Reviewerly/exHarmony huggingface dataset:
The effectiveness of artificial intelligence (AI) on daily educational activities of undergraduates in a modern and diversified university environment

Authors:
raw_author_name: Hamedi Mohd Adnan
raw_affiliation_string: Department of Media and Communication Studies, Faculty of Arts and Social Sciences, University of Malaya, Kuala Lumpur, Malaysia

Abstract: This study assessed the effectiveness of AI and AI-related technologies in the daily educational activities of undergraduate students in a modern and diversified university environment. The participants were 13 undergraduate Indonesian students participating in a mobility program for two weeks in Malaysia. A survey questionnaire designed with the help of the existing literature and ChatGPT, which includes ten (10) structured items and seven (7) open-ended questions, was used to collect the data. The relevant SPSS tests were used to analyze the data. Based on the results, of all 13 participants, 12 (92.3%) of them already experienced AI in their daily educational activities, and there were strong positive correlations between the attitudes toward AI and AI experiences, and attitudes toward AI and the effects of AI on education attributes, with correlation scores of .663 and .833 respectively. Based on the participant's answers to the qualitative questions, most of them believed that AI and AI technologies, such as ChatGPT, are helpful in daily educational activities and help them gain information regardless of time and space limitations and do their university-related assignments quickly. Based on the results, AI and AI-related technologies could transform different aspects of modern education.
Fuzzy evaluation model for physical education teaching methods in colleges and universities using artificial intelligence

Authors:
raw_author_name: Siyuan Li
raw_affiliation_string: Graduate School, Adamson University, 0900, Manila, Metro Manila, Philippines
raw_author_name: Chao Wang
raw_affiliation_string: Graduate School, Adamson University, 0900, Manila, Metro Manila, Philippines
raw_author_name: Ying Wang
raw_affiliation_string: Graduate School, Adamson University, 0900, Manila, Metro Manila, Philippines

Abstract: Abstract The evaluation of Physical Education Teaching Methods in Colleges and Universities faces two main challenges: an excess of evaluating elements and a lack of assessment framework. Hence, the research proposes the multi-feature fuzzy evaluation model based on artificial intelligence to streamline the evaluation process and provide an efficient framework for accessing teaching methods. The framework integrates natural/human language using fuzzy instructions considering three evaluation perspectives, including the management stage, instructors, and students and employs the enhanced cuckoo search optimization algorithm. After the teaching expert has determined each parameter's ratings, they are fed into the improved cuckoo search algorithm and solved using an unbiased function to assess the assessment's final result. It incorporates the students' mobility mechanism and movement vector deconstruction designed based on functional criteria. A system for evaluating the quality of instruction has been developed utilizing the proposed model with enhanced cuckoo search optimization. The results indicate that the proposed algorithm has achieved the highest scores across multiple assessment categories, average skill performances of 97.01%, learning progress of 87.36%, physical fitness of 93.49%, participation rate of 95.04%, student satisfaction of 95.49%, and physical education of 96.8% teaching efficiency. The usefulness of the proposed framework in enhancing physical education teaching methods has been demonstrated by comparing the results with traditional methods. It contributes to advancing pedagogical practices in the field.
Adaptive and fuzzy locality discriminant analysis for dimensionality reduction

Authors:
raw_author_name: Jingyu Wang
raw_affiliation_string: School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi’an, 710072, China; School of Astronautics, Northwestern Polytechnical University, Xi’an, 710072, China
raw_author_name: Hengheng Yin
raw_affiliation_string: School of Astronautics, Northwestern Polytechnical University, Xi’an, 710072, China
raw_author_name: Feiping Nie
raw_affiliation_string: School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi’an, 710072, China
raw_author_name: Xuelong Li
raw_affiliation_string: Key Laboratory of Intelligent Interaction and Applications, Ministry of Industry and Information Technology, Northwestern Polytechnical University, Xi’an, 710072, China

Abstract: Linear discriminant analysis (LDA) uses labeled samples for acquiring a discriminant projection direction, by which data of different categories are separated into distinct groups in a lower-dimensional subspace. In response to the issue that LDA lacks robustness to non-Gaussian data with rich local information, improvements on LDA explore the subspace manifold structure by building a fully connected similarity graph. However, these methods are vulnerable to the interference of noisy and redundant information. In this paper, we propose a new local LDA method, named adaptive and fuzzy locality discriminant analysis (AFLDA), which aims at extracting precise and compact features. Firstly, an adaptive and fuzzy k-means strategy is adopted, where the membership between data and corresponding subclass centers for each class is learned to establish a hybrid bipartite graph and capture local information. Secondly, we design a discrete and probability constraint imposed on the membership matrix to explore the intricate structure of multimodal data. Moreover, the subblock partition for each class makes data accommodate to multimodal subclasses. Thirdly, the maximum total scatter regularization term is introduced, which amply disperses the data to enhance the recognition of local structure and avoid trivial solutions. Finally, to eliminate the interference of noisy and redundant information, AFLDA learns an optimized subspace, where cluster centers and the membership matrix are updated alternately. Promising results in experiments illustrate the efficacy of the model.
Chasing snails: Automating the processing of EMCCD images of luminescence from opercula

Authors:
raw_author_name: G.A.T. Duller
raw_affiliation_string: Department of Geography and Earth Sciences, Aberystwyth University, Aberystwyth, Ceredigion, SY23 3DB, UK
raw_author_name: H.M. Roberts
raw_affiliation_string: Department of Geography and Earth Sciences, Aberystwyth University, Aberystwyth, Ceredigion, SY23 3DB, UK

Abstract: Opercula of the gastropod Bithynia tentaculata are composed of calcite, and are typically 2–4 mm in length. They emit a thermoluminescence (TL) signal that can be used for dose reconstruction, and spatially resolved TL data from them can be obtained using an electron multiplying charge coupled device (EMCCD). However, when multiple measurements are made of the same sample with imaging detectors such as the EMCCD, registering the different images is crucial so that when regions of interest (ROI) are defined they consistently relate to the same part of the specimen. Previous work on opercula has undertaken this registration by hand, but this is prohibitively time consuming and is also potentially prone to human error. An automated registration process is described, and its use is illustrated using a dose recovery experiment. Without registration more than half of the regions of interest defined across the operculum failed the recycling test, and for those ROIs which did pass recycling the dose recovery ratio varied from 0.7 to 1.2. After registration more than 97% of ROIs passed recycling and all these ROIs gave dose recovery ratios within two sigma of unity. The automated registration process described here has potential for application to other types of solid sample such as rock slices provided they are not perfectly circular.
Efficient vacuum-state preparation for quantum simulation of strongly interacting local quantum field theories

Authors:
raw_author_name: Thomas D. Cohen
raw_affiliation_string: Department of Physics and Maryland Center for Fundamental Physics, University of Maryland, College Park, Maryland 20742, USA
raw_author_name: Hyunwoo Oh
raw_affiliation_string: Department of Physics and Maryland Center for Fundamental Physics, University of Maryland, College Park, Maryland 20742, USA

Abstract: The authors propose a method to traverse the path in the parameter space of a Hamiltonian for preparing ground states. It is particularly efficient for problems in which the path length in parameter space is long, such as quantum field theories with large volumes.
Transformational Application of Artificial Intelligence and Machine Learning in Financial Technologies and Financial Services: A Bibliometric Review

Authors:
raw_author_name: Vijaya Kanaparthi

Abstract: In this study, I employ a multifaceted comprehensive scientometric approach to explore the intellectual underpinnings of AI and ML in financial research by examining the publication patterns of articles, journals, authors, institutions, and nations by leveraging quantitative techniques, that transcend conventional systematic literature reviews, enabling the effective analysis of vast scientometric and bibliographic data. By applying these approaches, I identify influential works, seminal contributions, thought leaders, topical clusters, research streams, and new research frontiers, ultimately fostering a deeper understanding of the knowledge structure in AI and ML finance research by considering publication records from 2010 to 2022 from several search engines and database sources. The present study finds a marked increase in publications from 2017 to 2022, which highlights a growing interest and expanding research activity in the field, indicating its potential significance and relevance in the contemporary academic landscape.
Authorship Attribution Methods, Challenges, and Future Research Directions: A Comprehensive Survey

Authors:
raw_author_name: Xie He
raw_affiliation_string: Behaviour-Centric Cybersecurity Center (BCCC), School of Information Technology, York University, 4700 Keele Street, Toronto, ON M3J 1P3, Canada
raw_author_name: Arash Habibi Lashkari
raw_affiliation_string: Behaviour-Centric Cybersecurity Center (BCCC), School of Information Technology, York University, 4700 Keele Street, Toronto, ON M3J 1P3, Canada
raw_author_name: Nikhill Vombatkere
raw_affiliation_string: Behaviour-Centric Cybersecurity Center (BCCC), School of Information Technology, York University, 4700 Keele Street, Toronto, ON M3J 1P3, Canada

Abstract: Over the past few decades, researchers have put their effort and paid significant attention to the authorship attribution field, as it plays an important role in software forensics analysis, plagiarism detection, security attack detection, and protection of trade secrets, patent claims, copyright infringement, or cases of software theft. It helps new researchers understand the state-of-the-art works on authorship attribution methods, identify and examine the emerging methods for authorship attribution, and discuss their key concepts, associated challenges, and potential future work that could help newcomers in this field. This paper comprehensively surveys authorship attribution methods and their key classifications, used feature types, available datasets, model evaluation criteria and metrics, and challenges and limitations. In addition, we discuss the potential future research directions of the authorship attribution field based on the insights and lessons learned from this survey work.
Rethinking Cost-Sensitive Classification in Deep Learning via Adversarial Data Augmentation

Authors:
raw_author_name: Raed Al Kontar
raw_affiliation_string: Industrial & Operation Engineering, University of Michigan, Ann Arbor, Michigan 48109;
raw_author_name: Maher Nouiehed
raw_affiliation_string: Industrial Engineering, American University of Beirut, Beirut 1107 2020, Lebanon;
raw_author_name: X. Jessie Yang
raw_affiliation_string: Industrial & Operation Engineering, University of Michigan, Ann Arbor, Michigan 48109;
raw_author_name: Corey Lester
raw_affiliation_string: College of Pharmacy, University of Michigan, Ann Arbor, Michigan 48109

Abstract: Cost-sensitive classification is critical in applications where misclassification errors widely vary in cost. However, overparameterization poses fundamental challenges to the cost-sensitive modeling of deep neural networks (DNNs). The ability of a DNN to fully interpolate a training data set can render a DNN, evaluated purely on the training set, ineffective in distinguishing a cost-sensitive solution from its overall accuracy maximization counterpart. This necessitates rethinking cost-sensitive classification in DNNs. To address this challenge, this paper proposes a cost-sensitive adversarial data augmentation (CSADA) framework to make overparameterized models cost sensitive. The overarching idea is to generate targeted adversarial examples that push the decision boundary in cost-aware directions. These targeted adversarial samples are generated by maximizing the probability of critical misclassifications and used to train a model with more conservative decisions on costly pairs. Experiments on well-known data sets and a pharmacy medication image (PMI) data set, made publicly available, show that our method can effectively minimize the overall cost and reduce critical errors while achieving comparable overall accuracy. Funding: Research reported in this publication was supported by the National Library of medicine of the National Institutes of Health in the United States under award number R01LM013624. Data Ethics &amp; Reproducibility Note: This paper abides by data ethics requirements. Data used are publicly available online at https://deepblue.lib.umich.edu/data/concern/data_sets/6d56zw997 . Codes to replicate the results of this paper are available at https://doi.org/10.24433/CO.2139841.v1 .
Unraveling metastable Markovian open quantum systems

Authors:
raw_author_name: Calum A. Brown
raw_affiliation_string: Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Wilberforce Road, Cambridge CB3 0WA, United Kingdom
raw_author_name: Katarzyna Macieszczak
raw_affiliation_string: Department of Physics, University of Warwick, Coventry CV4 7AL, United Kingdom

Abstract: We analyze the dynamics of metastable Markovian open quantum systems by unraveling their average dynamics into stochastic trajectories. We use quantum reset processes as examples to illustrate metastable phenomenology, including a simple three-state model whose metastability is of classical type, and a two-qubit model that features a metastable decoherence-free subspace. In the three-state model, the trajectories exhibit classical metastable phenomenology: fast relaxation into distinct phases and slow transitions between them. This extends the existing correspondence between classical and quantum metastability. It enables the computation of committors for the quantum phases, and the mechanisms of rare transitions between them. For the two-qubit model, the decoherence-free subspace appears in the unraveled trajectories as a slow manifold on which the quantum state has a continuous slow evolution. This provides a classical (nonmetastable) analog of this quantum effect. We discuss the general implications of these results, and we highlight the useful role of quantum reset processes for analysis of quantum trajectories in metastable systems.
Marketness and Governance: A Typology of Illicit Online Markets

Authors:
raw_author_name: Rasmus Munksgaard
raw_affiliation_string: Aalborg University, Aalborg, Denmark

Abstract: The preceding decade has seen an explosive growth in illicit online commerce, and scholars are increasingly occupied with the study of illicit products, their buyers and sellers, and the platforms and markets in which they are found. Despite sharing one basic characteristic, the exchange of illicit goods and services, there is extensive variation between illicit online markets: Some markets employ currencies, others are based on barter. Some markets exhibit strong social control, others appear anarchic. Traditional theoretical explanations to the organization of illicit markets, which assume that optimization or evolution drives their organization, find it difficult to explain these variations. I propose a typology of illicit online markets that appreciates this heterogeneity and uses it as a theoretical point of departure. Drawing on social control theory and economic sociology, I argue that illicit online markets can be separated across two axes: administrative governance and ”marketness.” This conceptual framework is not prone to functionalist inference and instead implies a less deterministic understanding of how illicit online markets come to be. In turn, this framework encourages the classification and comparison of individual markets, as well as comparative analyses of marketplaces.
Transformer‐based reranking for improving Korean morphological analysis systems

Authors:
raw_author_name: Jihee Ryu
raw_affiliation_string: Division of Computer Science and Engineering Jeonbuk National University Jeonju Republic of Korea; Language Intelligence Research Section Electronics and Telecommunications Research Institute Daejeon Republic of Korea
raw_author_name: Soojong Lim
raw_affiliation_string: Language Intelligence Research Section Electronics and Telecommunications Research Institute Daejeon Republic of Korea
raw_author_name: Seung‐Hoon Na
raw_affiliation_string: Division of Computer Science and Engineering Jeonbuk National University Jeonju Republic of Korea

Abstract: Abstract This study introduces a new approach in Korean morphological analysis combining dictionary‐based techniques with Transformer‐based deep learning models. The key innovation is the use of a BERT‐based reranking system, significantly enhancing the accuracy of traditional morphological analysis. The method generates multiple suboptimal paths, then employs BERT models for reranking, leveraging their advanced language comprehension. Results show remarkable performance improvements, with the first‐stage reranking achieving over 20% improvement in error reduction rate compared with existing models. The second stage, using another BERT variant, further increases this improvement to over 30%. This indicates a significant leap in accuracy, validating the effectiveness of merging dictionary‐based analysis with contemporary deep learning. The study suggests future exploration in refined integrations of dictionary and deep learning methods as well as using probabilistic models for enhanced morphological analysis. This hybrid approach sets a new benchmark in the field and offers insights for similar challenges in language processing applications.
Dual‐scale BERT using multi‐trait representations for holistic and trait‐specific essay grading

Authors:
raw_author_name: Minsoo Cho
raw_affiliation_string: Language Intelligence Research Section Electronics and Telecommunications Research Institute Daejeon Republic of Korea
raw_author_name: Jin‐Xia Huang
raw_affiliation_string: Language Intelligence Research Section Electronics and Telecommunications Research Institute Daejeon Republic of Korea
raw_author_name: Oh‐Woog Kwon
raw_affiliation_string: Language Intelligence Research Section Electronics and Telecommunications Research Institute Daejeon Republic of Korea

Abstract: Abstract As automated essay scoring (AES) has progressed from handcrafted techniques to deep learning, holistic scoring capabilities have merged. However, specific trait assessment remains a challenge because of the limited depth of earlier methods in modeling dual assessments for holistic and multi‐trait tasks. To overcome this challenge, we explore providing comprehensive feedback while modeling the interconnections between holistic and trait representations. We introduce the DualBERT‐Trans‐CNN model, which combines transformer‐based representations with a novel dual‐scale bidirectional encoder representations from transformers (BERT) encoding approach at the document‐level. By explicitly leveraging multi‐trait representations in a multi‐task learning (MTL) framework, our DualBERT‐Trans‐CNN emphasizes the interrelation between holistic and trait‐based score predictions, aiming for improved accuracy. For validation, we conducted extensive tests on the ASAP++ and TOEFL11 datasets. Against models of the same MTL setting, ours showed a 2.0% increase in its holistic score. Additionally, compared with single‐task learning (STL) models, ours demonstrated a 3.6% enhancement in average multi‐trait performance on the ASAP++ dataset.
Spoken‐to‐written text conversion for enhancement of Korean–English readability and machine translation

Authors:
raw_author_name: HyunJung Choi
raw_affiliation_string: Department of Artificial Intelligence University of Science and Technology Daejeon Republic of Korea
raw_author_name: Muyeol Choi
raw_affiliation_string: Integrated Intelligence Research Section Electronics and Telecommunications Research Institute Daejeon Republic of Korea
raw_author_name: Yohan Lim
raw_affiliation_string: Department of Artificial Intelligence University of Science and Technology Daejeon Republic of Korea
raw_author_name: Minkyu Lee
raw_affiliation_string: Integrated Intelligence Research Section Electronics and Telecommunications Research Institute Daejeon Republic of Korea
raw_author_name: Seung Yun
raw_affiliation_string: Integrated Intelligence Research Section Electronics and Telecommunications Research Institute Daejeon Republic of Korea
raw_author_name: Sang Hun Kim
raw_affiliation_string: Integrated Intelligence Research Section Electronics and Telecommunications Research Institute Daejeon Republic of Korea

Abstract: Abstract The Korean language has written (formal) and spoken (phonetic) forms that differ in their application, which can lead to confusion, especially when dealing with numbers and embedded Western words and phrases. This fact makes it difficult to automate Korean speech recognition models due to the need for a complete transcription training dataset. Because such datasets are frequently constructed using broadcast audio and their accompanying transcriptions, they do not follow a discrete rule‐based matching pattern. Furthermore, these mismatches are exacerbated over time due to changing tacit policies. To mitigate this problem, we introduce a data‐driven Korean spoken‐to‐written transcription conversion technique that enhances the automatic conversion of numbers and Western phrases to improve automatic translation model performance.