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42nd IEEE International Conference on Computer Communications, INFOCOM 2023

作者： Shen, Kai Zhang, Dayou Zhu, Zi Zhang, Lei Wang, Fangxin Wang, Dan The Chinese University of HongKong The Future Network of Intelligence Institute Shenzhen China The Chinese University of HongKong School of Science and Engineering Shenzhen China Shenzhen University College of Computer Science and Software Engineering China Peng Cheng Laboratory China The Guangdong Provincial Key Laboratory of Future Networks of Intelligence China The Hong Kong Polytechnic University Department of Computing Hong Kong

ISBN: (纸本)9798350334142

The outbreak of COVID-19 has dramatically promoted the explosive proliferation of multi-party realtime video streaming (MRVS) services, represented by Zoom and Microsoft Teams. Different from Video-on-Demand (VoD) or live streaming, MRVS enables all-to-all realtime video communication, bringing significant challenges to service providing. First, unreliable network transmission can cause network loss, resulting in delay increase and visual quality degradation. Second, the transformation from two-party to multi-party communication makes resource scheduling much more difficult. Moreover, optimizing the overall QoE requires a global coordination, which is quite challenging given the various impact factors such as bitrate and *** this paper, we propose the SJA framework, which is, to our best knowledge, the first server-driven joint loss and bitrate adaptation framework in multi-party realtime video streaming services towards maximized QoE. We comprehensively design an appropriate QoE model for MRVS services to capture the interplay among perceptual quality, variations, bitrate mismatch, loss damage, and streaming delay. We mathematically formulate the QoE maximization problem in MRVS services. A Lyapunov-based relaxation and the SJA algorithm are further designed to address the optimization problem with close-to-optimal performance. Evaluations show that our framework can outperform the SOTA solutions by 18.4% ∼ 46.5%. © 2023 IEEE.

关键词： Quality control

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Defining and Detecting the Defects of the Large Language Model-based Autonomous Agents

arXiv

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arXiv 2024年

作者： Ning, Kaiwen Chen, Jiachi Zhang, Jingwen Li, Wei Wang, Zexu Feng, Yuming Zhang, Weizhe Zheng, Zibin The School of Software Engineering Sun Yat-Sen University Peng Cheng Laboratory China The School of Software Engineering Sun Yat-Sen University China The School of Computer Science and Technology Harbin Institute of Technology China Peng Cheng Laboratory China The Department of New Networks Peng Cheng Laboratory China

Artificial intelligence (AI) agents are systems capable of perceiving their environment, autonomously planning and executing tasks. Recent advancements in Large Language Models (LLMs) have introduced a transformative paradigm for AI agents, enabling them to interact with external resources and tools through prompt techniques. This advancement has significantly extended the capabilities of LLMs, positioning LLM-based AI Agents as an important research area. In such agents, the workflow integrates developer-written code, which manages framework construction and logic control, with LLM-generated natural language that enhances dynamic decision-making and interaction. However, discrepancies between developer-implemented logic and the dynamically generated content of LLMs in terms of behavior and expected outcomes can lead to defects, such as tool invocation failures and task execution errors. These issues introduce specific risks, leading to various defects in LLM-based AI Agents, including service interruptions and incorrect output. Despite the importance of these issues, there is a lack of systematic work that focuses on analyzing LLM-based AI Agents to uncover defects in their code. To address this gap, we present the first study focused on identifying and detecting defects in LLM Agents. We collected and analyzed 6, 854 relevant posts from StackOverflow to define and classify eight types of agent defects. For each defect type, we provided detailed descriptions and illustrated with an example. Then, we designed a static analysis tool, named Agentable, to detect the defined defects. Agentable leverages Code Property Graphs (CPGs) and LLMs to analyze Agent workflows by efficiently identifying specific code patterns and analyzing natural language descriptions. To evaluate Agentable, we constructed two datasets: AgentSet, which consists of 84 real-world Agent projects, and AgentTest, which contains 78 Agent projects specifically designed to include various types of defect

关键词： Autonomous agents

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Downlink Transmit-End Beam Refinement for mmWave Communications Under Mobility

Downlink Transmit-End Beam Refinement for mmWave Communicati...

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2023 International Conference on Electrical engineering and Photonics, EExPolytech 2023

作者： Fokin, Grigoriy Grishin, Ilya Okuneva, Darina The Bonch-Bruevich Saint Petersburg State University of Telecommunications Laboratory of Software Defined Radio Saint Petersburg Russia The Bonch-Bruevich Saint Petersburg State University of Telecommunications Department of Communication Networks and Data Transmission Saint Petersburg Russia The Bonch-Bruevich Saint Petersburg State University of Telecommunications Department of Software Engineering and Computer Facilities Saint Petersburg Russia

ISBN: (纸本)9798350310481

This article discusses a method for adjusting the radiation pattern of a base station antenna array when conducting radio communications in the millimeter wavelength range for mobile user devices. The article describes a technique for selecting a training sequence of beams that minimizes the Cramer-Rao lower bound when estimating the transmission direction. The proposed technique makes it possible to reduce the beam adjustment time for transmission by 16 times compared to the conventional protocol, which improves the quality of service for highly mobile users. © 2023 IEEE.

关键词： Beamforming

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Realization and Performance Tests of a Dual-Polarization Fiber Optic Gyroscope 8

Realization and Performance Tests of a Dual-Polarization Fib...

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8th Symposium on Novel Photoelectronic Detection Technology and Applications

作者： Zhou, Tong Zhu, Lanxin Cao, Yuwen Chen, Yanjun Huang, Huimin Li, Zhengbin School of Software and Microelectronics Peking University Beijing102600 China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Peking University Beijing100871 China

ISBN: (数字)9781510653122

ISBN: (纸本)9781510653115

Interferometric fiber optical gyroscopes (IFOGs) have become one of the widely used sensors of inertial technology and rotational seismology, owing to their high precision and stability. In recent years, the dual-polarization IFOG, which allows two orthogonal polarization to work simultaneously, has been proved to achieve better measurement performance than the conventional minimum configuration through mutual compensation. In this work, a prototype of dual-polarization fiber-optic gyroscope is designed and realized. laboratory tests show the self-noise has reached 9 nrad/s/sqrt(Hz) over the frequency from 0.01 to 125 Hz and the angular random walk has achieved 2.6×10-5 deg/sqrt(hr). For an Open-loop IFOG, which does not need a confiscated feedback control system, it avoids the dead-zone of closed-loop IFOG. However, limited by the measurement range, as a result of the periodic and nonlinear response of open-loop demodulation, the scale factor of open-loop IFOG varies around 100~1000 parts per million (ppm) conventionally, which hinders its detection accuracy. In this work, with new measurement extension and linearity compensation methods, the prototype achieves an outstanding precision with scale factor nonlinearity up to 5 ppm within ±10 deg/s, and shows the potential of dual-polarization IFOGs for rotational seismology. © 2022 SPIE

关键词： Seismology

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FedIERF: Federated Incremental Extremely Random Forest for Wearable Health Monitoring

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Journal of Computer Science & Technology 2023年第5期38卷 970-984页

作者：胡春雨忽丽莎袁林陆佃杰吕蕾陈益强 Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center(National Supercomputer Center in Jinan)Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer ScienceJinan 250000China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan 25000China Institute of Information Technology Hebei University of Economics and BusinessShijiazhuang 050061China School of Information Science and Engineering Shandong Normal UniversityJinan 250358China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China

Wearable health monitoring is a crucial technical tool that offers early warning for chronic diseases due to its superior portability and low power ***,most wearable health data is distributed across dfferent organizations,such as hospitals,research institutes,and companies,and can only be accessed by the owners of the data in compliance with data privacy *** first challenge addressed in this paper is communicating in a privacy-preserving manner among different *** second technical challenge is handling the dynamic expansion of the federation without model *** address the first challenge,we propose a horizontal federated learning method called Federated Extremely Random Forest(FedERF).Its contribution-based splitting score computing mechanism significantly mitigates the impact of privacy protection constraints on model *** on FedERF,we present a federated incremental learning method called Federated Incremental Extremely Random Forest(FedIERF)to address the second technical *** introduces a hardness-driven weighting mechanism and an importance-based updating scheme to update the existing federated model *** experiments show that FedERF achieves comparable performance with non-federated methods,and FedIERF effectively addresses the dynamic expansion of the *** opens up opportunities for cooperation between different organizations in wearable health monitoring.

关键词： federated learning incremental learning random forest wearable health monitoring

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Small object detection algorithm for UAV images based on YOLOv5

Small object detection algorithm for UAV images based on YOL...

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2024 International Conference on Image Processing and Artificial Intelligence, ICIPAl 2024

作者： Song, Nan Wang, Ying Liu, Tianxu Henan International Joint Laboratory of Theories and Key Technologies on Intelligence Networks Henan University Kaifeng475004 China Software Engineering Intelligent Information Processing Innovation Base-Subject Innovation Base of Henan Higher Universities Henan University Kaifeng475004 China Henan Provincial Communications and Transportation Dispatch and Command Center Zhengzhou450000 China

ISBN: (纸本)9781510681514

In the field of deep learning-based object detection algorithms, the YOLO algorithm has attracted a lot of attention due to its advantages in speed and accuracy. However, there are problems such as low detection speed and poor detection accuracy in the application of UAV images. For this reason, an improved small object detection algorithm MTF-YOLOv5 based on the YOLOv5 algorithm is proposed. Firstly, MobileNet V3 is used to replace the main network to reduce the amount of network parameters and improve the running speed. Secondly, a small object detection layer is added to the original network structure to enhance the detection ability for small objects. Finally, the FocalModulation is introduced to replace the SPP module to improve accuracy. The improved algorithm is tested on the processed TinyPerson dataset. Experimental results show that the mean average precision (mAP) on the YOLOv5s algorithm improved by 4.1%, and the FPS increased from 86 to 136, improving the speed by 51%. While maintaining detection speed, the updated algorithm successfully raises detection accuracy. © 2024 SPIE.

关键词： Object detection

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Towards Evidence-Based Cybersecurity Assessment of Programmable Systems to Ensure the Protection of Critical IT Infrastructure 12

Towards Evidence-Based Cybersecurity Assessment of Programma...

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12th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS 2023

作者： Illiashenko, Oleg Kharchenko, Vyacheslav Odarushchenko, Oleg National Aerospace University 'KhAI' Department of Computer Systems and Networks Kharkiv61070 Ukraine Institute of Information Science and Technologies 'Alessandro Faedo' - Isti Cnr Software Engineering & Dependable Computing Laboratory Pisa56124 Italy Research and Production Enterprise Rpc Radics Llc Kropyvnytskyi25009 Ukraine

ISBN: (纸本)9798350358056

The paper is devoted to developing scientific principles, methods, means, and information technology of model-oriented verification and evidence-based assessment using functional safety and cybersecurity cases for programmable systems of critical applications (PSCA). In particular, information and control systems important for the safety of nuclear power plants (NPPs), aerospace systems, railway domains, etc., developed using Field Programmable Gate Arrays (FPGAs) and hardware platforms are reviewed. The goal is to ensure the guaranteed completeness and reliability of its functional safety and cybersecurity assessment by developing and implementing a set of formal and semi-formal methods and tools that consider defects of different nature - physical, design, trojans, and vulnerabilities that can be attacked and lead to a fatal system failure, which results in damaging the critical IT infrastructure. The methods shown are based on integrating algebraic, tabular, graph models, and case assessment methodology. The methods are implemented as appropriate technologies for evidence-based verification and evaluation of PSCA. For formal methods, a prototype of a translator of Very High-Speed Integrated Circuits Hardware Description Language (VHDL) code is developed into an algebra of behaviors. It provides evidence-based verification and a framework and tools to design reports on assessing the cybersecurity and functional safety of programmable systems. © 2023 IEEE.

关键词： critical IT infrastructure cybersecurity FPGA functional safety programmable systems of critical applications

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Identifying Code Changes for Architecture Decay via a Metric Forest Structure

Identifying Code Changes for Architecture Decay via a Metric...

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IEEE/ACM International Conference on Technical Debt (TechDebt)

作者： Wuxia Jin Yuyun Zhang Jiaowei Shang Yi Hou Ming Fan Ting Liu School of Software Engineering Xi’an Jiaotong University Xi’an China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University

During long-term software evolution, it is inevitable that an accumulation of changes leads to architectural erosion and debt. Diverse metric-based methods have been developed to identify architectural problems that violate design principles and degrade software maintainability. However, there still exists a gap between the implementation-level metrics and architecture-level metrics. Consequently, it hinders the comprehensibility, interpretability, and indicative(-bility) of the measurement results. To fill this gap, we propose the dbMIT to identify potential code changes that make architecture decay. Our dbMIT first integrates popular metrics such as the CK suite. Then dbMIT constructs a forest structure of metrics, serving as a knowledge base to relate the metrics across levels together. The forest aims to link the architecture-level metrics and implementation-level metrics. Via pre-defined rules using the forest structure, our dbMIT identifies code changes that potentially cause the architecture decay. The usage of forest structure of metrics makes it easy for developers to understand detection results, explain why the detected code changes are potential contributors to the decay, and indicate the code scope for resolution. We also contribute a web-based tool to integrate our dbMIT. Our experiments on the collected projects demonstrate the effectiveness of dbMIT against a history-based ground-truth.

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Imperceptible Face Forgery Attack via Adversarial Semantic Mask

arXiv

引用

arXiv 2024年

作者： Liu, Decheng Su, Qixuan Peng, Chunlei Wang, Nannan Gao, Xinbo State Key Laboratory of Integrated Services Networks School of Cyber Engineering Xidian University Shaanxi Xi’an710071 China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai201112 China State Key Laboratory of Integrated Services Networks School of Telecommunications Engineering Xidian University Shaanxi Xi’an710071 China Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Chongqing400065 China

With the great development of generative model techniques, face forgery detection draws more and more attention in the related field. Researchers find that existing face forgery models are still vulnerable to adversarial examples with generated pixel perturbations in the global image. These generated adversarial samples still can't achieve satisfactory performance because of the high detectability. To address these problems, we propose an Adversarial Semantic Mask Attack framework (ASMA) which can generate adversarial examples with good transferability and invisibility. Specifically, we propose a novel adversarial semantic mask generative model, which can constrain generated perturbations in local semantic regions for good stealthiness. The designed adaptive semantic mask selection strategy can effectively leverage the class activation values of different semantic regions, and further ensure better attack transferability and stealthiness. Extensive experiments on the public face forgery dataset prove the proposed method achieves superior performance compared with several representative adversarial attack methods. The code is publicly available at https://***/clawerO-O/ASMA. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Predictive Hiring System: Information Technology Consultants Soft Skills

Predictive Hiring System: Information Technology Consultants...

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International Conference on Advanced Intelligent Systems for Sustainable Development, AI2SD 2022

作者： Lamjid, Asmaa El Bouchti, Karim Ziti, Soumia Mohamed, Reda Oussama Labrim, Hicham Riadsolh, Anouar Belkacemi, Mourad Intelligence Processing System and Security Mohammed V University in Rabat Rabat Morocco Algorithms Networks Intelligent Systems and Software Engineering Research Faculty of Sciences Mohammed V University in Rabat Rabat Morocco Advanced Systems Engineering Laboratory National School of Applied Sciences Ibn Tofail University Kenitra Morocco Faculty of Sciences Mohammed V University in Rabat Rabat Morocco

ISBN: (纸本)9783031263835

Recruiting candidates who will perform well within any organization has become a challenge in the Human Resources sector. Generally, poor recruitment can negatively reflect the progression and evolution of the organism. Soft skills are personality traits and behaviors wanted by recruiters to provide high performance, and they are the skills that will help candidates succeed in their jobs. This work proposes a new predictive hiring model based on soft skills used in the human resources area for Information Technology Consultants. This model helps organizations’ recruiters choose the best candidates and avoid lousy recruitment. The implementation of the proposed model has been running under a machine learning algorithm, the Support Vector Machine algorithm using different functions to choose which one of the implementations is best displayed. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Support vector machines

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