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18th Colombian Conference on Computing

作者： Cortes-Navarroe, Daniel Augusto Alvarez-Calvoe, Santiago Alberto Cuesta-Quintero, Byron Rico-Bautista, Dewar Univ Francisco de Paula Santander Ocana Colombia

ISBN: (纸本)9783031752353;9783031752360

The implementation of artificial intelligence (AI) in education is today an innovative strategy that cannot be ignored when accompanying learning processes, with the aim of transforming educational systems and taking ways of learning to another level. Although there are studies on the application of AI in education, most focus on digital literacy or specific areas of knowledge, perhaps ignoring a correct integration of software that provides different teaching strategies focused on analyzing behavior patterns and interests of the student. Student, especially in early childhood, to project personalized learning strategies. Considering now the study carried out, a systematic mapping of literature about AI tools in education was carried out. Through a search in 4 digital databases, 124,210 documents on AI in education were obtained, which after applying inclusion and exclusion criteria, 64 relevant documents were selected that provide a solid basis for future research and offer a consensus on the current state. And the perspectives of AI in the educational field.

关键词： Adaptive learning Machine learning learning algorithms Gamification Personalized learning

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Multi-Granularity Causal Structure learning 38

Multi-Granularity Causal Structure Learning

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38th AAAI Conference on Artificial Intelligence (AAAI) / 36th Conference on Innovative Applications of Artificial Intelligence / 14th Symposium on Educational Advances in Artificial Intelligence

作者： Liang, Jiaxuan Wang, Jun Yu, Guoxian Xia, Shuyin Wang, Guoyin Shandong Univ Sch Software Jinan Peoples R China Shandong Univ SDU NTU Joint Ctr AI Res Jinan Peoples R China Chongqing Univ Posts & Telecom Chongqing Key Lab Computat Intelligence Chongqing Peoples R China Chongqing Univ Posts & Telecom MOE Key Lab Big Data Intelligent Comp Chongqing Peoples R China

ISBN: (纸本)1577358872

Unveiling, modeling, and comprehending the causal mechanisms underpinning natural phenomena stand as fundamental endeavors across myriad scientific disciplines. Meanwhile, new knowledge emerges when discovering causal relationships from data. Existing causal learning algorithms predominantly focus on the isolated effects of variables, overlook the intricate interplay of multiple variables and their collective behavioral patterns. Furthermore, the ubiquity of high-dimensional data exacts a substantial temporal cost for causal algorithms. In this paper, we develop a novel method called MgCSL (Multi-granularity Causal Structure learning), which first leverages sparse auto-encoder to explore coarse-graining strategies and causal abstractions from micro-variables to macro-ones. MgCSL then takes multi-granularity variables as inputs to train multilayer perceptrons and to delve the causality between variables. To enhance the efficacy on high-dimensional data, MgCSL introduces a simplified acyclicity constraint to adeptly search the directed acyclic graph among variables. Experimental results show that MgCSL outperforms competitive baselines, and finds out explainable causal connections on fMRI datasets.

关键词： learning algorithms

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Control-based algorithms for high dimensional online learning

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JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS 2020年第3期357卷 1909-1942页

作者： Ning, Hanwen Zhang, Jiaming Feng, Ting-Ting Chu, Eric King-wah Tian, Tianhai Zhongnan Univ Econ & Law Dept Stat Wuhan 430073 Peoples R China Hangzhou Dianzi Univ Sch Sci Dept Math Hangzhou 310018 Peoples R China Monash Univ Sch Math Sci Melbourne Vic 3800 Australia

In the era of big data, the high-dimensional online learning problems require huge computing power. This paper proposes a novel approach for high-dimensional online learning. Two new algorithms are developed for online high-dimensional regression and classification problems, respectively. The problems are formulated as feedback control problems for some low dimensional systems. The novel learning algorithms are then developed via the control problems. Via an efficient polar decomposition, we derive the explicit solutions of the control problems, substantially reducing the corresponding computational complexity, especially for high dimensional large-scale data streams. Comparing with conventional methods, the new algorithm can achieve more robust and accurate performance with faster convergence. This paper demonstrates that optimal control can be an effective approach for developing high dimensional learning algorithms. We have also for the first time proposed a control-based robust algorithm for classification problems. Numerical results support our theory and illustrate the efficiency of our algorithm. (C) 2020 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

关键词： learning algorithms

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Why Bring Science to the Forefront?

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COMPUTER 2025年第5期58卷 65-70页

作者： Hurlburt, George F. Univ Syst Maryland Southern Maryland Wildewood MD 20619 USA

Technology has reached unprecedented scale, interoperability, and societal clout as science ushers in an unparalleled Information Era. However, if protective artificial intelligence guardrails are to be effective, pub... 详细信息

关键词： Interoperability Modern Science Disciplines Popularity Science And Technology System Dynamics learning algorithms Nonlinear Systems Modern Technology Systems Theory Systems Science Network Science Big Tech Blockchain

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Machine learning Techniques for Frequency Comb Optimization 3

Machine Learning Techniques for Frequency Comb Optimization

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Quantum Sensing, Imaging, and Precision Metrology III 2025

作者： Floridia, Maximilian Wynn, Scott Nitzsche, Jayan Placke, Bishop Tyler, Matthew Diab, Jack Shariatdoust, Mirali S. Carbajo, Sergio Narang, Prineha Bertozzi, Andrea L. Department of Chemistry and Biochemistry University of California Los AngelesCA90095 United States Department of Computer Science and Engineering University of Washington SeattleWA98195 United States Department of Mathematics and Statistics Pomona College ClaremontCA91711 United States Department of Mathematics University of Nebraska LincolnNE68588 United States Department of Mathematics University of California Los Angeles 520 Portola Plaza Los AngelesCA90095 United States Department of Electrical and Computer Engineering University of California Los Angeles Los AngelesCA90095 United States California NanoSystems Institute 570 Westwood Plaza Los AngelesCA90095 United States Division of Physical Sciences University of California Los AngelesCA90095 United States Department of Mechanical and Aerospace Engineering University of California Los Angeles Los AngelesCA90095 United States

ISBN: (数字)9781510685338

ISBN: (纸本)9781510685321

We use supervised and unsupervised machine learning techniques to investigate how optical frequency combs may be used to identify gas molecules in the atmosphere. Dual comb heterodyne detection with GHz and THz repetition rates are often used as spectroscopic methods for probing ro-vibrational IR spectra of small molecules, proving to be promising in dispersive atmospheric sensing regimes. We simulate and recover an intensity spectrum from the molecule-specific IR absorption in the region spanned by the comb bandwidth. In practice, conventional frequency combs contain a narrow range of frequencies that may not span the spectral range associated with typical atmospheric target signals, prohibiting convenient molecule identification. We present several inexpensive, efficient machine learning methods to analyze optimal comb frequencies to maximize the ability to determine the molecular composition of a sample, based only on the interactions at those frequencies with respect to various channels of noise. Using a synthetic dataset, these methods are able to accurately identify molecules using simulated frequency combs, comparable to the ones used in practice. Furthermore, these methods withstand added noise (based on the imperfections of the experimental equipment) beyond what is expected in real applications. We also investigate the optimal comb generation parameters that provide the most informational value, and provide an analysis of the change in accuracy based on the number and size of allocated combs. The robustness of these methods suggests the techniques presented in this paper may be integrated into a laboratory setup and even in on-chip comb systems deployed in an atmospheric setting. This approach may be used as a detection framework for future quantum-enhanced systems via squeezed bi-photon comb inputs. © 2025 SPIE.

关键词： learning algorithms

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Visually Grounded Continual Language learning with Selective Specialization

Visually Grounded Continual Language Learning with Selective...

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Conference on Empirical Methods in Natural Language Processing (EMNLP)

作者： Ahrens, Kyra Bengtson, Lennart Lee, Jae Hee Wermter, Stefan Univ Hamburg Hamburg Germany

ISBN: (纸本)9798891760615

A desirable trait of an artificial agent acting in the visual world is to continually learn a sequence of language-informed tasks while striking a balance between sufficiently specializing in each task and building a generalized knowledge for transfer. Selective specialization, i.e., a careful selection of model components to specialize in each task, is a strategy to provide control over this trade-off. However, the design of selection strategies requires insights on the role of each model component in learning rather specialized or generalizable representations, which poses a gap in current research. Thus, our aim with this work is to provide an extensive analysis of selection strategies for visually grounded continual language learning. Due to the lack of suitable benchmarks for this purpose, we introduce two novel diagnostic datasets that provide enough control and flexibility for a thorough model analysis. We assess various heuristics for module specialization strategies as well as quantifiable measures for two different types of model architectures. Finally, we design conceptually simple approaches based on our analysis that outperform common continual learning baselines.(1) Our results demonstrate the need for further efforts towards better aligning continual learning algorithms with the learning behaviors of individual model parts.

关键词： learning algorithms

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DKAF: KB Arbitration for learning Task-Oriented Dialog Systems with Dialog-KB Inconsistencies 61

DKAF: KB Arbitration for Learning Task-Oriented Dialog Syste...

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61st Annual Meeting of the the Association-for-Computational-Linguistics (ACL)

作者： Vivek, Saley Vishal Das, Rocktim Jyoti Raghu, Dinesh Mausam Indian Inst Technol New Delhi India IBM Res New Delhi India

ISBN: (纸本)9781959429623

Task-oriented dialog (TOD) agents often ground their responses on external knowledge bases (KBs). These KBs can be dynamic and may be updated frequently. Existing approaches for learning TOD agents assume the KB snapshot contemporary to each individual dialog is available during training. However, in real-world scenarios, only the latest KB snapshot is available during training and as a result, the train dialogs may contain facts conflicting with the latest KB. These dialog-KB inconsistencies in the training data may potentially confuse the TOD agent learning algorithm. In this work, we define the novel problem of learning a TOD agent with dialog-KB inconsistencies in the training data. We propose a Dialog-KB Arbitration Framework (DKAF) which reduces the dialog-KB inconsistencies by predicting the contemporary KB snapshot for each train dialog. These predicted KB snapshots are then used for training downstream TOD agents. As there are no existing datasets with dialog-KB inconsistencies, we systematically introduce inconsistencies in two publicly available dialog datasets. We show that TOD agents trained with DKAF perform better than existing baselines on both these datasets.

关键词： learning algorithms

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Detecting Hydronephrosis Through Ultrasound Images Using State-of-the-Art Deep learning Models

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Ultrasound in Medicine and Biology 2023年第3期49卷 723-733页

作者： Lien, Wan-Ching Chang, Yi-Chung Chou, Hsin-Hung Lin, Lung-Chun Liu, Yueh-Ping Liu, Li Chan, Yen-Ting Kuan, Feng-Sen Department of Emergency Medicine National Taiwan University Hospital Taipei Taiwan Department of Emergency Medicine College of Medicine National Taiwan University Taipei Taiwan Department of Computer Science and Engineering National Chi Nan University Nantou Taiwan Department of Medical Affairs Ministry of Health and Welfare Taipei Taiwan Show Chwan Health Care System Taipei Taiwan Department of Research Planning of Omni Health Group Inc. Taipei Taiwan Department of Business Development Huasin H. T. Limited Taipei Taiwan

The goal of this study was to assess the feasibility of three models for detecting hydronephrosis through ultrasound images using state-of-the-art deep learning algorithms. The diagnosis of hydronephrosis is challenging because of varying and non-specific presentations. With the characteristics of ready accessibility, no radiation exposure and repeated assessments, point-of-care ultrasound becomes a complementary diagnostic tool for hydronephrosis;however, inter-observer variability still exists after time-consuming training. Artificial intelligence has the potential to overcome the human limitations. A total of 3462 ultrasound frames for 97 patients with hydronephrosis confirmed by the expert nephrologists were included. One thousand six hundred twenty-eight ultrasound frames were also extracted from the 265 controls who had normal renal ultrasonography. We built three deep learning models based on U-Net, Res-UNet and UNet++ and compared their performance. We applied pre-processing techniques including wiping the background to lessen interference by YOLOv4 and standardizing image sizes. Also, post-processing techniques such as adding filter for filtering the small effusion areas were used. The Res-UNet algorithm had the best performance with an accuracy of 94.6% for moderate/severe hydronephrosis with substantial recall rate, specificity, precision, F1 measure and intersection over union. The Res-UNet algorithm has the best performance in detection of moderate/severe hydronephrosis. It would decrease variability among sonographers and improve efficiency under clinical conditions. © 2022 World Federation for Ultrasound in Medicine & Biology

关键词： learning algorithms

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Compositional learning for Synchronous Parallel Automata 28th

Compositional Learning for Synchronous Parallel Automata

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28th International Conference on Fundamental Approaches to Software Engineering, FASE 2025, which was held as part of the International Joint Conferences on Theory and Practice of Software, ETAPS 2025

作者： Samadi, Mahboubeh Bastany, Aryan Hojjat, Hossein Tehran Institute for Advanced Studies Khatam University Tehran Iran University of Tehran Tehran Iran

ISBN: (纸本)9783031908996

Automata learning is an approach for extracting a model in the shape of an automaton from a black-box system. This approach has recently gained much attention in both industry and academia. In this paper, we introduce a compositional automata learning algorithm for systems comprising synchronous parallel components. Our algorithm assumes no prior knowledge about the number of components, their individual alphabets, and the synchronizing alphabets. The learning process is automatic and figures out the alphabet symbols on-the-fly during learning the components. We prove that the proposed algorithm terminates and correctly learns the individual components. We use a number of case studies from the industrial automotive domain and synthetic benchmarks to evaluate the performance of the proposed algorithm. The experimental results show that the algorithm requires significantly fewer input symbols and resets to learn the system compositionally. © The Author(s) 2025.

关键词： learning algorithms

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Early adverse physiological event detection using commercial wearables: challenges and opportunities

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NPJ DIGITAL MEDICINE 2024年第1期7卷 136页

作者： Phipps, Jesse Passage, Bryant Sel, Kaan Martinez, Jonathan Saadat, Milad Koker, Teddy Damaso, Natalie Davis, Shakti Palmer, Jeffrey Claypool, Kajal Kiley, Christopher Pettigrew, Roderic I. Jafari, Roozbeh Texas A&M Univ Dept Comp Sci & Engn College Stn TX 77843 USA MIT Lab Informat & Decis Syst Cambridge MA 02139 USA Texas A&M Univ Dept Elect & Comp Engn College Stn TX 77843 USA MIT Lincoln Lab Lexington MA 02421 USA Def Threat Reduct Agcy Ft Belvoir VA USA Texas A&M Univ Sch Engn Med Houston TX 77030 USA

Data from commercial off-the-shelf (COTS) wearables leveraged with machine learning algorithms provide an unprecedented potential for the early detection of adverse physiological events. However, several challenges inhibit this potential, including (1) heterogeneity among and within participants that make scaling detection algorithms to a general population less precise, (2) confounders that lead to incorrect assumptions regarding a participant's healthy state, (3) noise in the data at the sensor level that limits the sensitivity of detection algorithms, and (4) imprecision in self-reported labels that misrepresent the true data values associated with a given physiological event. The goal of this study was two-fold: (1) to characterize the performance of such algorithms in the presence of these challenges and provide insights to researchers on limitations and opportunities, and (2) to subsequently devise algorithms to address each challenge and offer insights on future opportunities for advancement. Our proposed algorithms include techniques that build on determining suitable baselines for each participant to capture important physiological changes and label correction techniques as it pertains to participant-reported identifiers. Our work is validated on potentially one of the largest datasets available, obtained with 8000+ participants and 1.3+ million hours of wearable data captured from Oura smart rings. Leveraging this extensive dataset, we achieve pre-symptomatic detection of COVID-19 with a performance receiver operator characteristic (ROC) area under the curve (AUC) of 0.725 without correction techniques, 0.739 with baseline correction, 0.740 with baseline correction and label correction on the training set, and 0.777 with baseline correction and label correction on both the training and the test set. Using the same respective paradigms, we achieve ROC AUCs of 0.919, 0.938, 0.943 and 0.994 for the detection of self-reported fever, and 0.574, 0.611, 0.601, an

关键词： learning algorithms

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