Reviewerly/exHarmony huggingface dataset:
Fractional-order quantum game chaotic map and its synchronization with application

Authors:
raw_author_name: Zeyu Liu
raw_affiliation_string: College of Science, Northwest A&F University, 22 Xinong Road, Yangling District, 712100, China
raw_author_name: Zhengyuan Lu
raw_affiliation_string: College of Science, Northwest A&F University, 22 Xinong Road, Yangling District, 712100, China
raw_author_name: Yunkang Zhou
raw_affiliation_string: College of Science, Northwest A&F University, 22 Xinong Road, Yangling District, 712100, China

Abstract: 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

Authors:
raw_author_name: Chunling Lou
raw_affiliation_string: Key Laboratory of Mobile Network Application Technology of Zhejiang Province, Ningbo 315211, China; School of Information Science and Engineering, Ningbo University, Ningbo, 315211, China
raw_author_name: Xijiong Xie
raw_affiliation_string: Key Laboratory of Mobile Network Application Technology of Zhejiang Province, Ningbo 315211, China; School of Information Science and Engineering, Ningbo University, Ningbo, 315211, China

Abstract: 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

Authors:
raw_author_name: Morakot Choetkiertikul
raw_affiliation_string: Faculty of Information and Communication Technology, Mahidol University, Nakhon Pathom, Thailand
raw_author_name: Akara Supratak
raw_affiliation_string: Faculty of Information and Communication Technology, Mahidol University, Nakhon Pathom, Thailand
raw_author_name: Suppawong Tuarob
raw_affiliation_string: Faculty of Information and Communication Technology, Mahidol University, Nakhon Pathom, Thailand

Abstract: 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

Authors:
raw_author_name: Macy A. Burchfield
raw_affiliation_string: Theory and Practice in Teacher Education, University of Tennessee, Knoxville, Tennessee, USA
raw_author_name: Joshua M. Rosenberg
raw_affiliation_string: Theory and Practice in Teacher Education, University of Tennessee, Knoxville, Tennessee, USA

Abstract: 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

Authors:
raw_author_name: Cheng Shi
raw_affiliation_string: Departement Mathematik und Informatik, Universität Basel, Basel 4051, Switzerland
raw_author_name: Liming Pan
raw_affiliation_string: School of Computer and Electronic Information, Nanjing Normal University, Nanjing 210023, China; School of Cyber Science and Technology, University of Science and Technology of China, Hefei 230026, China
raw_author_name: Hong Hu
raw_affiliation_string: Wharton Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA 19104-1686
raw_author_name: Ivan Dokmanić
raw_affiliation_string: Departement Mathematik und Informatik, Universität Basel, Basel 4051, Switzerland; Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801

Abstract: 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

Authors:
raw_author_name: Oumaima Saidani
raw_affiliation_string: Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia
raw_author_name: Muhammad Umer
raw_affiliation_string: Department of Computer Science and Information Technology, The Islamia University of Bahawalpur, Bahawalpur, 63100, Pakistan
raw_author_name: Nazik Alturki
raw_affiliation_string: Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia
raw_author_name: Shtwai Alsubai
raw_affiliation_string: Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, P.O. Box 151, 11942, Al-Kharj, Saudi Arabia
raw_author_name: Tai-Hoon Kim
raw_affiliation_string: School of Electrical and Computer Engineering, Yeosu Campus, Chonnam National University, 50, Daehak-ro, Yeosu-si, Jeollanam-do, 59626, Republic of Korea
raw_author_name: Imran Ashraf
raw_affiliation_string: Information and Communication Engineering, Yeungnam University, Gyeongsan, 38541, Republic of Korea

Abstract: 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

Authors:
raw_author_name: Amichai Painsky
raw_affiliation_string: Industrial Engineering Department, Tel Aviv University, Tel Aviv, Israel

Abstract: 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

Authors:
raw_author_name: Mary Frances Rice
raw_affiliation_string: University of New Mexico

Abstract: 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

Authors:
raw_author_name: Jing Chen
raw_affiliation_string: School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China
raw_author_name: Shengyi Yang
raw_affiliation_string: School of Physics and Mechatronic Engineering, Guizhou Minzu University, Guiyang, 550025, China
raw_author_name: Yue Ying
raw_affiliation_string: School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China
raw_author_name: Tian Li
raw_affiliation_string: School of Computer and Software, Nanjing Vocational University of Industry Technology, Nanjing, 210003, China
raw_author_name: Peng Li
raw_affiliation_string: School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China

Abstract: 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

Authors:
raw_author_name: Sophia Mueller
raw_affiliation_string: University of Miami, Coral Gables, Florida, USA
raw_author_name: Tom Kelleher
raw_affiliation_string: University of Florida, Gainesville, Florida, USA

Abstract: 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.
Construction of Knowledge Graph for Emergency Resources

Authors:
raw_author_name: Peng Wu
raw_affiliation_string: School of Intelligent Manufacturing, Nanjing University of Science and Technology, Nanjing 210094, China

Abstract: 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

Authors:
raw_author_name: Betül C. Czerkawski
raw_affiliation_string: College of Applied Science and Technology, University of Arizona, USA

Abstract: 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

Authors:
raw_author_name: C. V. Suresh Babu
raw_affiliation_string: Hindustan Institute of Technology and Science, India

Abstract: 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

Authors:
raw_author_name: Narissra Maria Punyanunt-Carter
raw_affiliation_string: Texas Tech University, USA

Abstract: 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.