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International Conference on Computer Supported Cooperative Work in Design

作者： Wenkai Chen Hui Zhang Chunming Yang Bo Li Xujian Zhao Yi Yang School of Computer Science and Technology Southwest University of Science and Technology Mianyang Shi China Sichuan Institute of Standardization Chengdu China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

The decision tree algorithm is an effective machine learning technique, but it cannot uncover causal relationships within data. To overcome this limitation, the causal decision tree was proposed, which combines causal discovery with decision tree principles. This method is particularly effective at identifying causal relationships in complex datasets. Currently, causal decision tree algorithms are being applied in various fields, including biology and politics. However, existing causal decision tree algorithms do not account the fairness. To fill this gap, this paper proposes the Fair Causal Decision Tree (FCDT), constructed through a fair post-pruning process. The FCDT ensures the algorithm's fairness while maintaining the causality inherent in the causal decision tree. To preserve the algorithm's causality, this paper introduces the Causal Structure Change Constraint (CSCC), which guarantees that the causal decision tree retains its strong causal properties when handling unfair nodes. This innovative constraint ensures that any structural modifications during the fairness adjust-ment process almost not decrease the algorithm's causality. The experiments were conducted on multiple datasets, and the results demonstrate that the proposed algorithm enhances fairness by approximately 10% compared to traditional causal decision tree algorithms, with only about a 1 % decrease in recall rate.

关键词： Measurement Machine learning algorithms Federated learning Cause effect analysis approximation algorithms Inference algorithms Biology Decision trees

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Fusion of Information in Multiple Particle Filtering in the Presence of Unknown Static Parameters

Fusion of Information in Multiple Particle Filtering in the ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xiaokun Zhao Marija Iloska Yousef El-Laham Monica F. Bugallo Department of Electrical and Computer Engineering Stony Brook University Stony Brook NY USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

An important and often overlooked aspect of particle filtering methods is the estimation of unknown static parameters. A simple approach for addressing this problem is to augment the unknown static parameters as auxiliary states that are jointly estimated with the time-varying parameters of interest. This can be impractical, especially when the system of interest is high-dimensional. Multiple particle filtering (MPF) methods were introduced to try to overcome the curse of dimensionality by using a "divide and conquer" approach, where the vector of unknowns is partitioned into a set of subvectors, each estimated by a separate particle filter. Each particle filter weighs its own particles by using predictions and estimates communicated from the other filters. Currently, there is no principled way to implement MPF methods where the particle filters share unknown parameters or states. In this work, we propose a fusion strategy to allow for the sharing of unknown static parameters in the MPF setting. Specifically, we study the systems which are separable in states and observations. It is proved that optimal Bayesian fusion can be obtained for state-space models with non-interacting states and observations. Simulations are performed to show that MPF with fusion strategy can provide more accurate estimates within fewer time steps comparing to existing algorithms.

关键词： Signal processing algorithms Estimation approximation algorithms Particle filters Vectors Acoustics State-space methods Partitioning algorithms Bayes methods Speech processing

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Multi-Pass Streaming Lower Bounds for Approximating Max-Cut

arXiv

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arXiv 2025年

作者： Fei, Yumou Minzer, Dor Wang, Shuo Department of EECS Massachusetts Institute of Technology United States Department of Mathematics Massachusetts Institute of Technology United States

In the Max-Cut problem in the streaming model, an algorithm is given the edges of an unknown graph G = (V, E) in some fixed order, and its goal is to approximate the size of the largest cut in G. Improving upon an earlier result of Kapralov, Khanna and Sudan, it was shown by Kapralov and Krachun that for all Ε > 0, no o(n) memory streaming algorithm can achieve a (1/2 + Ε)-approximation for Max-Cut. Their result holds for single-pass streams, i.e. the setting in which the algorithm only views the stream once, and it was open whether multi-pass access may help. The state-of-the-art result along these lines, due to Assadi and N, rules out arbitrarily good approximation algorithms with constantly many passes and n1−δ space for any δ > 0. We improve upon this state-of-the-art result, showing that any non-trivial approximation algorithm for Max-Cut requires either polynomially many passes or polynomially large space. More specifically, we show that for all Ε > 0, a k-pass streaming (1/2 + Ε)-approximation algorithm for Max-Cut requires ΩΕ (n1/3/k) space. This result leads to a similar lower bound for the Maximum Directed Cut problem, showing the near optimality of the algorithm of [Saxena, Singer, Sudan, Velusamy, SODA 2025]. Our lower bounds proceed by showing a communication complexity lower bound for the Distributional Implicit Hidden Partition (DIHP) Problem, introduced by Kapralov and Krachun. While a naive application of the discrepancy method fails, we identify a property of protocols called "globalness", and show that (1) any protocol for DIHP can be turned into a global protocol, (2) the discrepancy of a global protocol must be small. The second step is the more technically involved step in the argument, and therein we use global hypercontractive inequalities, and more specifically strong quantitative versions of the level d inequality for global functions. © 2025, CC BY.

关键词： approximation algorithms

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Machine Learning-Driven Surrogate Modeling and Quantum Metaheuristics for Gurney Flap Optimization in Darrieus Wind Turbines

Machine Learning-Driven Surrogate Modeling and Quantum Metah...

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Advancement in Computation & Computer Technologies (InCACCT), International Conference on

作者： Subhrajit Roy Siddharth Joshi Vishal Jaiswal Raj Kumar Singh Department of Mechanical Engineering Delhi Technological University Delhi India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

The optimization of Darrieus wind turbine designs is critical to improving their aerodynamic performance and energy efficiency. The present study focuses to enhance the (C P ) of the turbine by optimizing the design parameters of Gurney flaps attached to the trailing edges of the turbine rotors. Specifically, the flap angle and height are treated as the design variables. To explore the design space and generate random design points efficiently, Latin Hypercube Sampling (LHS) is employed. URANS simulations are carried out on these design points to provide high-fidelity data. A RBF surrogate model is then trained on this data, and is integrated with the Quantum-based Salp Swarm Algorithm (QSSA). The findings indicate a significant improvement in C P , with the optimized Gurney flap configurations outperforming the smooth baseline case by approximately 158% and the initial Gurney flap setup by 188%. Further, the performance of QSSA is shown with another popular evolutionary algorithm— the Harris Hawks Algorithm. This approach demonstrates the potential of data-driven optimization techniques in the energy sector.

关键词： Accuracy Computational modeling Computational fluid dynamics Metaheuristics Rotors Hypercubes approximation algorithms Data models Wind turbines Space exploration

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Enhanced Single Station PPP-RTK using CNES SSR Correction

Enhanced Single Station PPP-RTK using CNES SSR Correction

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IEEE Symposium on Position Location and Navigation (PLANS)

作者： Yu-Sheng Yang Shau-Shiun Jan Dept. of Aeronautics and Astronautics National Cheng Kung University Tainan City Taiwan

ISBN: (数字)9798331523176

ISBN: (纸本)9798331523183

The primary goal of this research is to develop an algorithm based on the concept of single-station augmented precise point positioning–real-time kinematic (PPP-RTK) to address the challenges posed by insufficient reference stations or limited satellite constellations provided by reference stations. This algorithm utilizes undifferenced and uncombined (UDUC) GNSS observations, integrated with global State Space Representation (SSR) corrections and regional atmospheric corrections. In a static experiment conducted under a fixed PPP-RTK scenario, the algorithm demonstrated excellent performance after convergence, achieving root-mean-square errors (RMSE) of 0.59 cm, 2.16 cm, and 2.98 cm in the east, north, and up directions, respectively. The fixed rate, representing the percentage of epochs with successfully resolved integer ambiguities, reached approximately 85%. In dynamic experiments, the RMSEs in the east, north, and up directions were 10.45 cm, 16.79 cm, and 20.51 cm, respectively, with a fixed rate of 65%.

关键词： Satellite constellations Global navigation satellite system Navigation Heuristic algorithms Kinematics approximation algorithms Convergence

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Task-Oriented Semantic Information Allocation Based on Rate Splitting for Cost Minimization

Task-Oriented Semantic Information Allocation Based on Rate ...

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IEEE Conference on Wireless Communications and Networking

作者： Jiarong Lu Xi Li Heli Zhang Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Task-oriented semantic communication effectively facilitates the completion of specific tasks by conveying users' interests. Nevertheless, when a base station conveys task-oriented semantic information to multiple users, these users exhibit both shared and distinct interests, which leads to increased redundancy and communication costs. To reduce transmission redundancy and cost, in this paper, a flexible and efficient multicast and unicast transmission scheme based on task-oriented semantic information is investigated for users with overlapping interests. Specifically, a joint energy-delay cost function is adopted to measure the communication cost caused by transmission redundancy, which facilitates a trade-off between energy consumption and transmission delay. Then, to minimize the network cost, this paper leverages the inherent benefits of common and private streams in rate splitting multiple access (RSMA) and design a semantic information allocation strategy. The RSMA common stream is further divided into the super-common and user-common streams. A semantic information allocation mechanism is established according to the common and private streams to flexibly allocate multiple semantic information. Then, semantic information selection on super-common stream, the proportional strategy of user-common stream, and rate splitting are jointly designed to minimize the network cost. The problem is a mixed-integer problem. Block coordinate descent and successive convex approximation technique are adopted to address it. An algorithm, namely SURA, is proposed for semantic information allocation in RSMA networks. Simulation results demonstrate the proposed SURA algorithm can adaptively allocate semantic information and reduce the network cost.

关键词： Costs Multicast algorithms Unicast Simulation Semantics Redundancy Minimization approximation algorithms Resource management Streams

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Spectrum and RFI Sensing using 2.4 GHz Digital Multi-Beam Circular Apertures with 360 FoV

Spectrum and RFI Sensing using 2.4 GHz Digital Multi-Beam Ci...

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IEEE Space Hardware and Radio Conference (SHaRC)

作者： A. Madanayake B. Thevathasan C. Rathnayaka U. Kumarasiri S. Sivasankar V. Ariyarathna Department of Electrical and Computer Engineering Florida International University Miami USA

ISBN: (数字)9798331507367

ISBN: (纸本)9798331507374

Radio spectrum monitoring across 360-degree field-of-view (FoV) is a fundamental requirement for RF situational awareness. RF awareness demands sensed intelligence on the direction of arrival, frequency occupancy, modulation type signal power, and bandwidth for each waveform present. This article includes an end-to-end design and realization of an array processor, consisting of sixteen uniformly spaced antennas in a circular array at 2.4 GHz, providing 360-degrees FoV using digital multi-beam beamforming. The algorithms encapsulate a low-complexity multi-beam circular array processor with each RF beam being processed by an approximate discrete Fourier transform (ADFT) algorithm. All directional sensing covered by sixteen simultaneous RF beams are 100 MHz in baseband. A fully parallel systolic-array processor for multi-beams is realized using Xilinx Virtex-6 FPGA and CASPER toolchain.

关键词： Radio frequency Array signal processing Bandwidth Parallel processing approximation algorithms Hardware Sensors Monitoring Antenna arrays Radiofrequency interference

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Bridging Security and Efficiency: Energy-Aware Clustering for Data Encryption in Smart Agriculture

Bridging Security and Efficiency: Energy-Aware Clustering fo...

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International Conference on Computing, Networking and Communications (ICNC)

作者： Huzaif Khan Lokesh Purohit Ali Jalooli Department of Computer Science California State University Dominguez Hills Carson CA USA

ISBN: (数字)9798331520960

ISBN: (纸本)9798331520977

Securing data communication in smart agriculture is essential to ensure data integrity and confidentiality in distributed sensor networks. While Cheon-Kim-Kim-Song (CKKS) homomorphic encryption offers strong security by allowing computation on encrypted data, its significant computational and energy overhead makes it unsuitable for energy-constrained devices like those in agricultural monitoring systems. We propose a novel framework to enable CKKS encryption in resource-limited settings through an optimized, energy-efficient clustering approach. By reducing the number of direct transmissions through data aggregation at cluster heads, our approach mitigates CKKS’s energy demands, making it feasible for low-power sensors. We formulate the clustering problem as an NP-hard optimization problem and present a real-time clustering algorithm that minimizes energy consumption while adapting to dynamic sensor energy levels. To evaluate our approach, we use the Gurobi optimizer to solve the problem to optimality and benchmark our algorithm’s performance. Results show that our solution achieves near-optimal energy efficiency, closely approximating the optimal solution while remaining computationally feasible for real-time applications. Additionally, our method improves packet delivery ratios and reduces end-to-end delay. This work provides a pathway for the practical implementation of advanced encryption schemes in energy-constrained environments.

关键词： Smart agriculture Energy consumption Heuristic algorithms Clustering algorithms Data aggregation approximation algorithms Energy efficiency Real-time systems Reliability Homomorphic encryption

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Variational Quantum Circuits for Molecular Classification Using Graph Neural Network

Variational Quantum Circuits for Molecular Classification Us...

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Quantum Communications, Networking, and Computing (QCNC), International Conference on

作者： Don Roosan Md Rahatul Ashakin Rubayat Khan Hasiba Mashed Khan Tiffany Khou Maria-Isabel Carnasciali Mohammad Rifat Haider School of Engineering and Computational Sciences Merrimack College USA Tekurai LLC San Antonio USA University of Nebraska Medical Center Omaha USA College of Pharmacy Western University of Health Sciences Pomona USA College of Public Health University of Georgia Athens USA

ISBN: (数字)9798331531591

ISBN: (纸本)9798331531607

The study explores the integration of quantum-enhanced feature mapping and optimization within Graph Neural Networks (GNNs) for molecular classification tasks. Utilizing IBM's Qiskit platform, we implement Variational Quantum Circuits (VQCs) to transform classical molecular features into quantum states, capturing complex correlations through quantum entanglement. Additionally, the Quantum Approximate Optimization Algorithm (QAOA) is employed for hyperparameter tuning to enhance model convergence and performance. Comparative analyses against traditional machine learning models demonstrate the superiority of the quantum-enhanced GNN, highlighting the potential of quantum computing in advancing molecular property prediction.

关键词： Quantum entanglement Computational modeling Transforms Quantum state approximation algorithms Graph neural networks Integrated circuit modeling Quantum circuit Optimization Tuning

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A Linear Search Algorithm for Resource Allocation in Frequency Domain Non-Orthogonal Multiple Access

A Linear Search Algorithm for Resource Allocation in Frequen...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Satoshi Denno Yuto Ohba Yafei Hou Faculty of Environmental Life Natural Science and Technology Okayama University Okayama Japan

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

This paper proposes a linear search algorithm for resource allocation in frequency domain non-orthogonal multiple access based on the low-density signature (LDS). Although the proposed linear search enables the non-orthogonal multiple access to achieve superior transmission performance, the proposed linear search makes the resource allocation implemented with lower and fixed computational complexity. The performance of the non-orthogonal access based on the proposed linear search is evaluated by computer simulation. The proposed linear search algorithm makes the non-orthogonal multiple access achieve a gain of about 6 dB at the BER of 10– 5 when the overloading ratio is set to 2. The complexity of the non-orthogonal access based on the proposed linear search algorithm is approximately half as much as that of the conventional low complexity resource allocation when the overloading ratio is 2, if the complexity is evaluated in terms of the number of additions.

关键词： Performance evaluation NOMA Frequency-domain analysis Computer simulation approximation algorithms Resource management Computational complexity Gain Consumer electronics Radio spectrum management

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