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12th International Workshop on Quantitative Approaches to software Quality, QuASoQ 2024

作者： Zou, Sheng Chen, Liqian Fan, Guangsheng Huang, Renjie Yin, Banghu College of Computer Science and Technology National University of Defense Technology Changsha410073 China State Key Laboratory of Complex & Critical Software Environment Changsha410073 China College of Systems Engineering National University of Defense Technology Changsha410073 China

The Fortran programming language is widely utilized in numerical computation and scientific computing. Fortran programs are prone to potential runtime errors related to numerical properties due to the large number of numerical operations. In this paper, we present F-IKOS, an abstract interpretation-based static analyzer for Fortran programs on top of IKOS, which soundly handles floating-point types in Fortran programs. Firstly, we translate Fortran programs to LLVM IR using compiler front-end Flang. After that, we extend IKOS to support sound floating-point analysis and then employ it to analyze the translated LLVM IR. Particularly, when analyzing floating-point types in programs, we first abstract floating-point expressions into real-number expressions with interval coefficients, and then linearize these expressions into real-number expressions with scalar coefficients. These linear expressions are subsequently handled by abstract domains originally designed for real-number types to produce sound analysis results. We have conducted experiments on representative Fortran programs to show the efficiency and effectiveness of F-IKOS. The experimental results are encouraging: F-IKOS soundly analyzes runtime errors in complex programs, outperforming other analyzers. © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： FORTRAN (programming language)

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An Intelligent Defect Detection Algorithm for PCB based on Deep Learning 6

An Intelligent Defect Detection Algorithm for PCB based on D...

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6th International Conference on Advances in Image Processing, ICAIP 2022

作者： Zhu, Xiangyuan Xiao, Xiuchun Lan, Zhiming Hong, Qiuhong Hou, Miao School of Computer Science and Software Zhaoqing University China College of Electronics and Information Engineering Guangdong Ocean University China

ISBN: (纸本)9781450397155

As an essential component of modern machines, printed circuit board (PCB) is widely used in various electronic products. Its quality significantly affects the quality of products. However, the production process of PCB is often accompanied with defects. In this paper, a defect detection algorithm is proposed. Data augmentation such as flipping, shifting, brightness adjustment, rotation, and Guass noise are carried out to diversify the dataset. You only look once (YOLO) v5s is then introduced to detect the PCB defects. Through parameter tuning and optimization, a trained detection model is achieved. F1-score and mean average precision (mAP) are used to assess the performance of the model. The experiment results show that the mAP and F1-score are 99.3% and 99.0%, respectively. The model developed based on YOLO-v5s algorithm can achieve superior performance, which is competent to detect the defects of PCBs. © 2022 ACM.

关键词： Printed circuit boards

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Stratifying large software files to improve prediction performance in software defect prediction 9

Stratifying large software files to improve prediction perfo...

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9th ACIS International Virtual Conference on Applied Computing and Information Technology, ACIT 2022

作者： Alshehri, Yasser Ali Alnazzawi, Noha Hijazi, Haneen Alharbi, Rawan Computer Science and Engineering Dept Yanbu Industrial College Saudi Arabia Dept of Software Engrg Hashemite University Jordan

ISBN: (纸本)9781450397605

Size is one of the significant factors associated with bugs, and it has been used to predict software faults. We believe that stratifying software files based on size can play an essential role in improving prediction performance. This study explored the effect of size by stratifying our sample based on each unit's size and distributing software units in multiple stratified groups based on an equal distribution approach. We stratified the Eclipse Europa project files, and we reported the performance of each stratified group and compared them. We used two popular classifiers, decision tree J48, and random forest, to implement this experiment. These classifiers presented similar results on the same group of files. The results indicated that predicting faults with large files is better than predicting those in small files. In addition, the results showed higher median values of all performance measures and less variation in each measure. © 2022 ACM.

关键词： Decision trees

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Hybrid Machine Learning Model for Face Recognition Using SVM

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computers, Materials & Continua 2022年第8期72卷 2697-2712页

作者： Anil Kumar Yadav R.K.Pateriya Nirmal Kumar Gupta Punit Gupta Dinesh Kumar Saini Mohammad Alahmadi IES College of Technology Bhopal462044India Maulana Azad National Institute of Technology Bhopal462003India Department of Information Technology Manipal University JaipurJaipur303007India Department of Computer and Communication Engineering Manipal University JaipurJaipur303007India Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah23218Saudi Arabia

Face recognition systems have enhanced human-computer interactions in the last ten ***,the literature reveals that current techniques used for identifying or verifying faces are not immune to *** Component Analysis-Support Vector Machine(PCA-SVM)and Principal Component Analysis-Artificial Neural Network(PCA-ANN)are among the relatively recent and powerful face analysis *** to PCA-ANN,PCA-SVM has demonstrated generalization capabilities in many tasks,including the ability to recognize objects with small or large data *** from requiring a minimal number of parameters in face detection,PCA-SVM minimizes generalization errors and avoids overfitting problems better than ***-SVM,however,is ineffective and inefficient in detecting human faces in cases in which there is poor lighting,long hair,or items covering the subject’s *** study proposes a novel PCASVM-based model to overcome the recognition problem of PCA-ANN and enhance face *** experimental results indicate that the proposed model provides a better face recognition outcome than PCA-SVM.

关键词： Face recognition system(FRS) face identification SVM discrete cosine transform(DCT) artificial neural network(ANN) machine learning

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Multimodal News Recommendation Based on Deep Reinforcement Learning 7

Multimodal News Recommendation Based on Deep Reinforcement L...

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7th International Conference on Intelligent Computing and Signal Processing, ICSP 2022

作者： Guo, Nan Fu, Zhangpeng Zhao, Qihui Northeastern University School of Computer Science and Engineering Shenyang China Northeastern University School of Software College Shenyang China

ISBN: (纸本)9781665478571

Multimodal news recommendation is a challenging problem due to the rapid expansion of Internet information, bringing different levels of knowledge expression such as text, images, audio, and video, etc. In this paper, we propose a multimodal news recommendation method based on a deep reinforcement learning framework to represent user interests as multimodal information. The proposed method feeds the multimodal fusion feature into the rainbow agent of deep reinforcement learning to learn news representation. The experiment on the MIND and IM-MIND datasets give the result of AUC, MRR, nDCG@5, and nDCG@10 scores, which outperform LSTUR, FIM, and DKN, NRMS, NPA, and DeepFM models. It shows that reinforcement learning is an excellent choice to fulfill recommendation tasks, and the multimodal fusion feature is effective for learning accurate news representations. © 2022 IEEE.

关键词： Deep learning

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Automatic Detection of Outliers in Multi-Channel EMG Signals Using MFCC and SVM

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Intelligent Automation & Soft Computing 2023年第4期36卷 169-181页

作者： Muhammad Irfan Khalil Ullah Fazal Muhammad Salman Khan Faisal Althobiani Muhammad Usman Mohammed Alshareef Shadi Alghaffari Saifur Rahman Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia Department of Software Engineering University of MalakandDir LowerPakistan Department of Electrical Engineering University of Engineering and TechnologyMardan23200Pakistan Faculty of Maritime Studies King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science University of Engineering and TechnologyMardan23200Pakistan

The automatic detection of noisy channels in surface Electromyogram(sEMG)signals,at the time of recording,is very critical in making a noise-free EMG *** an EMG signal contaminated by high-level noise is recorded,then it will be useless and can’t be used for any healthcare *** this research work,a new machine learning-based paradigm is proposed to automate the detection of low-level and high-level noises occurring in different channels of high density and multi-channel sEMG signals.A modiﬁed version of mel fre-quency cepstral coefﬁcients(mMFCC)is proposed for the extraction of features from sEMG channels along with other statistical parameters i-e complexity coef-ﬁcient,hurst exponent,and root mean *** state-of-the-art classiﬁers such as Support Vector Machine(SVM),Ensemble Bagged Trees,Ensemble Sub-space Discriminant,and Logistic Regression are used to automatically identify an EMG channel either bad or good based on these extracted ***-based analyses of these classiﬁers have also been considered based on total classi-ﬁcation accuracy,prediction speed(observations/sec),and processing *** proposed method is tested on 320 simulated EMG channels as well as 640 experi-mental EMG *** is used as our main classiﬁer for the detection of noisy channels which gives a total classiﬁcation accuracy of 99.4%for simulated EMG channels whereas accuracy of 98.9%is achieved for experimental EMG channels.

关键词： Machine learning surface electromyography support vector machine classiﬁcation features

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A Multiple Perceptual News Recommendation Method Based on Dynamic Multi-convolution Kernel CNN and Attention Mechanism

A Multiple Perceptual News Recommendation Method Based on Dy...

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2022 International Conference on computer, Artificial Intelligence, and Control engineering, CAICE 2022

作者： Guo, Nan Li, Jin-Hui Gao, Shen Northeastern University School of Computer Science and Engineering Shenyang China Northeastern University School of Software College Shenyang China

ISBN: (纸本)9781510655942

News recommendation is one of the important tasks in recommendation tasks. The existing recommendation process is essentially a system loop feedback process. The content fed back by the recommendation system will be greatly affected by the user's click behavior, while the news recommendation model usually learns from the user's click behavior, which will lead to the system recommending news that users have clicked/liked to watch with high probability and pushing other types of news with low probability, resulting in the problem of insufficient diversity. To effectively address this type of problem, we first analyzed the news data statistically and found the bias of "viewing frequency", and then proposed a multi-perceptual news recommendation method based on dynamic multi-convolution kernel CNN and attention mechanism. We use CNN with convolution kernels of different sizes to extract headline features in the news feature extractor. And then combine them in an attentive way. Form title features with more representations. The user feature extractor is divided into biased user feature extractor and unbiased user feature extractor. One is to learn biased user attributes and perceive user characteristics biased and the other is to learn unbiased user attributes and perceive user characteristics without prejudice. Then we use the cosine similarity loss, that is, to increase the difference between the two user characteristics through cosine similarity. The most important point is that ensure that the accuracy of the model is not reduced as much as possible while debiasing, we combine the two features in an attentive manner, and finally propose a ranking method to improve the diversity of recommendations. Extensive experiments show that our method can not only remove biases, but also achieve a high accuracy of the model. © 2022 SPIE.

关键词： Dynamics

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Quantum Reinforcement Learning for Cognitive Radio-Assisted UAV Traffic Offloading

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IEEE Transactions on Vehicular Technology 2025年

作者： He, Ying Huang, Chao Yu, F. Richard Liu, Zhiquan Zhou, Guang Shenzhen University College of Computer Science and Software Engineering China Jinan University China DeepRoute AI Inc. China

With the continuous development of communication industry, the demand for spectrum resources is gradually increasing. Simultaneously, unmanned aerial vehicles (UAVs) have been extensively studied as a means to assist with traffic offloading by establishing wireless links between base stations (BSs) and UAVs. However, the current spectrum resources are limited and far from optimal utilization. In response to this situation, cognitive radio (CR) technology and non-orthogonal multiple access (NOMA) technologies have been proposed to enhance the efficient utilization of spectrum resources. In this paper, we address the challenge of using CR technology to assist UAVs in traffic offloading within specific areas. At each instance, the UAV must consider the allocation of time for data collection, traffic transmission, and UAV charging. Given the dynamic nature of spectrum resources, UAVs must make adaptive decisions based on the current conditions. Finally, we model the aforementioned issues as a quantum reinforcement learning (QRL) system to expedite the training process. In particular, we design a quantum soft actor-critic algorithm employing a variable quantum circuit (VQC) and encode the experience data into a quantum summation tree while transforming it into a quantum priority experience replay buffer. Simulation results demonstrate the superior performance of the proposed framework. © 2025 IEEE.

关键词： Drones

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Maximizing IoT Security: An Examination of Cryptographic Algorithms

Maximizing IoT Security: An Examination of Cryptographic Alg...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Arya, Leena Sharma, Yogesh Kumar Kumar, Ramakrishna Padmanaban, Harish Devi, Suman Tyagi, Lalit Kumar Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering AP Guntur India College of Engineering National University of Science and Technology Department of Ece Muscat Oman Site Reliability and Software Engineering Professional Investment Banking HoustonTX United States Chaudhary Devi Lal University Department of Computer Science and Engineering Haryana Sirsa India G L Bajaj Institute of Technology Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350357769

The proliferation of Internet of Things (IoT) devices has created a ubiquitous network of interconnected sensors and devices that generate and exchange vast amounts of data. With this increased connectivity comes a pressing need for robust security measures to safeguard IoT communications' integrity, confidentiality, and authenticity. This paper categorizes cryptographic algorithms commonly employed in IoT security, including symmetric and asymmetric encryption methods, digital signatures, and key exchange protocols. By examining the attributes of these algorithms, their strengths, and potential vulnerabilities, this paper assesses their effectiveness within the unique context of IoT. Furthermore, the paper highlights the significance of cryptographic algorithm optimization in IoT security. Strategies for enhancing algorithm efficiency, such as algorithm selection, key management, and hardware acceleration are thoroughly explored. Through these case studies, this paper underscores the practical implications of cryptographic algorithm selection and optimization, illustrating how they can prevent security breaches and enhance IoT security. © 2023 IEEE.

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Beam Splitting Sensing for Terahertz Near-Field Channel Estimation 3

Beam Splitting Sensing for Terahertz Near-Field Channel Esti...

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3rd International Conference on Intelligent Communications and Computing, ICC 2023

作者： Tang, Shaochen Dong, Zhicheng Zhou, Weixi Cai, Donghong Zhou, Enzhi Lan, Ping College of Information Science Technology Tibet University Lhasa850000 China School of Computer Science Sichuan Normal University Chengdu610066 China College of Cyber Security Jinan University Guangzhou510632 China School of Computer and Software Engineering Xihua University Chengdu610039 China

ISBN: (纸本)9798350308327

Terahertz technology is considered as one of the key techniques for future 6G due to its ultra-wide bandwidth and ultra-high communication rate. However, terahertz communication channel estimation faces several significant challenges, one is that extremely-large-scale MIMO communication typically occurs in the near field, which will result in traditional far-field channel estimation schemes no longer being suitable. The other is the beam splitting effect caused by terahertz wideband communication, which leads to a large impact on the steering vector caused by different subcarrier frequencies. In order to solve these problems, this paper first studied the model of THz near-field beam splitting;secondly discussed the construction method of the THz near-field polar dictionary matrix;and then proposed a two-stage beam splitting sensing (Two-stage-BSS) algorithm to improve the accuracy of near-field channel estimation and reduce the impact of beam splitting. The experimental results validate the effectiveness of the proposed algorithm. © 2023 IEEE.

关键词： Channel estimation

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