Reviewerly/exHarmony huggingface dataset:
Image encryption algorithm based on 1D-SFACF with cross-cyclic shift and adaptive diffusion

Abstract In this paper, a new one-dimensional chaotic system is proposed, which is called one-dimensional sine-fractional-adjusted-cosine-fractional (1D-SFACF). The dynamic behavior of the 1D-SFACF is analyzed by LE, bifurcation graph, trajectory, and cobweb plot. Compared to most one-dimensional chaotic systems, the 1D-SFACF has good cryptographic properties, such as larger parameter space and better chaotic behavior. Based on the excellent expressiveness of the 1D-SFACF, we design a secure image encryption algorithm using 1D-SFACF, called the image encryption algorithm based on 1D-SFACF (SFACF-IE). SFACF-IE is divided into four steps. First, the control parameters and initial values of 1D-SFACF are generated through a natural noise using a hash function. Secondly, an adaptive diffusion strategy is proposed. The starting position of the plaintext diffusion is adaptively changed according to the characteristics of the plaintext. Then, use a cross-cyclic shift to scramble. Finally, a hash function is used to generate a feedback key, the scrambled image is the input of the hash function, and the ciphertext is obtained by diffusing again. Experimental evaluations show that the algorithm is resistant to common attacks.
Pre-large based high utility pattern mining for transaction insertions in incremental database

High utility pattern mining has been actively researched and applied to diverse applications because it can process the database by considering the quantity and importance of items. However, traditional high utility pattern mining methods aim to handle static databases, so they cannot meet the requirements of users who want to process the dynamic environments. Although methods to process incremental databases have been proposed, they have limitations that they perform the mining process on the entire database, including already processed data, whenever data are newly inserted. The pre-large concept is one of the techniques to process the dynamic database. Utilizing the pre-large technique, we can efficiently handle the transaction insertion using the extracted patterns of the previous mining process. In this paper, we propose a novel pre-large-based approach to discover high utility patterns from incremental databases. A list structure is proposed to store the utility information of patterns, so candidate patterns are not generated, and an additional database scan is not required. Performance evaluation performed on various real and synthetic datasets shows that the proposed algorithm is more efficient and effective than the latest approaches in a dynamic environment.
A Gaussian-multivariate Laplacian mixture distribution based robust cubature Kalman filter

In this paper, the state estimation problems of nonlinear systems with outlier-corrupted measurements are investigated. First, to model the non-Gaussian noises caused by the randomly occurring measurement outliers, we propose a new Gaussian-multivariate Laplacian mixture (GMLM) distribution and analyze its distribution characteristics. Then, the measurement likelihood probability density function (PDF) is formulated as the GMLM distribution and further expressed as a hierarchical Gaussian expression. Furthermore, by variational Bayesian (VB) method, a robust cubature Kalman filter is derived (GMLMRCKF). Finally, simulation test and real date are utilized to evaluate the effectiveness of our GMLMRCKF, the results illustrate that the proposed filter has better estimation accuracy and consistence in the case of non-stationary heavy-tailed noises than the existing robust filters, i.e., it has almost the same performance as the standard CKF in the absence of outliers and better robust performance in the presence of unknown outliers.
Deepfake Face Detection Using Deep InceptionNet Learning Algorithm

Deepfakes is digital manipulation techniques that use deep learning to produce deepfake (misleading) images and videos. Identifying deepfake images is the most difficult part of finding the original. Due to the increasing reputation of deep fakes, identifying original images and videos is more crucial to detect manipulated videos. This paper studies and experiments with different methods that can be used to detect fake and real images and videos. The Convolutional Neural Network (CNN) algorithm named InceptionNet has been used to identify deep fakes. A comparative analysis was performed in this work based on various convolutional Networks. This work uses the dataset from Kaggle with 401 videos of train sample and 3745 images were generated by augmentation process. The results were evaluated with the metrics like accuracy and confusion matrix. The results of the proposed model produces better results in terms of accuracy with 93 % on identifying deep fake images and videos
Hand Sign Language Detection - Using Deep Neural Network

Nearly 466 Million people on our Earth are Dumb and Deaf. These people are unable to hear or speak. Communication is a Major Aspectfor each and every Human being. It becomes difficult for these Inarticulate people to communicate with all people. These people have their own method to communicate which is by doing Hand gestures and making certain symbols with their hands. Our model will detect these Gestures and Predict what those gestures mean and what are they trying to communicate. With the help of a Deep Neural Network, the Model is trained and they are capable of detecting the Signs. For Binary Classification we have used logistic Regression, a 2-layer Neural network as in ANN(Artificial neural network).
NFT Application for Music Industry using Blockchain Smart contracts

The vast possibilities of the internet age bring an attraction for artists. As a matter of fact, reports of artist revenue have lowered the potential of music platforms. Streams that are difficult to comprehend, cultural structures inside lack reliable data, inadequate for the digital era, aspects of the music business and a drop in the majority of artists' wages. In this paper, A Non-Fungible Tokens (NFT) in Music Industry using Blockchain Technology are proposed to protect the music copyrights and get revenue rights from untrusted holders. In this model, the Musicians may effortlessly approve and maintain their music rights on a public ledger with the help of the blockchain with no middlemen or any other intermediate business companies.
Exposing the Vulnerabilities of Deep Learning Models in News Classification

News websites need to divide their articles into categories that make it easier for readers to find news of their interest. Recent deep-learning models have excelled in this news classification task. Despite the tremendous success of deep learning models in NLP-related tasks, it is vulnerable to adversarial attacks, which lead to misclassification of the news category. An adversarial text is generated by changing a few words or characters in a way that retains the overall semantic similarity of news for a human reader but deceives the machine into giving inaccurate predictions. This paper presents the vulnerability in news classification by generating adversarial text using various state-of-the-art attack algorithms. We have compared and analyzed the behavior of different models, including the powerful transformer model, BERT, and the widely used Word-CNN and LSTM models trained on AG news classification dataset. We have evaluated the potential results by calculating Attack Success Rates (ASR) for each model. The results show that it is possible to automatically bypass News topic classification mechanisms, resulting in repercussions for current policy measures.
Assessing the Effects of Lemmatisation and Spell Checking on Sentiment Analysis of Online Reviews

With many text preprocessing options, choosing the most efficient pipeline is important for accuracy and computational expense. Online text often contains non-standard English, spelling errors, colloquialisms, emojis, slang and other variations that affect current natural language processing tools, with no clear guidelines for preprocessing this type of text. In this work we analyse text preprocessing techniques using a dataset of online reviews scraped from iTunes and Google Play store. The objective is to measure the efficacy of different combinations of these techniques to maximise the amount of detected sentiment in a dataset of 438,157 reviews. Sentiment detection was performed by two state-of-the-art sentiment analysers (RoBERTa and VADER). Statistical analysis of the results suggest preprocessing strategies for maximising sentiment detected within mental health app reviews and similar text formats.
Unsupervised Estimation of Subjective Content Descriptions

An agent in pursuit of a task may work with a corpus containing text documents. One possible task of the agent is to retrieve documents of similar content and highlight relevant locations in retrieved documents. To perform information retrieval on the corpus, the agent may need additional data associated with the documents. Subjective Content Descriptions (SCDs) provide additional location-specific data for text documents. However, the agent needs SCDs referencing sentences of similar content across various documents in the corpus and most text documents are not associated with SCDs. Therefore, this paper presents UESM, an unsupervised approach to estimate SCDs for text documents, i.e., to associate any corpus with SCDs. In an evaluation, we show that the performance of UESM is on par with latent Dirichlet allocation, while UESM provides SCDs referencing sentences of similar content.
An Interdisciplinary Approach to Misinformation and Concept Drift in Historical Cannabis Tweets

This paper historically analyzes ten years of anglophone Cannabidiol (CBD) tweets using temporal topic modeling and proposes a classification method to detect commercial and non-commercial tweets to better understand topic changes over time. The topics in this conversation change as they affect, and are affected by, cannabis laws, policies and consumers. In the broader conversation on the effects of CBD, many claims of its effectiveness are exaggerated to the point of misinformation. Thus, analyzing the change in CBD topics over time has implications for how both advertising and misinformation regarding health spreads on social networks.
Sentiment Analysis for Women in STEM using Twitter and Transfer Learning Models

The science, technology, engineering and math (STEM) sector is integral to the nation's advancement and economy.However, the STEM workforce is perceived as maledominant, and women are systematically tracked away from it.There has been a rising popularity of the gender disparity problem in STEM across social media platforms.Attitudes relating to women influence the careers women choose to pursue.It is thus timely and important to assess the public's opinion on this topic.This paper proposes a sentiment analysis classification framework that detects the sentiment of social media tweets in relation to women in STEM.To this end, we extracted more than 250,000 relevant tweets and used various open-language models to uncover insights into the perceptions of women in STEM using various open-language models.The study evaluates the performance of multiple machine learning and deep learning methods.We also study the performance of state-of-the-art transformerbased models, including bidirectional encoder representations from transformers (BERT), BERTweet, and TimeLMs (Time Language Models), which achieves 96% accuracy in sentiment detection.Results reveal that people's attitude in response to women in STEM is generally positive on the Twitter platform.However, we observed a significant correlation between positive sentiment in tweets and dates celebrating women's achievements (e.g.International Day of Women and Girls in Science, and International Women's Day).This finding demonstrates the impact of such campaigns on the public's opinion.Therefore, promoting these events among the public can encourage more females to pursue careers in STEM.
Snape: Reliable and Low-Cost Computing with Mixture of Spot and On-Demand VMs

Cloud providers often have resources that are not being fully utilized, and they may offer them at a lower cost to make up for the reduced availability of these resources. However, customers may be hesitant to use such offerings (such as spot VMs) as making trade-offs between cost and resource availability is not always straightforward. In this work, we propose Snape (Spot On-demand Perfect Mixture), an intelligent framework to optimize the cost and resource availability by dynamically mixing on-demand VMs with spot VMs. Through a detailed characterization based on real production traces, we verify that the eviction of spot VMs is predictable to some extent. Snape also leverages constrained reinforcement learning to adjust the mixture policy online. Experiments across different configurations show that Snape achieves 44% savings compared to using only on-demand VMs while maintaining 99.96% availability, which is 2.77% higher than using only spot VMs.
User Perceptions of Actionability in Data Dashboards

This article reports on a multiphase study designed to understand how nonexpert users interact with COVID-19 data dashboards, particularly in terms of the dashboards’ actionability, or ability to support decision making. Analysis of the videos and transcriptions of user interviews shows the variable relevance of proposed criteria for dashboard actionability and suggests additional criteria for users’ emotional responses to data and for the presentation of data at degrees of personal and local granularity. These findings advance an understanding of how nonexpert audiences interact with and derive value from complex visualized data.
Contamination of digital evidence: Understanding an underexposed risk

The dangers of contamination have received considerable attention in the literature regarding the investigation of physical crime scenes and physical evidence. The understanding of contamination in the context of digital evidence appears to be much less understood. Based on experiences from the field of physical evidence, we develop a generalized definition of contamination that also covers digital evidence, namely the “inadvertent transfer of traits to an object of relevance at any point in the forensic process”. We illustrate the definition by presenting several examples and counterexamples for contamination of digital evidence. By addressing the specifics of digital evidence in this context, we argue that our definition can be useful to understand the risks arising through contamination in this domain.
Application of Artificial Intelligence and Robotics in Tourism and Hospitality Marketing

Rapid technological developments have affected business areas and caused differences in products and services. The tourism and hospitality industry has also been affected by these rapid developments, and the use and importance of new technologies have increased. In this context, this research aims to evaluate the use of artificial intelligence and robotic technologies in the field of tourism marketing with data obtained from secondary data. As a result of the research, it has been determined that artificial intelligence and robotic technology applications in the tourism sector offer personalized and new opportunities for tourists in their travel decisions. Within the scope of the research, it has been suggested that these opportunities offered through big data are important in reducing operating costs and ensuring personnel productivity and competitive advantage. Therefore, using artificial intelligence in tourism marketing positively contributes to the systematic analysis of customer information.
Eliciting Co-Creation Best Practices of Virtual Reality Reusable e-Resources

Immersive experiential technologies find fertile grounds to grow and support healthcare education. Virtual, Augmented, or Mixed reality (VR/AR/MR) have proven to be impactful in both the educational and the affective state of the healthcare student’s increasing engagement. However, there is a lack of guidance for healthcare stakeholders on developing and integrating virtual reality resources into healthcare training. Thus, the authors applied Bardach’s Eightfold Policy Analysis Framework to critically evaluate existing protocols to determine if they are inconsistent, ineffective, or result in uncertain outcomes, following systematic pathways from concepts to decision-making. Co-creative VR resource development resulted as the preferred method. Best practices for co-creating VR Reusable e-Resources identified co-creation as an effective pathway to the prolific use of immersive media in healthcare education. Co-creation should be considered in conjunction with a training framework to enhance educational quality. Iterative cycles engaging all stakeholders enhance educational quality, while co-creation is central to the quality assurance process both for technical and topical fidelity, and tailoring resources to learners’ needs. Co-creation itself is seen as a bespoke learning modality. This paper provides the first body of evidence for co-creative VR resource development as a valid and strengthening method for healthcare immersive content development. Despite prior research supporting co-creation in immersive resource development, there were no established guidelines for best practices.
arHateDetector: detection of hate speech from standard and dialectal Arabic Tweets

Abstract Hate speech has become a phenomenon on social media platforms, such as Twitter. These websites and apps that were initially designed to facilitate our expression of free speech, are sometimes being used to spread hate towards each other. In the Arab region, Twitter is a very popular social media platform and thus the number of tweets that contain hate speech is increasing rapidly. Many tweets are written either in standard, dialectal Arabic, or mix. Existing work on Arabic hate speech are targeted towards either standard or single dialectal text, but not both. To fight hate speech more efficiently, in this paper, we conducted extensive experiments to investigate Arabic hate speech in tweets. Therefore, we propose a framework, called arHateDetector, that detects hate speech in the Arabic text of tweets. The proposed arHateDetector supports both standard and several dialectal Arabic. A large Arabic hate speech dataset, called arHateDataset, was compiled from several Arabic standard and dialectal tweets. The tweets are preprocessed to remove the unwanted content. We investigated the use of recent machine learning and deep learning models such as AraBERT to detect hate speech. All classification models used in the investigation are trained with the compiled dataset. Our experiments shows that AraBERT outperformed the other models producing the best performance across seven different datasets including the compiled arHateDataset with an accuracy of 93%. CNN and LinearSVC produced 88% and 89% respectively.
Event Detection Using a Self-Constructed Dependency and Graph Convolution Network

The extant event detection models, which rely on dependency parsing, have exhibited commendable efficacy. However, for some long sentences with more words, the results of dependency parsing are more complex, because each word corresponds to a directed edge with a dependency parsing label. These edges do not all provide guidance for the event detection model, and the accuracy of dependency parsing tools decreases with the increase in sentence length, resulting in error propagation. To solve these problems, we developed an event detection model that uses a self-constructed dependency and graph convolution network. First, we statistically analyzed the ACE2005 corpus to prune the dependency parsing tree, and combined the named entity features in the sentence to generate an undirected graph. Second, we implemented an enhanced graph convolution network using the multi-head attention mechanism to understand the representation of nodes in the graph. Finally, a gating mechanism combined the semantic and structural dependency information of the sentence, enabling us to accomplish the event detection task. A series of experiments conducted on the ACE2005 corpus demonstrates that the proposed method enhances the performance of the event detection model.
Decision Making of Agile Patterns in Offshore Software Development Outsourcing: A Fuzzy Logic-Based Analysis

Computation intelligence techniques are important for making decisions in an agile-based offshore software development paradigm. Offshore development faces additional challenges, such as trust, communication and coordination, and socio-cultural and knowledge transfer. There is a need to determine the rankings of challenges considering their criticality concerning practitioners working in agile-based offshore software development. This paper aims to identify and rank agile challenges in offshore software development by applying computational intelligence techniques. From the systematic literature review, we identified 30 communication and coordination challenges. The distributed agile pattern catalog consists of 15 patterns, from which eight were used to solve communication and collaboration challenges. Many researchers have used fuzzy logic to quantify their results. We further applied the fuzzy analytical technique to determine the priority order concerning the criticality of the identified agile pattern catalog. The results showed that Central Code Repository Pattern ranked the most significant for solving communication and coordination challenges. Global Scrum Board Pattern and Synchronous Communication Pattern ranked second.
Demographic Factors Affecting Fuzzy Grading: A Hierarchical Linear Regression Analysis

Several factors affect students’ mathematics grades and standardized test results. These include the gender of the students, their socio-economic status, the type of school they attend, and their geographic region. In this work, we analyze which of these factors affect assessments of students based on fuzzy logic, using a sample of 29,371 Italian high school students from the 2018/19 academic year. To combine grades assigned by teachers and the students’ results in the INVALSI standardized tests, a hybrid grade was created using fuzzy logic, since it is the most suitable method for analyzing qualitative data, such as teacher-given grades. These grades are analyzed with a hierarchical linear regression. The results show that (1) boys have higher hybrid grades than girls; (2) students with higher socio-economic status achieve higher grades; (3) students from scientific lyceums have the highest grades, whereas students from vocational schools have the lowest; and (4) students from Northern Italy have higher grades than students from Southern Italy. The findings suggest that legislators should investigate appropriate ways to reach equity in assessment and sustainable learning. Without proper interventions, disparities between students might lead to unfairness in students’ future career and study opportunities.