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arXiv 2024年

作者： Chen, Zhirui Karthik, P.N. Chee, Yeow Meng Tan, Vincent Y.F. The Department of Industrial Systems Engineering and Management National University of Singapore Singapore The Institute of Data Science National University of Singapore Singapore The Department of Mathematics The Department of Electrical and Computer Engineering National University of Singapore Singapore

We study best arm identification (BAI) in linear bandits in the fixed-budget regime under differential privacy constraints, when the arm rewards are supported on the unit interval. Given a finite budget T and a privacy parameter Ε > 0, the goal is to minimise the error probability in finding the arm with the largest mean after T sampling rounds, subject to the constraint that the policy of the decision maker satisfies a certain Ε-differential privacy (Ε-DP) constraint. We construct a policy satisfying the Ε-DP constraint (called DP-BAI) by proposing the principle of maximum absolute determinants, and derive an upper bound on its error probability. Furthermore, we derive a minimax lower bound on the error probability, and demonstrate that the lower and the upper bounds decay exponentially in T, with exponents in the two bounds matching order-wise in (a) the sub-optimality gaps of the arms, (b) Ε, and (c) the problem complexity that is expressible as the sum of two terms, one characterising the complexity of standard fixed-budget BAI (without privacy constraints), and the other accounting for the Ε-DP constraint. Additionally, we present some auxiliary results that contribute to the derivation of the lower bound on the error probability. These results, we posit, may be of independent interest and could prove instrumental in proving lower bounds on error probabilities in several other bandit problems. Whereas prior works provide results for BAI in the fixed-budget regime without privacy constraints or in the fixed-confidence regime with privacy constraints, our work fills the gap in the literature by providing the results for BAI in the fixed-budget regime under the Ε-DP constraint. Copyright © 2024, The Authors. All rights reserved.

关键词： Decision making

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A prototype imaging method for feed estimation in beef cattle 10

A prototype imaging method for feed estimation in beef cattl...

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10th European Conference on Precision Livestock Farming, ECPLF 2022

作者： Xiong, Y. Psota, E.T. Brown-Brandl, T.M. Mote, B. Schmidt, T.B. Erickson, G.E. Animal Science Department University of Nebraska-Lincoln LincolnNE United States Biological Systems Engineering Department University of Nebraska-Lincoln LincolnNE United States Department of Electrical and Computer Engineering University of Nebraska-Lincoln LincolnNE United States

ISBN: (纸本)9788396536006

Research and commercial precision livestock management tools have been developed to provide individual management for swine and dairy industries. However, few tools have focused on beef production due to the extensive nature of the production system. Current feed management relies on visual assessment of residual feed by skilled workers, making it time-consuming and prone to error. This study tested the ability of a low-cost depth camera (Microsoft Azure Kinect® DK) mounted directly above an individual feeding bunk to estimate the weight of residual feed within the bunk. Two typical feedlot finishing diets with variation in composition and bulk density were tested: 1) high bulk density with more high moisture corn and 2) medium bulk density with more sweet bran. Each diet was weighed in increments and added into the bunk to approximately 23 kg. Twelve top-down depth images were captured for each feed addition, and the feed was randomly stirred after each picture. The volume of the feed was approximated by using background subtraction and voxel summing. The estimated volume and measured bulk density of each diet as-fed were used to calculate the estimated weight. Results show that a linear model can be used to determine the weight of high-density feed (Diet 1, SE of the estimate (S) = 0.52 kg), whereas a 2-order polynomial model was a better fit to estimate the weight for less dense feed (Diet 2, S = 0.45 kg). This work demonstrates that the proposed PLF tool can provide accurate bunk management information for beef cattle. © ECPLF 2022. All rights reserved.

关键词： Windows operating system

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Light-EvoOPT: A Lightweight Evolutionary Optimization Framework for Ultra-Large-Scale Mixed Integer Linear Programs

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IEEE Transactions on Evolutionary Computation 2025年

作者： Ye, Huigen Xu, Hua Coello, Carlos A. Coello Tsinghua University State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology Beijing100084 China Department of Computer Science D.F México07300 Mexico Tecnologico de Monterrey Faculty of Excellence The School of Engineering and Sciences N.L Monterrey Mexico

Machine Learning (ML)-based optimization frameworks emerge as a promising technique for solving large-scale Mixed Integer Linear Programs (MILPs), as they can capture the mapping between problem structures and optimal solutions to expedite their solution process. However, existing solution frameworks often suffer from high model computation costs, incomplete problem reduction, and reliance on large-scale solvers, leading to performance bottlenecks in ultra-large-scale problems with complex constraints. To address these issues, this paper proposes Light-EvoOPT, a Lightweight Evolutionary Optimization Framework for Ultra-Large-Scale Mixed Integer Linear Programs, which can be divided into four stages: (1) Problem Formulation for problem division to reduce model computational costs, (2) Model-based Initial Solution Prediction for predicting and constructing the initial solution using a small-scale training dataset, (3) Problem Reduction for both variable and constraint reduction, and (4) Evolutionary Optimization for current solution improvement employing a lightweight optimizer. Experiments on four benchmark datasets with tens of millions of variables and constraints and a real-world problem show that the proposed framework based on the sole use of a lightweight optimizer, trained on only one-thousandth of the scale of ultra-large-scale problems, is able to outperform state-of-the-art ML-based frameworks and advanced solvers (e.g. Gurobi) within a specified computational time, validating the feasibility and effectiveness of our proposed ML-based evolutionary optimization framework for ultra-large-scale MILPs. © 2025 IEEE. All rights reserved.

关键词： Mixed-integer linear programming

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A Comparative Study of PCA and LDA for Dimensionality Reduction in a 4-Way Classification Framework

Research Square

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Research Square 2024年

作者： Mabrouk, Besma Jazzar, Nessrine Sallemi, Lamia Hamida, Ahmed Ben Advanced Technologies for Medicine and Signals ATMS Department of Electrical and Computer Engineering National Engineers School Sfax University Tunisia Networked Objects Control and Communication Systems National Engineering School Sfax Tunisia

Alzheimer’s disease (AD), recognized as the second-most impactful neurological disorder and currently incurable, stands as the leading cause of dementia. An imperative research focus is efficiently diagnosing the stages of patients, distinguishing early or late Mild Cognitive Impairment and AD from those with normal cognitive function. Advancements in anatomical and diffusion-weighted imaging, coupled with machine learning techniques, have significantly progressed in this predictive domain. However, in real-world trials, datasets often contain numerous features, and the curse of dimensionality can introduce challenges such as increased computational complexity, overfitting, and diminished model interpretability. To address these issues, the present study explores the efficacy of Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) as dimensionality reduction techniques. LDA, a supervised technique emphasizing class separability, surpasses PCA, particularly in selecting features that significantly contribute to discriminating between classes. The 3D-LDA features obtained were subsequently assessed across various machine learning algorithms, leading to the establishment of a 4-way classification framework that utilized the K-Nearest Neighbors model. The outcome of this evaluation yielded an impressive accuracy rate of 87% in predicting the four different classes. © 2024, CC BY.

关键词： Nearest neighbor search

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Correction to: Generalized Robot Vision-Language Model via Linguistic Foreground-Aware Contrast (International Journal of computer Vision, (2025), 10.1007/s11263-024-02340-z)

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International Journal of computer Vision 2025年 1-1页

作者： Liu, Kangcheng Wang, Chaoqun Han, Xiaodong Liu, Yong-Jin Chen, Baoquan Changsha China Pasadena United States The School of Control Science and Engineering Shandong University Jinan China The School of Control Engineering Minjiang University Fuzhou China The Department of Computer Science and Technology Tsinghua University Beijing China The School of Artificial Intelligence Peking University Beijing China

The original version of this article contained inadvertent typos requiring the following revisions. The article title was given as "Generalized Robotic Vision-Language Learning Model via Linguistic Foreground-Aware Contrast" but should have been "Generalized Robot Vision-Language Model via Linguistic Foreground-Aware Contrast". The affiliation details for Author Chaoqun Wang were given as "The department of computer science and Technology, Tsinghua University, Beijing, China" but should have been "The School of control science and engineering, Shandong University, Jinan, China". The affiliation details for Author Xiaodong Han were given as "The School of control science and engineering, Shandong University, Jinan, China" but should have been "The School of control engineering, Minjiang University, Fuzhou, China". The affiliation details for Author Yong-Jin Liu were given as "The School of control engineering, Minjiang University, Fuzhou, China" but should have been "The department of computer science and Technology, Tsinghua University, Beijing, China". In this article, the part of the caption to Fig. 3 "Please refer to the colored figures online for detailed information" was inadvertently omitted. The complete caption of Fig. 3 is given here. Figure 3 Visualizations of projected point correlation maps over the indoor ScanNet (1st-4th rows) and the outdoor KITTI (5th-8th rows) with respect to the query points highlighted by yellow crosses. The View 1 and View 2 in each sample show the intra-view and cross-view correlations, respectively. We compare FAC with the state-of-the-art CSC (Hou et al., 2021) on segmentation (rows 1–4) and ProCo (Yin et al., 2022) on detection (rows 5–8). FAC clearly captures better feature correlations within and across views (columns 3–4). Please refer to the colored figures online for detailed information In this article, the footnote "The bold highlights the results of our proposed approaches" to Table 1 was inadvertently omitted. In Tab

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Performance Analysis of Pre-Processing techniques in Plantain Tree Disease Classification with Convolutional Neural Network 5

Performance Analysis of Pre-Processing techniques in Plantai...

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5th International Conference on Trends in Electronics and Informatics, ICOEI 2021

作者： Nandhini, M. Kala, K.U. Thangadarshini, M. Verma, S. Madhusudhana Pondicherry University Department of Computer Science Puducherry India Department of Computer Science and Engineering Puducherry India Rayalaseema University Department of Operations Research and Statistical Quality Control Andhra Pradesh India

ISBN: (纸本)9781665415712

The agricultural sector is considered as the backbone of almost all economies present in the world. Crop protection is remaining as a crucial factor in agriculture, since it determines the quantity and quality of products and increases the profit of the farmers. In the Indian economy, plantain tree cultivation is playing a major role. Plantain Tree is highly exposed to diseases such as Black Sigatoka/Yellow Sigatoka, Panama, Bunchy top, Moko, chlorosis, etc. This study aims at developing a DL model to facilitate crop protection by accurately identifying the diseases that affect the productivity of plantain trees by using the leaf images. The pre-processing of image data will also help to improve the classification accuracy and training time. The main aim of this study is the research investigation and performance comparison of various data augmentation and feature selection techniques that helps in the classification and detection of diseases in plantain trees. The experiments are carried out in real-time datasets, which are collected from different plantain tree cultivation in different fields of Tamil Nadu. The experimental results reveal the necessity to propose effective preprocessing techniques for improving the accuracy of the classification models. © 2021 IEEE.

关键词： Image classification

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An Ensembled Real-Time Hand-Gesture Recognition using CNN

An Ensembled Real-Time Hand-Gesture Recognition using CNN

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International Conference on Computing and Networking Technology (ICCNT)

作者： M N Kavitha S S Saranya M Prasad S Kaviyarasu N Ragunath P Rahul Department of Computer Science and Design Kongu Engineering College Erode TamilNadu India Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Coimbatore TamilNadu India Controller of Examinations K S Rangasamy College of Arts and Science (Autonomous) Namakkal TamilNadu India Bachelor of Science – Information Systems Kongu Engineering College Erode TamilNadu India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Hand sign recognition is a vital technology in the human-computer interaction, enabling individuals to communicate with machines naturally and effectively. An innovative approach for real-time hand sign identification with the help of CNN and OpenCV is introduced with the fusion of computer vision and deep learning that can accurately interpret and classify an extensive range of hand signs and gestures. This research contributes significantly to the fields of computer vision and human-computer interaction, offering a practical and efficient solution for hand sign recognition. The combination of CNN and OpenCV presents a promising avenue for enhancing accessibility and communication, especially in environments where verbal communication is limited or non-existent. The model is trained with multiple data so that the system can recognize the hand gestures more precisely. Pre-trained architectures like ResNet and MobileNet are combined with the CNN model using ensemble learning and the performance is improved when compared to all the three CNN architectures individually. The ensemble model provides better accuracy of 96 %. The potential applications of this technology are vast, from assisting the hearing-impaired in understanding sign language to more immersive and intuitive interactions. Overall, the approach holds the promise of bridging the gap between human gestures and machine understanding, opening new doors for meaningful interactions between individuals and intelligent systems.

关键词： Human computer interaction Deep learning computer vision Sign language Neural networks computer architecture Real-time systems Data models Ensemble learning Intelligent systems

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A Real-time Hand Gesture Recognition System for Human-computer and Human-Robot Interaction

A Real-time Hand Gesture Recognition System for Human-Comput...

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2022 International Conference of Yearly Reports on Informatics, Mathematics, and engineering, ICYRIME 2022

作者： Ponzi, Valerio Iacobelli, Emanuele Napoli, Christian Starczewski, Janusz Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Roma00185 Italy Institute for Systems Analysis and Computer Science Italian National Research Council Via dei Taurini 19 Roma00185 Italy Department of Computational Intelligence Czestochowa University of Technology al. Armii Krajowej 36 Czestochowa42-200 Poland

The proposed hand gesture recognition (HGR) system is designed to enhance human-computer interaction (HCI) and human-robot interaction (HRI), which are crucial areas of research aimed at improving the way humans interact with computer or robot systems. With the growing need for intelligent computers and robots in a range of applications, including healthcare, manufacturing, and education, both HCI and HRI have gained significant importance. In this context, the HGR system plays a vital role by enabling natural and intuitive communication between humans and technology through hand gestures. The presented system uses a single camera and efficient image processing techniques that enable real-time gesture detection. Unlike other methods, our approach employs a basic video camera, which is widely available on most computers, eliminating the need for expensive and specialized hardware. © 2022 Copyright for this paper by its authors.

关键词： Human robot interaction

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Privacy-Preserving Task-Oriented Semantic Communications Against Model Inversion Attacks

arXiv

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arXiv 2023年

作者： Wang, Yanhu Guo, Shuaishuai Deng, Yiqin Zhang, Haixia Fang, Yuguang School of Control Science and Engineering Shandong University Jinan250061 China Department of Computer Science City University of Hong Kong Hong Kong

Semantic communication has been identified as a core technology for the sixth generation (6G) of wireless networks. Recently, task-oriented semantic communications have been proposed for low-latency inference with limited bandwidth. Although transmitting only task-related information does protect a certain level of user privacy, adversaries could apply model inversion techniques to reconstruct the raw data or extract useful information, thereby infringing on users’ privacy. To mitigate privacy infringement, this paper proposes an information bottleneck and adversarial learning (IBAL) approach to protect users’ privacy against model inversion attacks. Specifically, we extract task-relevant features from the input based on the information bottleneck (IB) theory. To overcome the difficulty in calculating the mutual information in high-dimensional space, we derive a variational upper bound to estimate the true mutual information. To prevent data reconstruction from task-related features by adversaries, we leverage adversarial learning to train encoder to fool adversaries by maximizing reconstruction distortion. Furthermore, considering the impact of channel variations on privacy-utility trade-off and the difficulty in manually tuning the weights of each loss, we propose an adaptive weight adjustment method. Numerical results demonstrate that the proposed approaches can effectively protect privacy without significantly affecting task performance and achieve better privacy-utility trade-offs than baseline methods. © 2023, CC BY.

关键词： Economic and social effects

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Communication-Constrained STL Task Decomposition Through Convex Optimization

Communication-Constrained STL Task Decomposition Through Con...

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American control Conference (ACC)

作者： Gregorio Marchesini Siyuan Liu Lars Lindemann Dimos V. Dimarogonas Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden Thomas Lord Department of Computer Science University of Southern California Los Angeles CA USA

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

We propose a method to decompose signal temporal logic tasks for multi-agent systems under communication constraints. Specifically, given a task graph representing task dependencies among couples of agents in the system, we propose to decompose tasks assigned to couples of agents not connected in the communication graph by a set of sub-tasks assigned to couples of communicating agents over the communication graph. To this end, we parameterize the predicates' level set of tasks to be decomposed as hyper-rectangles with parametric centres and dimensions. Convex optimization is then leveraged to find optimal parameters maximising the volume of the predicate's level sets. Moreover, a formal treatment of conflicting conjunctions of formulas in the considered STL fragment is introduced, including sufficient conditions to avoid the insurgence of such conflicts in the final decomposition.

关键词： Sufficient conditions Level set Convex functions Task analysis Multi-agent systems

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