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5th International Conference on Big-data Service and Intelligent Computation, BDSIC 2023

作者： Turky, Ayad Hussain, Abir Bettayeb, Anissa M. Serhani, Mohamed Adel Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Electrical Engineering College of Engineering University of Sharjah Sharjah United Arab Emirates Information Technology Center University of Sharjah Sharjah United Arab Emirates Department of Information Systems College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9798400708923

Determining the best shortest path between locations in intelligent transportation systems is crucial but challenging. Traditional approaches, which assume fixed travel times, fall short of accurately reflecting dynamic factors such as weather, time, and day. This paper introduces a novel two-stage approach that integrates deep learning and ant colony optimization to address this limitation. Firstly, deep learning is employed to predict travel times, incorporating weather conditions as input for improved accuracy. Secondly, an ant colony optimization algorithm is utilized to identify the shortest route based on the predicted travel times. We evaluated the proposed approach using the New York City taxi benchmark dataset and observed superior performance compared to current state-of-the-art algorithms. © 2023 ACM.

关键词： Travel time

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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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Experimental Evaluation of Internet of Things Assisted Industrial Automation System with Smart Emergency Alert Mechanism

Experimental Evaluation of Internet of Things Assisted Indus...

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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Yamini, B. Sreeraj, S. Supriya, K. Vinithra Banu, T. Ragini, P.Y. Kurada, Phaneendra Srm Institute of Science and Technology School of Computing Department of Networking and Communications Tamil Nadu Chennai India Sri Krishna College of Engineering and Technology Department of M. Tech Cse Tamil Nadu Coimbatore India Panimalar Engineering College Department of Computer Science and Business Systems Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Department of Biosciences Saveetha School of Engineering Tamil Nadu Chennai India Department of Electronics and Communication Engineering MLR Institute of Technology Telangana Hyderabad India

ISBN: (纸本)9798350308167

Environmental sustainability, energy and raw material availability, and globalization are just a few of the major societal concerns that are generating new demands on a wide range of societal players. Efficiency, adaptability, sustainability, and competitiveness are some of the new criteria. These have been around for a while, and a variety of systems have been tuned locally in terms of a few of these requirements, but recent developments with regard to communication and computational characteristics have made them much more useful to guarantee new possibilities for combining various parts of society, allowing for optimum performance on a scale that is far greater. The impact of IoT on several sectors of the economy has been substantial. The Internet of Things (IoT) is a technology that allows us to manage physical things over the internet in order to save time and energy. There are several factories in India because of the country's rapid economic growth. However, industrial accidents were a severe problem in many sectors. However, industrial accidents were a big problem because they resulted in casualties and financial losses for businesses. This initiative aims to reduce the devastating effects of fires in high-risk industries including the oil, chemical and petrochemical sectors, where many lives and fortunes have been lost. It is crucial to have a system in place that can ensure the safety of the factory grounds and promptly alert the proper authorities should an emergency arise. In the past, a single bottle at a time would be loaded onto a conveyor and filled. This strategy is time-consuming and costly. Internet of Things (IoT) in Industries has been implemented to monitor and notify the individual in charge to take suitable measures in order to reduce manual overhead and therefore partially meet our needs. The manufacturing sector might benefit from AI-based management and oversight. 'Ecosystem having Manufacturing Relevant Implication' better describes the I

关键词： Accident prevention

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FIXED-BUDGET DIFFERENTIALLY PRIVATE BEST ARM IDENTIFICATION

arXiv

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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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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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The Cost of Balanced Training-Data Production in an Online Data Market 25

The Cost of Balanced Training-Data Production in an Online D...

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34th ACM Web Conference, WWW 2025

作者： Chaintreau, Augustin Maio, Roland Ziani, Juba Columbia University Computer Science Department New YorkNY United States Georgia Institute of Technology Industrial and Systems Engineering AtlantaGA United States

ISBN: (纸本)9798400712746

Many ethical issues in machine learning are connected to the training data. Online data markets are an important source of training data, facilitating both production and distribution. Recently, a trend has emerged of for-profit "ethical" participants in online data markets. This trend raises a fascinating question: Can online data markets sustainably and efficiently address ethical issues in the broader machine-learning economy? In this work, we study this question in a stylized model of an online data market. We investigate the effects of intervening in the data market to achieve balanced training-data production. The model reveals the crucial role of market conditions. In small and emerging markets, an intervention can drive the data producers out of the market, so that the cost of fairness is maximal. Yet, in large and established markets, the cost of fairness can vanish (as a fraction of overall welfare) as the market grows. Our results suggest that "ethical" online data markets can be economically feasible under favorable market conditions, and motivate more models to consider the role of data production and distribution in mediating the impacts of ethical interventions. © 2025 Copyright held by the owner/author(s).

关键词： Marketplaces

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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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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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