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The Fagnano Triangle Patrolling Problem (Extended Abstract) 1

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25th International Symposium on Stabilization, Safety, and Security of Distributed Systems, SSS 2023

作者： Georgiou, Konstantinos Kundu, Somnath Pralat, Pawel Department of Mathematics Toronto Metropolitan University TorontoON Canada Department of Computer Science Toronto Metropolitan University TorontoON Canada

ISBN: (数字)9783031442742

ISBN: (纸本)9783031442735

We investigate a combinatorial optimization problem that involves patrolling the edges of an acute triangle using a unit-speed agent. The goal is to minimize the maximum (1-gap) idle time of any edge, which is defined as the time gap between consecutive visits to that edge. This problem has roots in a centuries-old optimization problem posed by Fagnano in 1775, who sought to determine the inscribed triangle of an acute triangle with the minimum perimeter. It is well-known that the orthic triangle, giving rise to a periodic and cyclic trajectory obeying the laws of geometric optics, is the optimal solution to Fagnano’s problem. Such trajectories are known as Fagnano orbits, or more generally as billiard trajectories. We demonstrate that the orthic triangle is also an optimal solution to the patrolling problem. Our main contributions pertain to new connections between billiard trajectories and optimal patrolling schedules in combinatorial optimization. In particular, as an artifact of our arguments, we introduce a novel 2-gap patrolling problem that seeks to minimize the visitation time of objects every three visits. We prove that there exist infinitely many well-structured billiard-type optimal trajectories for this problem, including the orthic trajectory, which has the special property of minimizing the visitation time gap between any two consecutively visited edges. Complementary to that, we also examine the cost of dynamic, sub-optimal trajectories to the 1-gap patrolling optimization problem. These trajectories result from a greedy algorithm and can be implemented by a computationally primitive mobile agent. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Trajectories

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Mining Historical Multi-behavior Sequential Patterns for e-Commerce Recommendation 26th

Mining Historical Multi-behavior Sequential Patterns for e-...

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26th International Conference on Information Integration and Web Intelligence, iiWAS 2024

作者： Bandreddy, S. Ezeife, C.I. Gidado, Abdulrauf School of Computer Science University of Windsor 401 Sunset Avenue WindsorONN9B3P4 Canada

ISBN: (纸本)9783031780929

Existing recommender systems like HSPRec19 and HPCRec18 improve recommendation accuracy but do not further capture item-level user multi-behavior interactions, such as clicks, likes, carts, and purchases. While DCABRec21 and MBHT22 attempt to use user-item multi-behavior interactions, they are not sequential recommendation systems. This paper proposes a Multi-behavior Sequential Pattern Recommendation System (MBSPRec), an extension of the HSPRec19 system, which incorporates users’ multi-behavior patterns to enhance the quality of the user-item rating matrix input to recommendation system, reducing matrix data sparsity, and thereby improving recommendation accuracy. MBSPRec creates a Multi-behavior Sequential Database for user behavior types, ApproxMap Algorithm mines frequent sequential rules for generation input rating data for new items without previous user interaction. Experimental results of MBSPRec on publicly available datasets in comparison with existing systems of HSPRec19, MBHT22, and DCABRec21 show improved recommendation accuracy in terms of Precision, Recall, and Mean Reciprocal Rank, therefore reducing user-item rating data sparsity. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Collaborative filtering

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A New Transformer-Based Approach for Text Detection in Shaky and Non-shaky Day-Night Video 1

引用

7th Asian Conference on Pattern Recognition, ACPR 2023

作者： Halder, Arnab Shivakumara, Palaiahnakote Pal, Umapada Lu, Tong Blumenstein, Michael Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Faculty of Computer Science and Information Technology University of Malaya Kula Lumpur Malaysia Nanjing University Nanjing China University of Technology Sydney Sydney Australia

ISBN: (数字)9783031476372

ISBN: (纸本)9783031476365

Text detection in shaky and non-shaky videos is challenging because of variations caused by day and night videos. In addition, moving objects, vehicles, and humans in the video make the text detection problems more challenging in contrast to text detection in normal natural scene images. Motivated by the capacity of the transformer, we propose a new transformer-based approach for detecting text in both shaky and non-shaky day-night videos. To reduce the effect of object movement, poor quality, and other challenges mentioned above, the proposed work explores temporal frames for obtaining activation frames based on similarity and dissimilarity measures. For estimating similarity and dissimilarity, our method extracts luminance, contrast, and structural features. The activation frames are fed to the transformer which comprises an encoder, decoder, and feed-forward network for text detection in shaky and non-shaky day-night video. Since it is the first work, we create our own dataset for experimentation. To show the effectiveness of the proposed method, experiments are conducted on a standard dataset called the ICDAR-2015 video dataset. The results on our dataset and standard dataset show that the proposed model is superior to state-of-the-art methods in terms of recall, precision, and F-measure. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Chemical activation

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Long-Tailed Hashing with Wasserstein Quantization 27th

Long-Tailed Hashing with Wasserstein Quantization

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Fu, Zujun Lai, Hanjiang Pan, Yan School of Computer Science and Engineering Sun Yat-Sen University Guangzhou510006 China

ISBN: (纸本)9783031783043

Long-tailed hashing is to learn hash functions in unbalanced distribution datasets to represent images as binary hash codes for fast and accurate image retrieval. In contrast to balanced distribution datasets, unbalanced distributions are more common in the real world. However, Existing long-tailed hashing methods only focus on how to better learn from unbalanced datasets to improve performance, without giving good consideration to quantization error, which is very crucial in hash learning. In this paper, we propose a simple but efficient quantization method for long-tailed hashing. Specifically, to address the lack of samples in the tail classes, we take a uniform discrete distribution as the optimal target distribution. We use the Sliced Wasserstein distance as a measure of distribution distance. It makes good use of the discrete nature of hash functions and has low computational complexity. Then we formulate the optimization objective of the quantization error as minimizing the distance between the output of the learned hash function and this objective distribution, which can be added as an additional term of the loss function to existing long-tailed hashing methods. We conduct experiments on two long-tailed datasets, and the results show that our proposed method greatly improves the performance of existing long-tailed hashing methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Hash functions

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OpenVenus: An Open Service Interface for HPC Environment Based on SLURM 1

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7th International Conference on Smart Computing and Communication, SmartCom 2022

作者： Wan, Meng Cao, Rongqiang Wang, Yangang Wang, Jue Li, Kai Wang, Xiaoguang Yang, Qinmeng Computer Network Information Center Chinese Academy of Sciences Beijing100083 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (数字)9783031281242

ISBN: (纸本)9783031281235

With the emergence of more and more "AI + Field + HPC" applications, it is urgent to solve the problem of scheduling and management of High-Performance Computing (HPC) resources, as well as the fast and efficient "cloud service" of HPC applications. This engineering problem is particularly critical because it affects the progress of scientific research, the development period of the research platform, and the learning cost of scientists. To solve the problem, a set of reusable life cycle processes for HPC resources are designed. Based on the life cycle, we propose an open service interface based on HPC, which reduces the startup time under multiple refreshes and abnormal retries by using the mode of contention lock. The active interruption of users is a typical scenario in the startup phase. Furthermore, a read-write strategy with an overlay based on Singularity is implemented to save storage space and improve running speed. In order to evaluate the serviceability and performance of the proposed interface, we deploy the service on the Venus platform and make a startup comparison experiment. In addition, the reduction of storage for 100 users is also tested. The experimental results show that under the HPC environment with SLURM, the proposed open-service interface can effectively shorten 46% startup time of applications and services and reduce 25% storage at least for each user of the Venus platform. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： computer software reusability

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ICPR 2024 Competition on Multiple Sclerosis Lesion Segmentation—Methods and Results 27th

ICPR 2024 Competition on Multiple Sclerosis Lesion Segmenta...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Rondinella, Alessia Guarnera, Francesco Crispino, Elena Russo, Giulia Di Lorenzo, Clara Maimone, Davide Pappalardo, Francesco Battiato, Sebastiano Department of Mathematics and Computer Science University of Catania Catania Italy Department of Biomedical and Biotechnological Sciences University of Catania Catania Italy Department of Drug and Health Sciences University of Catania Catania Italy UOC Radiologia ARNAS Garibaldi Catania Italy Centro Sclerosi Multipla UOC Neurologia con Stroke Unit Azienda Ospedaliera per l’Emergenza Cannizzaro Catania Italy

ISBN: (纸本)9783031801389

This report summarizes the outcomes of the ICPR 2024 Competition on Multiple Sclerosis Lesion Segmentation (MSLesSeg). The competition aimed to develop methods capable of automatically segmenting multiple sclerosis lesions in MRI scans. Participants were provided with a novel annotated dataset comprising a heterogeneous cohort of MS patients, featuring both baseline and follow-up MRI scans acquired at different hospitals. MSLesSeg focuses on developing algorithms that can independently segment multiple sclerosis lesions of an unexamined cohort of patients. This segmentation approach aims to overcome current benchmarks by eliminating user interaction and ensuring robust lesion detection at different timepoints, encouraging innovation and promoting methodological advances. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Smartphone Indoor Fusion Localization with Trust Region-Based Magnetic Matching 18th

Smartphone Indoor Fusion Localization with Trust Region-Bas...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Zhu, Yu Jia, Yan Zou, Kaiyi Niu, Xiaoguang School of Computer Wuhan University Whuhan430072 China Cloud Network Operations Department China Telecom Group Co. Ltd. Beijing China

ISBN: (纸本)9783031714634

Magnetic indoor localization has attracted great attention in recent researches because of its advantage of not requiring additional equipment. However, existing magnetic matching methods have limited stability and accuracy due to insufficient consideration of gradient non-convergence and local optimal solution problem during the gradient descent iteration process. Moreover, in areas with inadequate magnetic features, the magnetic localization can be unreliable, leading to significant errors in certain regions. Therefore, we design a magnetic matching method using the trust region to adjust the utilization of gradient information during the matching process, dynamically balancing convergence efficiency and accuracy. By the way, we propose a fusion localization method FTRM, which enhances the robustness and accuracy of localization by determining the fusion weights of magnetic matching and pedestrian dead reckoning in the localization system through uncertainty, leading to more stable and accurate localization. The experiments show that the fusion localization system attain average accuracy of 0.545 m in real-world scenarios, achieve a 17.3% improvement. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Gradient methods

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LLMSecCode: Evaluating Large Language Models for Secure Coding 8th

LLMSecCode: Evaluating Large Language Models for Secure Cod...

引用

8th International Symposium on Cyber Security, Cryptology, and Machine Learning, CSCML 2024

作者： Rydén, Anton Näslund, Erik Schiller, Elad Michael Almgren, Magnus Computer Science and Engineering Chalmers University of Technology Gothenburg41296 Sweden

ISBN: (纸本)9783031769337

The rapid deployment of Large Language Models (LLMs) requires careful consideration of their effect on cybersecurity. Our work aims to improve the selection process of LLMs that are suitable for facilitating secure coding (SC). This raises challenging research questions, such as (RQ1) Which functionality can streamline the LLM evaluation? (RQ2) What should the evaluation measure? (RQ3) How to attest that the evaluation process is impartial? To address these questions, we introduce LLMSecCode, an open-source evaluation framework designed to assess LLM SC capabilities objectively. We validate the LLMSecCode implementation through experiments. We find a 10% and 9% difference in performance when varying parameters and prompts, respectively. We also compare some results to reliable external actors, where our results show a 5% difference. We strive to ensure the ease of use of our open-source framework and encourage further development by external actors. With LLMSecCode, we hope to encourage the standardization and benchmarking of LLMs’ capabilities in security-oriented code and tasks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Open systems

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Enhancing Freight Auditing Efficiency: Leveraging Hadoop MapReduce for Logistic Audit Assist 1

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Francis, Navya Ali, Anooja School of Computer Science and Engineering REVA University Bangalore India Department of Computer Science Kristu Jayanti College Bangalore India

ISBN: (数字)9789819789467

ISBN: (纸本)9789819789450

The logistics sector serves as a vital artery in global trade, with freight forwarders facilitating the movement of goods from source to destination. However, the accuracy of freight billing remains a persistent challenge, attributed to factors like tariff rate fluctuations, environmental variables, and fuel costs. Manual auditing of freight bills is prone to errors and inefficiencies, potentially leading to overpayments by organizations. This paper introduces a solution named Logistic Audit Assistance, which leverages the Hadoop MapReduce framework to streamline the auditing process for freight bills. By harnessing the capabilities of big data processing and analysis offered by Hadoop, Logistic Audit Assistance aims to deliver reliable, accurate, and consistent auditing outcomes, empowering stakeholders to make well-informed decisions and optimize future logistics operations. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Auditing efficiency Freight billing Freight forwarders Hadoop MapReduce Logistics Stakeholder empowerment

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Detection and Tracking of Pinus Radiata Catkins 1

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37th International Conference on Image and Vision Computing New Zealand, IVCNZ 2022

作者： Song, Eric Schofield, Sam Green, Richard Computer Science and Software Engineering University of Canterbury Christchurch New Zealand

ISBN: (数字)9783031258251

ISBN: (纸本)9783031258244

Pinus Radiata trees form pollen-producing catkins that can be harvested for pharmaceutical uses. Unmanned Aerial Vehicles (UAVs) may be well suited to the task of autonomously harvesting these catkins. We propose a method to reliably detect and track P. Radiata catkins in three dimensions that can be used for real-time guidance of a UAV. The method applies the YOLOv5 deep learning algorithm to detect catkins in the X-Y plane. A novel optimisation of the MeanShift algorithm is utilised to assist existing contour detection algorithms in segmenting individual catkins in the Z plane. A Kanade-Lucas-Tomasi tracker was used with RANSAC for accurate frame-to-frame tracking. The method achieved a Mean Average Precision of 0.87 on images taken at a commercial pine pollen farm. The method detected the depth of catkins at distances of up to 1200 mm to an accuracy of 2 mm, or 8 mm for occluded catkins. Detected catkins can be reliably tracked at speeds of 1ms. An average frame rate of 22 frames per second was achieved on an Intel i5 CPU, with the Meanshift optimisation performing up to 41 times faster than existing implementations. These results indicate that the proposed method could be used to successfully assist in the automated harvesting of P. Radiata catkins. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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