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Enhanced Route Optimization: Incorporating Road Safety Factors for Optimal Path Selection

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Enhanced Route Optimization: Incorporating Road Safety Facto...

International Conference on Power, Energy, Control and Transmission Systems (ICPECTS)

作者： R Subash Banu Priya Prathaban Ganeshkumar N Gopinathan S Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chennai India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai India Department of Computer Science and Engineering GITAM School of Technology Gandhi Institute of Technology and Management GITAM (Deemed to be University) Bengaluru Karnataka India Department of Artificial Intelligence and Data Science Sri Sai Ram Institute of Technology Chennai India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508852

Efficient route optimization is a crucial concern in modern transportation systems, influencing travel time, resource consumption, and user satisfaction. By empowering users to select their priority parameters, such as safety or efficiency, this project enables travellers to make eco-conscious choices and select routes that minimize their environmental footprint. Real-time data integration ensures users can bypass traffic jams and accidents, reducing emissions and fuel consumption. Furthermore, the focus on safety factors helps prevent accidents, thereby fostering safer road environments. Existing systems often fall short, providing limited customization and overlooking safety and sustainability priorities. To bridge these gaps, this project introduces an advanced route planning system that empowers users to select their priority parameters, integrates real-time data for up-to- the-minute updates, and prioritizes road safety. It introduces a pioneering approach that enhances the Dijkstra Algorithm by integrating real-time accident rate and travel time data into the route optimization process. By considering these additional factors, the algorithm produces routes that not only minimize distance but also prioritize safety and time efficiency in dynamic real-world conditions. The algorithm's multi-factor optimization considers distance, accident rates, and travel times, enabling the creation of routes that balance these criteria and offer improved decision-making for users through a user- friendly interface. The algorithm's dynamic adaptability, multi- factor optimization, and practical implementation showcase its potential to revolutionize how routes are planned, leading to safer, faster, and more informed travel experiences.

关键词： Analytical models Adaptation models Heuristic algorithms Real-time systems Road safety Safety Planning Optimization Public transportation Accidents

A Modified EACOP Implementation for Real-Parameter Single Objective Optimization Problems

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A Modified EACOP Implementation for Real-Parameter Single Ob...

Congress on Evolutionary Computation

作者： Andrea Tangherloni Vasco Coelho Francesca M. Buffa Paolo Cazzaniga Department of Computing Sciences Bocconi Institute for Data Science and Analytics Bocconi University Milan Italy Department of Informatics Systems and Communication University of Milano-Bicocca Milan Italy Department of Computing Sciences Bocconi University Milan Italy Bocconi Institute for Data Science and Analytics Department of Human and Social Sciences University of Bergamo Bergamo Italy

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

Evolutionary algorithms are effective techniques for optimizing non-linear and complex high-dimensional problems. However, most of them require a precise fine-tuning of their functioning settings to achieve satisfactory results. In this work, we propose a modified version of an evolutionary approach called the Evolutionary Algorithm for COmplex-process oPtimization (EACOP), designed to have a limited number of hyper-parameters. The base version of EACOP (bEACOP) combines different strategies, including the scatter search methodology, local searches, and a novel combination method based on path relinking to balance the exploration and exploitation phases. Our improved version (iEACOP) intensifies the exploration phase to escape from suboptimal search space areas where, on the contrary, bEACOP gets stuck. Our results show that iEACOP outperforms bEACOP on 27 out of 29 CEC 2017 test suite benchmark functions, exhibiting comparable performance against the three best algorithms of the CEC 2017 competition on single-objective bound-constrained real-parameter numerical optimization. The source code of bEACOP and iEACOP will be made publicly available on GitHub upon acceptance.

关键词： Parameter estimation Limiting Source coding Systems biology Evolutionary computation Benchmark testing Search problems

Performance evaluation of conditional handover in 5G systems under fading scenario

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arXiv 2024年

作者： Deb, Souvik Rathod, Megh Balamurugan, Rishi Ghosh, Shankar K. Singh, Rajeev K. Sanyal, Samriddha Department of Computer Science and Engineering Shiv Nadar Institution of Eminence Delhi NCR India Advanced Computing and Microelectronics Unit Indian Statistical Institute Kolkata India School of Computing and Data Science FLAME University Pune India

To enhance the handover performance in fifth generation (5G) cellular systems, conditional handover (CHO) has been evolved as a promising solution. Unlike A3 based handover where handover execution is certain after receiving handover command from the serving access network, in CHO, handover execution is conditional on the RSRP measurements from both current and target access networks, as well as on mobility parameters such as preparation and execution offsets. Analytic evaluation of conditional handover performance is unprecedented in literature. In this work, handover performance of CHO has been carried out in terms of handover latency, handover packet loss and handover failure probability. A Markov model accounting the effect of different mobility parameters (e.g., execution offset, preparation offset, time-to-preparation and time-to-execution), UE velocity and channel fading characteristics;has been proposed to characterize handover failure. Results obtained from the analytic model has been validated against extensive simulation results. Our study reveal that optimal configuration of Oexec, Oprep, Texec and Tprep is actually conditional on underlying UE velocity and fading characteristics. This study will be helpful for the mobile operators to choose appropriate thresholds of the mobility parameters under different channel condition and UE velocities. Copyright © 2024, The Authors. All rights reserved.

关键词： Packet loss

Correction to: Consistent Prompt Tuning for Generalized Category Discovery (International Journal of Computer Vision, (2025), 10.1007/s11263-024-02343-w)

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International Journal of Computer Vision 2025年 1-10页

作者： Yang, Muli Yin, Jie Gu, Yanan Deng, Cheng Zhang, Hanwang Zhu, Hongyuan Singapore Singapore School of Electronic Engineering Xidian University Xi’an China Norinco Group Testing and Research Institute Xi’an China College of Computing and Data Science Nanyang Technological University Singapore Singapore

The original publication of this article was published with several errors and has been corrected as follows. In the Section 4.3.1, the hyperlink for the Section 5 citation was missing and has been included. In Table 6, the significance ‘Reimplemented using CLIP’s image encoder’ was incorrectly provided for the method ‘CPT (Ours) and has been removed. In Table 12, the method name was incorrectly listed as ‘CLIP-GC’ but should read as ‘CLIP-GCD’. Additionally, the significance ‘Reimplemented using (Open/Meta) CLIP’s image encoder’ was missing for the method ‘CLIP-GCD’ and has been included. In Table 15, the incorrect significance ‘Reimplemented using CLIP’s image encoder’ was provided for the methods GCD (2022a), SimGCD (2023) and GPC (2023). This should be removed and the correct significance ‘Training with estimated category number’ has been included. In Table 22, the species "andrias acropora acuminata (staghorn coral)" was missing and has been included before the species "andrias davidianus (chinese giant salamander)." For completeness and transparency, the old incorrect and the corrected versions of Tables are displayed below. The original article has been corrected. Incorrect Table 6: GCD accuracy (%) in the customized setting on NICO dataset dataset → NICO-10 NICO-33 NICO-100 Method ↓ All Old New All Old New All Old New GCD (2022a) 95.1 97.3 94.0 38.6 58.4 25.1 48.0 67.0 39.1 PromptCAL (2023b) 89.6 80.9 93.6 30.5 42.4 22.5 45.3 53.4 41.5 SimGCD (2023) 97.3 97.0 97.4 45.9 67.7 46.3 65.1 37.4 PIM (2023) 96.4 97.2 96.0 42.2 67.6 24.9 49.5 67.5 41.0 GCD† 98.0 39.3 65.0 21.8 51.6 54.5 PromptCAL† 97.5 97.1 29.2 41.8 20.6 48.9 64.4 41.7 SimGCD† 97.5 46.6 29.1 69.4 44.6 PIM† 97.5 97.0 71.8 50.0 39.2 CPT (Ours)† 98.5 Bold value indicates the best results and underlined value indicates the second best results †Reimplemented using CLIP’s image encoder Corrected Table 6: GCD accuracy (%) in the customized setting on NICO dataset dataset → NICO-10 NICO-33 NICO-100 Method ↓

关键词：

Parks: A Doubly Infinite Family of NP-Complete Puzzles and Generalizations of A002464

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arXiv 2024年

作者： Minevich, Igor Cunningham, Gabe Karan, K. Aditya Gyllinsky, Joshua V. The School of Computing & Data Science Wentworth Institute of Technology BostonMA02115 United States EAi Technologies Vienna VA United States

The Parks Puzzle is a paper-and-pencil puzzle game that is classically played on a square grid with different colored regions (the parks). The player needs to place a certain number of "trees" in each row, column, and park such that none are adjacent, even diagonally. We define a doubly-infinite family of such puzzles, the (c, r)-tree Parks puzzles, where there need be c trees per column and r per row. We then prove that for each c and r the set of (c, r)-tree puzzles is NP-complete. For each c and r, there is a sequence of possible board sizes m × n, and the number of possible puzzle solutions for these board sizes is a doubly-infinite generalization of OEIS sequence A002464, which itself describes the case c = r = 1. This connects the Parks puzzle to chess-based puzzle problems, as the sequence describes the number of ways to place non-attacking kings on a chessboard so that there is exactly one in each column and row (i.e. to place non-attacking dragon kings in shogi). These findings add yet another puzzle to the set of chess puzzles and expands the list of known NP-complete problems described. © 2024, CC BY.

关键词： Trees (mathematics)

Dynamic Formation Tracking of Multi-agent Systems with Bounded Unknown Leader Input 5th

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Dynamic Formation Tracking of Multi-agent Systems with Bou...

5th Chinese Conference on Swarm Intelligence and Cooperative Control, CCSICC 2021

作者： Su, Piaoyi Dong, Xiwang Zhao, Qin Li, Qingdong Ren, Zhang School of Automation Science and Electronic Engineering Science and Technology on Aircraft Control Laboratory Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Beijing Institute of Control and Electronics Technology Beijing100038 China

ISBN: (纸本)9789811939976

This literature investigate the dynamic formation tracking problem of multiple Lagrange systems with linear nonautonomous leader over directed topology. In order to estimate the leader system, a distributed control scheme is proposed firstly by constructing adaptive distributed observer. Secondly, this paper design a nonlinear function to decrease the influence from the leader’s bounded unknown input. Then, the dynamic formation tracking problem of multiple Euler-Lagrange systems with nonlinear uncertainties and disturbances is solved by distributed tracking algorithm. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Multi agent systems

Multi-Stage Bidirectional Cross-Attention Model for Predicting Prognosis in Multiple Peritoneum Lesions with Clinical Information 22

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Multi-Stage Bidirectional Cross-Attention Model for Predicti...

22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Li, Haoshen Cai, Jieyuan Wei, Yiyuan Chen, Zifan Chen, Heyun Zhao, Jie Shi, Yanjie Dong, Bin Tang, Lei Zhang, Xiaotian Zhang, Li Center for Data Science Peking University China Peking University Cancer Hospital & Institute China Peking University National Engineering Laboratory for Big Data Analysis and Applications China Beijing International Center for Mathematical Research Peking University China Center for Machine Learning Research Peking University China Peking University Changsha Institute for Computing and Digital Economy China

ISBN: (纸本)9798331520526

Peritoneal metastasis occurs when cancer cells spread from the primary tumor to the peritoneum, leading to morphological alterations that significantly impact patient survival. The specific changes across multiple peritoneal sites can effectively indicate a patient's risk level and prognosis. However, the considerable variation in peritoneal shape, size, and location poses significant challenges for feature extraction and prognostic analysis. Traditional multi-instance learning approaches typically fuse instance features extracted by the backbone network at the final stage, but their lack of intermediate interaction limits the ability to capture both common and distinct features. To address this limitation, we propose a Multi-Stage BiDirectional Cross-Attention framework (MSBDCA). Our approach enhances feature extraction and prognostic analysis by facilitating interaction among different peritoneal instances and incorporating clinical information. Experimental comparisons with five baseline models demonstrate our method's superiority, achieving improvements of 8.7% in Area Under Curve (AUC) and 5.9% in Concordance Index (C_index). These results suggest a promising direction for survival and prognostic analysis using peritoneal lesions. Our code is available at https://***/HaoshenLi/MSBDCA. © 2025 IEEE.

关键词： deep learning multi-instance learning peritoneal metastasis analysis Survival analysis

GPSFormer: A Global Perception and Local Structure Fitting-based Transformer for Point Cloud Understanding

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arXiv 2024年

作者： Wang, Changshuo Wu, Meiqing Lam, Siew-Kei Ning, Xin Yu, Shangshu Wang, Ruiping Li, Weijun Srikanthan, Thambipillai Nanyang Technological University Singapore College of Computing and Data Science Nanyang Technological University Singapore Institute of Semiconductors Chinese Academy of Sciences China

Despite the significant advancements in pre-training methods for point cloud understanding, directly capturing intricate shape information from irregular point clouds without reliance on external data remains a formidable challenge. To address this problem, we propose GPSFormer, an innovative Global Perception and Local Structure Fitting-based Transformer, which learns detailed shape information from point clouds with remarkable precision. The core of GPSFormer is the Global Perception Module (GPM) and the Local Structure Fitting Convolution (LSFConv). Specifically, GPM utilizes Adaptive Deformable Graph Convolution (ADGConv) to identify short-range dependencies among similar features in the feature space and employs Multi-Head Attention (MHA) to learn long-range dependencies across all positions within the feature space, ultimately enabling flexible learning of contextual representations. Inspired by Taylor series, we design LSFConv, which learns both low-order fundamental and high-order refinement information from explicitly encoded local geometric structures. Integrating the GPM and LSFConv as fundamental components, we construct GPSFormer, a cutting-edge Transformer that effectively captures global and local structures of point clouds. Extensive experiments validate GPSFormer's effectiveness in three point cloud tasks: shape classification, part segmentation, and few-shot learning. The code of GPSFormer is available at https://***/changshuowang/GPSFormer. © 2024, CC BY-NC-SA.

关键词： Convolution

Towards Harmless Rawlsian Fairness Regardless of Demographic Prior

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arXiv 2024年

作者： Wang, Xuanqian Li, Jing Tsang, Ivor W. Ong, Yew-Soon School of Computer Science and Engineering Beihang University China Institute of High-Performance Computing Agency for Science Technology and Research Singapore Centre for Frontier AI Research Agency for Science Technology and Research Singapore College of Computing and Data Science Nanyang Technological University Singapore

Due to privacy and security concerns, recent advancements in group fairness advocate for model training regardless of demographic information. However, most methods still require prior knowledge of demographics. In this study, we explore the potential for achieving fairness without compromising its utility when no prior demographics are provided to the training set, namely harmless Rawlsian fairness. We ascertain that such a fairness requirement with no prior demographic information essential promotes training losses to exhibit a Dirac delta distribution. To this end, we propose a simple but effective method named VFair to minimize the variance of training losses inside the optimal set of empirical losses. This problem is then optimized by a tailored dynamic update approach that operates in both loss and gradient dimensions, directing the model towards relatively fairer solutions while preserving its intact utility. Our experimental findings indicate that regression tasks, which are relatively unexplored from literature, can achieve significant fairness improvement through VFair regardless of any prior, whereas classification tasks usually do not because of their quantized utility measurements. The implementation of our method is publicly available at https://***/wxqpxw/VFair. © 2024, CC BY.

关键词： Differential privacy