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Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems

作者： Nava Haghighi Sunny Yu James A. Landay Daniela Rosner Computer Science Stanford University Stanford California USA Symbolic Systems Program Stanford University Stanford California USA Computer Science Department Stanford University Stanford California USA Human Centered Design & Engineering University of Washington Seattle Washington USA

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A Two-stage Reconfiguration in Network Function Virtualization: Toward Service Function Chain Optimization

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IEEE Transactions on Network and Service Management 2025年

作者： Assis, Karcius D. R. Almeida, Raul C. Baghban, Hojjat Santos, Alex F. Boutaba, Raouf Electrical and Computer Engineering Department Salvador Brazil University of Waterloo David R. Cheriton School of Computer Science Canada Department of Electronics and Systems Recife Brazil Chang Gung University Department of Artificial Intelligence Taiwan Chang Gung University College of Intelligent Computing Taiwan Center of Science and Technology in Energy and Sustainability Brazil Postgraduate Program in Computer Science Bahia Brazil

Network Function Virtualization (NFV), as a promising paradigm, speeds up the service deployment by separating network functions from proprietary devices and deploying them on common servers in the form of software. Any service in NFV-enabled networks is achieved as a Service Function Chain (SFC) which consists of a series of ordered Virtual Network Functions (VNFs). However, migration of VNFs for more flexible services within a dynamic NFV-enabled network is a key challenge to be addressed. Current VNF migration studies mainly focus on single VNF migration decisions without considering the sharing and concurrent migration of VNF instances. In this paper, we assume that each deployed VNF is used by multiple SFCs and deal with the optimal placement for the contemporaneous migration of VNFs based on the actual network situation. We formalize the VNF migration and SFC reconfiguration problem as a mathematical model, which aims to minimize the VNF migration between nodes or the total number of core changes per node. The approach is a two-stage MILP based on optimal order to solve the reconfiguration. Extensive evaluation shows that the proposed approach can reduce the change in terms of location or number of cores per node in a 6-node and 14-node networks while ensuring network latency compared with the model without reconfiguration. © 2004-2012 IEEE.

关键词： Network function virtualization

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Porous silicon on paper sensors: Factors affecting biosensing performance 17

Porous silicon on paper sensors: Factors affecting biosensin...

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Frontiers in Biological Detection: From Nanosensors to Systems XVII 2025

作者： An, Huijin Laibinis, Paul E. Locke, Andrea K. Weiss, Sharon M. Interdisciplinary Material Science Program Vanderbilt University NashvilleTN37235 United States Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN37235 United States Department of Chemical and Biomolecular Engineering Vanderbilt University NashvilleTN37235 United States Department of Chemistry Vanderbilt University NashvilleTN37235 United States Department of Biomedical Engineering Vanderbilt University NashvilleTN37235 United States

ISBN: (纸本)9781510684249

A paper-based biosensor integrating a functionalized porous silicon (PSi) membrane as the active sensing element for quantifiable protein detection has been developed. For similar short-time exposures to an analyte, improved molecular transport in PSi membranes when on paper leads to larger signal changes compared to traditional PSi films that remain on a silicon substrate. In this work, we discuss controlling the incubation time of the analyte and the overall testing time of the sensor by incorporating different combinations of wicking and absorbent paper beneath the PSi membrane. With this control, the PSi-on-paper sensor platform has the potential to serve as an effective low-cost rapid diagnostic test with highly sensitive, quantitative readout for a wide range of analytes. © 2025 SPIE.

关键词： Porous silicon

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Mental Status Identification for Infertile Patients Using Artificial Intelligence Models 14th

Mental Status Identification for Infertile Patients Using Ar...

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14th International Conference on Frontier Computing, FC 2024

作者： Hsu, Hao-Siang Wang, Jong-Yi Chiou, Hai-Chi Wu, Fu-Hsing Lin, Chuen-Horng Chan, Yung‑Kuan Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung404336 Taiwan Department of Health Services Administration China Medical University Taichung406040 Taiwan Bachelor Degree Program of Artificial Intelligence National Taichung University of Science and Technology Taichung404336 Taiwan Department of Management Information Systems National Chung-Hsing University Taichung402202 Taiwan

ISBN: (纸本)9789819623945

Infertile patients may be a high-risk group of mental disorder. The precise identification of the mental status of infertile patients can provide decision support to healthcare professionals and may be helpful in providing preventive measures accordingly. Rare studies focused on the mental status identification using artificial intelligence (AI) models for infertile people. This study aimed to precisely identify the mental status (anxiety and depression) of infertile patients using three AI models, the XGBoost, decision tree, and stacking models. The dataset, retrieved from a structured questionnaire survey in 2011, which included 400 infertility outpatients seeking reproductive treatments at three teaching hospitals in Taiwan was adopted for training and testing AI models. Aforementioned AI models were designed and reached identification performances of accuracy, precision, recall and area under ROC curve (AUC) equaling 0.75–0.82, 0.70–0.80, 0.64–0.72, and 0.64–0.72, respectively. The XGBoost model (accuracy = 0.82, precision = 0.80, recall = 0.72, and AUC = 0.72) outperformed or equaled the decision tree and stacking models in terms of accuracy, precision, recall, and AUC values. Designed AI models for mental status identification can be integrated into the clinical decision support system (CDSS) to provide the anxiety and depression group automatically. Accordingly, the worsening of infertility-related anxiety and depression can be prevented. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： artificial intelligence infertile patients Mental status identification stacking model XGBoost

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Dimensionality reduction in 3D causal deep learning for neuroimage generation: an evaluation study

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Journal of Medical Imaging 2025年第2期12卷

作者： Ohara, Erik Y. Vigneshwaran, Vibujithan Souza, Raissa Vamosi, Finn G. Wilms, Matthias Forkert, Nils D. University of Calgary Biomedical Engineering Graduate Program Calgary AB Canada University of Calgary Department of Radiology Calgary AB Canada University of Calgary Hotchkiss Brain Institute Calgary AB Canada University of Calgary Alberta Children's Hospital Research Institute Calgary AB Canada University of Calgary Department of Computer Science Calgary AB Canada University of Calgary Departments of Pediatrics and Community Health Sciences Calgary AB Canada

Purpose: Causal deep learning (DL) using normalizing flows allows the generation of true counterfactual images, which is relevant for many medical applications such as explainability of decisions, image harmonization, and in-silico studies. However, such models are computationally expensive when applied directly to high-resolution 3D images and, therefore, require image dimensionality reduction (DR) to efficiently process the data. The goal of this work was to compare how different DR methods affect counterfactual neuroimage generation. Approach: Five DR techniques [2D principal component analysis (PCA), 2.5D PCA, 3D PCA, autoencoder, and Vector Quantised-Variational AutoEncoder] were applied to 23,692 3D brain images to create low-dimensional representations for causal DL model training. Convolutional neural networks were used to quantitatively evaluate age and sex changes on the counterfactual neuroimages. Age alterations were measured using the mean absolute error (MAE), whereas sex changes were assessed via classification accuracy. Results: The 2.5D PCA technique achieved the lowest MAE of 4.16 when changing the age variable of an original image. When sex was changed, the autoencoder embedding led to the highest classification accuracy of 97.84% while also significantly impacting the age variable predictions, increasing the MAE to 5.24 years. Overall, 3D PCA provided the best balance, with an age prediction MAE of 4.57 years while maintaining 94.01% sex classification accuracy when altering the age variable and 94.73% sex classification accuracy and the lowest age prediction MAE (3.84 years) when altering the sex variable. Conclusions: 3D PCA appears to be the best-suited DR method for causal neuroimage analysis. © 2025 Society of Photo-Optical Instrumentation Engineers (SPIE).

关键词： causal artificial intelligence causality deep learning dimensionality reduction generative models normalizing flows

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Email Spam Detection Using Logistic Regression and Explainable AI

Email Spam Detection Using Logistic Regression and Explainab...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Sumit Kanti Sarker Ratul Bhattacharjee Md Abu Sufian Md Solaiman Ahamed Md Abu Talha Faiza Tasnim K.M. Nazmul Islam Sharia Tasnim Adrita Department of Computer Science & Engineering (CSE) Faculty of Science & Technology (FST) American International University Bangladesh Dhaka Bangladesh Bachelor of Science in Information Technology (BSIT) Cybersecurity Program Concentrations Washington University of Science and Technology (WUST) VA USA Department of Electrical and Electronics Engineering (EEE) Faculty of Engineering (FE) American International University Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering (CSE) Faculty of Science & Engineering Northern University Bangladesh-(NUB) Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Email spam detection is crucial for ensuring a positive user experience and maintaining communication security. This study presents a novel spam detection approach leveraging Logistic Regression, optimized through hyperparameter tuning and enhanced with Explainable Artificial Intelligence. The proposed method is evaluated on a large-scale dataset of emails collected from a real-world spam detection system. Term Frequency-Inverse Document Frequency is utilized for feature extraction, converting email text into numerical representations. XAI introduces interpretability by highlighting critical features such as "pill," "PHP," and "Businessweek," which indicate spam classification patterns. Metrics such as precision, recall, and the confusion matrix are employed to assess the model’s performance, ensuring a balanced evaluation. Hyperparameter optimization using Grid Search CV achieves an optimal regularization parameter (C = 100) and maximum iterations (maxiter=100), resulting in an impressive F1 score of 98.76%, accuracy of 98.82%, precision of 98.62%, and recall of 98.90%.

关键词： Logistic regression Explainable AI Filtering Unsolicited e-mail Computational modeling Predictive models Transformers Frequency conversion Feature extraction Tuning

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Observation of Photonic Chiral Flatbands

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Physical Review Letters 2025年第10期134卷 103801-103801页

作者： Minho Choi Andrea Alù Arka Majumdar Department of Electrical and Computer Engineering University of Washington Seattle Washington 98195 USA Photonics Initiative Advanced Science Research Center City University of New York New York New York 10031 USA and Physics Program Graduate Center City University of New York New York New York 10031 USA Department of Electrical and Computer Engineering University of Washington Seattle Washington 98195 USA and Department of Physics University of Washington Seattle Washington 98195 USA

Distinct selectivity to the spin angular momenta of photons has garnered significant attention in recent years, for its relevance in basic science and for imaging and sensing applications. While nonlocal metasurfaces with strong chiral responses to the incident light have been reported, these responses are typically limited to a narrow range of incident angles. In this study, we demonstrate a nonlocal metasurface that showcases strong chirality, characterized by circular dichroism (∼0.6), over a wide range of incident angles ±5°. Its quality factor, circular dichroism and resonant frequency can be optimized by design. These findings pave the way to further advance the development of valley-selective optical cavities and augmented reality applications.

关键词： Natural frequencies

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High-performance UV polarization sensitive photodetector for a graphene(2D)/GaN(3D) junction with a non-centrosymmetric electric field

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Photonics Research 2025年第6期13卷 1544-1553页

作者： Can Zou Qing Liu Lu Zhang Xiao Tang Xiaohang Li Shuti Li Fangliang Gao Guangdong Engineering Research Center of Optoelectronic Functional Materials and Devices School of Electronic Science and Engineering (School of Microelectronics) South China Normal University Guangzhou 510631 China Advanced Semiconductor Laboratory Electrical and Computer Engineering Program CEMSE Division King Abdullah University of Science and Technology Thuwal 239556900 Saudi Arabia e-mail: lishuti@*** e-mail: gaofl@m.***

This study pioneers a high-performance UV polarization-sensitive photodetector by ingeniously integrating non-centrosymmetric metal nanostructures into a graphene ( Gr ) / Al 2 O 3 / GaN heterojunction. Unlike conventional approaches constrained by graphene’s intrinsic isotropy or complex nanoscale patterning, our design introduces asymmetric metal architectures (E-/T-type) to artificially create directional anisotropy. These structures generate plasmon-enhanced localized electric fields that selectively amplify photogenerated carrier momentum under polarized UV light (325 nm), synergized with Fowler-Nordheim tunneling (FNT) across an atomically thin Al 2 O 3 barrier. The result is a breakthrough in performance: a record anisotropy ratio of 115.5 (E-type, − 2 V ) and exceptional responsivity (97.7 A/W), surpassing existing graphene-based detectors by over an order of magnitude. Crucially, by systematically modulating metal geometry and density, we demonstrate a universal platform adaptable to diverse 2D/3D systems. This study provides a valuable reference for developing and practically applying photodetectors with higher anisotropy than ultraviolet polarization sensitivity.

关键词： Chemical vapor deposition Metals Nanostructures Quantum efficiency Surface plasmon resonance X-ray diffraction

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Machine Learning Stroke Prediction in Smart Healthcare:Integrating Fuzzy K-Nearest Neighbor and Artificial Neural Networks with Feature Selection Techniques

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computers, Materials & Continua 2025年第3期82卷 5115-5134页

作者： Abdul Ahad Ira Puspitasari Jiangbin Zheng Shamsher Ullah Farhan Ullah Sheikh Tahir Bakhsh Ivan Miguel Pires Department Information System Study Program Faculty of Science and TechnologyUniversitas AirlanggaSurabaya60286Indonesia School of Software Northwestern Polytechnical UniversityXi’an710072China Research Center for Quantum Engineering Design Faculty of Science and TechnologyUniversitas AirlanggaSurabaya60286Indonesia School of Computer Science and Software Engineering Shenzhen UniversityShenzhen518061China Cybersecurity Center Prince Mohammad Bin Fahd University617Al JawharahKhobarDhahran34754Saudi Arabia Cardiff School of Technologies Cardiff Metropolitan UniversityWestern AvenueCardiffCF52YBUK Instituto de Telecomunicacoes Escola Superior de Tecnologia e Gestao de AguedaUniversidade de Aveiroágueda3750-127Portugal

This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and Best First Search(BFS).The study demonstrates that BFS significantly enhances the performance of both *** BFS preprocessing,the ANN model achieved an impressive accuracy of 97.5%,precision and recall of 97.5%,and an Receiver Operating Characteristics(ROC)area of 97.9%,outperforming the Chi-Square-based ANN,which recorded an accuracy of 91.4%.Similarly,the F-KNN model with BFS achieved an accuracy of 96.3%,precision and recall of 96.3%,and a Receiver Operating Characteristics(ROC)area of 96.2%,surpassing the performance of the Chi-Square F-KNN model,which showed an accuracy of 95%.These results highlight that BFS improves the ability to select the most relevant features,contributing to more reliable and accurate stroke *** findings underscore the importance of using advanced feature selection methods like BFS to enhance the performance of machine learning models in healthcare applications,leading to better stroke risk management and improved patient outcomes.

关键词： Fuzzy K-nearest neighbor artificial neural network accuracy precision recall F-measure Chi-Square best search first heart stroke

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Exploring Augmented Reality's Influence on Cognitive Load and Emotional Dynamics within UAV Training Environments

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IEEE Transactions on Learning Technologies 2025年

作者： Rahimi, Fatema Sadeghi-Niaraki, Abolghasem Song, Houbing Wang, Huihui Choi, Soo-Mi Sejong University Department of Computer Science and Engineering Convergence Engineering for Intelligent Drone XR Research Center Seoul Korea Republic of Department of Information Systems BaltimoreMD21250 United States St. Bonaventure University Cybersecurity Program St. BonaventureNY14778 United States

This study investigates the cognitive and emotional processes involved in augmented reality (AR)-based learning. The study looks at learning outcomes, emotional responses, meditation, and attention using a comprehensive approach that includes self-assessment, EEG data gathering, and post-experiment questionnaires. Twelve participants, selected based on their English proficiency and lack of prior knowledge of the course material, engaged in AR-based learning, while a baseline reading condition was included to contextualize cognitive and emotional engagement. The study findings indicate that the AR group's participants demonstrated notably elevated attention and meditation levels, indicating heightened engagement and focus that is advantageous for efficient assimilation and retention of knowledge. Furthermore, AR learners reported feeling less tired and exhausted, which may have mitigated the negative emotional states that are frequently connected to learning activities. However, no significant differences in negative emotions were observed between the reading and AR groups. These results emphasize the value of customized AR environments for education goals and the need for more study to maximize learning outcomes and affective experiences in AR learning contexts. © 2008-2011 IEEE.

关键词： engineering education

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