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检索条件"任意字段=Israel Conference on Computer Systems and Software Engineering"

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Research on the Collaborative Mechanism of P4Runtime and SAI in White Box Switches Based on the SONiC System 5

Research on the Collaborative Mechanism of P4Runtime and SAI...

引用

5th IEEE International conference on Power, Electronics and computer Applications, ICPECA 2025

作者： Pan, Zhuyu Wang, Hongchao Yan, Chen Zhang, Zishi School of Electronic and Information Engineering Beijing Jiaotong University Beijing China

ISBN: (纸本)9798331533694

With the advancement of software-Defined Networking (SDN) technology, network programmability and flexibility have been significantly enhanced. However, effectively integrating traditional network systems with emerging programmable platforms remains a critical research challenge. This paper proposes a fusion architecture based on the P4Runtime protocol to achieve deep integration of SONiC white box switches and the P4 platform. By introducing SDN programmability, we design and implement a hybrid model that enables traditional network architectures to achieve advanced functionalities with the assistance of external SDN controllers. This paper provides a detailed discussion of the architecture design and implementation process, including P4 code development, compilation, target configuration file generation, and interaction mechanisms between the control plane and the data plane. Furthermore, we validate the applicability and performance of the architecture in various scenarios, offering robust support for future research and applications in programmable networks. © 2025 IEEE.

关键词： Internet protocols

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Knowledge Graph of Unmanned Aerial Vehicle Reconnaissance Research 24

Knowledge Graph of Unmanned Aerial Vehicle Reconnaissance Re...

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3rd International conference on Signal Processing, computer Networks and Communications, SPCNC 2024

作者： Zhang, Xiaodong Qin, Xi Cai, Juesong University of Information Engineering Henan Zhengzhou China

ISBN: (纸本)9798400710834

The role of unmanned aerial vehicles (UAVs) in modern warfare is becoming increasingly significant, with the potential to disrupt the rules of warfare and change the form of conflict. This has garnered high attention from major military nations around the world. UAV reconnaissance is one of the key research directions within UAV studies. This paper employs quantitative and visual methods to analyze the current state and developmental trends within the field of UAV reconnaissance. By utilizing the visualization software CiteSpace and extracting relevant literature from the Web of Science Core Collection database, this study leverages bibliometric analysis and scientific mapping techniques to explore domestic and international attention, research dynamics, development contexts, research hotspots, and trends in this field. The findings reveal that Collaborative Combat, Mission Planning, Cooperative Reconnaissance, Task Analysis, Remote Sensing, and Deep Reinforcement Learning are research hotspots in this domain. Moreover, the integration of UAVs with emerging technologies such as artificial intelligence is anticipated to become a potential research trend in the future, enhancing the operational effectiveness and autonomy of UAV systems. © 2024 Copyright held by the owner/author(s).

关键词： Knowledge graph

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Towards a Probabilistic Framework for Analyzing and Improving LLM-Enabled software 18

Towards a Probabilistic Framework for Analyzing and Improvin...

引用

18th IEEE International conference on software Testing, Verification and Validation Workshops, ICSTW 2025

作者： Baldonado, Juan Manuel Bonomo-Braberman, Flavia Braberman, Victor A. Universidad de Buenos Aires ICC UBA/CONICET and DC FCEN Buenos Aires Argentina

ISBN: (纸本)9798331534677

Ensuring the reliability and verifiability of large language model (LLM)-enabled systems remains a significant challenge in software engineering. We propose a probabilistic framework for systematically analyzing and improving these systems by modeling and refining distributions over clusters of semantically equivalent outputs. This framework facilitates the evaluation and iterative improvement of Transference Models- key software components that utilize LLMs to transform inputs into outputs for downstream tasks. To illustrate its utility, we apply the framework to the autoformalization problem, where natural language documentation is transformed into formal program specifications. Our case illustrates how distribution-aware analysis enables the identification of weaknesses and guides focused alignment improvements, resulting in more reliable and interpretable outputs. This principled approach offers a foundation for addressing critical challenges in the development of robust LLM-enabled systems. © 2025 IEEE.

关键词： Utility programs

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Adaptive Testing for LLM-Based Applications: A Diversity-Based Approach 18

Adaptive Testing for LLM-Based Applications: A Diversity-Bas...

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18th IEEE International conference on software Testing, Verification and Validation Workshops, ICSTW 2025

作者： Yoon, Juyeon Feldt, Robert Yoo, Shin KAIST School of Computing Daejeon Korea Republic of Chalmers University Dept. of Computer Science & Engineering Gothenburg Sweden

ISBN: (纸本)9798331534677

The recent surge of building software systems powered by Large Language Models (LLMs) has led to the development of various testing frameworks, primarily focused on treating prompt templates as the unit of testing. Despite the significant costs associated with test input execution and output assessment, the curation of optimized test suites is yet overlooked in these tools, which calls for tailored test selection or prioritization strategies. In this paper, we show that diversity-based testing techniques, such as Adaptive Random Testing (ART) with appropriate string distance metrics, can be effectively applied to the testing of prompt templates. Our proposed adaptive testing approach adjusts the conventional ART process to this context by selecting new test inputs based on scores derived from existing test suite and their labelling results. Our results, obtained using various implementations that explore several string-based distances, confirm that our approach enables the discovery of failures with reduced testing budgets and promotes the generation of more varied outputs. © 2025 IEEE.

关键词： software testing

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Resource and Performance Improvements of Optimized Convolutional Neural Networks for FPGA Implementations of Automatic Modulation Recognition 59

Resource and Performance Improvements of Optimized Convoluti...

引用

59th Annual conference on Information Sciences and systems, CISS 2025

作者： Rothe, Joshua A Shajaiah, Haya Johns Hopkins University Whiting School of Engineering BaltimoreMD United States

ISBN: (纸本)9798331513269

Automatic Modulation Recognition (AMR), commonly found in software defined radios, relies on speed and accuracy to effectively interpret the modulation type of incoming signals. Convolutional Neural Networks (CNNs) are growing in popularity over traditional algorithms due to their excellent performance with classification-type problems - but these are typically resource intensive, and Radio Frequency (RF) receivers are typically part of a larger system that can benefit from less resources being tied to this classification task. Field Programmable Gate Array (FPGA) implementations provide better performance than CPU and GPU implementations in most cases, and the added benefit of these functions being off the processor allows the entire system to perform better. Since resource utilization is such a critical component of effective CNN implementation, and it is often inversely tied to performance, the effectiveness of various hardware optimization techniques as well as their effects on performance should be considered by the designer. This work evaluates the tradeoffs of various model optimizations and how they affect both implementation and performance, synthesizing the models using Xilinx's quantization-aware FINN library. In this work, a methodology is presented for the generation of I and Q signals for CNN model training and evaluation, and the performance and hardware utilization benchmarks are evaluated to determine both design considerations and effective approaches for optimizing these models for real-world implementation. © 2025 IEEE.

关键词： Convolutional neural networks

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Brick by Brick - Findings of an of-the-Shelf Automation System Application for Building Block Component Group Disassembly 9

Brick by Brick - Findings of an of-the-Shelf Automation Syst...

引用

9th International conference on Mechanical engineering and Robotics Research, ICMERR 2025

作者： Briechle, Dominique Geger, Tobias Schartow, Lena Rausch, Andreas Institute for Software and Systems Engineering Clausthal University of Technology Clausthal-Zellerfeld Germany

ISBN: (纸本)9798331509262

Automation is one of the key drivers in today's global economy. It ensures the conduction of a manifold of standardized processes which helps tackle the decreasing amount of skilled workers in certain areas as well as reducing cost. Although automation is broadly used nowadays, the overall applicability of automated processes is often limited to use cases with predefined process steps of minor flexibility. However, new and upcoming sustainable industries that are focusing on product life-cycle prolonging (e.g. repair, refurbish, and remanufacture (3Rs)) options to enable Circular Economy-friendly treatment, require a high amount of adaptability in automated systems to tackle the high variability of product compositions in disassembly scenarios. The goal of the conducted case study is therefore to investigate the possibilities of a nowadays available of-the-shelf automation system regarding its functions and mechanisms towards enabling automated adaptability by using disassembly scenarios of component groups consisting of different building blocks as an example scenario and the concluding future research areas. The paper, therefore, investigates the different methods offered by the default system and applies them to various levels of disassembly scenarios. The conducted study helps to clarify the capabilities of the system's functionalities in the overall goal to enhance adaptability in disassembly processes. © 2025 IEEE.

关键词： Automation Circular Economy Cobots computer Vision Disassembly

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Matched Filter Based 3-Dimensional Near-Field Compensation for Coherent Sensing with Radars

Matched Filter Based 3-Dimensional Near-Field Compensation f...

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2025 International conference on Electronics, Information, and Communication, ICEIC 2025

作者： Cho, Wanjei Park, Jeong-Hoon Ham, Doyoung Kim, Seong-Cheol Seoul National University Department of Electrical and Computer Engineering Seoul Korea Republic of

ISBN: (纸本)9798331510756

This paper proposes a matched filter-based method to compensate for the near-field effects that occur in radar systems when using multiple separate receiving antenna arrays in a coherent manner, caused by the bigger effective antenna aperture due to the increased physical dimensions of the whole radar system. The simulation results show that the proposed method successfully combines the radar from 3 different receiving antenna arrays and triples the peak power compared to a single receiving antenna array. © 2025 IEEE.

关键词： Matched filters

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Artificial Intelligence Techniques for the Ransomware Detection: Current Methods and Challenges 1

Artificial Intelligence Techniques for the Ransomware Detect...

引用

1st International conference on AIML-Applications for engineering and Technology, ICAET 2025

作者： Rakibe, Priya Jawale, Madhuri Computer Engineering Research Centre Sanjivani College of Engineering Kopargaon Savitribai Phule Pune University Pune India Sanjivani College of Engineering Kopargaon Savitribai Phule Pune University Dept. of Information Technology Pune India

ISBN: (纸本)9798350355611

Recently, Artificial Intelligence (AI) Techniques have received significant interest across the different fields for data analysis and decision-making. Ransomware detection using AI has been a challenging research problem for the last decade in hybrid communication systems like blockchainintegrated Internet of Things (IoT) architectures. Ransomware is a form of malicious software that restricts access to a victim's resources by altering system login credentials or encrypting digital data. Consequently, typical approaches used to identify other types of threats are inadequate for recognizing ransomware. Different AI techniques recently have been used for the detection of such threats, but an appropriate level of accuracy and minimum false rate with automation are vital challenges. This study presents a systematic review of the current methods for ransomware detection using different AI techniques. The ransomware detection methods under categories such as rule-based, machine learning-based, and deep learning-based are reviewed in this study. The comparative analysis of these methods is provided quantitatively and qualitatively. As per the recent progress in the domain of ransomware detection, the current research problems are identified and discussed as the outcome of the study. The roadmap for future work is described as the research gap analysis output. © 2025 IEEE.

关键词： Adversarial machine learning

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Generation of Human-Cyber-Physical Controller Based on Formal Synthesis 2nd

Generation of Human-Cyber-Physical Controller Based on For...

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2nd International conference on the Frontiers of Robotics and software engineering, FRSE 2024

作者： Zhang, Yong Yang, Dong Dong, Yanqi Liu, Sirui Dong, Wei College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9789819631605

In response to the varied application scenarios of Human-Cyber-Physical systems (HCPS) and the dynamic roles humans play, we have introduced three specialized controller frameworks: H-CP, C-HP, and HP-CP. We designed and implemented these controllers using a formal synthesis methodology, ensuring a rigorous approach to system design. By incorporating Linear Temporal Logic (LTL) into our formal synthesis process, we have enhanced the safety and efficiency of the system. To validate our methodology, we conducted three experiments. The results of these experiments demonstrate the effectiveness of our controllers in a range of HCPS applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cyber Physical System

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MDE in the Era of Generative AI 17th

MDE in the Era of Generative AI

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17th International conference on Verification and Evaluation of computer and Communication systems, VECoS 2024

作者： Alaoui Mdaghri, Ahmed Ouederni, Meriem Chaari, Lotfi University of Nantes Nantes France Toulouse INP University of Toulouse IRIT Toulouse France

ISBN: (纸本)9783031853555

Domain-Specific Languages (DSLs) play a vital role in software development, enabling the concise expression of domain-specific concepts and requirements. In this study, we propose a novel approach leveraging Large Language Models (LLMs) to assist the DSLs modelling starting from natural language description. Our solution is a proof of concept where Model Driven engineering (MDE) is revisited taking advantage from the power of generative AI. Starting from human friendly description and domain modelling language document type, LLM-based system extracts relevant domain knowledge and builds the corresponding DSL model. Such a model is then validated through an iterative process. We applied our proposal to several case studies from different application domains including software engineering, healthcare, and finance. Furthermore, we consider a wide range of existing LLMs usually adapted for code generation. We also study the effectiveness of our solution through multi-criteria experimental evaluation. Lastly, the results demonstrate the feasibility and efficiency of our LLM driven MDE for DSL development, and then advancing domain-specific modelling practices. By doing so, we would enable the developer to save time and effort for further tasks such as functional properties’ verification. A demo as well as a web application for our developed solution are available online via the following link https://***/demo-ecore-gen/. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： software design

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