Reviewerly/exHarmony huggingface dataset:
Text Classification Based on Natural Language Processing and Machine Learning in Multi Label Corpus

The rapid development of the Internet has led to a geometric expansion of text information resources online. Among them, corpus, as the basic data source of natural language processing based on statistical language model, its construction and application have become a hot issue in current language processing research. After consulting a large number of relevant literature and materials, it was found that many researchers have provided new ideas for multi label corpus text classification methods. However, this article was adding its own understanding and taking this as the direction and basis. In the introduction, the research significance of text classification was introduced, and then academic research and analysis were carried out on the two key sentences of corpus text classification and natural language processing in multi tag corpus text classification. This article then utilized an algorithm model to provide a theoretical basis for the study of multi label corpus text classification methods; At the end of the article, a simulation comparative experiment would be conducted, and the experiment would be summarized and discussed; In the Enterprise L corpus, the difference in recall rates before and after the use of Entrance 1 was 5.5%, the difference in recall rates before and after the use of Entrance 2 was 7.8%, the difference in recall rates before and after the use of Entrance 3 was 3.3%, and the difference in recall rates before and after the use of Entrance 4 was 4.5%. At the same time, with the continuous research of natural language processing and machine learning, the research on text classification methods of multi tag corpus is also facing new opportunities and challenges.
An Employment of the Evolutionary Algorithms in Training of Neuro Fuzzy Inference System for Forecasting Personnel Radiation Exposure

Evolutionary algorithms have gained widespread recognition as a viable approach to numerous optimization problems that are characterized by infeasible optimal solutions, owing to the presence of large search spaces and computational limitations. Forecasting personnel radiation exposure can be one of these problems. Radiation exposure poses risks to various practitioners as well as patients in the healthcare facilities. In this study, we model the problem as a specific time series instance. Moreover, we investigate the impact of the training an adaptive neuro fuzzy system using evolutionary algorithms, namely, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), on the overall performance of forecasting personnel radiation exposure. The results show that GA and PSO could provide effective solution. On the other hand, they might be highly affected by the initial state of the fuzzy inference system leading to unstable performances. We recommend further experimentation with a combination of other advanced optimization and machine learning methods to assure the most effective results.
Feature Extraction with Differential Evolution Algorithm

In this study, we presented a feature extraction approach that addresses the difficulties arising from high dimensionality and large datasets in classification tasks. Our method combined differential evolution algorithmwith Support Vector Machines (SVM). The experimental results demonstrated that SVM struggled when applied to large datasets. The proposed approach aimed to identify the most relevant features from the dataset, ultimately improving the accuracy of classification tasks. By reducing the data's dimensionality, we achieved storage efficiency and enhanced the classification process. This research provides valuable insights into the effectiveness of DE algorithm in overcoming challenges related to high-dimensional and large datasets, making a significant contribution to the field of feature selection and classification. Future research can explore the application of these approaches to other datasets and explore ways to further improve the efficiency and effectiveness of classification tasks.
The Effectiveness of E-Learning System on Students' of Al-Zaytoonah university of Jordan: A Case Study

This research aims to identify the effectiveness of the e-learning system used in distance education on the students of Al-Zaytoonah University of Jordan, <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Because of the orientation of higher education institutions and ensuring their quality to adopt the electronic and blended education system, the research was conducted with the aim of knowing the effectiveness of the electronic learning system used at Al-Zaytoonah University of Jordan. The study adopted the quantitative statistical analysis method using frequencies and percentages. The number of male students reaches 84 and the number of female students reaches 93 from the scientific and humanities colleges and different educational levels, through a questionnaire that is distributed electronically through a link sent to the students. The results of the research showed that there is a positive effectiveness of the e-learning system used in distance education on the students of Al-Zaytoonah University of Jordan, particularly, on the system axis, and that there are no differences due to the variable of gender and college on these axes, as the respondents expressed the efficiency and effectiveness of the system E-learning by 86%, the ease of handing over assignments by 95%, understanding the study material and the possibility of referring to it through the e-learning system by 94%..
Intelligent Agents and Neural Fuzzy Logic: Enhancing Agent Intelligence in Complex Environments

Intelligent agents have become increasingly significant in numerous fields including autonomous systems, robotics, and smart environments. Neural fuzzy logic, an amalgamation of fuzzy logic and neural networks, offers a propitious solution to incumbent-addressed roadblocks to sub-optimal decision-making and limited adaptability. This paper presents a methodology for integrating neural fuzzy logic into intelligent agent systems, with a focus on a case study involving a robotic vacuum cleaner, constructing a smart cleaning device that continues to learn and optimize its cleaning performance based on experiences. The proposed approach leverages the power of neural fuzzy logic and robotics to create an intelligent and adaptive cleaning device. The iterative nature of the proposed approach allows the robot vacuum cleaner to continuously adapt its behavior based on real-time feedback and learning capabilities, enhancing the intelligence and autonomy of the agent.
OSINT-Based Tool for Social Media User Impersonation Detection Through Machine Learning

With the explosion of social media platform usage in recent years, privacy and online security have become major concerns. Malicious users create fake social media profiles, posing as regular people or public figures to gather personal information, damage reputations, or show off their social engineering skills. As a result, social media platforms, such as Facebook, Instagram, and others, provide the perfect place for identity theft. Malicious users can create fake social media profiles with the same name and profile image as another person. In this paper, Open Source Intelligence (OSINT) platform is used to collect and analyze publicly available information to identify potential impersonators. The proposed approach employs web scraping, machine learning, and web development modules in Python and can be hosted on the AWS cloud for optimal performance and scalability. It accurately scrapes social media platforms (Facebook and Instagram) and presents potential impostor profiles based on a user's uploaded photo only, or the photo with a name, or the photo with the name and a username. The proposed model achieved an accuracy of 88% with precision and recall matrices of 86% and 89% respectively.
Examining the Validity of Inference Rules: Utilizing Truth Tables and Contrapositive Analysis

In mathematical discrete logic, rules of inference play a fundamental role in reasoning and drawing valid conclusions. This paper focuses on proving the common rules of inference using truth tables for the contrapositive. The contrapositive is a powerful tool that allows for the examination of the logical relationships between conditional statements. By constructing truth tables for the contrapositive of each rule of inference, we can systematically evaluate the validity of the rules. The analysis involves assigning truth values to the premises and determining the truth values of the conclusions based on the contrapositive truth tables. The results demonstrate the consistency and soundness of the rules of inference, providing a rigorous foundation for logical reasoning in mathematical discrete logic. This approach showcases the applicability of truth tables and the contrapositive in establishing the validity of the rules of inference, thereby enhancing our understanding and utilization of logical principles in mathematical discrete logic.
Security Challenges Review in Agile and DevOps Practices

Agile and DevOps methodologies are becoming increasingly popular in software development, as they offer many benefits to software development teams and the organizations they work for. Agile methods depend on speed in development, repetition, and an increase in focus on the main characteristics and functions of the system. The DevOps approach aims at continuous integration, continuous delivery, continuous improvement, and faster feedback. Security is a critical component of Agile and DevOps methodologies. Integrating security into the development process from the outset can help to reduce the risk of security vulnerabilities, improve collaboration between development and security teams, enable rapid response to security incidents, increase automation, and ensure compliance with regulatory requirements. In conclusion, security has challenges in agile and DevOps approaches, so this paper discusses the most important challenges of combining ensuring security and continuous development.
Enhancing Password Security via Supervised Learning

Text-based passwords are currently the most widely used form of authentication and are expected to remain so. However, since these passwords are typically made up of meaningful strings, machine learning and deep learning algorithms can help developers assess their strengths and predict their vulnerability to brute-force attacks. Advanced techniques such as Long Short-Term Memory (LSTM) and Generative Adversarial Networks (GAN) can generate lists of similar and expected text passwords by learning the patterns of how users create and select their passwords. This paper explores using machine learning algorithms to categorize passwords into three levels: strong, moderate, and weak. We also evaluate whether machine learning or deep learning techniques can learn the patterns used in hashing techniques. Furthermore, we have developed a password generation model using Gated Recurrent Unit (GRU) to create new passwords based on learned patterns. With this approach, we aim to improve password security and enhance the user experience of creating and managing passwords.
Unmasking Nationality Bias: A Study of Human Perception of Nationalities in AI-Generated Articles

We investigate the potential for nationality biases in natural language processing (NLP) models using human evaluation methods. Biased NLP models can perpetuate stereotypes and lead to algorithmic discrimination, posing a significant challenge to the fairness and justice of AI systems. Our study employs a two-step mixed-methods approach that includes both quantitative and qualitative analysis to identify and understand the impact of nationality bias in a text generation model. Through our human-centered quantitative analysis, we measure the extent of nationality bias in articles generated by AI sources. We then conduct open-ended interviews with participants, performing qualitative coding and thematic analysis to understand the implications of these biases on human readers. Our findings reveal that biased NLP models tend to replicate and amplify existing societal biases, which can translate to harm if used in a sociotechnical setting. The qualitative analysis from our interviews offers insights into the experience readers have when encountering such articles, highlighting the potential to shift a reader’s perception of a country. These findings emphasize the critical role of public perception in shaping AI’s impact on society and the need to correct biases in AI systems.
Multicalibrated Regression for Downstream Fairness

We show how to take a regression function that is appropriately multicalibrated and efficiently post-process it into an approximately error minimizing classifier satisfying a large variety of fairness constraints. The post-processing requires no labeled data, and only a modest amount of unlabeled data and computation. The computational and sample complexity requirements of computing are comparable to the requirements for solving a single fair learning task optimally, but it can in fact be used to solve many different downstream fairness-constrained learning problems efficiently. Our post-processing method easily handles intersecting groups, generalizing prior work on post-processing regression functions to satisfy fairness constraints that only applied to disjoint groups. Our work extends recent work showing that multicalibrated regression functions are omnipredictors (i.e. can be post-processed to optimally solve unconstrained ERM problems) to constrained optimization problems.
Evaluation of targeted dataset collection on racial equity in face recognition

Algorithmic audits of industry face recognition models have recently incentivized companies to diversify their data collection methods, which in turn has reduced error disparities along demographic lines, such as gender or race. We argue that it is important to understand exactly how various forms of targeted data collection mitigate performance disparities in these updated face recognition models. We propose an empirical framework to assess the impact of additional dataset collection targeted towards various racial groups. We apply our framework to three racially-annotated benchmark datasets using three standard face recognition models. Our findings empirically validate the notion that the introduction of data from the demographic group with the initially-lowest performance improves performance on that group significantly more than adding from other groups. We also observe that in all settings, the introduction of data from a previously omitted group does not harm the performance of other groups. Furthermore, investigation of feature embeddings reveals that performance increases are associated with a larger separation among images of different identities. Despite the commonalities we observe across datasets, we also find key differences: for example, in one dataset, training on one racial group generalizes well across all groups. These differences speak to the criticality of re-applying empirical evaluation methods, such as the methods in this work, when introducing new datasets or models.
A Bio-inspired Energy Efficient Dynamic Task Scheduling (BEDTS) scheme and classification for virtualization CDC

Cloud Data Centers (CDCs) have evolved into an essential computing infrastructure for businesses. These applications generate a variety of data traffic with varying needs that must be examined and processed on CDCs. Every CDC is made up of multiple servers, virtual infrastructures, and physical connections that manage the internet's informative traffic. CDCs make use of a number of critical technologies, such as virtualization and Service Level Agreements (SLA). Virtualization makes it easier to share cloud computing resources (for example, by separating a powerful Physical Machine (PM) into a series of Virtual Machines (VM)) whose power is comparatively less. Even though virtualization increases the use of PMs by establishing a set of VMs for offering customised services to satisfy the needs of end-users, it also introduces another difficulty to cloud computing: map the VMs to the appropriate PMs. This is referred to as the VM deployment problem, and it is a Nondeterministic Polynomial time problem (NP-problem). CDC task scheduling is appropriate to augment the energy efficiency and resource usage in cloud computing. In the case of real-time tasks in virtualized CDC, the Bio-Inspired Energy Efficient Dynamic Task Scheduling (BEDTS) method is proposed. The Adaptive Elephant Herding Optimization (AEHO) technique is introduced in the BEDTS algorithm for optimal selection of VMs with resource restrictions and jobs. Initially, heterogeneous jobs and VMs are identified using a previous scheduling record. The Bayes classifier and Historical Scheduling Record (HSR), which allow for the identification of both the task type and VM type, serve as the foundation for task categorization. Then, related tasks are combined and then scheduled to make the best use of the host's operational status. When compared to previous approaches, experimental results reveal that BEDTS considerably enhances the scheduling performance on the whole, attains improved CDC resource utilisation, boosts task guarantee ratio, lowers average response time, and decreases the energy usage.
Safety Issues in Conversational Systems

This paper addresses two critical safety issues in conversational systems and methods to mitigate these problems. In section 1, I will discuss the problems faced by online conversational systems due to the actions of malicious users. It will particularly focus on the cyberpredator problem, which often targets vulnerable individuals, especially young children. In this section, I will review existing models to detect such predators, including my previous work, and present the results. In section 2, I will discuss safety issues related to conversational agents based on the Large Language Models. I highlight the limitations of existing works in assessing the safety of the models and propose a research topic that I plan to undertake to address them.
Optimizing Generative Adversarial Networks Models for Non-Pneumatic Tire Design

Pneumatic tires1 are used in a wide range of industries. However, they are difficult to design and rely on the knowledge of experienced designers. To aid in the design of pneumatic tires, this paper suggests the use of Generative Adversarial Networks (GAN) models. The 2000 created images for training were used after removing highly sim-ilar images using Mean Squared Error (MSE) and Structural Similarity Index (SSIM). To find the best model for generating patterns of regularly shaped non-pneumatic tires, GAN, Deep Convolu-tional Generative Adversarial Networks (DCGAN), StyleGANv2 ADA and ProjectedGAN were compared and analyzed. In the qualitative evaluation, the GAN, DCGAN, and StyleGAN v2-ADA models showed that the circle shape was distorted and did not maintain a consistent pattern, but ProjectedGAN showed that the circle remained consistent and the pattern was less distorted than the other GAN models. When evaluating quantitative metrics, ProjectedGAN performed best among several techniques that measure the difference be-tween the generated and actual image distributions.
Usability Heuristics and Guidelines for RESTful APIs and Application to the Twitter API

According to ISO/IEC 9126-1, usability refers to a software's capability for being understood, learned, used, and attractive to the user under specific conditions. Therefore, ensuring usability should be a priority in software development. The main goal of this paper is to develop a set of usability heuristics and guidelines for designing RESTful APIs. This is done through a comprehensive analysis of REST principles using an extended, general-purpose usability model as a guide. The efficacy of these heuristics and guidelines was tested with a case study, namely, the REST API for Twitter developers. This usability study consisted in a heuristic evaluation, which is a technique introduced by Nielsen and Molich in 1990. Prior to this, it is necessary to characterize the REST API's context of use according to the characteristics of the users, the tasks and the environments. The goal is to gain a better perspective on the problems found during the usability study.
FulltextAttention

This study proposes a novel approach, FulltextAttention, for classifying lengthy documents, particularly scientific papers. The proposed model tackles the length constraint issue of transformer-based attention mechanisms by dividing the documents into fixed-size chunks. It consists of two modules: The document segmentation module splits the input document into manageable fixed-size chunks, while the attention module performs token-level and chunk-level attention in a two-step process. Unlike the existing transformer-based models, FulltextAttention is not limited by the length of the input sequence by utilizing the RNN-based bidirectional GRU (BiGRU). The attention module employs Bahdanau Attention that is applicable to RNNs. The proposed model was trained by optimizing parameters to minimize the loss through the Bahdanau attention process after passing the feature embedding through the BiGRU layer. Our experiments on scientific papers of RF-EMF (Radio Frequency Electromagnetic Field) show that the proposed model using full-text achieves comparable performance to the state-of-the-art transformer-based BERT model using abstract data. Although the limited size of the full-text dataset affected the model's performance, this can be addressed by augmenting the dataset in the future. Furthermore, the proposed model exhibits potential for efficient utilization in other downstream tasks, such as question and answering (Q&A).
Reckoning with the Disagreement Problem: Explanation Consensus as a Training Objective

As neural networks increasingly make critical decisions in high-stakes settings, monitoring and explaining their behavior in an understandable and trustworthy manner is a necessity. One commonly used type of explainer is post hoc feature attribution, a family of methods for giving each feature in an input a score corresponding to its influence on a model’s output. A major limitation of this family of explainers in practice is that they can disagree on which features are more important than others. Our contribution in this paper is a method of training models with this disagreement problem in mind. We do this by introducing a Post hoc Explainer Agreement Regularization (PEAR) loss term alongside the standard term corresponding to accuracy, an additional term that measures the difference in feature attribution between a pair of explainers. We observe on three datasets that we can train a model with this loss term to improve explanation consensus on unseen data, and see improved consensus between explainers other than those used in the loss term. We examine the trade-off between improved consensus and model performance. And finally, we study the influence our method has on feature attribution explanations.