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检索条件"任意字段=International Symposium and Workshop on Systems Engineering of Computer Based Systems"

共 37407 条记录，以下是21-30 订阅

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Design and Implementation of Operation and Maintenance Management System for Teaching Building based on IoT+WebGIS 3D Visualization 24

Design and Implementation of Operation and Maintenance Manag...

引用

8th international Conference on Electronic Information Technology and computer engineering, EITCE 2024

作者： Zhao, Yongheng Seng, Bingfeng Liu, Xiaojing Wang, Shuhong School of Computer Technology and Application Qinghai University Qinghai Xining China Qinghai Provincial Key Laboratory of Media Integration Technology and Communication Qinghai Xining China

ISBN: (纸本)9798400710094

Building an operation and maintenance system for teaching buildings that integrates"three-dimensional visualization" and"digital informatization" is essential for realizing a smart campus. To address the limitations of current systems in real-time monitoring and visualization, this study integrates Internet of Things (IoT) technology with WebGIS three-dimensional visualization techniques to develop an operation and maintenance management system, taking the teaching building of the School of computer Science at Qinghai University as a case study. The system achieves real-time data collection and dynamic monitoring through IoT devices, and integrates multiple functions, including live three-dimensional model display, indoor and outdoor environmental monitoring, fire alarm and early warning, classroom and equipment management, security monitoring, and energy consumption analysis. This system not only facilitates the transformation of traditional teaching buildings into smart and information-based infrastructure, but also provides operational staff with efficient and intelligent management tools. © 2024 Copyright held by the owner/author(s).

关键词： Fire alarm systems

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Recognition of Remakes and Fake Facial Images 19th

Recognition of Remakes and Fake Facial Images

引用

19th international workshop on Data Privacy Management, DPM 2024, 8th international workshop on Cryptocurrencies and Blockchain Technology, CBT 2024 and 10th workshop on the Security of Industrial Control systems and of Cyber-Physical systems, CyberICPS 2024 which were held in conjunction with the 29th European symposium on Research in computer Security, ESORICS 2024

作者： Bobulski, Janusz Kubanek, Mariusz Department of Computer Sciences Czestochowa University of Technology Czestochowa42-201 Poland

ISBN: (纸本)9783031823619

computer Vision techniques are extensively utilised in entertainment, enhancing realism in games and movies. Within video games, these techniques enable the recognition of objects, characters, and player movements, thereby enabling more intelligent game responses to player actions, resulting in more prosperous and more immersive experiences. Moreover, integrating deep learning methodologies with computer vision facilitates the automatic generalisation of special effects and enhances live broadcasts with interactive elements. However, such approaches also present challenges, as they can be exploited to manipulate and fabricate information, such as swapping faces in images or videos. This phenomenon is particularly evident in social media, where various forms of counterfeit or manipulated content proliferate, commonly called fake news. Our article proposes a novel convolutional neural network-based method for detecting alterations in real facial images and distinguishing them from artificially generated ones. Our technique, with its potential to revolutionise the field, enables the classification of facial photos into three categories: authentic faces, authentic faces with applied modifications (e.g., through photo editing software), and artificially generated facial images. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Fairness Analysis of Machine Learning-based Code Reviewer Recommendation 5th

Fairness Analysis of Machine Learning-Based Code Reviewer Re...

引用

5th international workshop on Algorithmic Bias in Search and Recommendation

作者： Mohajer, Mohammad Mahdi Belle, Alvine Boaye Harzevili, Nima Shiri Wang, Junjie Hemmati, Hadi Wang, Song Jiang, Zhen Ming (Jack) York Univ Toronto ON Canada Chinese Acad Sci Inst Software Beijing Peoples R China

ISBN: (纸本)9783031719745;9783031719752

Ensuring the fairness of machine learning (ML) applications is critical to the reliability of modern artificial intelligence systems. Despite extensive study on this topic, the fairness of ML models in the software engineering (SE) domain has not yet been explored well. As a result, many ML-powered software systems, particularly those utilized in the software engineering community, continue to be prone to fairness issues. Taking one of the typical SE tasks, i.e., code reviewer recommendation, as a subject, this paper investigates the fairness of ML applications in the SE domain, specifically focusing on the code reviewer recommendation task. Our empirical study demonstrates that existing ML-based code reviewer recommendation systems exhibit unfairness and discriminating behaviors. Specifically, male reviewers get, on average, 7.25% more recommendations than female code reviewers compared to their distribution in the reviewer set. This paper also investigates why the studied ML-based code reviewer recommendation systems are unfair and provides solutions to mitigate the unfairness. For instance, such systems may recommend male reviewers at a significantly higher rate than female reviewers in a discriminatory manner. Our study further indicates that the existing mitigation methods can enhance fairness significantly in projects with a similar distribution of protected and privileged groups. Still, their effectiveness in improving fairness on imbalanced or skewed data is limited.

关键词： Fairness Machine Learning Code Reviewer Recommendation

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Card-based Cryptographic Protocols for Three-Input Functions with a Standard Deck of Cards Using Private Operations 19th

Card-Based Cryptographic Protocols for Three-Input Function...

引用

作者： Kobayashi, Naoki Manabe, Yoshifumi School of Informatics Kogakuin University Tokyo Shinjuku163-8677 Japan

ISBN: (纸本)9783031823480

This paper shows card-based cryptographic protocols to calculate several Boolean functions with a standard deck of cards using private operations. They are multi-party secure computations executed by multiple semi-honest players without computers. The protocols use private operations that are executed by a player at a place where the other players cannot see. Most card-based cryptographic protocols use a special deck of cards that consists of many cards with two kinds of marks. Though these protocols are simple and efficient, the users need to prepare such special cards. Few protocols were shown that use a standard deck of playing cards, though the protocols with a standard deck of cards can be easily executed in our daily lives. It was shown that logical AND, logical XOR, and copy protocols can be executed with the minimum number of cards. However, the protocols for complicated functions are not known. This paper shows that by using private operations, all of the following Boolean functions can be calculated without additional cards other than the input cards: (1) any three input Boolean functions, (2) half adder and full adder, and (3) any n-input symmetric Boolean functions. The results show the effectiveness of private operations in card-based cryptographic protocols. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Boolean functions

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A RAG-based Question-Answering Solution for Cyber-Attack Investigation and Attribution 19th

A RAG-Based Question-Answering Solution for Cyber-Attack In...

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作者： Rajapaksha, Sampath Rani, Ruby Karafili, Erisa University of Southampton University Road SouthamptonSO17 1BJ United Kingdom

ISBN: (纸本)9783031823619

In the constantly evolving field of cybersecurity, it is imperative for analysts to stay abreast of the latest attack trends and pertinent information that aids in the investigation and attribution of cyber-attacks. In this work, we introduce the first question-answering (QA) model and its application that provides information to the cybersecurity experts about cyber-attacks investigations and attribution. Our QA model is based on Retrieval Augmented Generation (RAG) techniques together with a Large Language Model (LLM) and provides answers to the users’ queries based on either our knowledge base (KB) that contains curated information about cyber-attacks investigations and attribution or on outside resources provided by the users. We have tested and evaluated our QA model with various types of questions, including KB-based, metadata-based, specific documents from the KB, and external sources-based questions. We compared the answers for KB-based questions with those from OpenAI’s GPT-3.5 and the latest GPT-4o LLMs. Our proposed QA model outperforms OpenAI’s GPT models by providing the source of the answers and overcoming the hallucination limitations of the GPT models, which is critical for cyber-attack investigation and attribution. Additionally, our analysis showed that when the RAG QA model is given few-shot examples rather than zero-shot instructions, it generates better answers compared to cases where no examples are supplied in addition to the query. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cyber attacks

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Closed-Loop System Identification of an Unstable Nonlinear System Using Koopman Operator 4

Closed-Loop System Identification of an Unstable Nonlinear S...

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4th international symposium on Instrumentation, Control, Artificial Intelligence, and Robotics, ICA-SYMP 2025

作者： Maneeloke, Benjaset Boonto, Sudchai Sanposh, Peerayot Tipsuwan, Yodyium Chinthaned, Natthawut Tanaprasitpattana, Warit Kasetsart University Dept. of Electrical Engineering Bangkok Thailand King Mongkut's University of Technology Thonburi Dept. of Control Systems and Instrumentation Engineering Bangkok Thailand Kasetsart University Dept. of Computer Engineering Bangkok Thailand

ISBN: (纸本)9798331505400

This paper presents a novel methodology for closed-loop system identification of unstable nonlinear systems using the Koopman operator with Extended Dynamic Mode Decomposition with control (EDMDc). The study highlights the critical role of selecting appropriate observable functions to develop accurate and efficient Koopman models. We demonstrate that the resulting Koopman models exhibit excellent fitting and validation properties and retain the stabilizability of the original nonlinear systems. These models are verified through time and frequency responses under closed-loop control using a Linear Quadratic Regulator (LQR), confirming their effectiveness. Future work will extend this framework to more complex systems and incorporate machine learning techniques to refine the selection of observable functions. This approach aims further to enhance the adaptability and robustness of Koopman-based control strategies. © 2025 IEEE.

关键词： Dynamic mode decomposition

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How Well Do LLMs Understand DEECo Ensemble-based Component Architectures 12th

How Well Do LLMs Understand DEECo Ensemble-Based Component A...

引用

12th international symposium on Leveraging Applications of Formal Methods, Verification and Validation

作者： Topfer, Michel Khalyeyev, Danylo Bures, Tomis Hnetynka, Petr Plasil, Frantisek Charles Univ Prague Prague Czech Republic

ISBN: (纸本)9783031751066;9783031751073

Ensemble-based component systems have been used for many years to develop collective adaptive systems (CAS). The DEECo component model offers a framework for modeling and implementing ensemble-based component systems. Being expressive enough and having semantics specifically tailored towards dynamically evolving systems, DEECo has proven to be fairly powerful in modeling complex and dynamic architectures. At the same time, its specific semantics turned out to be a hurdle for newcomers when expressing their design intention and understanding its implications and side effects. We see quite a potential in employing large language models (LLMs) to simplify creating and refining the DEECo architectures. Since this constitutes a large research scope, we focus in this paper on initial experiments demonstrating how well generic LLMs understand the advanced concepts of ensemble-based CAS embodied in DEECo and asses what expectation from LLMs is realistic in this context. Our results indicate that LLMs can indeed understand ensemble-based architectures, but it depends on the form in which the architecture is presented in the textual form. Using external DSL, which is very self-explanatory gave good results out of the box. Specifications embedded in existing programming languages needed prior explanation of how to interpret them.

关键词： Specification languages

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Short Paper: Machine Learning-based Secure Malware Detection Using Features from Binary Executable Headers 19th

Short Paper: Machine Learning-Based Secure Malware Detection...

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作者： Bulut, Irfan Rúa, Enrique Argones COSIC KU Leuven Kasteelpark Arenberg 10 Leuven3001 Belgium

ISBN: (纸本)9783031823619

As the number of intelligent systems has dramatically increased, malware has become increasingly harmful. Consequently, detecting previously unknown malware swiftly has emerged as a critical issue in cybersecurity, aiming to prevent these threats from becoming widespread hazards that harm users and systems. To address this challenge, machine learning methods have been employed to rapidly detect and classify malware. These methods can be either dynamic or static analysis. Dynamic analysis allows for the collection of significant features not obtainable through static analysis alone. However, malware authors often employ techniques to evade automated malware analysis, leading to decreased true detection rates due to sandbox evasion strategies. In this study we focus on static analysis for malware detection, specifically on Portable Executable (PE) files. For this purpose, we propose a novel feature extraction pipeline, and a methodology aimed at finding reliably secure detectors, where the main priority is to avoid at all cost false negatives. For this, we leverage the use of the AutoML (auto-sklearn) framework and perform a reliability analysis of different configurations after consequently setting the working points to avoid false negatives. Our experiments, performed using the up-to-date MalwareBazaar malware dataset, show that it is possible to find consistently robust ML detectors when using the proposed static feature sets for PE files. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cybersecurity

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Fault-Tolerant Controller Design for PMSM Drives with Single-Phase Fault Inverters 4

Fault-Tolerant Controller Design for PMSM Drives with Single...

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4th international symposium on Instrumentation, Control, Artificial Intelligence, and Robotics, ICA-SYMP 2025

作者： Buakaew, Sornchai Pongyart, Witthawas King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok Thailand

ISBN: (纸本)9798331505400

Permanent magnet synchronous motors (PMSMs) are commonly used in various electrical drive applications due to their high efficiency and performance. However, these systems are susceptible to several types of faults. To ensure uninterrupted operation in the event of a fault, researchers have focused on developing fault-tolerant control systems. This paper presents a fault-tolerant controller design for PMSM drives with single-phase fault inverters. A fault-tolerant inverter is introduced, and an energy-based controller is implemented in the current loop, with a PI-controller governing speed control. The proposed control system is tested under both normal and fault conditions, demonstrating its ability to maintain motor torque and speed while ensuring system stability in both scenarios. © 2025 IEEE.

关键词： Permanent magnets

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Enhancing Domain-Specific ASR Performance Using Finetuning and Zero-Shot Prompting: A Study in the Medical Domain 6

Enhancing Domain-Specific ASR Performance Using Finetuning a...

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6th Doctoral symposium on Intelligence Enabled Research, DoSIER 2024

作者： Maulik, Utsav Bandyopadhyay Mitra, Pabitra Sarkar, Sudeshna Dept. of Computer Science and Engineering IIT Kharagpur West Bengal India

Domain Adaptation has emerged as an important development in Speech Recognition systems for improving the transcription accuracy of the input audio. This study explores the enhancement of Domain-specific Automatic Speech Recognition performance through finetuning and postprocessing using Large Language Models, focusing specifically on the medical domain. We investigate how domain-specific finetuning and advanced text postprocessing techniques can significantly improve transcription accuracy in medical contexts, reducing errors in specialized terminology, acronyms, and abbreviations. Our findings highlight the benefits of integrating Large Language Model based postprocessing with Automatic Speech Recognition systems to achieve better results in complex domains. © 2025 Copyright for this paper by its authors.

关键词： Speech recognition

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