device for Elderly and Visually Impaired Individuals

The aging population and visually impaired individuals often face mobility challenges and are at higher risk of accidents and falls. In this research, we propose the design and development of an IoT-based smart tracking stick that can assist elderly and visually impaired people in their daily activities. The need for assistive technologies to support mobility and independence among elderly and visually impaired individuals is becoming more crucial. In this research, we propose the design, development, and evaluation of an IoT-based smart tracking stick that leverages sensor technologies and smartphone connectivity to enhance the safety and quality of life of elderly and visually impaired individuals. The stick can be located by a smartphone via SMS, which provides real-time tracking and location data to caregivers or family members. The proposed smart stick is equipped with various sensors such as GPS - GSM, LDR sensor, Ardiuno Microcontoller, force sensor, water level sensor, ultrasonic sensor and various other sensor's. A force sensor can be utilized by the stick to detect the presence of a impaired individual and also ensure that they are holding it securely.
Variable-based Learning Considering Topic Specificity in Heterogeneous Data Clustering Tasks

Recently, data mining via interdisciplinary co-creation has attracted considerable social attention, and various data have been published, for free or for a fee. Data publication encourages the exchange and combination of data between different institutions, which is helpful for interdisciplinary data collaboration. However, issues pertaining to designing high-quality data for interdisciplinary data discovery remain in data search. Variables are frameworks for data, and reflect the data topics and intent of the data design. In this study, the relationships between data topics and variables for a large dataset were quantitatively investigated to provide suggestions for data design and exploration. The probability of occurrence of variables and their pairs for each topic was determined to elucidate the relationship between the topics and variables; subsequently, clustering was applied based on these relationships.
A Comprehension Analysis of Heart Disease Recognition using Machine Learning

Cardiovascular disease (CVD) is a challenging condition to manage, impacting a significant global population. Within the realm of cardiology, rapid and accurate identification of heart disease is imperative for effective intervention. As a result of the availability of data with varied features, many feature determination algorithms are available in textual form. The chosen subset of characteristics must be continuous to be used in practical applications. This study suggests a sequential feature selection strategy for identifying fatal events in heart disease patients while they are receiving therapy and choosing the most crucial traits. There are several machine learning algorithms employed, including, RF, LR, DT, SVM, ANN, and KNN for heart disease recognition. The HD framework performed significantly better than the traditional CVD risk score. The Machine Learning paradigm is effective for HD prediction in individuals with medium to high cardiovascular risk.
Data Set Development for Gesture Recognition and Implementation using MATLAB and Xilinx Vivado

Gesture recognition system is an algorithmic based system which can interpret different hand gestures, which are useful in human machine interface. In this paper, two datasets are used for training and testing. The first data set developed by the authors, has a total of 250 images, across 10 classes. Images of ten different hand gestures of one of the authors were captured and segregated into ten corresponding classes. For each hand gesture, 25 different images are captured in varying positions. To increase number of images in the data set, a second data set which consists of 1000 images, across 10 classes, is also considered from an open source. In the first data set, 250 images are considered for training and the resolution of each image is 432 x 578. In the second data set, 1000 images are used for training. The trained images are converted into gray scale and stored in the binary format. A gesture recognition algorithm is designed and 90 images are used for testing in both datasets. The gesture recognition algorithm is modeled in MATLAB for accuracy of 99.2% for the first data set and 98.6% for the second data set. The next stage of the project is conversion of designed MATLAB code into verilog code using HDL coder. The verilog code is functionally simulated, synthesized, and implemented in Xilinx vivado.
A study on the results of word guessing game based on XGBoost regression modeling

Wordle is a word-guessing game from The New York Times in which the player solves puzzles through a limited number of attempts, receiving feedback for each attempt. In this paper, we build a predictive class model and use machine learning to analyze puzzle features in this game related to the number of reported results and the number of attempts required to successfully solve the puzzle, and bring EERIE's feature data file into the model to obtain percentage predictions. Then, a K-means cluster analysis model was developed, and three different clustering categories with difficulty levels of easy, medium, and hard were obtained by using all the metric items of the number of attempts of the players to solve the puzzles as independent variables. Finally, the prediction results of the percentage of all solution attempts of EERIE were imported into the cluster analysis model, and it was concluded that the difficulty attribute of EERIE was hard. This paper provides a better direction for setting puzzles that can attract more people to participate in the game.
Network Retrieval of English Teacher Education Resources Based on Ant Colony Algorithm

This article analyzes the characteristics of online education resources and the concept, classification, and system structure of the English teacher education resource network model, and briefly analyzes the advantages and disadvantages of the English teacher education network; And combined with elite strategy, an elite ant colony algorithm was proposed; Based on the WEB logs of the education resource management system, the pheromone update mechanism of the elite ant colony algorithm has been redesigned to play a greater role in the English teacher education resource network.
Multi-view Deep Markov Models for Time Series Anomaly Detection

We consider the problem of multi-view anomaly detection for multi-view time series data. This task aims to find time steps in time series instances that have inconsistent features across multiple views. To solve the problem, we propose a multi-view deep Markov model that can learn sequential structures in complex high-dimensional multi-view time series data. In our proposed model, each view is modeled by a sequence of latent states and the state transition function is shared across views. Therefore, the inconsistencies in views data will lead to inconsistencies in transitions between latent states across views, which makes the likelihood of abnormal time steps not high. We rely on that property to compute the multi-view anomaly score of each time step in all time series instances. The extensive experiments show that the proposed model is effective in detecting multi-view anomalies in time series data.
A Cascade CNN Model based on Adaptive Learning Rate Thresholding for Reliable Face Recognition

Convolutional models may effectively identify persons quickly through automatic face analysis. A convolutional neural network architecture known as the CNN cascade structure uses numerous deep convolution layers to extract hierarchical characteristics from the input image. Cascade modeling has several drawbacks, including high computational costs and complexity, difficult training, little feedback, accumulation of errors, sensitivity to model order, and challenging interpretation. In order to improve model performance due to these many issues, we propose adding a dynamic learning rate (DLR). It entails gradually modifying the learning rate in response to the model’s performance during training. The loss of training or validation error is a concern with cascade models. The CNN cascade structure and the DLR technique are combined in this paper to present a novel deep-learning methodology for reliable face recognition as a biometric security solution. The proposed cascading CNN model based on DLR was assessed on 3D face images from the MIT CBCL database. It allows achieving a higher accuracy of up to 99.65% with 5 epochs.
One to Rule them All: A Study on Requirement Management Tools for the Development of Modern AI-based Software

Modern system architectures are rapidly adopting AI-based functionality. As a result, new requirements about software trustworthiness must be considered during the entire software development life cycle of applications. While several requirement management tools are available to track and monitor requirements over time, it is still unknown to what extent these tools can cope with these new demands imposed by AI. In this paper, we contribute by performing a qualitative and quantitative analysis of different requirement management tools and their performance in managing AI-related requirements effectively. Through a rigorous analysis performed by a consortium formed by different industry and academic partners, we evaluate the suitability of five different requirement management tools. Our results indicate that while several tools are available for managing requirements, it is currently challenging to find a tool that can manage AI requirements mainly because tools do not comply with the required aspects imposed by regulatory entities. Lastly, we also shared our lessons learned and experiences from selecting requirement tools that can be used in team-based consortium projects.
Mineralogical Marvels: Deciphering the Geological Alchemy from Stones to Wealth

This research endeavors to dissect the mineralogical intricacies governing the alchemical metamorphosis of geological substrates into economic wealth. Employing a multifaceted approach, this study scrutinizes the crystallographic and chemical underpinnings of economically consequential minerals. Crystallographically, an exploration of intricate structures elucidates the nexus between crystal lattice configurations and resultant mineral properties, instrumental in ascertaining economic worth. Concurrently, an in-depth chemical analysis discerns the elemental intricacies dictating mineral value, establishing a nexus between compositional intricacies and economic significance. The investigation extends to the geological crucible where ore formation processes and diverse geological settings are analyzed. This includes an examination of the dynamic interplay of factors engendering the concentration of economically salient minerals. Consequently, this study accentuates the geological environments fostering mineral richness and includes pertinent case studies. Furthermore, an economic trajectory is traced, covering extraction, processing methodologies, and market dynamics. This encompasses an appraisal of contemporary mining and processing technologies, along with a discerning scrutiny of the geopolitical intricacies steering global mineral markets. In conclusion, this research amalgamates its findings into a cohesive narrative, presenting a nuanced understanding of the geological genesis of mineral wealth and its implications for scientific and economic paradigms.
The Impact of Gender, Education, and Age on Installing a Proximity Tracing Application: Survey on a German Population

During a crisis such as COVID-19 citizens of countries all over the world were asked to use a proximity tracing application voluntarily and in­stall it on their smartphones. Even though the use of the application in times of the pandemic crises was promoted as crucially important, many citizens re­fused to install it. In this paper, we raised the question of why. Previous litera­ture confirmed the impact of universal UTAUT predictors, namely, social in­fluence, performance expectancy and effort expectancy, on intention to use. However, the impact of the predictors has not yet been confirmed in actual use. We propose a research model to examine the direct influence of the pre­dictors on actual use. Furthermore, we assess if the impact of age, gender and education on PTA’s use behavior is significant. We present our preliminary re­sults on data collected in Germany.
The role of conflict and information in the resolution of problems of unfamiliar physics concepts

ABSTRACT IS AVAILABLE IN THE PDF
Graph Coordinates and Conventional Neural Networks - An Alternative for Graph Neural Networks

Graph-based data present unique challenges and opportunities for machine learning. Graph Neural Networks (GNNs), and especially those algorithms that capture graph topology through message passing for neighborhood aggregation, have been a leading solution. However, these networks often require substantial computational resources and may not optimally leverage the information contained in the graph’s topology, particularly for large-scale or complex graphs.We propose Topology Coordinate Neural Network (TCNN) and Directional Virtual Coordinate Neural Network (DVCNN) as novel and efficient alternatives to message passing GNNs, that directly leverage the graph’s topology, sidestepping the computational challenges presented by competing algorithms. Our proposed methods can be viewed as a reprise of classic techniques for graph embedding for neural network feature engineering, but they are novel in that our embedding techniques leverage ideas in Graph Coordinates (GC) that are lacking in current practice.Experimental results, benchmarked against the Open Graph Benchmark Leaderboard, demonstrate that TCNN and DVCNN achieve competitive or superior performance to message passing GNNs. For similar levels of accuracy and ROC-AUC, TCNN and DVCNN need far fewer trainable parameters than contenders of the OGBN Leaderboard. The proposed TCNN architecture requires fewer parameters than any neural network method currently listed in the OGBN Leaderboard for both OGBN-Proteins and OGBN-Products datasets. Conversely, our methods achieve higher performance for a similar number of trainable parameters. These results hold across diverse datasets and edge features, underscoring the robustness and generalizability of our methods. By providing an efficient and effective alternative to message passing GNNs, our work expands the toolbox of techniques for graph-based machine learning. A significantly lower number of tunable parameters for a given evaluation metric makes TCNN and DVCNN especially attractive for resource limited IoT/mobile devices and for reducing power consumption of ML models.
Efficient Dynamic Federated Learning for Imbalanced Data

With the advent of data driven era, new challenges have emerged that can hinder their efficient and reliable utilization. At the center of this era is data. However, public, and unrestricted access to data may pose privacy and security threats. Federated learning methods have proposed to address this problem. While the main concern in federated learning has been preserving privacy of data and maintaining the efficiency of centralized learning methods, the structure and distribution of data has not been studied sufficiently. In this paper, we present a federated machine learning approach capable of accommodating imbalanced datasets. Our method dynamically adjusts the learned parameters to prevent biased classifications. Our empirical results indicates that the proposed method managed to improve accuracy by 22%.
Design and Implementation of an ERC-20 Smart Contract on the Ethereum Blockchain

As technology continues to develop, there is a growing need to find sustainable solutions in all industries, including cryptocurrency. Due to the high energy consumption that cryptocurrencies are known for, there have been efforts to reduce waste consumption and in turn minimize the carbon footprint. We support the trends for creating an environment-friendly crypto token using the ERC-20 standard on the Ethereum blockchain. We outline the various aspects that make a token more sustainable and highlight the potential benefits of such tokens. Our proposal involves the design of a smart contract that incorporates eco-friendly features such as lower energy consumption, carbon offsetting, and more efficient methods or algorithms. We also discuss the importance of transparency and accountability in the design and implementation of such tokens. This paper discusses not only the practical tools and steps necessary in creating a crypto token but also highlights the challenges associated with creating a more sustainable token.
Exploiting Large Language Models (LLMs) through Deception Techniques and Persuasion Principles

With the recent advent of Large Language Models (LLMs), such as ChatGPT from OpenAI, BARD from Google, Llama2 from Meta, and Claude from Anthropic AI, gain widespread use, ensuring their security and robustness is critical. The widespread use of these language models heavily relies on their reliability and proper usage of this fascinating technology. It is crucial to thoroughly test these models to not only ensure its quality but also possible misuses of such models by potential adversaries for illegal activities such as hacking. This paper presents a novel study focusing on exploitation of such large language models against deceptive interactions. More specifically, the paper leverages widespread and borrows well-known techniques in deception theory to investigate whether these models are susceptible to deceitful interactions. This research aims not only to highlight these risks but also to pave the way for robust countermeasures that enhance the security and integrity of language models in the face of sophisticated social engineering tactics. Through systematic experiments and analysis, we assess their performance in these critical security domains. Our results demonstrate a significant finding in that these large language models are susceptible to deception and social engineering attacks.