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19th Annual Conference of China Electrotechnical Society, ACCES 2024

作者： Song, Deng Yang, Qi Zeng, Huarong Zhong, Yao Huang, Huan Xie, Quan Meihua Mountain Freezing Climate Disaster and Equipment Safety Observation and Research Station of Guizhou Province Guiyang550002 China Guizhou Power Grid Limited Liability Company Power Science Research Institute Guiyang550002 China School of Big Data and Information Engineering Guizhou University Guiyang550025 China

ISBN: (纸本)9789819613823

Since the large-scale ice disaster occurred in 2008, DC ice melting technology has gradually become a common configuration for substations in medium and heavy ice-covered areas of China, which has become the most effective measure for transmission lines to resist large-scale ice-covered disasters in winter. However, it has been found that many communication links and low ice melting wiring efficiency exist in DC ice melting operation process, causing the transmission line to take too long from outage to resumption. Aiming at the existing problems, the time-consuming characteristics of various wiring forms of ice-melting transmission lines that have been carried out in the past of Guizhou Power Grid are obtained: the dispatcher command model is inefficient in melting ice due to too many communication links. In order to improve work efficiency, this paper proposes an ice melting work-group model that gives transmission line management authority to the person in charge of on-site ice melting work. The advantages of the ice melting work-group mode in the efficiency of ice melting on transmission lines are verified by comparing the two modes in terms of time consumption in engineering application. Finally, for the long time-consuming operation process of Ice-melting wiring operations, a one-key sequence control is proposed, the transformation requirements for substation are given and the efficiency of the method is verified, which has guiding significance for the further improvement of deicing efficiency in the future. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Efficiency

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Enhancing Online Teaching Effectiveness Through Hand-Raising Detection 4

Enhancing Online Teaching Effectiveness Through Hand-Raising...

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4th International Conference on Information science and Education, ICISE-IE 2023

作者： Zhu, Xiaoke Dong, Zhiwei Chen, Xiaopan Shi, Yufeng Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng China School of Computer and Information Engineering Henan University Kaifeng China Henan University Henan Key Laboratory of Big Data Analysis and Processing Kaifeng China

ISBN: (纸本)9798350394610

After the outbreak of the COVID-19 pandemic, online teaching has gradually emerged as an indispensable component of education. Despite its convenience, online education lacks the immediate interactive experience inherent in traditional classrooms. This paper presents a meticulously designed DS-YOLOv8 network model, specifically tailored for real-time recognition of students raising their hands under cameras. On our proprietary dataset, this network model exhibits exceptional accuracy in hand-raising detection, showcasing a 1.3% improvement in the AP50 index compared to the baseline YOLOv8 model. Our focus lies on the application of hand-raising detection within online classrooms with an objective to provide enhanced support for teachers by promptly identifying students raising their hands and thereby improving teaching efficiency while enriching the interactive experience of students in virtual learning environments. © 2023 IEEE.

关键词： Students

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Metal Corrosion Rate Prediction of Small Samples Using an Ensemble Technique

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Computer Modeling in Engineering & sciences 2023年第1期134卷 267-291页

作者： Yang Yang Pengfei Zheng Fanru Zeng Peng Xin Guoxi He Kexi Liao State Key Laboratory of Oil Gas Reservoir Geology and Exploitation Southwest Petroleum UniversityChengdu610500China School of Earth Sciences and Technology Southwest Petroleum UniversityChengdu610500China Spatial Information Technology and Big Data Mining Research Center School of Earth Sciences and TechnologySouthwest Petroleum UniversityChengdu610500China Sichuan Xinyang Anchuang Technology Co. Ltd.Chengdu610500China SichuanWater Conservancy College Chengdu610500China CCDC Safety EnvironmentQuality Supervision&Testing Research InstituteGuanghan618300China

Accurate prediction of the internal corrosion rates of oil and gas pipelines could be an effective way to prevent pipeline *** this study,a proposed framework for predicting corrosion rates under a small sample of metal corrosion data in the laboratory was developed to provide a new perspective on how to solve the problem of pipeline corrosion under the condition of insufficient real *** approach employed the bagging algorithm to construct a strong learner by integrating several KNN learners.A total of 99 data were collected and split into training and test set with a 9:1 *** training set was used to obtain the best hyperparameters by 10-fold cross-validation and grid search,and the test set was used to determine the performance of the *** results showed that theMean Absolute Error(MAE)of this framework is 28.06%of the traditional model and outperforms other ***,the proposed framework is suitable formetal corrosion prediction under small sample conditions.

关键词： Oil pipeline bagging KNN ensemble learning small sample size

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Leveraging Deep Learning Models for Machine-Generated Text Detection Using Transformer-Based Models

Leveraging Deep Learning Models for Machine-Generated Text D...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Suthanthiradevi, P. Revathy, G. Rejini, K. Muthu Lakshmi, V. School of Computing Srm institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Chennai India Vels Institute of science Technology and Advanced studies Department of Computer science and engineering Chennai117 India Amrita college of Engineering and Technology Department of Computer Science Engineering Nagercoil India St. Joseph's College of Engineering Department of Computer Science and Engineering OMR Chennai India

ISBN: (纸本)9798331543617

GPT is a large language model (LLM) derived from natural language processing that can generate a human-like text using machine learning. However, these models raise questions about authenticity and reliability of material, particularly in fields such as journalism, social media, and academia, despite their usefulness for automating text-based tasks. Detecting machine-generated text is thus an important difficulty in ensuring content integrity. This study investigates the use of huge language models as a technique for recognizing machine-generated material. The author proposes a comprehensive detection model by evaluating the language patterns, syntactic structures, and stylistic traits that separate AI-generated literature from human writing. In addition, this research investigate the possibilities of fine-tuning models designed expressly for text identification tasks and evaluate their performance using LLM - Detect AI Generated Text datasets. In digital ecosystems, LLMs are effective at detecting AI-generated text, providing a novel approach for content moderation, academic integrity checks, and synthetic media detection. An increasingly AI-powered future will require a model that can discriminate between human and machine-generated writing in real-time. According to experimental findings, the CNN architecture's design combined with the use of DistilBERT embeddings allows for the effective and efficient classification of AI generated text data, achieving an exceptional 98% accuracy rate. © 2024 IEEE.

关键词： Adversarial machine learning

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A Blockchain of Blockchains Structure Based on Asset Trading Scheme

A Blockchain of Blockchains Structure Based on Asset Trading...

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IEEE International Symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Siyuan Huang Yuling Chen Chaoyue Tan Hui Dou Yun Luo Yuxiang Yang State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China

In the realm of blockchain applications, the issue of value isolation, commonly referred to as the value island problem, imposes inherent limitations on the applicability of corresponding assets. To overcome this challenge, researchers have dedicated their efforts to exploring diverse solutions for facilitating cross-chain transactions involving various assets. These solutions include centralized exchanges and hash time lock protocols. However, these approaches are subject to significant limitations. Centralized exchanges, while providing transactional functionalities, undermine the decentralized nature intrinsic to blockchain technology. Conversely, hash time lock protocols necessitate a high level of trust between transacting parties. Furthermore, although decentralized exchanges have emerged to support atomic cross-chain exchanges, their transactional architectures are restricted to either a bilateral relationship between two blockchains or a subset of pre-established blockchains. Therefore, the construction of a non-trust-based, decentralized exchange that supports multiple blockchain networks remains an ongoing challenge. In this research, we propose Vchain, a cross-chain relay transaction scheme, designed with a BoBs(Blockchain of Blockchains) structure, to address the aforementioned challenges. Vchain enables the participation of various accounts within blockchain ecosystems that support smart contract functionalities. It ensures the security and integrity of user transactions by leveraging third-party cryptocurrency assets as collateral. Moreover, users have the flexibility to selectively choose transaction counterparties based on the quantity of collateral assets provided, thereby maximizing their transactional interests. Consequently, Vchain enables non-trust-based, multi-chain asset cross-chain transactions, addressing the limitations of existing solutions.

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Global Convergence of Natural Policy Gradient with Hessian-aided Momentum Variance Reduction

arXiv

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arXiv 2024年

作者： Feng, Jie Wei, Ke Chen, Jinchi School of Data Science Fudan University Shanghai China School of Mathematics East China University of Science and Technology Shanghai China

Natural policy gradient (NPG) and its variants are widely-used policy search methods in reinforcement learning. Inspired by prior work, a new NPG variant coined NPG-HM is developed in this paper, which utilizes the Hessian-aided momentum technique for variance reduction, while the sub-problem is solved via the stochastic gradient descent method. It is shown that NPG-HM can achieve the global last iterate ϵ-optimality with a sample complexity of O(ϵ−2), which is the best known result for natural policy gradient type methods under the generic Fisher non-degenerate policy parameterizations. The convergence analysis is built upon a relaxed weak gradient dominance property tailored for NPG under the compatible function approximation framework, as well as a neat way to decompose the error when handling the sub-problem. Moreover, numerical experiments on Mujoco-based environments demonstrate the superior performance of NPG-HM over other state-of-the-art policy gradient methods. © 2024, CC BY.

关键词： Reinforcement learning

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On Simplifying Large-Scale Spatial Vectors: Fast, Memory-Efficient, and Cost-Predictable k-means

arXiv

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arXiv 2024年

作者： Ji, Yushuai Liu, Zepeng Wang, Sheng Sun, Yuan Peng, Zhiyong School of Computer Science Wuhan University China Big Data Institute Wuhan University China La Trobe Business School La Trobe University Australia

The k-means algorithm can simplify large-scale spatial vectors, such as 2D geo-locations and 3D point clouds, to support fast analytics and learning. However, when processing large-scale datasets, existing k-means algorithms have been developed to achieve high performance with significant computational resources, such as memory and CPU usage time. These algorithms, though effective, are not well-suited for resource-constrained devices. In this paper, we propose a fast, memory-efficient, and cost-predictable k-means called Dask-means. We first accelerate k-means by designing a memory-efficient accelerator, which utilizes an optimized nearest neighbor search over a memory-tunable index to assign spatial vectors to clusters in batches. We then design a lightweight cost estimator to predict the memory cost and runtime of the k-means task, allowing it to request appropriate memory from devices or adjust the accelerator’s required space to meet memory constraints, and ensure sufficient CPU time for running k-means. Experiments show that when simplifying datasets with scale such as 106, Dask-means uses less than 30MB of memory, achieves over 168 times speedup compared to the widely-used Lloyd’s algorithm. We also validate Dask-means on mobile devices, where it demonstrates significant speedup and low memory cost compared to other state-of-the-art (SOTA) k-means algorithms. Our cost estimator estimates the memory cost with a difference of less than 3% from the actual ones and predicts runtime with an MSE up to 33.3% lower than SOTA methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Nearest neighbor search

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Effective Source Code Vectorization for Vulnerability Detection Using Deep Learning and Attention Mechanism

SSRN

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SSRN 2023年

作者： Zhu, Erzhou Chang, Jiaqi Luo, Siyuan Ma, Zhujuan School of Computer Science and Technology Anhui University Hefei230601 China School of Big Data and Artificial Intelligence Anhui Xinhua University Hefei230088 China

With the increasing availability of computational power, deep learning methods have been widely used for detecting software vulnerabilities in recent years. In contrast to traditional machine learning technology, deep learning methods have the merits of low computational overhead and high vulnerability detection accuracy, and they do not depend on expert knowledge to extract vulnerability features. However, the performance of many existing deep vulnerability detection methods is degraded by inadequate information about the syntax and semantics of source code. This paper proposes Vulnerability Detection based on Deep learning and Attention mechanisms (VDDA), an effective software vulnerability detection model based on deep learning and an attention mechanism. In VDDA, the bidirectional long short-term memory (BLSTM) deep model is used to alleviate the need for the feature engineering of traditional machine learning techniques. With the Joern analysis tool, the source code is converted to code property graphs (CPG) to retain the affluent syntax and semantic information. Several improvements, including depth-first traversal-based CPG optimization, three-direction code slicing, slice organization with code blocks, and the separation of function names from variable names in code symbolization, were made to effectively convert source code into vectors that could be taken as the only input to the underlying deep learning model. Meanwhile, because different parts of the vector play different roles in vulnerability detection, the attention mechanism was integrated with BLSTM to further improve vulnerability detection performance. The experimental results on two datasets of different scales demonstrated that the proposed VDDA outperforms many existing methods in vulnerability detection. © 2023, The Authors. All rights reserved.

关键词： Semantics

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AudioTime: A Temporally-aligned Audio-text Benchmark dataset

AudioTime: A Temporally-aligned Audio-text Benchmark Dataset

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zeyu Xie Xuenan Xu Zhizheng Wu Mengyue Wu MoE Key Lab of Artificial Intelligence AI Institute X-LANCE Lab Shanghai Jiao Tong University Shanghai AI Lab School of Data Science Shenzhen Research Institute of Big Data CUHK-Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recent advances in audio generation have enabled the creation of high-fidelity audio clips from free-form textual descriptions. However, temporal relation, a critical feature for audio content, is currently underrepresented in mainstream models, resulting in an imprecise temporal controllability. Specifically, users cannot accurately control the timestamps of sound events using free-form text. One significant challenge is the absence of a high-quality, temporally-aligned audio-text dataset, which is essential for training models with temporal control. The more temporally-aligned the annotations, the better the models can understand the precise relationship between audio outputs and temporal textual prompts. Therefore, we propose a temporally-aligned audio-text dataset, AudioTime. It provides text annotations rich in temporal information such as timestamps, duration, frequency, and ordering, covering almost all aspects of temporal control. Additionally, we offer a comprehensive test set and evaluation metric to assess the temporal control performance of text-to-audio generation models. Examples are available on the $AudioTime - Demo$.

关键词： Measurement Training Time-frequency analysis Analytical models Annotations Signal processing Benchmark testing Controllability Speech processing Frequency control

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KAC-Unet: A Medical Image Segmentation With the Adaptive Group Strategy and Kolmogorov-Arnold Network

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Lin, Shiying Hu, Rong Li, Zuoyong Lin, Qinghua Zeng, Kun Wu, Xiang Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Mining and Applications School of Computer Science and Mathematics Fuzhou350118 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control School of Computer and Big Data Fuzhou350121 China Fuzhou University Affiliated Provincial Hospital Provincial Clinical Medical College Fujian Medical University Department of Urology Fuzhou350001 China

In the field of deep learning-based medical image segmentation, convolutional neural networks (CNNs) extract image features by combining linear convolutional layers with nonlinear activation functions. However, excessive stacking of linear layers in the network limits the model’s ability to capture fine-grained details. In addition, the feature distribution imbalance caused by the traditional fixed grouping strategy (FGS) can affect the deep model’s capacity to perceive the overall structure of the image. To address these challenges, we propose a medical image segmentation framework, called Kolmogorov-Arnold Network with the adaptive group strategy and contextual Transformer based on Unet (KAC-Unet). First, we propose the adaptive group strategy (AGS) to balance the grouping of different input channels, alleviating the performance degradation caused by differences in group information. Then, we propose the Shift Tokenized Kolmogorov-Arnold Network (KAN) Block to capture complex features in medical images through flexible nonlinear transformations and shift operations. Extensive experiments are conducted on three medical image segmentation datasets. The results demonstrate the effectiveness and superiority of our proposed method compared with state-of-the-art algorithms. © 1963-2012 IEEE.

关键词： Convolutional neural networks

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