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arXiv 2023年

作者： Naor, Moni Nissim, Kobbi Stemmer, Uri Yan, Chao Department of Computer Science and Applied Math Weizmann Institute of Science Blavatnik School of Computer Science Tel Aviv University Google Research Department of Computer Science Georgetown University United States

A private learner is trained on a sample of labeled points and generates a hypothesis that can be used for predicting the labels of newly sampled points while protecting the privacy of the training set [Kasiviswannathan et al., FOCS 2008]. Research uncovered that private learners may need to exhibit significantly higher sample complexity than non-private learners as is the case with, e.g., learning of one-dimensional threshold functions [Bun et al., FOCS 2015, Alon et al., STOC 2019]. We explore prediction as an alternative to learning. Instead of putting forward a hypothesis, a predictor answers a stream of classification queries. Earlier work has considered a private prediction model with just a single classification query [Dwork and Feldman, COLT 2018]. We observe that when answering a stream of queries, a predictor must modify the hypothesis it uses over time, and, furthermore, that it must use the queries for this modification, hence introducing potential privacy risks with respect to the queries themselves. We introduce private everlasting prediction taking into account the privacy of both the training set and the (adaptively chosen) queries made to the predictor. We then present a generic construction of private everlasting predictors in the PAC model. The sample complexity of the initial training sample in our construction is quadratic (up to polylog factors) in the VC dimension of the concept class. Our construction allows prediction for all concept classes with finite VC dimension, and in particular threshold functions with constant size initial training sample, even when considered over infinite domains, whereas it is known that the sample complexity of privately learning threshold functions must grow as a function of the domain size and hence is impossible for infinite domains. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

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A Robust Cold-Start Approach Using Phylogram

A Robust Cold-Start Approach Using Phylogram

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International Conference on Optimization and Applications (ICOA)

作者： Breno Caetano da Silva Jorge Luiz Franco Hermes Manoel Galvão Castelo Branco Eric Tokuda Alexandre Cláudio Botazzo Delbem Computer Science Department Federal Institute of Maranhão Teresina Brazil Inst. of Math. and Computer Science University of São Paulo São Carlos Brazil Electrical Engineering Department Federal University of Piaui Teresina Brazil

ISBN: (数字)9798350387353

ISBN: (纸本)9798350387360

In Active Learning (AL) applications, it is crucial to develop a model that efficiently represents data using a limited number of labeled samples. In this context, an oracle is often employed to label samples that are deemed representative and informative. However, one of the primary limitations of AL lies in the need for an initial set of labeled samples, which may not be representative of the entire data space. To address this issue, cold-start strategies are implemented with the goal of selecting an appropriate initial set of samples for labeling, ensuring that the initial model is robust and effective. We propose a novel approach – PhyIL – that harness the advantages of phylogram analysis and community detection techniques to smartly perform the cold-start selection in AL. The method facilitates the choice of samples that best represent the diversity and complexity of the data, which is fundamental for the subsequent success of the active learning process. We evaluated the proposed approach using nine real-world databases and nine traditional ML models. The findings indicate that PhyIL offers an effective and viable solution for the initial sample selection, when compared to the random selection.

关键词： Training Databases Data models Complexity theory Labeling Standards Optimization Context modeling

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Link Prediction with Relational Hypergraphs

arXiv

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arXiv 2024年

作者： Huang, Xingyue Romero, Miguel Barceló, Pablo Bronstein, Michael M. Ceylan, Ismail Ilkan Department of Computer Science University of Oxford United Kingdom Department of Computer Science Universidad Católica Chile Inst. for Math. and Comp. Eng. Universidad Católica de Chile & IMFD Chile CENIA Chile

Link prediction with knowledge graphs has been thoroughly studied in graph machine learning, leading to a rich landscape of graph neural network architectures with successful applications. Nonetheless, it remains challenging to transfer the success of these architectures to relational hypergraphs, where the task of link prediction is over k-ary relations, which is substantially harder than link prediction with knowledge graphs. In this paper, we propose a framework for link prediction with relational hypergraphs, unlocking applications of graph neural networks to fully relational structures. Theoretically, we conduct a thorough analysis of the expressive power of the resulting model architectures via corresponding relational Weisfeiler-Leman algorithms and also via logical expressiveness. Empirically, we validate the power of the proposed model architectures on various relational hypergraph benchmarks. The resulting model architectures substantially outperform every baseline for inductive link prediction, and lead to state-of-the-art results for transductive link prediction. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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Adaptive particle swarm optimization model for resource leveling

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Evolving Systems 2023年第4期14卷 593-604页

作者： Lin, Jerry Chun-Wei Lv, Qing Yu, Dehu Srivastava, Gautam Chen, Chun-Hao School of Information and Control Engineering Qingdao University of Technology Qingdao China Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway School of Civil Engineering Qingdao University of Technology Qingdao China School of Civil Engineering Shandong Jianzhu University Jinan China Department of Math and Computer Science Brandon University Brandon Canada Research Centre for Interneural Computing China Medical University Taichung Taiwan Department of Information and Finance Management National Taipei University of Technology Taipei Taiwan

In the process of engineering project construction, the balanced allocation of resources has an important impact on the purchase of actual materials, the progress of the site construction and the arrangement of temporary facilities. Although there have been many studies on the use of evolutionary computation (EC) to solve the fixed duration of the resource-leveling problem, the optimization effect of standard EC to solve the resource-balancing problem in large-scale network design planning is reduced due to the phenomenon of premature convergence of PSO and the complexity of large-scale network planning. Therefore, in this paper, an adaptive dissipative particle swarm optimization (ADPSO) algorithm is used to solve the resource balancing optimization problem for different network-plans scales, and the feasibility of the developed model is verified using four different scaled network plans (databases). At the same time, a solution to the dynamic resource-leveling problem with optimization for a fixed duration is proposed. The results show that the proposed models are suitable for solving the problem of resource-leveling with a fixed duration of optimization of different scale network plans, and their accuracy and stability are significantly improved compared to the state-of-the-art approaches. Moreover, the proposed models are accurate and suitable for combining the calculation method of network planning time parameters with the ADPSO algorithm to realize dynamic resource balancing optimization. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Particle swarm optimization (PSO)

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Containerized computer Vision Applications on Edge Devices

Containerized Computer Vision Applications on Edge Devices

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IEEE International Conference on Edge Computing (EDGE)

作者： Osamah I. Alqaisi Ali Şaman Tosun Turgay Korkmaz Department of Computer Science University of Texas at San Antonio San Antonio TX USA Dep. of Math. and Computer Science The University of North Carolina Pembroke NC USA

The proliferation of IoT devices has led to various computer vision applications, where addressing bandwidth and latency challenges through edge nodes presents significant benefits. However, there are still existing gaps and a need for improvements to optimize IoT applications, especially in the field of computer vision, by overcoming limited resources and enhancing device performance. Addressing these challenges is essential to unlock the full potential of IoT applications in real-world scenarios. This paper evaluates the use of lightweight container technology for computer vision applications which using different algorithms, such as Haar Cascades, HOG and CNN with YOLO algorithm, on edge devices and provides a comprehensive comparison and analysis of different versions of computer vision applications in containers in terms of processing ability, and performance. It focuses on containerizing computer vision applications using Docker to achieve safe execution of multiple applications on these devices without interference and to enable flexibility, efficiency, portability, scalability, and isolation. The study also examines the resource usage, execution time, and receiving time of containerized computer vision applications. The research findings significantly advance our understanding of computer vision processing in IoT and edge computing, thereby opening up new avenues for real-time computing scenarios. These insights have the potential to drive transformative advancements in the field, enabling more efficient and accurate computer vision applications in IoT and paving the way for enhanced real-time decision-making, automation, and intelligent systems.

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Towards Early Diagnosis and Intervention: An Ensemble Voting Model for Precise Vital Sign Prediction in Respiratory Disease

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IEEE Journal of Biomedical and Health Informatics 2023年 PP卷 1-13页

作者： Ahmed, Usman Lin, Jerry Chun-Wei Srivastava, Gautam Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway Dept. of Math and Computer Science Brandon University Canada

Worldwide, cardiovascular and chronic respiratory diseases account for approximately 19 million deaths annually. Evidence indicates that the ongoing COVID-19 pandemic directly contributes to increased blood pressure, cholesterol, as well as blood glucose levels. Timely screening of critical physiological vital signs benefits both healthcare providers and individuals by detecting potential health issues. This study aims to implement a machine learning-based prediction and classification system to forecast vital signs associated with cardiovascular and chronic respiratory diseases. The system predicts patients' health status and notifies caregivers and medical professionals when necessary. Utilizing real-world data, a linear regression model inspired by the Facebook Prophet model was developed to predict vital signs for the upcoming 180 seconds. With 180 seconds of lead time, caregivers can potentially save patients' lives through early diagnosis of their health conditions. For this purpose, a Naïve Bayes classification model, a Support Vector Machine model, a Random Forest model, and genetic programming-based hyper tunning were employed. The proposed model outdoes previous attempts at vital sign prediction. Compared with alternative methods, the Facebook Prophet model has the best mean square in predicting vital signs. A hyperparameter-tuning is utilized to refine the model, yielding improved short- and long-term outcomes for each and every vital sign. Furthermore, the F-measure for the proposed classification model is 0.98 with an increase of 0.21. The incorporation of additional elements, such as momentum indicators, could increase the model's flexibility with calibration. The findings of this study demonstrate that the proposed model is more accurate in predicting vital signs and trends. IEEE

关键词： Decision trees

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Dispatching Point Selection for a Drone-Based Delivery System Operating in a Mixed Euclidean-Manhattan Grid

arXiv

引用

arXiv 2023年

作者： Sorbelli, Francesco Betti Corò, Federico Das, Sajal K. Pinotti, Cristina M. Shende, Anil Department of Computer Science and Mathematics University of Perugia Italy Department of CS Missouri University of Science and Technology United States Department of Math. CS & Physics Roanoke College United States

In this paper, we present a drone-based delivery system that assumes to deal with two different mixed-areas, i.e., rural and urban. In these mixed-areas, called EM-grids, the distances are measured with two different metrics, and the shortest path between two destinations concatenates the Euclidean and Manhattan metrics. Due to payload constraints, the drone serves a single customer at a time returning back to the dispatching point (DP) after each delivery to load a new parcel for the next customer. In this paper, we present the 1-Median Euclidean-Manhattan grid Problem (MEMP) for EM-grids, whose goal is to determine the drone’s DP position that minimizes the sum of the distances between all the locations to be served and the point itself. We study the MEMP on two different scenarios, i.e., one in which all the customers in the area need to be served (full-grid) and another one where only a subset of these must be served (partial-grid). For the full-grid scenario we devise optimal, approximation, and heuristic algorithms, while for the partial-grid scenario we devise optimal and heuristic algorithms. Eventually, we comprehensively evaluate our algorithms on generated synthetic and quasi-real data. © 2023, CC BY.

关键词： Heuristic algorithms

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Rank Flow Embedding for Unsupervised and Semi-Supervised Manifold Learning

arXiv

引用

arXiv 2023年

作者： Valem, Lucas Pascotti Pedronette, Daniel Carlos Guimarães Latecki, Longin Jan Department of Statistics Applied Math. and Computing State University of São Paulo Rio Claro Brazil Department of Computer and Information Sciences Temple University Philadelphia United States

Impressive advances in acquisition and sharing technologies have made the growth of multimedia collections and their applications almost unlimited. However, the opposite is true for the availability of labeled data, which is needed for supervised training, since such data is often expensive and time-consuming to obtain. While there is a pressing need for the development of effective retrieval and classification methods, the difficulties faced by supervised approaches highlight the relevance of methods capable of operating with few or no labeled data. In this work, we propose a novel manifold learning algorithm named Rank Flow Embedding (RFE) for unsupervised and semi-supervised scenarios. The proposed method is based on ideas recently exploited by manifold learning approaches, which include hypergraphs, Cartesian products, and connected components. The algorithm computes context-sensitive embeddings, which are refined following a rank-based processing flow, while complementary contextual information is incorporated. The generated embeddings can be exploited for more effective unsupervised retrieval or semi-supervised classification based on Graph Convolutional Networks. Experimental results were conducted on 10 different collections. Various features were considered, including the ones obtained with recent Convolutional Neural Networks (CNN) and Vision Transformer (ViT) models. High effective results demonstrate the effectiveness of the proposed method on different tasks: unsupervised image retrieval, semi-supervised classification, and person Re-ID. The results demonstrate that RFE is competitive or superior to the state-of-the-art in diverse evaluated scenarios. Copyright © 2023, The Authors. All rights reserved.

关键词： Embeddings

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'Simple Mistakes' in College Algebra: an Analysis of Students' Perceptions of Their Errors Using Attribution Theory

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International Journal of Research in Undergraduate math.matics Education 2025年 1-28页

作者： Ryals, Megan Hill-Lindsay, Sloan Pilgrim, Mary E. Burks, Linda C. Center for Applied Mathematics University of Virginia 156 Engineer’s Way Charlottesville 22904 VA United States Department of Math and Statistics San Diego State University 5500 Campanile Dr San Diego 92182 CA United States Department of Mathematics and Computer Science Santa Clara University 500 El Camino Real Santa Clara 95053 CA United States

Analyzing their mistakes affords students opportunities for improved learning and success in problem solving disciplines, especially when students are able to identify reasons for their errors and modify their future study behavior. To help students make appropriate adjustments, instructors must understand the language students use to describe their own academic performance. This study explores what students mean when they say they made “simple mistakes.” Eight students participated in interviews where they reviewed errors from a recent exam, explaining why errors were simple or not simple. We analyzed the interview data using Causal Attribution Theory and discovered how students’ attributions of their errors influence their classification of their errors as simple or not simple. Results include descriptions of simple and not simple distinctions for three categories of students and evidence of similarities among students within each category regarding their self-efficacy and persistence through struggle. The analysis also reveals how students plan to adjust their study approach based on their classification of their errors. Across categories, students believe simple mistakes are preventable through repetitive practice or memorization, while not simple mistakes require help seeking, but students within different categories differ in how they use these strategies. The results show that that different students can use the same language to convey very different meanings for their own error categorizations and that there is need for instructors to probe these meanings before providing guidance. Our results also provide support for previous calls for interventions aimed at retraining students’ causal attribution styles towards controllable, unstable causes. © The Author(s) 2025.

关键词： Attribution theory College Algebra Motivation Student errors

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Graph Convolutional Networks based on Manifold Learning for Semi-Supervised Image Classification

arXiv

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arXiv 2023年

作者： Valem, Lucas Pascotti Pedronette, Daniel Carlos Guimarães Latecki, Longin Jan The Department of Statistics Applied Math. and Computing State University of São Paulo Rio Claro Brazil Department of Computer and Information Sciences Temple University Philadelphia United States

Due to a huge volume of information in many domains, the need for classification methods is imperious. In spite of many advances, most of the approaches require a large amount of labeled data, which is often not available, due to costs and difficulties of manual labeling processes. In this scenario, unsupervised and semi-supervised approaches have been gaining increasing attention. The GCNs (Graph Convolutional Neural Networks) represent a promising solution since they encode the neighborhood information and have achieved state-of-the-art results on scenarios with limited labeled data. However, since GCNs require graph-structured data, their use for semi-supervised image classification is still scarce in the literature. In this work, we propose a novel approach, the Manifold-GCN, based on GCNs for semi-supervised image classification. The main hypothesis of this paper is that the use of manifold learning to model the graph structure can further improve the GCN classification. To the best of our knowledge, this is the first framework that allows the combination of GCNs with different types of manifold learning approaches for image classification. All manifold learning algorithms employed are completely unsupervised, which is especially useful for scenarios where the availability of labeled data is a concern. A broad experimental evaluation was conducted considering 5 GCN models, 3 manifold learning approaches, 3 image datasets, and 5 deep features. The results reveal that our approach presents better accuracy than traditional and recent state-of-the-art methods with very efficient run times for both training and testing. Copyright © 2023, The Authors. All rights reserved.

关键词： Convolutional neural networks

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