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IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C)

作者： Yadi Hu Jinlong Tao Yu Liang Zhewei Xu Zhangtao Jia Beijing Institute of Computer Technology and Application Beijing China

ISBN: (数字)9798350365658

ISBN: (纸本)9798350365665

This paper primarily analyzes the impact of tester capabilities on the quality of aerospace software testing and elaborates on how to assess tester abilities from both technical and cognitive perspectives. The research was supported by the Aerospace Software Testing Center of the China Aerospace Science and Industry Corporation (CASIC), which allowed researchers to collect data on the center's testing personnel and conduct interviews with relevant testers. The findings of this research have been used by the center to coordinate the efforts to improve quality and human resource management. This paper provides valuable insights and guidance for the field of aerospace software testing, contributing to the enhancement of tester capabilities and software testing quality, and ultimately promoting the reliability and safety of aerospace software.

关键词： Software testing Technical requirements Software quality Reliability theory Aerospace testing Aerospace safety Software reliability Security Problem-solving Aerospace industry

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SQAT-LD: SPeech Quality Assessment Transformer Utilizing Listener Dependent Modeling for Zero-Shot Out-of-Domain MOS Prediction

SQAT-LD: SPeech Quality Assessment Transformer Utilizing Lis...

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2023 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2023

作者： Shen, Kailai Yan, Diqun Dong, Li Ren, Ying Wu, Xiaoxun Hu, Jing Ningbo University Faculty of Electrical Engineering and Computer Science China Zhejiang Key Laboratory of Mobile Network Application Technology China

ISBN: (纸本)9798350306897

In this paper, we propose the speech quality assessment transformer utilizing listener dependent modeling (SQAT-LD) mean opinion score (MOS) prediction system, which was submitted to the 2023 VoiceMOS Challenge. The system is based on a combination of self-supervised learning (SSL) models and listener-dependent modeling. Due to this challenge's emphasis on real-world and challenging zero-shot out-of-domain MOS prediction in three different voice evaluation scenarios, we specifically designed a two-branch module to predict scores and weights for each frame, aiming to achieve better generalization. In the challenge, our system achieved fourth place in Track 1a, second place in Track 1b and first place in Track 2. Additionally, we conducted an ablation study to investigate the effectiveness of our proposed method. © 2023 IEEE.

关键词： Forecasting

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An Efficient Vision based Method for Detecting Drowsiness in Real-time 6

An Efficient Vision based Method for Detecting Drowsiness in...

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6th International Conference on Intelligent Computing and Control Systems, ICICCS 2022

作者： Pulluri, Rahul Ranjana, P. Hindustan Institute of Technology & Science Department of Computer Application Chennai603103 India

ISBN: (纸本)9781665410359

The driver feeling drowsy while driving is a common situation which has become a major reason for the cause of accidents on road. A drunk person and a person in drowsy state are in the same condition with blur eyes and have slow thought process. There has been various research carried out to detect the drowsy state. Haar Cascade classifiers method has been used a lot in the previous studies for face detection;however, the problem with haar cascade is that they are highly liable to wrong face detections and need adjustment of parameters to detect MultiScale function. This paper proposes a system that utilizes Python, OpenCV and Dlib face detector which uses HOG and Linear SVM. Dlib is a library written in C++, which is highly suitable for solving complex problems. In this paper, Dlib is used for detecting faces and facial landmarks. The proposed algorithm is a real time algorithm to check the eye status from the video input captured in the real time. It considers the position of the landmarks detected on the face and extract the eye region by using both eyes' facial landmarks. The Eye Aspect Ratio is calculated and depending upon the ratio, the system decides to alarm or not. The proposed system gives better accurate results compared to the Haar cascade. © 2022 IEEE.

关键词： Aspect ratio

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A Rate-Distortion-Classification Approach for Lossy Image Compression

arXiv

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arXiv 2024年

作者： Zhang, Yuefeng Beijing Institute of Computer Technology and Application Beijing China

In lossy image compression, the objective is to achieve minimal signal distortion while compressing images to a specified bit rate. The increasing demand for visual analysis applications, particularly in classification tasks, has emphasized the significance of considering semantic distortion in compressed images. To bridge the gap between image compression and visual analysis, we propose a Rate-Distortion-Classification (RDC) model for lossy image compression, offering a unified framework to optimize the trade-off between rate, distortion, and classification accuracy. The RDC model is extensively analyzed both statistically on a multi-distribution source and experimentally on the widely used MNIST dataset. The findings reveal that the RDC model exhibits desirable properties, including monotonic non-increasing and convex functions, under certain conditions. This work provides insights into the development of human-machine friendly compression methods and Video Coding for Machine (VCM) approaches, paving the way for end-to-end image compression techniques in real-world applications. © 2024, CC BY-NC-SA.

关键词： Signal distortion

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Multi-Modal Instrument Recognition Method Based on Improved YOLOv5s and ESPNet 6

Multi-Modal Instrument Recognition Method Based on Improved ...

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6th IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2024

作者： Shen, Shixian Wang, Jianguo Feng, Yong Li, Yingna Kunming University of Science and Technology Yunnan Key Laboratory of Computer Technologies Application Kunming China China Copper Industry Co. Ltd Kunming China

ISBN: (纸本)9798350374315

Addressing the challenges of high cost, low accuracy, and poor real-time performance in anomaly detection within industrial production processes, this study proposes a multi-modal instrument recognition method based on improved YOLOv5s and ESPNet. Specifically, a dynamic non-monotonic focusing mechanism is introduced into the YOLOv5s object detection model, enhancing its ability to accurately detect the position of pointer-type instruments. Simultaneously, affine and perspective transformations are employed to rectify skewed and rotated images. Secondly, variable convolutions are integrated into the ESPNet segmentation network to adaptively capture nonlinear deformation elements of pointers and dials in images, thereby extracting comprehensive key information from both. Subsequently, a Hough transform operation is applied to fit the straight line representing the pointer, while contour tracking methods are utilized to extract the valid range of dial data. Based on linear proportional relationships, the reading of pointer-type instruments is computed. Finally, a comprehensive model is built by integrating image-acquired instrument data with corresponding sensor data, enabling anomaly detection from a multimodal data analysis perspective. Experimental results demonstrate an identification accuracy of 96.21% for pointer-type instruments, with an average detection speed of 0.216 seconds and a data anomaly detection rate of 99.43%. This method accurately and rapidly identifies data anomalies, meeting the demands for key indicator monitoring in industrial production. © 2024 IEEE.

关键词： Hough transforms

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A Blockchain-Based Key Management Scheme in FANETs

A Blockchain-Based Key Management Scheme in FANETs

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Electronic Information Engineering and computer Science (EIECS), 2021 International Conference on

作者： Datong Yin Lei Han Beijing Institute of Computer Technology and Application Beijing China

ISBN: (数字)9798331531409

ISBN: (纸本)9798331531416

Flying Ad-Hoc Networks (FANETs) enable the deployment of Unmanned aerial vehicles (UAVs) in a clustered manner to execute complex missions, yet their inherent openness poses significant challenges to communication security. However, as a crucial part of ensuring communication security, most existing FANET key management schemes rely on base stations or other infrastructures. This reliance not only complicates communication between UAVs and base stations but also makes the system vulnerable to single point of failure. To address these issues, we introduce a key management scheme based on Blockchain. This scheme in-tegrates the key tree with the secret sharing scheme, which achieves the autonomous generation and updating of group keys, the dynamic joining or exiting of UAVs, and the withdraw of malfunctioning UAVs. Security and performance analyses demonstrate that our scheme effectively resists a variety of security threats, exhibits lower computational overhead and energy consumption compared to similar schemes, making it well-suited for heterogeneous FANET environments.

关键词： Resistance computer science Energy consumption Base stations Resists Ad hoc networks Performance analysis Complexity theory Vehicle dynamics Eavesdropping

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A real-Time framework for dangerous behavior detection based on deep learning 4

A real-Time framework for dangerous behavior detection based...

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4th International Conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2022

作者： Yang, Heng Liu, Pengjie Li, Shiyuan Liu, Hongyu Wang, Haofeng Ark Key Laboratory Beijing Institute of Computer Technology and Application Beijing China

ISBN: (纸本)9781450398343

Identifying dangerous behaviors of workers in complex production operation scenarios is an important research field of intelligent monitoring technology. When implementing practical applications, it often has complex and diverse function requirements and business logic. To this end, this paper proposes a unified and simple framework for monitoring unsafe behavior based on deep learning technology. We first conduct data analysis and demand logic disassembly based on six actual production scenarios and decouple the complex task requirement as a coupled problem of object detection function, adaptive and variable scene recognition function, behavior analysis function, and safety logic reasoning function. Then we build a unified detection framework and use four sub-modules for real-Time and high-efficiency detection, which are a perceptron-based efficient scene recognition module, a Yolov5s-based real-Time object detection module, an area-based behavior judgment module, and a configurable safety rule inference module. While maintaining the characteristics of splitting and combining these modules for different application requirements, their effects on the test set have also reached the best. Among them, the accuracy rate of the scene recognition module has reached 100.00%, and the mAP of the object detection module has reached 99.06%, average FPS of the overall framework reached 72.05. © 2022 ACM.

关键词： Object detection

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Bit level image encryption algorithm based on hyperchaotic system

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Optoelectronics Letters 2023年第3期19卷 186-192页

作者： MAN Zhenlong ZHANG Yue ZHOU Ying LU Xiaoli WANG Zhaoquan School of Electronic and Information Engineering Liaoning Technical UniversityHuludao 125100China Advanced Interactive Technology and Application Laboratory Computer Network Information CenterChinese Academy of SciencesBeijing 100190China

Because chaotic systems are unpredictable,ergodic and sensitive to initial values and parameters,they are often used in the field of *** avoid the bad randomness of the random key generated by the low dimensional chaotic map system,a 5-dimensional multi-wing hyperchaotic system is adopted in this *** key stream generated by the chaotic system is related to the plaintext *** it can effectively resist the attacks of selecting *** plaintext graph is decomposed into binary form by bit plane decomposition technique,and then these bit planes are divided into high and low *** designed control matrix is used to identify the specific scrambling mode,and each bit is permuted within and between ***,bit diffusion is used to change the pixel value of each *** analysis and numerical simulation show that the algorithm has good encryption performance for image encryption.

关键词： chaotic system algorithm

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AI-Enabled Learning for Imbalanced Class Data using Spark Streaming Concepts 21

AI-Enabled Learning for Imbalanced Class Data using Spark St...

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21st OITS International Conference on Information technology, OCIT 2023

作者： Tiwari, Sadhana Zohair, Mohammad Chandra, Ritesh Agarwal, Sonali Agarwal, Parikshit Indian Institute of Information Technology Allahabad Department of Information Technology Prayagraj India Jamia Millia Islamia Department of Computer Engineering New Delhi India SIET Department of Computer Application Prayagraj India

ISBN: (纸本)9798350358230

Chronic Diseases are a very significant problem around the world. Manhandling medical cases often leads to significant repercussions in the treatment process and needs to be detected and dealt with early on. Unusual clinic actions are often related to medical errors, enabling detection and alerting. Huge leaps in machine learning, data science, and big data analytics techniques have led to innovative ways to deal with these issues. As a result, mortality rates can be greatly reduced in all sorts of medical cases. In this work, a new ensemble method is proposed for multiclass classification applied on the US-Chronic Disease Indicator (US-CDI) dataset and validated using UCI-Diabetes data by applying K-means clustering to it to detect chronic disease. The proposed hybrid model, KD-Boost Ensemble, is based on the concept of ensemble voting mechanisms, which combine the predictions of the K-neighbors classifier, Decision Tree classifier, and Gradient Boosting classifier to obtain improved accuracy over the dataset when compared to the individual conventional classifiers. This chronic disease classification achieved 96% accuracy using the proposed model. The devised model is validated through the diabetes dataset in a real-time environment and attains 98% accuracy. © 2023 IEEE.

关键词： Decision trees

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IntML-KNN: A Few-Shot Radio Frequency Fingerprint Identification Scheme for Lora Devices

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IEEE Signal Processing Letters 2025年 32卷 2259-2263页

作者： Cao, Xin Tan, Weijie Gao, Qiangqiang Hu, Zhilong Li, Chunguo Guizhou University College of Computer Science and Technology Guiyang550025 China Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610025 China Ministry of Industry and Information Technology Laboratory of Cryptograph Application Technology Innovation and Testing Verification Beijing100015 China Southeast University School of Information Science and Engineering Nanjing210096 China

Deep learning (DL) is widely used in radio frequency fingerprint identification (RFFI). However, in few-shot case, traditional DL-based RFFI need to construct auxiliary dataset to realize radio frequency fingerprint identification. To address this issue, we propose a few-shot RFFI (FS-RFFI) method based on interpolating metric learning and KNN (IntML-KNN). Specifically, the method first extends the dataset with data augmentation, and CutMix interpolation. Secondly, combining with metric learning to enhance the generalization capacity of the model. Finally, KNN algorithm is designed to realize device classification and detection. The proposed IntML-KNN method is verified on the commercial available LoRa dataset. The experimental results indicate that the proposed scheme exhibits strong classification and generalization performance in FS-RFFI. Meanwhile, the recognition rate of the proposed IntML-KNN reaches 97.00% with only 10% samples. © 1994-2012 IEEE.

关键词： Deep learning

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