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International Conference on Recent Trends in Advance computing (ICRTAC)

作者： Gouranga Mandal Anurag De Arindam Dey Sudeepthi Gangarapu Sharmila Rani Chekuru Chekuri Nandini School of Computer Science and Engineering Vellore Institute of Technology Andhra Pradesh India School of Computer Science and Engineering With Specialization in Data Analytics Vellore Institute of Technology Andhra Pradesh India

This article proposes a technique to create a pothole detection system that can operate both during the day and at night. This model was developed using the real-time object identification YOLO (You Only Look Once) technique. The potholes are located using a pre-trained model with YOLO v5. In its creation, the PyTorch framework is utilized. The first YOLO model created using the PyTorch framework is YOLOv5, which is a lot more user-friendly and lightweight. In a manner similar to real-time object detection systems, the proposed method displays potholes in real time and emphasizes them with boundary boxes for accurate identification. The accuracy achieved with this method is 97% during the day and 92% at night which is found to be satisfactory.

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Extremal Maximal Entanglement

arXiv

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arXiv 2024年

作者： Zhang, Wanchen Ning, Yu Shi, Fei Zhang, Xiande School of Mathematical Sciences University of Science and Technology of China Hefei230026 China Hefei National Laboratory University of Science and Technology of China Hefei230088 China Hefei National Laboratory Hefei230088 China Department of Computer Science School of Computing and Data Science University of Hong Kong 999077 Hong Kong

A pure multipartite quantum state is called absolutely maximally entangled if all reductions of no more than half of the parties are maximally mixed. However, an n-qubit absolutely maximally entangled state only exists when n equals 2, 3, 5, and 6. A natural question arises when it does not exist: which n-qubit pure state has the largest number of maximally mixed ⌊n/2⌋-party reductions? Denote this number by Qex(n). It was shown that Qex(4) = 4 in [Higuchi et al. Phys. Lett. A (2000) ] and Qex(7) = 32 in [Huber et al. Phys. Rev. Lett. (2017) ]. In this paper, we give a general upper bound of Qex(n) by linking the well-known Turán’s problem in graph theory, and provide lower bounds by constructive and probabilistic methods. In particular, we show that Qex(8) = 56, which is the third known value for this problem. © 2024, CC BY.

关键词： Quantum entanglement

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Fu-W:A Hyperspectral Image Classification Algorithm Combining Mini Graph Convolutional Networks and Convolutional neural network 23

Fu-W:A Hyperspectral Image Classification Algorithm Combinin...

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6th International Conference on Big data Technologies, ICBDT 2023

作者： Shi, Jiechuan Liu, Kun Yuan, Hao Wang, Can Yang, Bo School of Information Science and Engineering University of Jinan Jinan250022 China China High-resolution Earth Observation System Shandong Center of Data and Application University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan250022 China

ISBN: (纸本)9798400707667

Convolutional Neural Network (CNN) is a widely used neural network in deep learning, and Graph Convolutional Network (GCN) is one of the most effective semi -supervised methods. It spread node information in a conversion way. In this article, we have studied the differences between CNN and GCN in the classification of high -spectrum image. Because the traditional GCN algorithm needs to build an adjacent matrix on all data, the calculation cost is very high, especially in large -scale remote sensing problems. MinigCNS uses a small -batch learning method to solve the problem of CCN calculation costs, and then it has not solved the problem of low efficiency of single model classification. This article studies the advantages of minigcns and CNN, and proposes a weighted fusion network FU-W, which weighs the minigcns and CNN weighted integration to break the bottleneck of single model performance. We experimented with the fusion algorithm on the two high -spectrum data sets, and its overall accuracy can reach 88.8%in Indian Pines. The experiment proves the superiority of the fusion strategy for a single CNN or GCN model. © 2023 ACM.

关键词： Convolutional neural networks

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Few-Shot Learning-Based Machine Fault Diagnosis Using EMD-Gammatone Spectrogram with Limited Labeled Audio dataset

Few-Shot Learning-Based Machine Fault Diagnosis Using EMD-Ga...

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International Conference on Big data and Smart computing (BIGCOMP)

作者： Mahe Zabin Syed Tasnimul Karim Ayon Farhan Md Siraj Mehedi Hasan Shuvo Ho-Jin Choi Jia Uddin School of Computing Korea Advanced Institute of Science and Technology Daejeon Republic of Korea Department of Computer Science and Engineering BRAC University Dhaka Bangladesh Department of Computer Science and Engineering Dhaka University of Engineering & Technology Gazipur Dhaka Bangladesh AI and Big Data Department Endicott College Woosong University Daejeon Republic of Korea

ISBN: (数字)9798331529024

ISBN: (纸本)9798331529031

The deep learning models are computationally burdensome, and their accuracy depends on the number of labeled datasets used in training. The scarcity of the labeled dataset in industrial machine diagnosis is a significant concern as it is pretty impossible to collect data from faulty machines in industrial environments. To address this issue, in this paper, we presented a few-shot learning-based machine fault diagnosis model on a lightweight SqueezeNet architecture using generated texture images from audio sensor data. Subsets of two public audio datasets, MIMII and ToyADMOS, containing limited data- 10 images per class were used to validate the model. The experimental results demonstrate that the model has exhibited 89.6% and 98.3% accuracy for MIMII and ToyADMOS, respectively, with only ten labeled images of each fault class. As the model has only 73 thousand trainable parameters and requires only 300 KB of memory, it is expected to be deployable in a portal device for machine fault diagnosis.

关键词： Fault diagnosis Training Accuracy Sensitivity Computational modeling Memory management data models Few shot learning Spectrogram Portals

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Capacity Maximization for Double Passive/Active RIS-Aided MISO Systems in Multi-User Scenarios

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IEEE Internet of Things Journal 2025年

作者： Peng, Xin Liu, Chenyu Yu, Chao Xie, Wenwu Yang, Liang Hunan Institute of Science and Technology School of Information Science and Engineering Yueyang414006 China Hunan Engineering Research Center for Autonomous Data Perception and Processing in Industrial Intelligent Networking Yueyang414006 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

This paper addresses the problem of capacity maximization in double reconfigurable intelligent surface (RIS) -aided multiple-input single-output (MISO) systems under multi-user scenarios. A system model is proposed for double RIS-aided MISO configurations, incorporating both passive RIS (PRIS) and active RIS (ARIS). The primary objective is to maximize the minimum channel capacity across all users, and a corresponding optimization problem is formulated to achieve this goal. To overcome the challenges posed by the fractional structure of the non-convex objective function, auxiliary variables are introduced, and the bisection method is utilized. Given the highly coupled and nonconvex nature of the optimization variables, a joint alternating optimization (AO) algorithm is designed based on the bisection method and successive convex approximation (SCA), enabling iterative determination of the beamforming vectors at the base station and the reflection coefficients of RISs. The results indicate that deploying double RISs substantially enhances the system’s channel capacity through reflected links. Notably, the ARIS protocols yield higher performance gains, particularly under scenarios involving high total power and large amplification factors. In multi-user environments, the double RIS configuration significantly outperforms the single RIS setup due to the increased degrees of freedom, especially when supported by ARIS protocols. These findings underscore the substantial potential and advantages of double RIS configurations in complex communication environments. © 2014 IEEE.

关键词： Convex optimization

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Transformer doctor: diagnosing and treating vision transformers 24

Transformer doctor: diagnosing and treating vision transform...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Jiacong Hu Hao Chen Kejia Chen Yang Gao Jingwen Ye Xingen Wang Mingli Song Zunlei Feng College of Computer Science and Technology Zhejiang University and State Key Laboratory of Blockchain and Data Security Zhejiang University College of Computer Science and Technology Zhejiang University School of Software Technology Zhejiang University Bangsheng Technology Co. Ltd. Electrical and Computer Engineering National University of Singapore College of Computer Science and Technology Zhejiang University and Bangsheng Technology Co. Ltd. College of Computer Science and Technology Zhejiang University and State Key Laboratory of Blockchain and Data Security Zhejiang University and Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security School of Software Technology Zhejiang University and State Key Laboratory of Blockchain and Data Security Zhejiang University and Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security

ISBN: (纸本)9798331314385

Due to its powerful representational capabilities, Transformers have gradually become the mainstream model in the field of machine vision. However, the vast and complex parameters of Transformers impede researchers from gaining a deep understanding of their internal mechanisms, especially error mechanisms. Existing methods for interpreting Transformers mainly focus on understanding them from the perspectives of the importance of input tokens or internal modules, as well as the formation and meaning of features. In contrast, inspired by research on information integration mechanisms and conjunctive errors in the biological visual system, this paper conducts an in-depth exploration of the internal error mechanisms of Transformers. We first propose an information integration hypothesis for Transformers in the machine vision domain and provide substantial experimental evidence to support this hypothesis. This includes the dynamic integration of information among tokens and the static integration of information within tokens in Transformers, as well as the presence of conjunctive errors therein. Addressing these errors, we further propose heuristic dynamic integration constraint methods and rule-based static integration constraint methods to rectify errors and ultimately improve model performance. The entire methodology framework is termed as Transformer Doctor, designed for diagnosing and treating internal errors within transformers. Through a plethora of quantitative and qualitative experiments, it has been demonstrated that Transformer Doctor can effectively address internal errors in transformers, thereby enhancing model performance. For more information, please visit https://***/.

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Machine Learning is All You Need: A Simple Token-Based Approach for Effective Code Clone Detection

Machine Learning is All You Need: A Simple Token-Based Appro...

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International Conference on Software Engineering (ICSE)

作者： Siyue Feng Wenqi Suo Yueming Wu Deqing Zou Yang Liu Hai Jin Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab Jinyinhu Laboratory Wuhan China School of Cyber Science and Engineering HUST Wuhan China Nanyang Technological University Singapore School of Computer Science and Technology HUST Wuhan China

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

As software engineering advances and the code demand rises, the prevalence of code clones has increased. This phenomenon poses risks like vulnerability propagation, underscoring the growing importance of code clone detection techniques. While numerous code clone detection methods have been proposed, they often fall short in real-world code environments. They either struggle to identify code clones effectively or demand substantial time and computational resources to handle complex clones. This paper introduces a code clone detection method namely Toma using tokens and machine learning. Specifically, we extract token type sequences and employ six similarity calculation methods to generate feature vectors. These vectors are then input into a trained machine learning model for classification. To evaluate the effectiveness and scalability of Toma, we conduct experiments on the widely used BigCloneBench dataset. Results show that our tool outperforms token-based code clone detectors and most tree-based clone detectors, demonstrating high effectiveness and significant time savings.

关键词： Codes Scalability Computational modeling Cloning Machine learning Detectors Feature extraction

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Validity Matters: Uncertainty-Guided Testing of Deep Neural Networks

Validity Matters: Uncertainty-Guided Testing of Deep Neural ...

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作者： Jiang, Zhouxian Li, Honghui Wang, Rui Tian, Xuetao Liang, Ci Yan, Fei Zhang, Junwen Liu, Zhen School of Computer Science and Technology Beijing Jiaotong University Beijing China Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing China Collaborative Innovation Center of Railway Traffic Safety Beijing Jiaotong University Beijing China Faculty of Psychology Beijing Normal University Beijing China School of Transportation Science and Engineering Harbin Institute of Technology Harbin China School of Automation and Intelligence Beijing Jiaotong University Beijing China

Despite numerous applications of deep learning technologies on critical tasks in various domains, advanced deep neural networks (DNNs) face persistent safety and security challenges, such as the overconfidence in predicting out-of-distribution samples and susceptibility to adversarial examples. Thorough testing by exploring the input space serves as a key strategy to ensure their robustness and trustworthiness of these networks. However, existing testing methods focus on disclosing more erroneous model behaviours, overlooking the validity of the generated test inputs. To mitigate this issue, we investigate devising valid test input generation method for DNNs from a predictive uncertainty perspective. Through a large-scale empirical study across 11 predictive uncertainty metrics for DNNs, we explore the correlation between validity and uncertainty of test inputs. Our findings reveal that the predictive entropy-based and ensemble-based uncertainty metrics effectively characterize the input validity demonstration. Building on these insights, we introduce UCTest, an uncertainty-guided deep learning testing approach, to efficiently generate valid and authentic test inputs. We formulate a joint optimization objective: to uncover the model's misbehaviours by maximizing the loss function and concurrently generate valid test input by minimizing uncertainty. Extensive experiments demonstrate that our approach outperforms the current testing methods in generating valid test inputs. Furthermore, incorporating natural variation through data augmentation techniques into UCTest effectively boosts the diversity of generated test inputs. © 2024 John Wiley & Sons Ltd.

关键词： Adversarial machine learning

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Performance Analysis of Differential Amplifiers Using Cadence with GPDK45, GPDK90, and GPDK180

Performance Analysis of Differential Amplifiers Using Cadenc...

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Devices for Integrated Circuit (DevIC)

作者： Tomal Suvro Sannyashi Sk Babul Akhtar Tanmay Sinha Roy Biswajit Jana Soumen Ghosh Electronics and Communication Engineering Swami Vivekananda University Kolkata Electrical and Electronics Engineering Institute of Engineering and Management Kolkata School of Computer Science Engineering and Technology Bennett University UP Computer Science and Engineering (Data Science) Haldia Institute of Technology West Bengal

ISBN: (数字)9798350391107

ISBN: (纸本)9798350391114

This paper presents a comprehensive analysis of differential amplifier circuits designed using three process technologies: GPDK45, GPDK90, and GPDK180. Utilizing Cadence design and simulation tools, we perform detailed simulations to evaluate and compare key performance parameters of these architectures. The analysis focuses on critical metrics such as propagation delay, gain, power consumption, and bandwidth, which play a pivotal role in determining the overall efficiency and suitability of differential amplifiers for various applications. The simulation results highlight the impact of technology scaling on the performance characteristics, offering insights into trade-offs and optimization strategies. By systematically comparing the outputs across these process nodes, the study provides valuable guidelines for the selection of process technology based on specific design requirements and application domains.

关键词： Performance evaluation Power demand Simulation Bandwidth Very large scale integration Differential amplifiers Performance analysis Optimization Propagation delay Guidelines

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Sentiment Analysis Using Machine Learning Methods

Sentiment Analysis Using Machine Learning Methods

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2024 International Conference on Intelligent and Innovative Practices in Engineering and Management, IIPEM 2024

作者： Badholia, Abhishek Diwan, Tarun Dhar Narooka, Preeti Khatkale, Pravin B Vishnoi, Ankit Kaushik, Keshav Shri Shankaracharya Institute of Professional Management and Technology Department of Data Science Raipur India Atal Bihari Vajpayee University Bilaspur India Manipal University Artificial Intelligence and Machine Learning School of Computer and Engineering Jaipur India Sanjivani University Maharashtra Kopargaon India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun India Amity University Punjab Amity School of Engineering and Technology Mohali India

ISBN: (纸本)9798350390049

Machine learning (ML) will be utilized to evaluate sentiment analysis in this project. This study aims to do this. Sentiment analysis is a popular natural language processing area. This is a natural language processing subfield. This section identifies textual senses. Due to the growing availability of digital text across platforms, sentiment analysis is now vital for many applications. This is because sentiment analysis has become essential. This technology might be used for market research, political analysis, and consumer feedback. A few instances. This study analyzes several machine learning methods, to name a few. This category includes SVM, Naive Bayes, Random Forest, and RNNs. To clarify, several datasets are used to assess these methodologies. To get more information. Preprocessing and feature engineering are crucial to sentiment analysis model construction, as this paper shows. The research emphasises these two phases. In addition, it provides insights into how these approaches are used in constantly changing digital situations. © 2024 IEEE.

关键词： Adversarial machine learning

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