Reviewerly/exHarmony huggingface dataset:
Public Value-creation Through Applicationof Artificial Intelligence(AI) in COVID-19 Management: PolicyLessons from India

The COVID-19 pandemic had posed significant challenges for public health systems worldwide. Countries had been experimenting with innovative solutions to address the crisis. Artificial Intelligence (AI) has emerged as a promising technology in this regard, with its potential to improve disease detection, contact tracing, and vaccine distribution. The Government of India had administered more than 1,000 million vaccines in a short period since the outbreak of COVID-19, that is, in March 2020. The government harnessed the potential of AI for providing services to citizens during this uncertain time. This article aims to explore the potential of AI in creating public value in the context of COVID-19 management in India. The analysis is based on primary and secondary sources such as research articles, reports published by government and non-government organisations, online databases, and discussion papers. The findings of the study suggest that AI played a critical role in enhancing the effectiveness of COVID-19 management in India. The application of AI has led to significant improvements in disease surveillance, diagnosis, and treatment, thereby creating public value. The study highlights that the assimilation of AI-enabled services increases the citizens’ participation and overall satisfaction and enhances public trust in the government initiatives. The study recommends for a supportive policy environment which fosters innovation and collaboration between stakeholders in leveraging AI. The study also highlights the need for a robust regulatory framework which ensures data privacy and security.
Adaptive Particle Swarm Optimization Applied in Conjunction with Support Vector Machine

This study showcases the outcomes of implementing the Support Vector Machine (SVM) algorithm with an adaptive particle swarm optimization (APSO) technique. The researchers primarily focused on examining the performance of the linear kernel SVM and found that APSO was able to identify the optimal value for the involved constant. In order to test the effectiveness of the proposed approach, two commonly used optimization techniques were employed—the Grid search algorithm and the typical Particle Swarm Optimization (PSO). These methods were applied to a breast cancer classification task to provide choices and help validate the concept. After conducting the experiments, it was found that by combining the support vector machine (SVM) with the adaptive PSO, satisfactory results can be obtained. The results obtained were conclusive, demonstrating that this method is an effective and promising approach for breast cancer classification. The findings suggest that the proposed approach has the potential to provide accurate and reliable predictions for the classification of breast cancer. By utilizing a combination of the adaptive particle swarm optimization (APSO) technique, it is possible to enhance the performance of the SVM algorithm and improve the accuracy of the classification model.
DPFL-AES: Differential Privacy Federated Learning Based on Adam Early Stopping

Federated learning (FL) is a distributed machine learning framework that protects data privacy on the premise that the data does not leave the local area, and can effectively solve the data islands and data privacy security issues faced by artificial intelligence. However, attackers can launch attribute inference attacks or membership inference attacks on customers by analyzing the parameters in shared model training. To solve this problem, we combine the concepts of differential privacy (DP) and federated learning, and propose a differentially private federated learning scheme based on the Adam Early Stopping algorithm (DPFL-AES). The algorithm inherits the advantages of differential privacy and federated learning, and hides the basic parameters uploaded by the client during the training process. Furthermore, as the model converges, the noise should be reduced accordingly later in the federated learning training process. We added an early stopping (ES) mechanism to the algorithm. In the later stage of federated learning training, as the model converges, the number of communication rounds and the addition of noise are reduced, thereby improving the performance of the model. The algorithm balances data security and availability under a shared model. Finally, we verified the feasibility and practicability of the algorithm through theoretical analysis and experimental simulation on the real MNIST dataset.
Auxiliary Information Guided Self-Attention for Image Quality Assessment

Image quality assessment (IQA) is an important problem in computer vision with many applications. We propose a transformer-based multi-task learning framework for the IQA task. Two subtasks: constructing an auxiliary information error map and completing image quality prediction, are jointly optimized using a shared feature extractor. We use ViT as a feature extractor for feature extraction and guide ViT to focus on image quality-related features by building auxiliary information error map subtask. In particular, we propose a fusion network that includes a channel focus module. Unlike the fusion methods commonly used in previous IQA methods, we use the fusion network, including the channel attention module, to fuse the auxiliary information error map features with the image features, which facilitates the model to mine the image quality features for more accurate image quality assessment. And by jointly optimizing the two subtasks, ViT focuses more on extracting image quality features and building a more precise mapping from feature representation to quality score. With slight adjustments to the model, our approach can be used in both no-reference (NR) and full-reference (FR) IQA environments. We evaluate the proposed method in multiple IQA databases, showing better performance than state-of-the-art FR and NR IQA methods.
Imaginarity of Gaussian states

It is a long-standing debate that why quantum mechanics uses complex numbers but not real numbers only. To address this topic, in recent years, the imaginarity theory has been developed in the way of quantum resource theory. However, the existing imaginarity theory mainly focuses on the quantum systems with finite dimensions. Gaussian states are widely used in many fields of quantum physics, but they are in the quantum systems with infinite dimensions. In this paper we establish a resource theory of imaginarity for bosonic Gaussian states. To do so, under the Fock basis, we determine the real Gaussian states and real Gaussian channels in terms of the means and covariance matrices of Gaussian states. Also, we provide two imaginary measures for Gaussian states based on the fidelity.
A Morphological Perspective on Verb Formation: A Contrast Analysis Affixes of English, Indonesia and Banjar Kuala Language of South Borneo

The purpose of this research is to find similarities and differences, as well as to describe the types of prefixes and suffixes in English, Indonesian and Banjar in Indonesian Borneo, to develop a description of the socio-cultural background of speakers of Banjar Kuala language and describe the morphology and syntax of the Banjar Kuala language: Formation of verbs in Banjar Kuala Borneo/South Kalimantan. Describes verbs formed from affixes, namely prefixes and suffixes, infixes, and prefixes + suffixes like the prefix maN-/mam-/ ( /maNujun/ 'fishing', /mambanu/ 'selling water', and the prefix maN-/ma- and ending -an like, /unjun/ 'kail' becomes /maunjunan/ 'all for fish' and /mambanuan/ 'sell all the water' other prefixes and suffixes maN- and -i: /maunjuni/ 'fishing' in a certain place/.and other verbs like /manjukuni/ 'give a boat' and prefix maN - and suffix i- + suffix -will /maunjanikan/ 'fishing for', /manjukuniakan/ 'to run a boat for' This research project clearly illustrates the differences in verb formation between the Banjar-Kuala language in South Kalimantan, Indonesia and the international language, namely English .both can be seen from the use of affixes in the formation of verbs, the similarity of the two can form verbs with the addition of prefixes and suffixes, while the difference is that English cannot form verbs from infixes, but Banjar language can form verbs from the same infixes as Indonesian and other languages in the world.
Boosting QA Performance Through SA-Net and AA-Net with the Read+Verify Framework

This paper proposes an ensemble model for the Stanford Question Answering Dataset (SQuAD) with the aim of improving performance compared to baseline models such as Albert, and Electra. The proposed ensemble model incorporates Sentence Attention (SA-Net) and Answer Attention (AA-Net) components, which leverage attention mechanisms to emphasize important information in sentences and answers, respectively. Additionally, the model adopts a read+verify architecture. In the Read stage, the model's focus is on accurately predicting answer text, while in the Verify stage, it emphasizes the ability to determine the presence or absence of an answer, providing a probability for the existence of an answer. To enhance the training data, techniques for data augmentation are utilized, including Synonyms Replacement and Random Insertion. The experiment results demonstrate significant improvements on the Albert and Electra baseline models, highlighting the effectiveness of the proposed ensemble model for SQuAD.
A Comparative Study for Predicting the Product Type Based on the Characteristics of the Customer Complaint for Orange Jordan Telecommunication Company

This paper aims to investigate Telecom's products that customers complain about. Telecom companies can use customer complaints to improve their services and loyalty through insightful analysis of complaint categories. The dataset used in this paper was obtained from a well-known company in Jordan named Orange Jordan. The dataset contains complaints received from several clients at the company. The dataset has undergone several preprocessing steps, including cleaning, encoding, and feature selection. Several machine learning classifiers were used to classify the complaints by product type, such as Internet or mobile products. Based on the classification metrics, the Decision Tree model was shown to be the reliable model that can accurately predict the product type for the selected dataset, where it performed slightly better than the KNN model and outperformed the GussianNB model. To enhance the performance of the classification model, it is recommended that the data retrieved from the company system be improved to avoid missing values occurring in the dataset.
Arabic News Summarization based on T5 Transformer Approach

The problem of automatic text summarization is one of the main challenging problems in the field of natural language processing. With the huge amount of data available on the internet, automatic text summarization techniques become necessary to summarize this large number of documents to extract information quickly and efficiently. In this work, the automatic text summarization problem has been investigated using Transfer Learning with a customized Unified Text-to-Text Transformer T5 (t5-arabic-base) model. The t5-arabic-base was fine-tuned on Aljazeera.net news dataset and produced state-of-the-art performance in abstractive automatic text summarization. The experiments achieved F1-measure for ROUGE1, ROUGE2, and ROUGEL equal to 62.84%, 54.84%, 61.98%, respectively. Finally, we explained the model's reasoning process using heat maps and saliency maps. In addition to that, the model's sensitivity to slight perturbations to the input was discussed by using adversarial examples generated using Input Reduction and HotFlip techniques.
The Performance of ChatGPT in Emotion Classification

Natural language processing (NLP) and sentiment analysis empower artificial intelligence systems to identify the perspectives and emotions conveyed in text. This capability has grown in importance and accuracy in recent times. ChatGPT, an OpenAI-developed language model, has garnered considerable attention due to its exceptional NLP abilities. In this paper, ChatGPT's effectiveness in emotion classification, using the Dair-ai/emotion dataset [1], is evaluated. ChatGPT has achieved an accuracy of 58%.
Analysis of TOR Artifacts and Traffic in Windows 11: A Virtual Lab Approach and Dataset Creation

The rapid spread of TOR (The Onion Router) for anonymized communication and internet browsing raises relevant security concerns due to its frequent use in illicit activities. This research is centered around a comprehensive analysis of TOR artifacts on Windows 11 and the creation of a relevant dataset to aid researchers. The study commences with a thorough review and comparative assessment of the existing literature on TOR artifacts and traffic examination. Windows 11 TOR browser artifacts are identified and placed together with those in Windows 10 and preceding versions. The paper's core methodology involves establishing a virtual lab environment, facilitating integration, simulating TOR network traffic, and gathering network forensics artifacts. A comprehensive dataset, assimilated from traffic logs procured from tools such as Wireshark, snort, Suricata, and pfsense firewall, is designed and used to construct machine learning models for TOR traffic detection. Combining these traffic logs into a particular dataset enhances the accuracy and efficiency of the machine-learning models. The findings of this study provide important insights into the evolving landscape of TOR browser artifacts and traffic patterns on different Windows versions. The proposed models showcase impressive accuracy in identifying TOR traffic. This research is crucial in enhancing our knowledge of TOR traffic analysis. It lays the groundwork for further investigations to enhance the detection and prevention of malicious activities utilizing the TOR network. Moreover, it contributes to developing a valuable dataset that can aid ML researchers in future endeavors.
Design and FPGA Implementation of the Henon Chaotic Map for Image Encryption

Due to the ever increasing usage of multimedia content sharing and the inherent nature of the insecure links used for transmission, it is quite necessary to provide a fast and secure encryption algorithm. Current symmetric ciphers possess properties that may reveal some content of the plaintext images, especially when a block cipher is used without any chaining. Chaotic encryption has been proposed to allow for hiding inherent properties of the images and to generate almost uniform histograms of the cipher image (i.e.; hiding any visual properties). To that end, we propose the use of hardware to implement chaos-based ciphers for faster execution time. The results of this work show that such an implementation is feasible, and even favorable, when compared to software-based image encryption.
Reversed Forward Feature Selection Algorithm for Website Fingerprinting Attacks Mitigation

Due to the vast and fast growth of the internet, privacy is becoming a concern. Web browser data privacy is a part of the overall users' privacy. Since the attackers took social engineering to the next level, achieving the web browser history data secured from privacy breaches becomes difficult. In this work, we implemented three types of web fingerprinting attack using ML algorithms (DT_Attack, RF_Attack and AdaB_Attack) with the best performance results (Accuracy 99.9%, precision 100%, recall 100% and execution time 339 seconds) achieved using the Decision Tree classifier (DT_Attack). Then we proposed a defense mechanism using the forward feature selection algorithm named Reverse Forward Feature selection, which contributes to reducing the efficiency of the previously implemented web fingerprinting attacks. More specifically, accuracy, precision, and recall of the implemented attacks were reduced on all attack types between 44.8-54.3%, 24.3-38.6%, 53.6-70.2% respectively, without adding any overhead in terms of bandwidth and execution time.
Arabic Paraphrasing Detection Using Multiple Extracted Features

Paraphrase detection identifies whether two given texts have the same meaning despite differences in wording and structure. Improving these applications' accuracy and effectiveness is crucial, especially for Arabic language, where additional challenges exist due to its complex nature and limited online resources. This paper investigates and compares the applicability of various statistical techniques and machine learning classifiers for identifying paraphrasing in Arabic texts. Given the limited availability of Arabic datasets, A new dataset is proposed that comprises 143 short texts collected from Al-Manara journal, which was paraphrased under the guidance of an Arabic linguistics specialist. This study examines the effectiveness of various statistical and machine learning techniques for feature extraction, such as unigram, bigram, TF-IDF, and word embedding. Moreover, it assesses the performance of several machine learning classifiers for paraphrase detection, including SVM, DT, K-NN, RF, and deep learning (BERT) using different metrics such as accuracy, precision, recall and F-measure. Experiments showed that bigram technique performed the best. Additionally, all machine learning classifiers outperformed the bigram technique in all metrics.
Explainable Artificial Intelligence in Drones: A Brief Review

The lack of clarity of most artificial intelligence algorithms which have many applications has led to the emergence of explainable artificial intelligence (XAI), which includes machine learning algorithms that introduce a clear explanation of learning decisions for human users. In the field of using drones which have recently been expanded in several applications, the drone designers face many challenges due to the weather and the difficulty of making a decision during the flight. Therefore, explainable artificial intelligence may play a significant role in these models of unmanned aerial vehicle. This paper presents a brief review for the recent articles that were published in the field of using XAI in drone's applications.
Generating Fashion Sketches Using Generative Adversarial Networks

Generative Adversarial Networks (GANs) are an active area of research in deep learning and computer vision. They are concerned with generating and manipulating photorealistic synthetic images as well as manipulating real ones. This work used two types of GANs to generate new fashion sketches. The first type was Deep Convolutional GAN (DCGAN), which is one of the earliest GAN models. The results were acceptable compared with the size of the data and the basic types of networks used. For the second model, fashion sketches were generated using StyleGAN-ADA, a powerful model developed mainly for limited datasets. The results were much better visually, with an Fréchet Inception Distance (FID) score of 15.69 and a Kernel Inception Distance (KID) score of 3.2e-3 for 50k generated images against the entire dataset. Besides its ability to be used for data augmentation in projects that use fashion sketches, our research can be a source of inspiration for fashion designers.
Prediction Of Cryptocurrencies Prices Using Long Short Term Memory and Technical Indicators

The most well-known and widely adopted digital currency, Bitcoin, was created in 2009. It is the most dominant cryptocurrency in trading volume, with around 0.47 of the total market volume. Most of the previous studies in the cryptocurrency field were done to predict Bitcoin prices, but not many of them focused on the altcoins. In this study, we focused on 2 of the most famous altcoins, Ethereum and Litecoin, to predict their prices using Long-short term memory. In this study, we explore the impact of the technical indicators and the correlation of Bitcoin with the altcoins. We found that there was a significant improvement when using technical indicators along with correlation with Bitcoin. The error decreased noticeably, especially in Lite coin, where MAPE decreased by as much as 0.41.
Towards a Comprehensive Metric for Evaluating Text Simplification Systems

Accurately evaluating text simplification (TS) systems is a major challenge in optimizing existing models and developing new TS approaches. Human evaluation stands as the gold standard for TS quality assessment, it involves expert human annotators who assess simplified texts based on grammaticality (G), meaning preservation (M), and simplicity (S) criteria. Typically, the overall quality of a TS system is calculated by taking the arithmetic mean of these criteria. However, this method can be misleading due to the complex relationships between these criteria. This study investigates the nuanced relationships between the G, M, and S criteria and introduces two new formulas for combining them into a unified metric that more accurately reflects the overall quality of a TS system. The experimental results highlight the effectiveness of BERTscore and BLEU in evaluating TS systems, particularly when high-quality references are available, and their strong correlations with the unified metrics we propose to express overall system performance.
Predicting Bug Severity Using Machine Learning and Ensemble Learning Techniques

Software quality can be adversely affected by various bugs in the system. Bug identification and fixing are part of every software development life cycle. However, in practice, large and long-lived systems may encounter an enormous number of bugs during their lifetime. Additionally, identifying and fixing a large number of software bugs requires a vast amount of allocated resources that may hinder the software budget. Thus, proper management including bug classification and prioritization is needed. Therefore, this paper employs modern Machine Learning techniques to help developers identify and classify bugs based on various factors including their severity level. Six different datasets are used to evaluate the proposed ML models. Features are extracted from bug descriptions using natural language processing techniques like TF-IDF. Evaluated models include K-Nearest Neighbors, Naïve Bayes, Neural Networks, Random Forest, Support Vector Machine, and various ensemble learning methods. All candidate models are evaluated based on their accuracy for each of the used datasets. Results show that the Neural Networks model exceeds on all six datasets, achieving an accuracy that ranges from 93% to 95%. It also exceeds in other measures as well including precision, recall, and F1-Score. These results indicate that the proposed method performs satisfactorily when identifying bugs in source code.
Detecting ChatGPT Generated Fake Reviews Using Supervised Machine Learning

Customers rely on online reviews to make purchasing decisions. However, the prevalence of fake reviews leads to a challenge in trusting the authenticity of online reviews. The manual detection of these fake reviews is hard and time-consuming. This paper presents an automated fake review detection method using text mining and supervised machine learning. In addition, a new fake reviews dataset called ChatGPT-Fake-It is collected from Google Play and chatGPT. Real reviews about the Facebook mobile application are collected from the Google Play store, while fake reviews are generated using chatGPT. A set of lexical and sentimental features are extracted from the textual reviews to build three classifiers. The experimental results reveal that the decision tree-based method achieved better accuracy, precision, recall, and F1-score than other classifiers.