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Facilitating Black Students’ Career development in the Mathematical sciences: A Closer Look at Two HBCUs

PRIMUS 2025年第2期35卷 135-151页

作者： Jett, Christopher C. Jones Williams, Morgin College of Education & Human Development Georgia State University United States Department of Computer Science and Mathematics University of South Carolina Beaufort United States

The mathematics major and subsequent workforce can be difficult for Black scholars to navigate given the racial challenges that abound in the field. Despite these challenges, historically Black colleges and universities (HBCUs) have a stellar track record of supporting Black mathematics majors and facilitating their entry in math-intensive fields. This article explores 2 distinct qualitative studies─(1) a study with 16 Black men informed by Black masculinity theory and (2) a study with 6 Black women informed by Black feminist theory─with participants who earned mathematics degrees at 2 traditionally single-gender HBCUs. In both studies, participants felt supported as they recognized that these 2 HBCUs exerted efforts to facilitate their career development in the mathematical sciences. Drawing upon the findings and lessons learned from these studies, this article provides implications for facilitating Black students’ career development in the mathematical sciences via undergraduate mathematics programs. © 2025 Taylor & Francis Group, LLC.

关键词： Black feminist theory Black masculinity theory career development HBCU undergraduate mathematics education

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Vocal pitch enables differential motor learning of speech segments

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Journal of Neurophysiology 2025年第5期133卷 1382-1391页

作者： Karlin, Robin Tesch, Emily Tang, Ding-Lan Zeng, Yuyu Niziolek, Caroline A. Parrell, Benjamin Department of Speech Language and Hearing Sciences University of Missouri Columbia MO United States Department of Speech and Hearing Science Arizona State University Tempe AZ United States Academic Unit of Human Communication Learning and Development (HCLD) The University of Hong Kong Hong Kong Waisman Center University of Wisconsin-Madison Madison WI United States Department of Communication Sciences and Disorders University of Wisconsin-Madison Madison WI United States

Sensory feedback is crucial for accurate motor control. One process of movement correction is sensorimotor adaptation, or motor learning in response to perceived sensory errors. Recent studies demonstrate that people can adapt to opposing errors on a single movement given a context that differentiates when each error occurs. In speech production, linguistic structure (e.g., the same vowel in different words) can provide context for differential adaptation, but it is unclear whether this is restricted to the same effectors (i.e., lips, tongue, jaw) or also includes movements of other speech effectors (i.e., the larynx). Reaching studies show that contextual movements need not be produced with the same effector as the learning target, but so far, they have only tested left-right pairs. We present three simultaneous adaptation experiments in speech that examine whether laryngeal movements for pitch can provide context for oral articulator movements for vowels. In each experiment, the resonances that correlate with vowel articulator position were perturbed in three directions that were predictable given a pitch context. First, Mandarin speakers differentially adapted given pitch contexts that signal differences in word meaning, suggesting that lexical pitch provides context for vowels. Second, English speakers differentially adapted given arbitrary pitch matching contexts on the word “head,” suggesting that nonmeaningful pitch movements provide context for vowels. Third, English speakers did not differentially adapt when listening to contextual pitch, indicating that mere auditory input of pitch is insufficient. Together, these results suggest that sensorimotor context for learning can be provided by effectors other than the learning target. © 2025 The Authors.

关键词： altered auditory feedback contextual learning sensorimotor adaptation speech production

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The Conformer Encoder May Reverse the Time Dimension

The Conformer Encoder May Reverse the Time Dimension

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Robin Schmitt Albert Zeyer Mohammad Zeineldeen Ralf Schlűter Hermann Ney Computer Science Department Human Language Technology and Pattern Recognition RWTH Aachen University Aachen Germany AppTek GmbH Aachen Germany

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

We sometimes observe monotonically decreasing cross-attention weights in our Conformer-based global attention-based encoder-decoder (AED) models, negatively affecting performance compared to monotonically increasing attention weights. Further investigation shows that the Conformer encoder reverses the sequence in the time dimension. We analyze the initial behavior of the decoder cross-attention mechanism and find that it encourages the Conformer encoder self-attention to build a connection between the initial frames and all other informative frames. Furthermore, we show that, at some point in training, the self-attention module of the Conformer starts dominating the output over the preceding feed-forward module, which then only allows the reversed information to pass through. We propose methods and ideas of how this flipping can be avoided and investigate a novel method to obtain label-frame-position alignments by using the gradients of the label log probabilities w.r.t. the encoder input frames.

关键词： Training Signal processing Acoustics Decoding Speech processing

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Classification Error Bound for Low Bayes Error Conditions in Machine Learning

arXiv

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arXiv 2025年

作者： Yang, Zijian Eminyan, Vahe Schlüter, Ralf Ney, Hermann Machine Learning and Human Language Technology Group Lehrstuhl Informatik 6 Computer Science Department RWTH Aachen University Germany AppTek GmbH Germany

In statistical classification and machine learning, classification error is an important performance measure, which is minimized by the Bayes decision rule. In practice, the unknown true distribution is usually replaced with a model distribution estimated from the training data in the Bayes decision rule. This substitution introduces a mismatch between the Bayes error and the model-based classification error. In this work, we apply classification error bounds to study the relationship between the error mismatch and the Kullback-Leibler divergence in machine learning. Motivated by recent observations of low model-based classification errors in many machine learning tasks, bounding the Bayes error to be lower, we propose a linear approximation of the classification error bound for low Bayes error conditions. Then, the bound for class priors are discussed. Moreover, we extend the classification error bound for sequences. Using automatic speech recognition as a representative example of machine learning applications, this work analytically discusses the correlations among different performance measures with extended bounds, including cross-entropy loss, language model perplexity, and word error rate. Copyright © 2025, The Authors. All rights reserved.

关键词： Speech recognition

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Classification Error Bound for Low Bayes Error Conditions in Machine Learning

Classification Error Bound for Low Bayes Error Conditions in...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zijian Yang Vahe Eminyan Ralf Schlüter Hermann Ney Computer Science Department Machine Learning and Human Language Technology Group Lehrstuhl Informatik 6 RWTH Aachen University Germany AppTek GmbH Germany

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

关键词： Error analysis Measurement uncertainty Linear approximation Training data Machine learning Signal processing Loss measurement Data models Bayes methods Speech processing

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Using the interactive software FossilSketch to teach micropaleontology to undergraduate students

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Journal of Geoscience Education 2025年第2期73卷 133-153页

作者： Stepanova, Anna Belanger, Christina Anwar, Saira Stanley, Christine Nath, Ankur Cherian, Josh Hammond, Tracy Department of Computer Science and Engineering Texas A&M University College Station TX United States Department of Geology and Geophysics Texas A&M University College Station TX United States Department of Multidisciplinary Engineering Texas A&M University College Station TX United States Department of Educational Administration and Human Resource Development Texas A&M University College Station TX United States Institute for Engineering Education and Innovation Texas A&M University College Station TX United States

Micropaleontology is a critical tool for determining the ages of geologic records, reconstructing ancient environments, and monitoring modern ecosystem health. However, most students are not exposed to micropaleontology in their college coursework. To enable non-expert instructors to integrate microfossil identification training in their undergraduate courses, we developed FossilSketch, an interactive web-based educational tool that introduces students to the basics of micropaleontology and guides students through a scaffolded learning experience that develops microfossil identification skills. Here we test the impact of FossilSketch on students’ ability to learn micropaleontology skills, such as identification of microfossils to genus level and basics of fossil data analysis, using data on students’ performance and survey responses collected in an undergraduate paleontology course for geology majors at a large public university. A total of 112 students took part in this study. Analysis of classroom assessments showed that junior and senior geology majors who used FossilSketch were better able to understand the process of microfossil identification, recognize morphological characteristics, and achieve a correct identification than those who did not use FossilSketch. Students who used FossilSketch needed to ask the teaching assistant fewer questions and felt better prepared for specimen-based work than students who did not use FossilSketch. These results suggest that FossilSketch improves students’ understanding of the microfossil identification process. © 2024 National Association of Geoscience Teachers.

关键词： micropaleontology Taxonomy undergraduate education

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GMOCSO: Multi-objective Cat Swarm Optimization Algorithm based on a Grid System

arXiv

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arXiv 2025年

作者： Ahmed, Aram M. Hassan, Bryar A. Rashid, Tarik A. Noori, Kaniaw A. Saeed, Soran Ab.M. Ahmed, Omed H. Umar, Shahla U. Computer Science and Engineering Department University of Kurdistan Hewlêr Erbil Iraq Department of Computer Science College of Science Charmo University Chamchamal Sulaimani Iraq Database Technology Department Technical College of Informatics Sulaimani Polytechnic University Sulaimani Iraq Department of Information Technology University of Human Development Sulaymaniyah Iraq Network Department College of Computer Science and Information Technology Kirkuk University Kirkuk Iraq

This paper presents a multi-objective version of the Cat Swarm Optimization Algorithm called the Grid-based Multiobjective Cat Swarm Optimization Algorithm (GMOCSO). Convergence and diversity preservation are the two main goals pursued by modern multi-objective algorithms to yield robust results. To achieve these goals, we first replace the roulette wheel method of the original CSO algorithm with a greedy method. Then, two key concepts from Pareto Archived Evolution Strategy Algorithm (PAES) are adopted: the grid system and double archive strategy. Several test functions and a real-world scenario called the Pressure vessel design problem are used to evaluate the proposed algorithm's performance. In the experiment, the proposed algorithm is compared with other well-known algorithms using different metrics such as Reversed Generational Distance, Spacing metric, and Spread metric. The optimization results show the robustness of the proposed algorithm, and the results are further confirmed using statistical methods and graphs. Finally, conclusions and future directions were presented.. © 2025, CC BY.

关键词： Pressure vessels

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Applied intelligence with deep learning assisted automated sarcasm recognition in twitter data

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Alexandria Engineering Journal 2025年 128卷 79-91页

作者： Muhammad Swaileh A. Alzaidi Taghreed Ali Alsudais Majdy M. Eltahir Shouki A. Ebad Asma Alshuhail Mohammed Alshahrani Sultan Alanazi Department of English Language College of Language Sciences King Saud University P. O. Box 145111 Riyadh Saudi Arabia Department of Teaching and Learning College of Education and Human Development Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia Department of Information Systems Applied College at Mahayil King Khalid University Saudi Arabia Center for Scientific Research and Entrepreneurship Northern Border University Arar 73213 Saudi Arabia Department of Information Systems College of Computer Sciences & Information Technology King Faisal University Saudi Arabia Computer Department Applied College Najran University Najran 66462 Saudi Arabia Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj 16273 Saudi Arabia

Sarcasm is essential in human interaction, particularly on social networking, where individuals share their feelings through criticism, humour, and satire. Sarcasm detection is vital in understanding the context of communication and the sentiment on platforms such as Twitter. This presents sarcasm detection as a complex problem in natural language processing (NLP). The challenges and importance upsurge, particularly in languages such as Arabic and Urdu, where resources for NLP are inadequate. The classical rule-based techniques need to improve performance owing to sarcasm's context-based and subtle nature. On the other hand, the current developments in deep learning (DL) and NLP offer efficient solutions. Therefore, this study presents an Applied Intelligence with Deep Learning Assisted Automated Sarcasm Recognition Approach (AIDL-ASRA) on Twitter Data. The presented AIDL-ASRA technique concentrates on identifying sarcastic and non-sarcastic contexts in tweets. In the AIDL-ASRA technique, the initial pre-processing stage is executed with different subprocesses. Next, Global Vectors for Word Representation (GloVe)-based word embedding process occurs. The AIDL-ASRA technique utilizes the Discriminative Deep Belief Networks (DDBN) method for sarcasm recognition. The DBN model's effectiveness is improved using the cat swarm optimization (CSO) model. A set of experiments were involved in evaluating the performance of the AIDL-ASRA method under the Arabic sarcastic dataset comprising 10,000 tweets. The experimental validation of the AIDL-ASRA method attained a superior accuracy value of 91.25 % over existing techniques under distinct measures.

关键词： Sarcasm Recognition Sentiment Analysis Twitter Data Deep Learning Cat Swarm Optimization Word Embedding

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AI-Enhanced Child Handwriting Analysis: A Framework for the Early Screening of Dyslexia and Dysgraphia

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SN computer science 2025年第5期6卷 1-26页

作者： Rangasrinivasan, Sahana Sumi Suresh, M.S. Olszewski, Abbie Setlur, Srirangaraj Jayaraman, Bharat Govindaraju, Venu Department of Computer Science and Engineering University at Buffalo Buffalo 14260 NY United States Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri Kerala Kollam 690525 India College of Education and Human Development University of Nevada-Reno Reno 89557 NV United States Amrita Institute of Advanced Research Elburn 60119 IL United States

Dyslexia and dysgraphia are two specific learning disabilities (SLDs) that are prevalent among children. To minimize the negative impact these SLDs have on a child’s academic and social-emotional development, it is crucial to identify dyslexia and dysgraphia at an early age, enabling timely and effective intervention. The first step in this process is screening, which helps determine if a child requires further instruction or a more in-depth assessment. Current screening tools are expensive, require additional administration time beyond regular classroom activities, and are designed to screen exclusively for one condition, not for both dyslexia and dysgraphia, which often share some common behavioral characteristics. Most dyslexia screeners focus on speech and oral tasks and exclude writing activities. However, analyzing children’s writing samples for behavioral signs of dyslexia and dysgraphia can offer valuable insights into the screening process, which can be time-consuming. As a solution, we propose a co-designed framework for building artificial intelligence (AI) tools that could boost the efficiency of screening and aid practitioners such as speech-language pathologists (SLPs), occupational therapists, general educators, and special educators by simplifying their tasks. This paper reviews current screening methods employed by practitioners, the use of AI-based systems in identifying dyslexia and dysgraphia, and the handwriting datasets available to train such systems. The paper also outlines a framework for developing an AI-integrated screening tool that can identify writing-based behavioral indicators of dyslexia and dysgraphia in children’s handwriting. This framework can be used in conjunction with current screening tools like the Dysgraphia and Dyslexia Behavioral Indicator Checklist (DDBIC). The paper also proposes a methodology for collecting children’s offline and online handwriting samples to build a valuable dataset for developing AI solutions. The pr

关键词： Artificial intelligence Child handwriting Dysgraphia Dyslexia Early screening

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A TBP DICOM format for total-body scanner-independent lesion evolution detection 6

A TBP DICOM format for total-body scanner-independent lesion...

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Biophotonics in Exercise science, Sports Medicine, Health Monitoring Technologies, and Wearables VI 2025

作者： Xue, Minghao Huang, Wei-Lun Tashayyod, Davood Kang, Jun Gandjbakhche, Amir Armand, Mehran Johns Hopkins University Whiting School of Engineering Department of Computer Science BaltimoreMD United States Johns Hopkins School of Medicine Department of Dermatology BaltimoreMD United States Eunice Kennedy Shriver National Institute of Child Health and Human Development BethesdaMD United States University of Arkansas Institute for Integrative and Innovative Research Department of Mechanical Engineering AR United States Lumo Imaging RockvilleMD United States

ISBN: (纸本)9781510683747

Total-body photography (TBP) has the potential to revolutionize early detection of skin cancers by monitoring minute changes in lesions over time. However, there is no standardized Digital Imaging and Communications in Medicine (DICOM) format for TBP. In order to accommodate various TBP data types and sophisticated data preprocessing pipelines, we propose three TBP Extended Information Object Definitions (IODs) for 2D regional images, dermoscopy images, and 3D surface meshes. We introduce a comprehensive pipeline integrating advanced image processing techniques, including 3D DICOM representation, super-resolution enhancement, and style transfer for dermoscopic-like visualization. Our framework tracks individual lesions across multiple TBP scans from different imaging systems and provides cloud-based storage with a customized DICOM viewer. To demonstrate the effectiveness of our approach, we validate our framework using TBP datasets from multiple imaging systems. Our framework and proposed IODs enhance TBP interoperability and clinical utility in dermatological practice, potentially improving early skin cancer detection. © 2025 SPIE.

关键词： Digital Imaging and Communications in Medicine (DICOM)

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