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2025 2nd International Conference on algorithms, software Engineering and Network Security (ASENS)

作者： Ruiqi Wang He Wang Huajun Shi The 32nd Research Institute of China Electronics Technology Group Corporation Shanghai China

ISBN: (数字)9798331520298

ISBN: (纸本)9798331520304

High-Resolution Remote Sensing Images often exhibit significant variations in object scales, with small objects being dense and having complex backgrounds. To address the issues of missed detection and false detection of small targets in remote sensing images, this paper proposes a small target detection method, SORA-DETR, which is an improvement based on RT-DETR. A lightweight small target feature fusion module, SPD-CSP Omnikernel Fusion Module (SOFM), is introduced. This module integrates low-level image features into the CNN-based Cross-scale Feature Fusion of RT-DETR to enhance the detection capability for small targets. Furthermore, C2f is used to replace the ResNet-18 in RT-DETR, while a Residual Attention Block (RAB) is applied to improve the flow of features through the network and suppress irrelevant features. On the DOTAv1.5 dataset, compared to RT-DETR r18, the mAP50 improved by 2.7% and 1.8%, while the number of parameters decreased by 24%, demonstrating significant improvements in both accuracy and lightweight performance.

关键词： Accuracy Attention mechanisms software algorithms Object detection Network security Feature extraction Real-time systems Remote sensing software engineering

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Computer Vision Methods in Applied Problems of Classifying Objects in Images

Computer Vision Methods in Applied Problems of Classifying O...

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Wave Electronics and its Application in Information and Telecommunication Systems (WECONF)

作者： Yuri Leokhin Timur Fatkhulin Konstantin Boitsov Moscow Technical University of Communications and Informatics Moscow Russia

ISBN: (数字)9781665465687

ISBN: (纸本)9781665465694

This research is devoted to solving problems at enterprises related to the classification of objects in various types of images, including pictures of pipes, using computer vision methods. The purpose of the research is to improve the quality of pipe classification using computer vision methods. The relevance of the work is due to the fact that control of all operations at the enterprise from the standpoint of quality and efficiency requires automation of both key procedures and individual (specific) situations that arise daily or on a specific order from time to time. At present, all enterprises and operational procedures are required to undergo automation using modern methods, engineering and various software products. Accounting for the number of pipes during their storage and transfer to production is one of the problems that require full or partial automation using computer vision technologies. The object of the research is computer vision methods used to classify objects in an image. The subject of the research is the performance indicators of computer vision methods used to classify pipes. The following research methods served as the methodological basis for the work: comparison, description, measurement, scientific abstraction method, as well as analysis and generalization. The conclusion provides the main findings obtained as a result of the research.

关键词： YOLO Computer vision Automation software algorithms Lightning Production software Classification algorithms Telecommunications Object recognition

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DISCOVER: A Cyberinfrastructure Testbed for Distributed Computing and Networking in Rural and Remote Environments

DISCOVER: A Cyberinfrastructure Testbed for Distributed Comp...

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IEEE International Symposium on World of Wireless Mobile and Multimedia Networks (WoWMoM)

作者： Alireza Ebrahimi Connor Gouin Sayed Pedram Haeri Boroujeni Juan Carlos Tique Rangel Tolunay Seyfi Truong X. Nghiem Abolfazl Razi Morgan Vigil-Hayes Paul L. Heinrich Fatemeh Afghah Department of Electrical and Computer Engineering Clemson University Clemson USA School of Computing Clemson University Clemson USA School of Informatics Computing and Cyber Systems Northern Arizona University Flagstaff Arizona USA Department of Electrical and Computer Engineering University of Central Florida Florida USA

ISBN: (数字)9798331538323

ISBN: (纸本)9798331538330

The Distributed Sensing and Computing Over Sparse Environments (DISCOVER) testbed is a pioneering cyberinfrastructure initiative designed to advance research in distributed computing and networking tailored to rural, remote, and sparsely populated regions. Supported by the National Science Foundation (NSF), DISCOVER integrates a network of configurable Internet-of-Things (IoT) nodes—including stationary sensors, drones, and terrestrial rovers—across three key sites: Northern Arizona University (NAU), Clemson University, and Navajo Technical University (NTU). This collaboration offers a unique platform to explore innovative algorithms and methodologies addressing the technical challenges of under-served areas, with an emphasis on environmental and civil disaster response. The testbed enables a wide range of experiments, such as regional-scale data collection, heterogeneous networked services, distributed artificial intelligence (AI), distributed multi-robot control, and communication-aware software for resource-constrained networks. An online portal enhances accessibility, allowing researchers to request resources, upload experimental code, and retrieve data, with pre-integrated deep learning models for applications like human posture detection, object detection, and wildfire detection. This paper outlines the testbed’s architecture, operational sites, supported experiment types, ongoing research efforts, and its educational and outreach impacts, highlighting its role in fostering scientific innovation.

关键词： Wireless communication Wireless sensor networks Technological innovation Wildfires software algorithms US Government Sensors Distributed computing Artificial intelligence Drones

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PatchFinder: Leveraging Visual Language Models for Accurate Information Retrieval Using Model Uncertainty

PatchFinder: Leveraging Visual Language Models for Accurate ...

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Roman Colman Minh Vu Manish Bhattarai Martin Ma Hari Viswanathan Daniel O'Malley Javier E. Santos Earth and Environmental Sciences Los Alamos National Laboratory Los Alamos NM USA Theoretical Division Los Alamos National Laboratory Los Alamos NM USA

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

For decades, corporations and governments have re-lied on scanned documents to record vast amounts of information. However, extracting this information is a slow and tedious process due to the sheer volume and complexity of these records. The rise of Vision Language Models (VLMs) presents a way to efficiently and accurately extract the information out of these documents. The current au-tomated workflow often requires a two-step approach in-volving the extraction of information using optical char-acter recognition software, and subsequent usage of large language models for processing this information. Unfor-tunately, these methods encounter significant challenges when dealing with noisy scanned documents, often requiring computationally expensive language models to handle high information density effectively. In this study, we propose PatchFinder, an algorithm that builds upon VLMs to improve information extraction. First, we devise a confidence-based score, called Patch Confi-dence, based on the Maximum Softmax Probability of the VLMs' output to measure the model's confidence in its predictions. Using this metric, PatchFinder determines a suitable patch size, partitions the input document into overlapping patches, and generates confidence-based predictions for the target information. Our experimental results show that PatchFinder, leveraging Phi-3v, a 4.2 billion parameter VLM, achieves an accuracy of 94% on our dataset of 190 noisy scanned documents, outperforming ChatGPT-4o by 18.5 percentage points.

关键词： Visualization Uncertainty Accuracy Computational modeling software algorithms Predictive models Information retrieval software Noise measurement Data mining

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Delay-Sensitive Task Pre-Migration in SDN-Based Computing Power Networks

Delay-Sensitive Task Pre-Migration in SDN-Based Computing Po...

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IEEE Conference on Wireless Communications and Networking

作者： Qi Shi Yuze Jin Wei Wang Chenhui Gu Zhaoyang Zhang College of Information Science and Electronic Engineering Zhejiang University Hangzhou P.R. China National (Hangzhou) Novel Internet Exchange Hangzhou P.R. China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The prevalence of computational-intensive tasks stimulate the emergence of Computing Power Networks (CPN), where software Defined Network (SDN) is utilized for flexible control. Considering the coexistence of in-execution tasks in the network and the arrival of emergency tasks, pre-migration is essential for reserving computing resources in advance, while it results in extra transmission cost in data plane and control cost in control plane. In this paper, we propose an analytical framework of task pre-migration policy in the SDN-based CPN to reduce system cost while meeting the delay budget. Specially, we consider the delay in SDN control plane induced by pre-migration flows, which involves $\ell_{0}-\mathbf{norm}$ and causes the problem non-convex and discontinuous. To overcome the challenges, we propose an iterative approximation algorithm to handle $\ell_{0}-\mathbf{norm}$ constraints, which transforms the original problem into a continuous function. Then we propose a dynamic programming based algorithm to minimize the approximation error, aiming at delay guarantee. The superiority of the proposed algorithm is verified through simulations, especially for heavy task offloading.

关键词： Energy consumption Costs Heuristic algorithms software algorithms Transforms Approximation algorithms Approximation error Iterative algorithms Delays software defined networking

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Digitalization Solutions for Automated Material Handling Systems in Semiconductor Smart Factories

Digitalization Solutions for Automated Material Handling Sys...

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China Semiconductor Technology International Conference (CSTIC)

作者： Ruiji Wang Semiconductor AMHS R&D Center Huaxin (Jiaxing) Intelligent Manufacturing Co. LTD Jiaxing China

ISBN: (数字)9798350362190

ISBN: (纸本)9798350362206

Automated Material Handling Systems (AMHS) are an essential component of semiconductor smart factories. Traditional AMHS vendors focus mainly on transport management and path calculation based on automated handling equipment. To realize the digital transformation of modern semiconductor fabs, HIMC has introduced a comprehensive AMHS digital solution tailored to the semiconductor smart factories. In addition to Material Control System (MCS), Multiple OHT Controller (MOC) and subsystem control software, the solution also incorporates intelligent order dispatching and path optimization modules developed through algorithms and artificial intelligence technologies. Additionally, it employs digital twin technology, 3D rendering techniques, and big data analytics to achieve the real-time synchronization of the physical and digital worlds by converting two-dimensional data into three-dimensional visualizations. Through the implementation of these digitalization solutions, AMHS can achieve enhanced automation, efficiency, and sustainability, thereby improving the productivity and competitiveness of semiconductor smart factories.

关键词： Automation Three-dimensional displays software algorithms Materials handling Rendering (computer graphics) software Real-time systems

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Intelligence Control Based Fault Detection in Transmission Lines

Intelligence Control Based Fault Detection in Transmission L...

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： Radhika Dora Dandaboina Veeranjaneyulu Tirupathi Keerthi Priya Kunchi Sunny Kumar Department of EEE Geethanjali College of Engineering and Technology Hyderabad Telangana

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Electrical Power System is very large, complex which is spread over a large area. A fault in a circuit disturbs normal operation of the system. Fault usually causes flow of excessive current, abnormal voltages. Detecting faults can help greatly in protection of power system. A modern solution for fault detection of power system is by implementing Wavelet Transform. A Wavelet Transform is a tool for analyzing the transient signals based on mathematical formula. Transient signals include temperature over voltages, temperature over currents etc., so this method is used to detect faults in power system by by low pass filter and high pass filter. Voltage signal is taken under fault conditions and being processed by using wavelet tool. If the energy calculated is greater than corresponding threshold value, then we can conclude that there is a fault disturbance in a line.

关键词： Wavelet transforms Power transmission lines Fault detection software algorithms Electrical fault detection Wavelet analysis software Power systems Transient analysis Voltage control

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A reinforcement learning-based metaheuristic algorithm for on-demand ride-pooling

A reinforcement learning-based metaheuristic algorithm for o...

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International Conference on Intelligent Environments (IE)

作者： Klavdiya Bochenina Laura Ruotsalainen Computer Science Department University of Helsinki Helsinki Finland

ISBN: (数字)9798350386790

ISBN: (纸本)9798350386806

In this paper, we propose a ride-pooling algorithm which selects the parameters of a heuristic dispatching procedure using reinforcement learning. This meta-heuristic approach is applicable for the selection of arbitrary parameters of a centralized dispatching algorithm and is aimed to find sequences of decisions to optimize target function over the simulation period, to capture dynamics of demand and supply in the ride-pooling system. A reinforcement learning environment is implemented in SUMO traffic simulation software. The experimental study compares the results of the metaheuristic with the strategies based on the exhaustive search and shows that the proposed approach is able to find a near-optimal solution with advantage of 2%-10% compared to baselines.

关键词： Heuristic algorithms Metaheuristics software algorithms Transportation Reinforcement learning Traffic control Dispatching

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Adapting Computational Skills for AI Integration

Adapting Computational Skills for AI Integration

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Conference on software Engineering Education & Training (CSEE&T)

作者： Hanya Elhashemy Harold Abelson Tilman Michaeli Technical University Of Munich Massachusetts Institute of Technology Munich Germany Massachusetts Institute of Technology Massachusetts USA Technical University Of Munich Munich Germany

ISBN: (数字)9798350378979

ISBN: (纸本)9798350378986

In today's data-driven world, the importance of data literacy is paramount. However, software engineering education has not adequately addressed integrating comprehensive data science curricula, leaving students ill-equipped for the future of artificial intelligence (AI), which is built on the foundations of data science. This gap is exacerbated by the lack of tailored courses and the intimidating nature of existing tools for begin-ners. Consequently, students often miss out on essential skills like data cleanup, real-world application of machine learning (ML) algorithms, and the integration of big data in software products. This paper addresses these challenges by proposing a novel approach to applied data science for software engineering students. We argue for a shift from traditional algorithm-focused teaching to a curriculum emphasizing real-world problem-solving, lever-aging data science techniques. By empowering students to define and tackle their own data-driven projects, we aim to increase motivation, enhance data literacy, and instill a data-thinking mindset in future software engineers to prepare them for the AI world. Overall, this paper contributes to the advancement of software engineering education for young learners by offering a comprehensive framework, the data action educational framework (DAEF), and a data science toolkit that enables DAEF by empowering learners to create original data-driven mobile apps.

关键词： Machine learning algorithms Education software algorithms Machine learning Data science software Mobile applications

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A Novel Minimum Attribute Reduction Algorithm Based on Hierarchical Elitist Role Model Combining Competitive and Cooperative Co-evolution

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Chinese Journal of Electronics 2023年第4期22卷 677-682页

作者： DING Weiping WANG Jiandong GUAN Zhijin College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China School of Computer Science and Technology Nantong University Nantong China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

Minimum attribute reduction in rough set theory is an NP-hard problem, which is difficult to use traditional evolution methods to solve. In this paper, a novel and efficient minimum Attribute reduction algorithm (named HERCo 2 AR) based on Hierarchical elitist role model combining competitive and cooperative coevolution is proposed. Through such an iterative process of competitive and cooperative co-evolution, the various subpopulations are better optimized by different elitists, and reasonable decomposition of interacting attribute sets can co-adapt to emerge due to the evolutionary pressure of hierarchical elitist role. The hierarchical elitist role model is very effective in the protection and promotion of outstanding individuals, and it can accelerate to direct the global optimal attribute reduction. Experimental results demonstrate that HERCo 2 AR achieves the better feasibility and effectiveness than existing state-of-the-art attribute reduction algorithms, and the quality of the global optimal solution can be significantly improved as well.

关键词： Technological innovation NP-hard problem Education software algorithms Rough sets software Iterative methods

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