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International Conference on Software engineering (ICSE)

作者： Maolin Sun Yibiao Yang Ming Wen Yongcong Wang Yuming Zhou Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology China Hubei Key Laboratory of Distributed System Security Services Computing Technology and System Lab Cluster and Grid Computing Lab Hubei Engineering Research Center on Big Data Security National Engineering Research Center for Big Data Technology and System State Key Laboratory for Novel Software Technology Nanjing University China School of Computer Science and Technology Huazhong University of Science and Technology China

SMT solvers check the satisfiability of logic formulas over first-order theories, which have been utilized in a rich number of critical applications, such as software verification, test case generation, and program synthesis. Bugs hidden in SMT solvers would severely mislead those applications and further cause severe consequences. Therefore, ensuring the reliability and robustness of SMT solvers is of critical importance. Although many approaches have been proposed to test SMT solvers, it is still a challenge to discover bugs effectively. To tackle such a challenge, we conduct an empirical study on the historical bug-triggering formulas in SMT solvers' bug tracking systems. We observe that the historical bug-triggering formulas contain valuable skeletons (i.e., core structures of formulas) as well as associated atomic formulas which can cast significant impacts on formulas' ability in triggering bugs. Therefore, we propose a novel approach that utilizes the skeletons extracted from the historical bug-triggering formulas and enumerates atomic formulas under the guidance of association rules derived from historical formulas. In this study, we realized our approach as a practical fuzzing tool HistFuzz and conducted extensive testing on the well-known SMT solvers Z3 and cvc5. To date, HistFuzz has found 111 confirmed new bugs for Z3 and cvc5, of which 108 have been fixed by the developers. More notably, out of the confirmed bugs, 23 are soundness bugs and invalid model bugs found in the solvers' default mode, which are essential for SMT solvers. In addition, our experiments also demonstrate that HistFuzz outperforms the state-of-the-art SMT solver fuzzers in terms of achieved code coverage and effectiveness.

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A Lightweight Attention Network for Camouflaged Object Fixation Prediction

A Lightweight Attention Network for Camouflaged Object Fixat...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Qingbo Wu Guanxing Wu Shengyong Chen Key Laboratory of Computer Vision and Systems (Ministry of Education) Tianjin University of Technology Tianjin China Engineering Research Center of Learning-Based Intelligent System (Ministry of Education) Tianjin University of Technology Tianjin China School of Computer Science and Engineering Tianjin University of Technology Tianjin China

ISBN: (数字)9798350394085

ISBN: (纸本)9798350394092

This paper proposes a novel encoder-decoder architecture for camouflaged object fixation prediction (COFP), which builds on a large-kernel decomposition technique and depth-wise separable convolution embedded attention. Specifically, the encoder with the large-kernel decomposition adopts a Visual Attention Network (VAN) as the backbone to extract more accurate features, and the decoder predicts fixation maps from concealed objects using the attention module incorporating depth-wise separable convolution. Both designs aim to reduce the number of learnable parameters, making the model lighter and simultaneously maintaining the performance of COFP. In addition, we propose using the reverse Kullback-Leibler Divergence (KLDiv) as the loss function. Extensive experiments demonstrate that the proposed method performs well against SOTA approaches in quality and quantity.

关键词： Industrial electronics Visualization Accuracy Convolution Feature extraction Decoding

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Compatible Secure Key Distribution in Passive Optical Network based on Fiber Polarization Reciprocity

Compatible Secure Key Distribution in Passive Optical Networ...

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OptoElectronics and Communications Conference, OECC

作者： Da Lin Taihang Qiu Lei Deng Qi Yang Zhijun Yan Xiaoxiao Dai Xiaojing Gao Mengfan Cheng National Engineering Research Center for Next Generation Internet Access System School of Optical and Electronic Information Huazhong University of Science and Technology (HUST) Wuhan China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China The School of Computer Science China University of Geosciences Wuhan China

In this work, we propose using Delta-Sigma modulation (DSM) techniques to improve the traditional channel reciprocal key distribution scheme in access network scenarios. The proposed approach relies only on digital fi... 详细信息

ISBN: (数字)9798350308396

ISBN: (纸本)9798350308402

关键词： Optical fiber polarization Point-to-multipoint communications Optical filters Optical polarization Filtering Delta-sigma modulation Passive optical networks

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Dual-Use Secure Key Distribution Protocol Based on FEC-Aided Unidirectional BER Estimation

Dual-Use Secure Key Distribution Protocol Based on FEC-Aided...

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OptoElectronics and Communications Conference, OECC

作者： Di Wu Lei Deng Qi Yang Zhijun Yan Xiaoxiao Dai Xiaojing Gao Mengfan Cheng National Engineering Research Center for Next Generation Internet Access System School of Optical and Electronic Information Huazhong University of Science and Technology (HUST) Wuhan China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China The School of Computer Science China University of Geosciences Wuhan China

ISBN: (数字)9798350308396

ISBN: (纸本)9798350308402

We propose a unidirectional dual-use secure key distribution protocol based on the forward-error-correction-aided bit error rate estimation. Hybrid automatic repeat request and adaptive coding techniques are integrated to enhance the reliability of communication section. A simulation is conducted in a turbulent channel to verify the feasibility of the proposed protocol.

关键词： Protocols Bit error rate Estimation Parity check codes Reliability Automatic repeat request Optical devices Adaptive coding

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An intelligent guided-airborne virus detection 21

An intelligent guided-airborne virus detection

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21st IEEE International Conference on sciences and Techniques of Automatic Control and computer engineering, STA 2022

作者： Hadded, Ahmed Ayed, Mossaad Ben Alshaya, Shaya A. National Engineering School of Sfax Sfax University Computer and Embedded System Laboratory Sfax3038 Tunisia Electronic Industrial ENISo Sousse University Tunisia ENIS Sfax University Computer and Embedded System Laboratory Sfax3038 Tunisia Majmaah University Faculty of Sciences and Humanities Sciences Department of Computer Science Saudi Arabia Prince Sultan University Dean of Digital Transformation and E-transactions Majmaah11952 Saudi Arabia

ISBN: (纸本)9781665482615

Health has recently faced many challenges, including improving a healthy environment and reducing human life's dangers and economic crises. The last pandemic COVID-19 had badly affected survivor sectors with infection and lockdown exigence. Scientists proposed several solutions to reduce the negative impact of a such pandemic by proposing systems for earlier detection of viruses. The use of metamaterials as an emerging technology in the biosensors field allows a high accuracy. This paper presents a method for detecting and capturing airborne viruses using metasurface technology. The goal is to develop a system that can identify and capture these viruses using FET sensors. The accuracy of the detection is tied to the concentration of aerosols. The model proposes a guided flow of aerosols that positively impacts the detection of viruses through the FET biosensor. The simulation results based on Concentration and airflow velocity delays prove the proposed model's performance. © 2022 IEEE.

关键词： Biosensors

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IoT-Enabled Smart Manhole Management system for Real-time Status, Water Level, and Gas Detection

IoT-Enabled Smart Manhole Management System for Real-time St...

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Intelligent systems for Cybersecurity (ISCS), International Conference on

作者： Md Asif Imran S M Masfequier Rahman Swapno Gunjan Chhabra Keshav KaushiK Md. Arif Anzum Mahi Md Babul Islam Rezaul Haque Dept. of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka Bangladesh Department of CSE Graphic Era Hill University Dehradun Uttarakhand India Adjunct Professor Graphic Era Deemed to be University Dehradun Uttarakhand India School of Computer Science University of Petroleum and Energy Studies Dehradun Uttarakhand India Dept. of Computer Modeling Electronic and System Engineering UNICAL Rende Italy Applied Bahari Big Data Research House Dhaka Bangladesh Department of Computer Modeling Electronic and System Engineering UNICAL Rende Italy

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

Regular monitoring of the maintenance manhole system is imperative to uphold the city's health and cleanliness standards. Our study offers an intelligent manhole that monitors the manhole's condition, water level, and gases. We tested the status, gas, and water levels of our revolutionary, innovative manhole in several places. Our system's Blink app position tracking of our smart manhole is critical. This capability improves monitoring efficiency and speeds up issue resolution. Our work revolutionizes municipal utility maintenance and safety by optimizing manhole performance. Integrating a Node MCU, GPS module, gas sensor, ultrasonic sensor, and Android Blynk software creates a complete real-time monitoring and data-collecting system. Combining these technologies, our Manhole Monitoring system improves safety and streamlines maintenance, making municipal infrastructure more robust and efficient. This paper highlights the promising features of IoT and shows how it may be used to improve the smart manhole. In the future, this creative method may result in a more durable and better intelligent city.

关键词： Surveillance Urban areas Robot vision systems Transforms Cameras Real-time systems Maintenance

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Comparative Analysis of Epileptic Seizure Prediction: Exploring Diverse Pre-Processing Techniques and Machine Learning Models

Comparative Analysis of Epileptic Seizure Prediction: Explor...

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IEEE UP Section Conference on Electrical computer and Electronics (UPCON)

作者： Md. Simul Hasan Talukder Rejwan Bin Sulaiman Gunjan Chhabra Suman Avdhesh Yadav Rezaul Haque Md Babul Islam Keshav Kaushik Nuclear Safety Security and Safegurad Division Bangladesh Atomic Energy Regulatory Authority Dhaka Bangladesh School of Computer Science and Technology Northumbria University Newcastle UK Dept. of CSE Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Amity School of Engineering & Technology Noida India Dept of Computer Science and Engineering East West University Dhaka Bangladesh Department of Computer Modeling Electronic and System Engineering (DIMES) University of Calabria Calabria Italy School of Computer Science University of Petroleum and Energy Studies Dehradun India

Precise prediction and detection of epilepsy, a common neurological condition marked by sporadic and repeated seizures, is necessary for efficient management and patient care. To do accurate and reliable seizure detection, machine learning (ML) on electroencephalogram (EEG) recordings is a good choice because of its capacity to provide insightful information about brain activity during seizures. In this work, we do a thorough comparative analysis of five machine learning methods for the prediction of epileptic seizures using EEG data: Random Forest (RF), Decision Tree (DT), Extra Trees (ET), Logistic Regression (LR), and Gradient Boosting (GB). Firstly, to assure the quality of the data and enable precise model training, the dataset underwent extensive preprocessing, which included cleaning, normalization, treatment of outliers, and oversampling. These preprocessing techniques significantly enhanced the models' performance. In our study, we evaluate the accuracy of each model; the LR classifier obtains 56.95% accuracy, while both GB and DT achieve 97. 17% accuracy. Outperforming the findings of previous investigations, the ET model showed the best performance with an accuracy of 99.29%. Achieving 98.99% accuracy was another accomplishment for the RF model. Our results imply that the ET model not only performed better than the other models in the comparison analysis but also more effectively than the most advanced outcomes from earlier research. Regarding the accurate and dependable prediction of epileptic seizures using EEG data, the ET model is a solid option due to its remarkable performance.

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Multi-Turn Jailbreaking Large Language Models via Attention Shifting 39

Multi-Turn Jailbreaking Large Language Models via Attention ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Du, Xiaohu Mo, Fan Wen, Ming Gu, Tu Zheng, Huadi Jin, Hai Shi, Jie China National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security China Cluster and Grid Computing Lab School of Computer Science and Technology HUST China JinYinHu Laboratory China Huawei International China

ISBN: (纸本)157735897X

Large Language Models (LLMs) have achieved significant performance in various natural language processing tasks but also pose safety and ethical threats, thus requiring red teaming and alignment processes to bolster their safety. To effectively exploit these aligned LLMs, recent studies have introduced jailbreak attacks based on multi-turn dialogues. These attacks aim to prompt LLMs to generate harmful or biased content by guiding them through contextual content. However, the underlying reasons for the effectiveness of multi-turn jailbreaks remain unclear. Existing attacks often focus on optimizing queries and escalating toxicity to construct dialogues, lacking a thorough analysis of the inherent vulnerabilities of LLMs. In this paper, we first conduct an in-depth analysis of the differences between single-turn and multi-turn jailbreaks and find that successful multi-turn jailbreaks can effectively disperse the attention of LLMs on keywords associated with harmful behaviors, especially in historical responses. Based on this, we propose ASJA, a new multi-turn jailbreak approach by shifting the attention of LLMs, specifically by iteratively fabricating the dialogue history through a genetic algorithm to induce LLMs to generate harmful content. Extensive experiments on three LLMs and two datasets show that our approach surpasses existing approaches in jailbreak effectiveness, the stealth of jailbreak prompts, and attack efficiency. Our work emphasizes the importance of enhancing the robustness of LLMs’ attention mechanism in multi-turn dialogue scenarios for a better defense strategy. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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A public data-set for synchronous motor electrical faults diagnosis with CNN and LSTM reference classifiers

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Energy and AI 2023年第4期14卷 221-237页

作者： Zhenglong Sun Ram Machlev Qianchao Wang Juri Belikov Yoash Levron Dmitry Baimel Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of Education(Northeast Electric Power University)JilinChina The Andrew and Erna Viterbi Faculty of Electrical&Computer Engineering Technion-Israel Institute of TechnologyHaifa 3200003Israel Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education School of Energy and EnvironmentSoutheast UniversityNanjing210096China Department of Software Science Tallinn University of TechnologyAkadeemia tee 15a12618 TallinnEstonia Department of Electrical and Electronics Engineering Sami Shamoon College of EngineeringBeer-Sheva84100Israel

In the last two decades,motor operation monitoring tools have become a necessity,and many studies focus on the detection and diagnosis of motor electrical ***,at present,a core obstacle that prevents the direct comparison of such classification techniques is the lack of a standard database that can be used as a *** view of this,we offer here a public experimental data-set that has beendesigned specifically for the comparison of synchronous motor electrical fault *** data-set comprises five types of motor electrical faults:open phase between inverter and motor;short circuit/leakage current between two phases;short circuit/leakage current in phase-to-neutral;rotor excitation voltage disconnection;and variation of rotor excitation *** addition,each fault has been recorded as a four-dimensional signal:three phase voltages;three phase currents;motor speed;and motor *** package includes two deep-learning reference classifiers that are based on a convolutional neural network(CNN)and long short term memory(LSTM).Due to the good performance of these classifiers,we suggest that they can be used by the community as benchmarks for the development of new and better motor electrical fault classification *** database and the reference classifiers are examined and insights regarding different combinations of features and lengths of recording points are *** developed code is available online,and is free to use.

关键词： Motor faults Synchronous motors Public data-set Classification Deep-learning CNN LSTM

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Path planning for multi-quadrotor 3D boundary surveillance using non-autonomous discrete memristor hyperchaotic system

arXiv

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arXiv 2024年

作者： Harisankar, R. Kaushik, Abhishek Muni, Sishu Shankar School of Electronic System and Automation Digital University Kerala Kerala Thiruvananthapuram695317 India School of Computer Science and Engineering Digital University Kerala Kerala Thiruvananthapuram695317 India School of Digital Sciences Digital University Kerala Kerala Thiruvananthapuram695317 India

Recent studies have shown that chaotic maps are well-suited for applications requiring unpredictable behaviour, making them a valuable tool for enhancing unpredictability and complexity. A method is developed using 3D parametric equations to make boundary surveillance more robust and flexible to effectively cover and adapt to the ever-changing situations of boundary surveillance. A non-autonomous discrete memristor hyperchaotic map is utilized to significantly enhance the unpredictability of trajectories in boundary surveillance. Since the scaling property is an affine transformation, and the hyperchaotic systems are invariant under affine transformations, the adaptability of hyperchaotic trajectories is visually demonstrated by applying the scaling property for robust path planning. Furthermore, hyperchaotic systems enable the generation of mutually independent trajectories with slight variations in initial conditions, allowing multiple quadrotors to operate simultaneously along a single guiding path. This minimizes the chance of overlap and interference, ensuring effective and coordinated surveillance. © 2024, CC BY-NC-SA.

关键词： Trajectories

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