STEM Workshops and Students’ Interest in Mathematics, Physics, and Computer Science: Machine Learning Approach

Συγγραφείς: Aleksandar Milenković, Dragutin Ostojić, Dalibor Rajković, Milan Milikić

Πηγή: International Journal of Instruction. 19:367-386

STEM Workshops and Students’ Interest in Mathematics, Physics, and Computer Science: Machine Learning Approach.

Συγγραφείς: Milenković, Aleksandar¹ aleksandar.milenkovic@pmf.kg.ac.rs, Ostojić, Dragutin¹ dragutin.ostojic@pmf.kg.ac.rs, Rajković, Dalibor¹ d.rajkovicrs@gmail.com, Milikić, Milan² milikic.milan@yahoo.com

Πηγή: International Journal of Instruction. Jan2026, Vol. 19 Issue 1, p367-386. 20p.

Θεματικοί όροι: STEM education, Mathematics, Physics, Student engagement, Sociodemographic factors, Computer science, Student interests, Machine learning

Integrated trucks assignment and scheduling problem with mixed service mode docks: A Q-learning based adaptive large neighborhood search algorithm: A Q-learning based adaptive large neighborhood search algorithm

Συγγραφείς: Yueyi Li, Mehrdad Mohammadi, Xiaodong Zhang, Yunxing Lan, Willem van Jaarsveld

Πηγή: European Journal of Operational Research. 333:117-137

Θεματικοί όροι: FOS: Computer and information sciences, Computer Science - Machine Learning, Adaptive large neighborhood search, Optimization and Control (math.OC), Q-Learning algorithm, FOS: Mathematics, Truck assignment and scheduling, Mixed service mode, Mathematics - Optimization and Control, Assignment, Machine Learning (cs.LG)

Σύνδεσμος πρόσβασης: http://arxiv.org/abs/2412.09090
https://research.tue.nl/en/publications/72ff8157-c154-45bf-8f8e-ff89e6eefc6e
https://doi.org/10.1016/j.ejor.2025.12.036

Electrical impedance tomography for anisotropic media: A machine learning approach to classify inclusions

Συγγραφείς: Romina Gaburro, Patrick Healy, Shraddha Naidu, Clifford Nolan

Συνεισφορές: ULRR

Πηγή: Computers & Mathematics with Applications. 214:51-74

Θεματικοί όροι: anisotropic inclusions, FOS: Computer and information sciences, Computer Science - Machine Learning, machine learning, FOS: Mathematics, Mathematics - Numerical Analysis, Numerical Analysis (math.NA), 65N21, 35R30, 68T99, electrical impedance tomography, Machine Learning (cs.LG)

Περιγραφή αρχείου: application/pdf

Σύνδεσμος πρόσβασης: http://arxiv.org/abs/2502.04273

Hardness of Learning Boolean Functions from Label Proportions

Συγγραφείς: Venkatesan Guruswami, Rishi Saket

Συνεισφορές: Venkatesan Guruswami and Rishi Saket

Πηγή: ACM Transactions on Computation Theory. 18:1-15

Θεματικοί όροι: FOS: Computer and information sciences, Computer Science - Computational Complexity, Computer Science - Machine Learning, Hardness, Computational learning, Computer Science - Data Structures and Algorithms, Learning from label proportions, Data Structures and Algorithms (cs.DS), Boolean functions, Computational Complexity (cs.CC), Machine Learning (cs.LG)

Περιγραφή αρχείου: application/pdf

Σύνδεσμος πρόσβασης: http://arxiv.org/abs/2403.19401
https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FSTTCS.2023.37

Edge-Assisted ML-Aided Uncertainty-Aware Vehicle Collision Avoidance at Urban Intersections

Συγγραφείς: Dinesh Cyril Selvaraj, Christian Vitale, Tania Panayiotou, Panayiotis Kolios, Carla-Fabiana Chiasserini, Georgios Ellinas

Πηγή: IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

Θεματικοί όροι: FOS: Computer and information sciences, Computer Science - Machine Learning, Collision Avoidance, Trajectory Predictions, Uncertainty Estimation, Intelligent Transportation System, Machine Learning, Connected Autonomous Vehicles, Machine Learning (cs.LG)

Περιγραφή αρχείου: application/pdf

Σύνδεσμος πρόσβασης: http://arxiv.org/abs/2404.14523
https://ieeexplore.ieee.org/document/10185090
https://doi.org/10.1109/TIV.2023.3296190
https://hdl.handle.net/11583/2983096

AdaServe: Accelerating Multi-SLO LLM Serving with SLO-Customized Speculative Decoding

Συγγραφείς: Zikun Li, Zhuofu Chen, Remi Delacourt, Gabriele Oliaro, Zeyu Wang, Qinghan Chen, Shuhuai Lin, April Yang, Zhihao Zhang, Zhuoming Chen, Yi-Hsiang Lai, Xinhao Cheng, Xupeng Miao, Zhihao Jia

Πηγή: Proceedings of the 21st European Conference on Computer Systems. :36-54

Θεματικοί όροι: FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Computation and Language, Artificial Intelligence (cs.AI), Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Artificial Intelligence, Distributed, Parallel, and Cluster Computing (cs.DC), Computation and Language (cs.CL), Machine Learning (cs.LG)

Σύνδεσμος πρόσβασης: http://arxiv.org/abs/2501.12162

Aligning Multiple Knowledge Graphs in A Single Pass

Συγγραφείς: Yaming Yang, Zhe Wang, Ziyu Guan, Wei Zhao, Weigang Lu, Xinyan Huang, Jiangtao Cui, Xiaofei He

Πηγή: Proceedings of the ACM Web Conference 2026. :3588-3599

Θεματικοί όροι: FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Computation and Language, Computation and Language (cs.CL), Machine Learning (cs.LG)

Σύνδεσμος πρόσβασης: http://arxiv.org/abs/2408.00662

How to Strategize Human Content Creation in the Era of GenAI?

Συγγραφείς: Seyed A. Esmaeili, Kevin Lim, Kshipra Bhawalkar, Zhe Feng, Di Wang, Haifeng Xu

Πηγή: Proceedings of the ACM Web Conference 2026. :285-296

Θεματικοί όροι: FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial Intelligence (cs.AI), Computer Science - Computer Science and Game Theory, Computer Science - Artificial Intelligence, Computer Science - Human-Computer Interaction, Computer Science and Game Theory (cs.GT), Human-Computer Interaction (cs.HC), Machine Learning (cs.LG)

Σύνδεσμος πρόσβασης: http://arxiv.org/abs/2406.05187

GPU-accelerated Multi-relational Parallel Graph Retrieval for Web-scale Recommendations

Συγγραφείς: Zhuoning Guo, Guangxing Chen, Qian Gao, Xiaochao Liao, Jianjia Zheng, Lu Shen, Hao Liu

Πηγή: Proceedings of the ACM Web Conference 2026. :7769-7778

Θεματικοί όροι: FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Distributed, Parallel, and Cluster Computing, Distributed, Parallel, and Cluster Computing (cs.DC), Information Retrieval (cs.IR), Computer Science - Information Retrieval, Machine Learning (cs.LG)

Σύνδεσμος πρόσβασης: http://arxiv.org/abs/2502.11490

In pursuit of gene variation of consequence to human health and disease

Additional Titles: From the invention of Sanger sequencing, to the birth of current highthroughput and long-read methodologies, sequencing technology has become an vital tool for scientific research. Biologists released the first version of the human genome in 2001, and continued to refine it over the following years until the complete and final genome sequence was published in 2022. In parallel, the 1000 Genome project has revealed the extent of human genetic variation and polymorphisms, filling a gap in our knowledge about the diversity of the human mutational landscape. Transcriptome sequencing provides a means to study the changes in gene expression patterns and related signaling pathways affected by diseases and other biological processes. With the advancement of computer science, machine learning has been introduced into the field of biological and medical research. Using ML approaches scientists hope to find the biological signals and patterns hidden within massive datasets. The first chapter of this thesis provides an overview of the human genome, transcriptome research and different machine learning algorithms, including their applications in biological and medical research. The last chapter centers around two projects I worked on during my Ph.D. In the first project, simply called DNA prediction, we employed a Central model, a Markov model and a bi-directional Markov model to estimate the probability of the occurrence of four nucleotide types at a site based on its context sequence - the input for these models were the human reference genome. The results show that the base prediction of the human genome was above 50% on average, which should be compared to random guessing (25%). We applied the predicted results to SNP databases, and found that the alternative alleles showed higher probabilities than reference bases for somatic SNPs. In addition, we developed a substitution model to calculate the base mutability. Here, we found that the α matrix relies on a much smaller conte

Συγγραφείς: Liang, Yuhu

Πηγή: Liang , Y 2023 , ' In pursuit of gene variation of consequence to human health and disease ' .

Όροι ευρετηρίου: other

Σύνδεσμος: https://researchprofiles.ku.dk/da/publications/955ed63a-56ff-4724-8336-1b07587d2055

In pursuit of gene variation of consequence to humanhealth and disease

Additional Titles: From the invention of Sanger sequencing, to the birth of current high-throughput and long-read methodologies, sequencing technology has becomean vital tool for scientific research. Biologists released the first version of thehuman genome in 2001, and continued to refine it over the following yearsuntil the complete and final genome sequence was published in 2022. Inparallel, the 1000 Genome project has revealed the extent of human geneticvariation and polymorphisms, filling a gap in our knowledge about the diver-sity of the human mutational landscape. Transcriptome sequencing providesa means to study the changes in gene expression patterns and related signal-ing pathways affected by diseases and other biological processes. With theadvancement of computer science, machine learning has been introduced intothe field of biological and medical research. Using ML approaches scientistshope to find the biological signals and patterns hidden within massive datasets.The first chapter of this thesis provides an overview of the human genome,transcriptome research and different machine learning algorithms, includingtheir applications in biological and medical research.The last chapter centers around two projects I worked on during my Ph.D.In the first project, simply called DNA prediction, we employed a Centralmodel, a Markov model and a bi-directional Markov model to estimate theprobability of the occurrence of four nucleotide types at a site based on its con-text sequence - the input for these models were the human reference genome.The results show that the base prediction of the human genome was above50% on average, which should be compared to random guessing (25%). Weapplied the predicted results to SNP databases, and found that the alternativealleles showed higher probabilities than reference bases for somatic SNPs. Inaddition, we developed a substitution model to calculate the base mutability.Here, we found that the α matrix relies on a much smaller context sequences,and i

Συγγραφείς: Liang, Yuhu

Πηγή: Liang , Y 2023 , ' In pursuit of gene variation of consequence to humanhealth and disease ' .

Όροι ευρετηρίου: other

Σύνδεσμος: https://researchprofiles.ku.dk/da/publications/eb6fac81-f0ac-48a2-865d-9db6c28100e3

MUST: Multi-Scale Structural-Temporal Link Prediction Model for UAV Ad Hoc Networks

Συγγραφείς: Cunlai Pu, Fangrui Wu, Rajput Ramiz Sharafat, Guangzhao Dai, Xiangbo Shu

Πηγή: IEEE Transactions on Knowledge and Data Engineering. 38:3765-3777

Θεματικοί όροι: FOS: Computer and information sciences, Computer Science - Machine Learning, Machine Learning (cs.LG)

Σύνδεσμος πρόσβασης: http://arxiv.org/abs/2505.09331

Epidemiological model calibration via graybox Bayesian optimization

Συγγραφείς: Puhua Niu, Byung-Jun Yoon, Xiaoning Qian

Πηγή: Infect Dis Model

Θεματικοί όροι: FOS: Computer and information sciences, Computer Science - Machine Learning, Statistics - Machine Learning, Machine Learning (stat.ML), Article, Machine Learning (cs.LG)

Σύνδεσμος πρόσβασης: https://pubmed.ncbi.nlm.nih.gov/41551338
http://arxiv.org/abs/2412.07193

Graph Fourier Transformer with Structure-Frequency Information

Συγγραφείς: Yonghui Zhai, Yang Zhang 0013, Minghao Shang, Lihua Pang, Yaxin Ren

Πηγή: ICASSP 2026 - 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). :6036-6040

Θεματικοί όροι: FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Graphics, Graphics (cs.GR), Machine Learning (cs.LG)

Σύνδεσμος πρόσβασης: http://arxiv.org/abs/2504.19740

Dynamic Feature Selection on Variable Feature Sets using Features of Features

Συγγραφείς: Katsumi Takahashi, Koh Takeuchi 0001, Hisashi Kashima

Πηγή: ICASSP 2026 - 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). :3741-3745

Θεματικοί όροι: FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Information Theory, Information Theory (cs.IT), Machine Learning (cs.LG)

Σύνδεσμος πρόσβασης: http://arxiv.org/abs/2503.09181

RelUNet: Relative Channel Fusion U-Net for Multichannel Speech Enhancement

Συγγραφείς: Ibrahim Aldarmaki, Thamar Solorio, Bhiksha Raj, Hanan Aldarmaki

Πηγή: ICASSP 2026 - 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). :21031-21035

Θεματικοί όροι: FOS: Computer and information sciences, Computer Science - Machine Learning, Sound (cs.SD), Audio and Speech Processing (eess.AS), FOS: Electrical engineering, electronic engineering, information engineering, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing, Machine Learning (cs.LG)

Σύνδεσμος πρόσβασης: http://arxiv.org/abs/2410.05019

Sample Weight Averaging for Stable Prediction

Συγγραφείς: Han Yu 0009, Yue He 0001, Renzhe Xu, Dongbai Li, Jiayin Zhang, Wenchao Zou, Peng Cui 0001

Πηγή: ICASSP 2026 - 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). :6206-6210

Θεματικοί όροι: FOS: Computer and information sciences, Computer Science - Machine Learning, Machine Learning (cs.LG)

Σύνδεσμος πρόσβασης: http://arxiv.org/abs/2502.07414

S2TX: Cross-Attention Multi-Scale State-Space Transformer for Time Series Forecasting

Συγγραφείς: Zihao Wu, Juncheng Dong, Haoming Yang, Vahid Tarokh

Πηγή: ICASSP 2026 - 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). :1341-1345

Θεματικοί όροι: FOS: Computer and information sciences, Computer Science - Machine Learning, Machine Learning (cs.LG)

Σύνδεσμος πρόσβασης: http://arxiv.org/abs/2502.11340

Multi-Scale Conditional Generative Modeling for Microscopic Image Restoration

Συγγραφείς: Luzhe Huang, Xiongye Xiao, Shixuan Li, Jiawen Sun, Yi Huang, Aydogan Ozcan, Paul Bogdan

Πηγή: ICASSP 2026 - 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). :6336-6340

Θεματικοί όροι: FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Computer Vision and Pattern Recognition (cs.CV), Image and Video Processing (eess.IV), Computer Science - Computer Vision and Pattern Recognition, FOS: Electrical engineering, electronic engineering, information engineering, Electrical Engineering and Systems Science - Image and Video Processing, Machine Learning (cs.LG)

Σύνδεσμος πρόσβασης: http://arxiv.org/abs/2407.05259