Reviewerly/exHarmony huggingface dataset:
Machine learning and deep learning for user authentication and authorization in cybersecurity: A state-of-the-art review

In the continuously developing field of cyber security, user authentication and authorization play a vital role in protecting personal information and digital assets from unauthorized use. As the field of cyber security expands, traditional user authentication and authorization approaches are not enough to prevent unauthorized access to personal information. Therefore, Machine Learning and Deep Learning models are introduced in cybersecurity. To assist researchers and cybersecurity experts in their research endeavours, a comprehensive and informative study is required covering the state-of-the-art advancements. Therefore, this research aimed to explore the field of Machine Learning and Deep Learning-based user authentication and authorization. More specifically, this paper intends to explore the diverse application domains of Machine Learning and Deep Learning-based user authentication and authorization. The paper also analyzes the commonly used datasets, pre-processing methods and Machine Learning and Deep Learning algorithms in user authentication and authorization. After that, this study conducts a thorough and detailed examination of some state-of-the-art articles' results and experimental details to enhance comprehension of the present advancements. Finally, the study engages in a comprehensive discussion concerning the various challenges encountered and outlines potential avenues for future research. This systematic review provides an all-encompassing overview of Machine Learning and Deep Learning-based user authentication and authorization, covering its application domains, models, analysis of state-of-the-art results, challenges, and research directions. It serves as a valuable resource for interdisciplinary studies.
Knowledge Distillation via Token-level Relationship Graph based on the Big Data technologies

In the big data era, characterized by vast volumes of complex data, the efficiency of machine learning models is of utmost importance, particularly in the context of intelligent agriculture. Knowledge distillation (KD), a technique aimed at both model compression and performance enhancement, serves as a pivotal solution by distilling the knowledge from an elaborate model (teacher) to a lightweight, compact counterpart (student). However, the true potential of KD has not been fully explored. Existing approaches primarily focus on transferring instance-level information by big data technologies, overlooking the valuable information embedded in token-level relationships, which may be particularly affected by the long-tail effects. To address the above limitations, we propose a novel method called Knowledge Distillation with Token-level Relationship Graph (TRG) that leverages token-wise relationships to enhance the performance of knowledge distillation. By employing TRG, the student model can effectively emulate higher-level semantic information from the teacher model, resulting in improved performance and mobile-friendly efficiency. To further enhance the learning process, we introduce a dynamic temperature adjustment strategy, which encourages the student model to capture the topology structure of the teacher model more effectively. We conduct experiments to evaluate the effectiveness of the proposed method against several state-of-the-art approaches. Empirical results demonstrate the superiority of TRG across various visual tasks, including those involving imbalanced data. Our method consistently outperforms the existing baselines, establishing a new state-of-the-art performance in the field of KD based on big data technologies.
Investigating the behavioural intentions of museum visitors towards VR: A systematic literature review

This study investigates the behavioural intentions of museum visitors towards Virtual Reality (VR) technology, spanning the years 2010–2023. It evaluates literature quality, explores research theories, and identifies trends. Findings indicate that most museum visitors hold positive attitudes towards VR, necessitating further research on generational perspectives. Quantitative research methods dominate (77.8%), with 38.9% employing the Technology Acceptance Model (TAM) or its extensions. Key influencing factors include perceived usefulness, information quality, perceived ease of use, and psychological cognition. The study underscores the importance of comprehending generational differences among museum visitors and advocates for adopting comprehensive theoretical models in future research within the museum domain. This research contributes to understanding how museum visitors engage with VR technology, emphasising nuanced approaches considering generational variations and recommending more inclusive theoretical frameworks.
Fractional-order quantum game chaotic map and its synchronization with application

In this paper, according to the knowledge of discrete fractional calculus and quantum game chaotic map, we first get the fractional quantum game chaotic map, which describes the dynamics of a quantum duopoly game. Then, the phase space, maximum Lyapunov exponent, and bifurcation diagram of the proposed map are obtained for the purpose of observing dynamic behaviors compared with the original quantum game chaotic map. After that, the synchronous controller is designed and the chaos synchronization of the fractional quantum game chaotic map is observed. Finally, we develop the fractional quantum game chaotic map into the chaotic sequence generator and image encryption scheme.
Multi-view universum support vector machines with insensitive pinball loss

Multi-view learning (MVL) is a promising field that seeks to make the most of information shared across different views, ultimately improving generalization performance. Numerous multi-view support vector machines (SVMs) have been proposed and shown great success. However, hingle loss is widely used by existing methods, which makes these SVMs sensitive to noise and unstable when re-sampling. Furthermore, many researchers have demonstrated the effectiveness of universum data for supervised learning. This paper proposes a novel MVL method called multi-view universum support vector machine with insensitive pinball loss (Pin-MvUSVM). Specifically, we first integrate the pinball loss into the multi-view learning framework, which is dependent on the quantization distance and is robust to noise. Then, we utilize universum data to explore prior knowledge for MVL, which improves the generalization performance. Moreover, we develop multi-view universum twin support vector machine with insensitive pinball loss (Pin-MvUTSVM) as a low-complexity alternative to improve computational efficiency. It solves a pair of smaller-scale quadratic programming problems (QPPs) and utilizes the dual form of the model to solve two convex optimization problems for obtaining the prediction classifier. We conduct classification experiments on four multi-view datasets, and our results show that the proposed convex models outperform several state-of-the-art models.
TeReKG: A temporal collaborative knowledge graph framework for software team recommendation

Successful software development requires a cohesive team with the right mix of technical skills and the ability to collaborate effectively. However, forming a software team that can execute tasks with precision and efficiency requires a deep understanding of each member’s competence, experience, and cooperation history. Previously, automated software team selection has evaluated technical skills, cohesion, and cooperation history. However, the previous method had some limitations. Particularly, local features directly calculated from team members were subjective to the researchers’ views, and the method ignored the temporal aspect of open-source software development. To overcome these limitations, this paper proposes a knowledge-graph software team recommendation framework called TeReKG. This framework encapsulates temporal collaboration patterns and uses a temporal knowledge graph to encode software collaboration history, technical abilities, task dependencies, and project structure. TeReKG was against state-of-the-art team recommendation algorithms using three popular open-source software projects: Moodle, Apache, and Atlassian. The evaluation results show that TeReKG outperforms the state-of-the-art baselines in both single-role and team recommendation tasks. These findings demonstrate that knowledge graph embedding can be effectively utilized in automated recommendation tasks in software engineering. Additionally, this highlights the potential for knowledge graphs to capture global information that can benefit various software development applications, including impact prediction of software repositories, code clone detection, and source code retrieval.
Informed or outdated consent? An investigation into the media release policies of school districts in the United States

School media release policies determine whether, how, and where children's personally identifiable information (PII) is shared, but these key policies and the forms that parents are asked to sign at the start of each year have not yet been considered in light of concerns about the privacy of students and educational institutions' use of social media. In this study, we examined the available media release forms for 120 of the largest school districts in the United States to understand how they communicate and inform their communities of their media release policies. Through the use of qualitative content analysis, we systematically coded 90 available media release forms to evaluate which types, where a child's personally identifiable information might be shared, and whether potential risks of sharing were depicted in the consent process. 95.5% of the available media release forms we analyzed mentioned which PII schools and school districts can share if parents consent and 87.7% mention the various forms of media through which PII can be shared. However, only 13.3% mentioned specific social media platforms, though prior research has suggested that the use of social media by educational institutions is very widespread. Further, just 4.4% of the forms in our sample mentioned any specific risks that may accompany the sharing of PII, despite the articulation of clearly identified risks being an ethical recommendation for consent processes (e.g., the Belmont Report). In conclusion, there exists a significant gap between media release policies in educational institutions and the comprehensive, informed consent that parents should receive, highlighting a need for improved transparency and communication about the risks and avenues of PII sharing.
Homophily modulates double descent generalization in graph convolution networks

Graph neural networks (GNNs) excel in modeling relational data such as biological, social, and transportation networks, but the underpinnings of their success are not well understood. Traditional complexity measures from statistical learning theory fail to account for observed phenomena like the double descent or the impact of relational semantics on generalization error. Motivated by experimental observations of "transductive" double descent in key networks and datasets, we use analytical tools from statistical physics and random matrix theory to precisely characterize generalization in simple graph convolution networks on the contextual stochastic block model. Our results illuminate the nuances of learning on homophilic versus heterophilic data and predict double descent whose existence in GNNs has been questioned by recent work. We show how risk is shaped by the interplay between the graph noise, feature noise, and the number of training labels. Our findings apply beyond stylized models, capturing qualitative trends in real-world GNNs and datasets. As a case in point, we use our analytic insights to improve performance of state-of-the-art graph convolution networks on heterophilic datasets.
White blood cells classification using multi-fold pre-processing and optimized CNN model

White blood cells (WBCs) play a vital role in immune responses against infections and foreign agents. Different WBC types exist, and anomalies within them can indicate diseases like leukemia. Previous research suffers from limited accuracy and inflated performance due to the usage of less important features. Moreover, these studies often focus on fewer WBC types, exaggerating accuracy. This study addresses the crucial task of classifying WBC types using microscopic images. This study introduces a novel approach using extensive pre-processing with data augmentation techniques to produce a more significant feature set to achieve more promising results. The study conducts experiments employing both conventional deep learning and transfer learning models, comparing performance with state-of-the-art machine and deep learning models. Results reveal that a pre-processed feature set and convolutional neural network classifier achieves a significantly better accuracy of 0.99. The proposed method demonstrates superior accuracy and computational efficiency compared to existing state-of-the-art works.
Good-bootstrap: simultaneous confidence intervals for large alphabet distributions

Simultaneous confidence intervals (SCI) for multinomial proportions are a corner stone in count data analysis and a key component in many applications. A variety of schemes were introduced over the years, mostly focussing on an asymptotic regime where the sample is large and the alphabet size is relatively small. In this work we introduce a new SCI framework which considers the large alphabet setup. Our proposed framework utilises bootstrap sampling with the Good-Turing probability estimator as a plug-in distribution. We demonstrate the favourable performance of our proposed method in synthetic and real-world experiments. Importantly, we provide an exact analytical expression for the bootstrapped statistic, which replaces the computationally costly sampling procedure. Our proposed framework is publicly available at the first author's Github page.
E-learning in Times of Radical Uncertainty

As teachers and teacher educators in the United States, we offer this editorial based on our lived experiences with the political, social, and environmental precarities where we live. Among these precarities are our looming U.S. presidential election after the last one included a plot by bad actors to abscond with our State’s electoral votes, our constant struggle for water in a desert state, and our weak overall internet connectivity. We call our wonderings about how we are living in and somehow surviving these times, &lt;i&gt;radical uncertainties&lt;/i&gt;. In this editorial, we canvas radical uncertainties related to e-learning about digital literacies, generative text, datafication, and the camera gaze. Alongside these uncertainties, we offer alternative avenues for collective thinking based on Chicana Feminist epistemologies. We intend for readers to consider how local knowing can and should inform e-learning planning and practice.
Multi-type concept drift detection under a dual-layer variable sliding window in frequent pattern mining with cloud computing

Abstract The detection of different types of concept drift has wide applications in the fields of cloud computing and security information detection. Concept drift detection can indeed assist in promptly identifying instances where model performance deteriorates or when there are changes in data distribution. This paper focuses on the problem of concept drift detection in order to conduct frequent pattern mining. To address the limitation of fixed sliding windows in adapting to evolving data streams, we propose a variable sliding window frequent pattern mining algorithm, which dynamically adjusts the window size to adapt to new concept drifts and detect them in a timely manner. Furthermore, considering the challenge of existing concept drift detection algorithms that struggle to adapt to different types of drifting data simultaneously, we introduce an additional dual-layer embedded variable sliding window. This approach helps differentiate types of concept drift and incorporates a decay model for drift adaptation. The proposed algorithm can effectively detect different types of concept drift in data streams, perform targeted drift adaptation, and exhibit efficiency in terms of time complexity and memory consumption. Additionally, the algorithm maintains stable performance, avoiding abrupt changes due to window size variations and ensuring overall robustness.
U.S. and Japanese Consumer Attitudes Toward Tailored and Targeted Communication with Human and Chatbot Agents

Consumers in the United States (n = 290) and Japan (n = 221) participated in 2 × 2 experiments designed to test effects of agent type (online human or chatbot) and communication strategy (targeted or tailored) on attitudes toward messages and agents. In both national samples, tailored messages were more effective in positively affecting attitudes. In addition, perceived humanness was found to mediate the relationships between agent type and attitudes such that when human or artificial agents are perceived as more human in text exchanges, perceived humanness in turn increases attitudes toward both the agents and messages. Hypothesized effects based on individualism-collectivism were not found. Theoretical and practical implications for designing interactive message strategy via conversational chatbots in an era of artificial intelligence (AI) and global communication are discussed.
Statistical inference in circular structural model and fitting circles to noisy data

It is well known that commonly used algorithms for circle fitting perform poorly when sampling distribution of the points is not symmetric with respect to the circle center, e.g., when the points are sampled from a circle arc. To overcome this difficulty we introduce and study a parametric circular structural model. In this model the points on the circumference are assumed to be sampled according to the von Mises distribution with unknown concentration and mean direction parameters. We develop maximum likelihood and method of moments estimators of the circle center and radius, and study their statistical properties. In particular, we show that the proposed maximum likelihood estimator is asymptotically normal and efficient. We also develop a test of uniformity for the sampling distribution along the circle. Based on the derived theoretical results we propose a numerically stable circle fitting algorithm, investigate its accuracy in a simulation study, and illustrate its behavior in a real data example.
Construction of Knowledge Graph for Emergency Resources

Knowledge graphs can effectively organize and represent information related to emergency resources for unforeseen sudden events. In this study, we construct a model layer for the knowledge graph of emergency resources, focused on sudden events, through the classification and analysis of unforeseen disaster measures. This study defines eight interconnected entity types, each characterised by a set of attributes and engaging in one or more relationships with other entity types. Utilizing 121 incident investigation reports from the emergency management departments of various provinces and cities over the past five years, we select five entities with the highest frequency of occurrence along with their corresponding four relationships. We then design an extraction plan for these entities and relationships. Based on the completed knowledge graph data, we formulate 14 questions related to emergency resources for sudden events and construct 19 corresponding question-and-answer templates using a template-based question-answering (QA) approach. We retrieve the corresponding Cypher statement templates through template mapping and obtain the question answers through querying. Finally, we design a knowledge graph question-and-answer system using the Django web framework, which includes entity queries and knowledge QA functions, specifically for emergency resources related to sudden events.
Designing Language Learning Experiences With Generative AI Tools

Artificial intelligence (AI), particularly generative AI, can present many opportunities for language learners to practice and improve their language skills, receive timely feedback on their performance, and customize their learning based on their needs and language proficiency. AI's benefits are not limited to second language (L2) learners. Instructors can also benefit from the novel generative AI technologies by using them in curriculum and lesson design, developing new teaching and assessment materials, or addressing diverse learner skills and needs. Despite AI's advantages, the main issue is how to design L2 environments effectively so learners can receive the best benefits from AI while reducing some associated drawbacks. This chapter argues that learning experience design (LXD) presents a road map for L2 instructors as they incorporate generative AI into their instruction. If the learning design is random and left to good intentions, achieving meaningful learning outcomes will also be left to chance. Following proven LXD guidelines may help alleviate the confusion around AI.
Unravelling the Evolution of Generative AI in Communication Education

This chapter traces the historical evolution of human-machine communication, highlighting generative AI's transformative role. It begins with the origins of AI and early communication technologies, including Alan Turing's pioneering work and the development of Turing machines, as well as early AI experiments in human-machine interaction. The narrative then transitions to AI's evolution and the emergence of generative models, spotlighting advancements like recurrent neural networks (RNNs) and long short-term memory (LSTM) networks, which are fundamental to coherent text generation. The pivotal role of generative AI in language and communication learning is explored, showcasing its impact on writing skills and comprehension. Ethical considerations in AI usage are discussed, including bias, privacy, and ethical communication practices. The chapter concludes by envisioning AI's future in communication pedagogy, emphasizing collaboration between educators and AI developers and recapitulating historical insights for the integration of AI in education.
The Influence of Generative AI on Interpersonal Communication Dynamics

In this comprehensive exploration, the interaction between generative AI and interpersonal communication is examined. The initial sections delve into the characteristics and limitations of AI-generated responses, highlighting the challenges of context and non-verbal cues interpretation. The potential for AI-driven interpersonal skill development is presented. The discussion progresses to address the changing dynamics in the classroom, contrasting traditional communication training with AI-augmented methods. The efficacy of AI in group discussions and role-plays is assessed, with a central focus on whether AI augments or diminishes human connections. The final sections explore the potential of generative AI in reshaping our understanding of effective communication and the necessity for educators to uphold the human element while leveraging AI's skill-enhancing capabilities. This comprehensive review offers insights into the evolving landscape of AI and interpersonal communication, shedding light on its opportunities, challenges, and the path forward.