Reviewerly/exHarmony huggingface dataset:
Modular Analysis of Dataset Balancing Techniques For Binary Classification

This paper presents a modular analysis of dataset balancing techniques for binary classification. The study’s objective is to analyze the performance or check the necessity of different balancing techniques. Several balancing techniques, such as Random Oversampling, Random Undersampling, Synthetic Minority Over-sampling Technique (SMOTE), NearMiss and Tomek Links, and their combinations are evaluated using performance metrics such as accuracy, macro F1-score, and Micro F1-Score.The results reveal that Balancing techniques are not really necessary and hardly impact the performance of models especially in binary classification tasks. It is hoped that the findings will aid practitioners in making informed decisions in selecting balancing techniques for their problem domain.
APDO: A Hybrid Aquila Optimizer and Prairie Dog Optimization Metaheuristic Algorithm for Global, Optimization

We propose a hybrid metaheuristic algorithm that combines the strengths of the Aquila Optimizer (AO) and the Prairie Dog Optimization (PDO) algorithms. The proposed algorithm is named the Aquila Prairie Dog Optimization (APDO) algorithm. During the initialization phase of the APDO algorithm, chaotic maps are employed to enhance the exploration capabilities, as they introduce randomness into the initialization process. In addition, opposition-based learning is incorporated during the swarming process, wherein the objective is to consider the opposite values of the current solutions, to expand the diversity of the swarm and to avoid local optimums. Moreover, to assess and evaluate the performance of the APDO algorithm, a comprehensive comparative analysis was conducted by benchmarking it against five widely recognized metaheuristics, across a diverse set of challenging optimization problems, encompassing: unimodal, multimodal, hybrid and composition functions. Finally, the performance evaluation of the APDO algorithm was conducted using the Friedman post hoc Dunn’s test, which revealed compelling results indicating that the APDO algorithm demonstrated superior performance in the majority of cases, when compared to the other benchmarked algorithms, thereby showcasing its competitiveness and efficacy in tackling diverse optimization problems.
HortiTECH: Design and Fabrication of a Tree Plantation Drone

This paper describes the design and fabrication of a tree plantation drone named as HortiTECH. A large region of the earth must be reforested for a healthy and greener ecosystem, which appears to be a difficult endeavor if carried out manually. Every year, millions of trees are planted throughout the world, but the traditional process is time-consuming and costly. This difficulty is readily solved by employing drones, which are several times faster than traditional tree plantation methods. Our aim is to design a HortiTECH hexacopter drone that can plant capsuled seedlings from the air over specified topographical areas, as well as design seedpods out of biodegradable materials and a shooting mechanism for them using a Paintball gun. The drone can fly 10 to 20 feet above ground level (AGL) while following a specified planting pattern thanks to 3D mapping and processing. The drone is anticipated to initially plant ten tamarind seeds in a single journey.
Human Activity Recognition using Convolutional Neural Network

This study delves into Human Activity Recognition (HAR) using wearable inertial measurement units (IMUs), aiming to enhance classification accuracy in systems reliant on a single sensor. By leveraging convolutional neural networks and denoising autoencoders, the research seeks to outperform previous benchmarks and diminish dependence on manually curated features. Data from a single IMU, capturing seven activities like - running, jumping, walking, falling, sitting, standing, lying; was segmented using a time-window approach. Data augmentation techniques were employed to address class imbalances. The optimized 8-layer CNN model achieved an F1-Score of 0.972, notably improving accuracy for the underrepresented ’falling’ activity. The research’s implementations are made available online.
Empirical Exploration of the Relationship Between Coupling and Method Interaction-Based Cohesion Measures for Object-Oriented Classes

Developing high-quality systems is an essential goal in software engineering. Coupling and cohesion are two highly considered quality attributes. For object-oriented systems, class cohesion refers to the relatedness of members of a class, whereas class coupling refers to the relationship between a class and other classes. To maintain a high-quality level, developers must aim at increasing class cohesion and reducing class coupling. Existing cohesion measures follow different measurement approaches. Interaction-based cohesion measures consider the degree of interaction between each pair of methods in a class. Aiming at providing quality-based guidelines for software developers, this study empirically explores the relationship between eight coupling and five interaction-based cohesion measures. The study involves classes in three open-source applications. The study results indicate that cohesion and coupling measures are inversely correlated. In addition, the results show that the correlation between cohesion and coupling measures is mostly weak, which empirically supports the expectation that these measures quantify different quality aspects. The strength of the correlation dramatically differs from one cohesion measure to another due to the differences between these measures in their formulas and measurement approaches.
A Comparative Analysis of Customer Service Chatbots: Efficiency, Usability and Application

A computer programme that imitates and processes human interaction, either through the use of voice or text communication, is known as a chatbot. Its purpose is to be of assistance in the process of finding a solution to a problem. The transformation brought on by advances in technology has had an effect on every industry. The chatbot provides assistance with a wide variety of tasks, including Reservations, Customer Service, and a great number of other services. The fast development of technologies relating to artificial intelligence and natural language processing has resulted in an increase in the use of chatbots in a variety of fields, most notably in the field of customer service. Customers could receive advice that is prompt, accurate, and personalised through the use of chatbots, which has the potential to completely transform customer service. Because it can automate customer service and reduce the amount of work that needs to be done by humans, it has gained a lot of popularity in the business world. Which can help businesses improve the experience they provide for their customers. The purpose of this research is to undertake a comparative review of customer service chatbots, with a particular emphasis on their efficiency, usability, and application across a variety of business sectors. The research will uncover best practises, difficulties, and potential for improvement by analysing a variety of chatbot solutions.
Superfast AI and ML on Mobile

In recent years, the fields of Machine Learning (ML) and Artificial Intelligence (AI) have witnessed a transformative expansion, impacting various domains of science and technology. The advent of compact computational devices, notably smartphones, has led to a paradigm shift where computationally demanding tasks, such as computer vision and speech recognition, can be executed efficiently. A significant avenue of research is the deployment of neural networks on resource-constrained devices, particularly microcontrollers. These devices are often characterized by their limited performance, memory, and power capabilities. Neural Architecture Search (NAS) has emerged as a revolutionary approach to tailor network models for these constrained environments, obviating the need for manual and often error-prone adjustments by researchers [1]. While several comprehensive reviews on NAS exist [1], [2], there is a notable gap in literature regarding its application specifically to edge devices, such as microcontrollers (MCUs). This research aims to bridge this gap, offering a holistic overview of recent advancements in this niche. We contrast and compare various methodologies developed in recent years against benchmark applications tailored for mobile devices. Further, we elucidate potential avenues for future exploration and innovation in this domain.
Natural Data Synthesis using AI

In this research endeavor, the objective is to reconstruct the temperature field—chosen as a representative study—drawing from a limited set of noisy measurements reminiscent of genuine experimental scenarios. Pursuant to this, a dictionary-learning methodology is introduced, seeking to identify a sparse amalgamation of dictionary components. Sparse coefficients are ascertained utilizing both basis pursuit and LASSO methodologies, with a subsequent comparative analysis of these optimization constructs. Within the scope of this study, the dictionary is curated leveraging a priori dependable solutions, such as those derived from numerical solvers, in their unaltered state.
Data Leakage in Kuwait: Reasons and Solution

With numerous incidents highlighting the susceptibility of people and organizations to cyber threats, data leakage is a growing concern in the world. These dangers can include sophisticated hacking attempts as well as unintentional human errors that reveal private data. Such compromised data can have far-reaching effects, including substantial financial losses, damaged reputations, operations disruption, and even legal repercussions. In this paper, we measured the data leakage in Kuwait and how it can affect people and their stability. Ending by, recommend best methodologies must done to remedy this type of attack that may has huge disaster in the future.
Text-based Q&amp;A: Automated Question Generation and Answering for Enhanced Data Processing

In the age of information overload, where the world is getting increasingly digital, traditional methods of learning are becoming tedious and extensively outdated. Imagine a system with just a few clicks that can quickly generate perplexing questions and enlightening solutions from a given text. Likewise, this paper represents a groundbreaking system that uses stateof-the-art of natural language processing techniques to analyze subject-specific chapters to create questions and corresponding solutions of varying lengths. The system’s versatility as an ideal tool for a wide range of users, including students, researchers, and educators, is a result of its capability to handle a wide range of domains. By offering questions of insight along with appropriate answers, this aforementioned structure demonstrated its extraordinary accuracy and competency in our studies on an array of informational datasets. Altering the learning process and promoting knowledge discovery, this program is flexible in delivering brief or comprehensible solutions to inquiries, having the potential to completely change how individuals interact with written material, whether they are reading for short reference or conducting any depth research. Our suggested framework establishes a dynamic platform for immediate information that enables people to learn substantially more and comprehend any topic in depth. With this leading-edge method, bid goodbye to exhausting manual question generation and get ready to embrace a new era of seamless and fruitful learning with this cutting-edge system.
Unveiling Twitter Sentiments: Analyzing Emotions and Opinions through Sentiment Analysis on Twitter Dataset

Social media plays a vital role in our daily lives. To understand and interpret emotions and opinions expressed on social media platforms, analyzing sentiment is very important. Our study is based on Twitter sentiment analysis. Our aim is to classify tweets automatically as positive, negative, or neutral based on their content using natural language processing and machine learning algorithms. The dataset we used for our analysis is extracted from the website called mendeley data and also we have added some tweets manually which covers various topics. To remove noise, including URLs, hashtags, punctuations, and user mentions, and to retain essential textual content and emojis, we pre-processed the dataset. Additionally, for our research, we used VADER (Valence Aware Dictionary and sentiment Reasoner) and Transformers-RoBERTa to analyze the sentiment of various tweets. We evaluate the performance of these two models using evaluation metrics such as accuracy, precision, recall and F1-score, and also confusion metrics on the testing set. We also discuss the study’s limitations and conclude that machine learning-based sentiment analysis models are a reliable tool for the sentiment analysis of the twitter dataset.
Assessing English Language Writing and Readability Skills using Long Short-Term Memory Model

In English writing courses, students’ ability to write effectively relies on how well they comprehend the texts that are used in teaching. In order to determine the comprehension level, it is fundamental to have a readability score measurement of written texts. The readability score refers to a numeric measure that defines the level at which a learner can comprehend a text. Traditional readability formulas such as Flesch Reading Ease, Dale-Chall and FleschKincaid Grade Level and other formulas are widely used to assess the text’s readability score. In contrast, the Long ShortTerm Memory is a variant of Deep Learning Recurrent Neural Network that is designed to handle sequences of data such as text and time series. Long ShortTerm Memory can extract several features from the text including sentence patterns and readability scores. This research proposes a Long Short-Term Memory model that can predict the readability score of texts that is scored using traditional formulas.
The Role of Business Communication on the Systematised Operations of Business Ventures: A Comprehensive Review

The exchange of ideas, information, or instructions is a fundamental feature of all organisations. This exchange of information may take place between different sections or departments or externally with other organisations. In the context of business communication, the identification of how this process operates most effectively and what essential elements are involved in the process for the sole purpose of enhancement of business activities and profit-making is vital. Still, most of the companies in the business sector have failed to pay due recognition for communication practices which have resulted in inefficiency in operations. The goal of this review study is to identify areas and highlight and explain the importance of good communication in the management process and business growth. The study is a review of literature that adopts qualitative research methodology and with secondary data. In this paper, the review of literature related to business communication has identified the necessity of maintaining an ideal communication protocol within and outside of the organization. The role of business communication is better understood through the identification of functions of key elements such as sender, business information, receiver, and feedback. Further, the role of communication in the business context is better understood through the identification and adaptation of the ideal type or mixture of types of communication: upward, downward, lateral/horizontal, and external. The study has also identified the enhancement of communication practices with the help of digital ICT technologies.
Subdomain Protection is Needed: An SPF and DMARC-Based Empirical Measurement Study and Proactive Solution of Email Security

SPF and DMARC are two important email authen-tication protocols that can effectively reduce the risk of spoofing attacks and improve email security. In this paper, we provide an empirical measurement study of how well SPF and DMARC are deployed and managed. We perform an active measurement on the Alexa Top Million Domains and their subdomains. For the first time, we present a measurement of subdomain configuration. SPF and DMARC adoption is growing, but still more than 70% of domains do not have proper configurations. More than 90% of all domains lack subdomain configurations. Through experiments, we show that in the absence of effective SPF and DMARC configurations, domains and subdomains can be used by attackers to send spoofed emails. To address this issue, we provide a complete set of proactive email security defense solutions. We summarize detailed mitigation measures and email security assessment methodologies. We also propose the SPF Macro-based Abnormal Email Detection System (SMAEDS), which enables proactive defense against spoofed email attacks. We recommend that the community pay more attention to the systemic issues of SPF and DMARC deployment. We hope that this work can help improve the security of the email ecosystem and reduce the risk of phishing attacks.
ALBUS: a Probabilistic Monitoring Algorithm to Counter Burst-Flood Attacks

Modern DDoS defense systems rely on probabilistic monitoring algorithms to identify flows that exceed a volume threshold and should thus be penalized. Commonly, classic sketch algorithms are considered sufficiently accurate for usage in DDoS defense. However, as we show in this paper, these algorithms achieve poor detection accuracy under burst-flood attacks, i.e., volumetric DDoS attacks composed of a swarm of medium-rate sub-second traffic bursts. Under this challenging attack pattern, traditional sketch algorithms can only detect a high share of the attack bursts by incurring a large number of false positives. In this paper, we present ALBUS, a probabilistic monitoring algorithm that overcomes the inherent limitations of previous schemes: ALBUS is highly effective at detecting large bursts while reporting no legitimate flows, and therefore improves on prior work regarding both recall and precision. Besides improving accuracy, ALBUS scales to high traffic rates, which we demonstrate with an FPGA implementation, and is suitable for programmable switches, which we showcase with a P4 implementation.
Defending against Poisoning Attacks in Federated Learning from a Spatial-temporal Perspective

In federated learning, the central server aggregates local model updates from the participants in the network to generate a global model. For the purpose of protecting clients' privacy, the server is designed to have no visibility into how these updates are generated. The nature of federated learning makes detecting and defending against malicious model up-dates a challenging task. Unlike existing works that struggle to defend against poisoning attacks from a spatial perspective, the paper considers mitigating the impact of attacks from a spatial-temporal perspective. This paper proposes Fedmvae, a robust federated learning framework. Fedmvae uses multiple variational autoencoder models to detect and exclude malicious model updates from a spatial perspective. Moreover, to handle poisoning attacks with time-varying features, we propose generating a robust global model update according to momentum-based update speculation and historical global updates. Fedmvae is tested with extensive experiments on both IID and non-IID datasets, showing a competitive performance over existing aggregation methods under both Byzantine attacks and backdoor attacks.
BlindFilter: Privacy-Preserving Spam Email Detection Using Homomorphic Encryption

Spam filtering services typically operate via cloud outsourcing, which exposes sensitive and private email content to the cloud server spam filter. Homomorphic encryption (HE) can address this issue by ensuring that user emails remain encrypted throughout all stages of the spam detection process on the cloud server. However, existing HE-based approaches are computationally infeasible due to the nature of HE operations. This paper proposes BlindFilter, a distributed, lightweight, HE-based spam email detection approach that consists of clients and servers collaborating to perform spam detection operations securely. BlindFilter employs WordPiece encoding and a modified Naive Bayes classifier, mitigating the need for multiplications and comparisons that would be prohibitive in terms of computation when applied with HE. Our experimental results demonstrate the efficacy of BlindFilter, with F1 scores exceeding 97% across two public email datasets. Furthermore, BlindFilter proves to be efficient as it can process an email in an average of 482.78 milliseconds. Our analysis also reveals that BlindFilter is robust against model extraction attacks, in which malicious users attempt to deduce the features of BlindFilter from query-response pairs.
Perception of EFL Mahasaraswati Denpasar University Students Toward Code Mixing on Social Media

This study elaborates with EFL (English Foreign Language) students’ responses on Google Form. This study focused on how to do code mixing and perception of EFL students toward code mixing on social media. Hoffman’s theory (1991) is implemented to compare with students’ responses about how to doing code mixing on social media and Ulya’s formula (2015) is used to analyzed closed questions about perception toward code mixing. To analyzed the data, descriptive qualitative and quantitative methods were used in this study. The data were obtained from an online questionnaire in Google Form. In collecting the data, this study applies an online survey technique. For the data analysis, it was represented using formal and informal methods. The result shows there are 5 similarities between how to students doing code mixing and reason for applying code-mixing from Hoffman’s theory. Whilst, about perception, it was found that students had 15 positive perception, 6 neutral and 4 negatives.
Analysis of the Grammatical Cohesion in the Descriptive Text Written by the Fourth Semester Students of Universitas Mahasaraswati Denpasar

The title of this research was an analysis of the grammatical cohesion used in the descriptive text written by the fourth semester students of Universitas Mahasaraswati Denpasar. The objectives of this research are to find out the types of grammatical cohesion and how are the used of grammatical cohesion in the descriptive text written by fourth semester students of Universitas Mahasaraswati Denpasar. To analyze the data, the researcher used the theory of cohesion proposed by Halliday and Hasan (1976). The method that the researcher use in this study was the qualitative and quantitative method. As a result, there are two types of grammatical cohesion found in the descriptive text written by fourth semester students, they are reference, and conjunction. There is no ellipsis and substitution type found in the descriptive text. The most dominant type in the text found was the conjunction. The used of conjunction are the additive conjunction with a total use of 47%, the adversative conjunction with a total use of 16%, the temporal conjunction with a total use of 6% and causal conjunction with a total use of 4%.