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8th International Conference on Information Technology: IoT and Smart City, ICIT 2020

作者： Zhou, Yanyun Shi, Fei Chen, Juan School of Information Science and Engineering Xinjiang University and Key Laboratory of Signal Detection and Processing China

ISBN: (纸本)9781450388559

Aiming at the disadvantages of embedded system in practical teaching, a pocket experiment system based on STM32F4 is developed. The system includes input unit, output unit, logic control unit, power supply unit, peripheral unit and data transmission unit. It has the characteristics of rich resources, low cost, and easy to carry, which meets the needs of students' mobility experiments and extra-curricular innovation practice training. Practical application shows that the system not onlystimulates students' enthusiasm for learning, but also trains their creative ability and practical application ability. © 2020 ACM.

关键词： Computer programming

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Residual Graph Convolution Collaborative Filtering with Asymmetric neighborhood aggregation

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Neural Computing and Applications 2024年第22期36卷 13989-14003页

作者： Wang, Tao Qin, Jiwei Ma, Chao School of Computer Science and Technology Xinjiang University Urumqi830046 China Xinjiang Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi830046 China

Due to the superior performance of graph convolutional networks (GCNs) in feature extraction and representation, researchers have introduced GCNs to collaborative filtering (CF) to improve the accuracy of recommendations. However, existing GCN-based CF models still have three shortcomings: degrading the model performance due to symmetric normalization in neighborhood aggregation, underutilizing the node (user or item) representations at lower layers, and failing to alleviate the over-smoothing effect in graph convolution operations. We present a Residual Graph Convolution Collaborative Filtering model with Asymmetric neighborhood aggregation (ARGCCF) to tackle the above shortcomings. Firstly, ARGCCF utilizes asymmetric normalization in neighborhood aggregation, enabling better preservation of high-degree node information for more precisely learning node representations. Secondly, the propagation of ARGCCF embeddings proceeds in two steps. During the initial propagation, a residual connection is established between the node representation of the current layer and the previous layer to utilize the low-level layer’s node representation fully. The embeddings generated after the initial propagation are propagated again in the same graph convolutional layer, which obtains more refined embedding representations to alleviate the over-smoothing problem. Finally, experimentations on five truthful datasets exhibit that ARGCCF has better-improved performance over several mainstream methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Collaborative filtering

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End-To-End Residual Stage Network for COVID-19 detection 4

End-To-End Residual Stage Network for COVID-19 Detection

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4th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2023

作者： Chen, Hongwei Huang, Zhihua Lu, Kexin Liu, Hanzheng Xinjiang University Key Laboratory of Signal Detection and Processing in Xinjiang Uygur Autonomous Region Urumchi China

ISBN: (纸本)9798350324303

Currently, the COVID-19 virus is in a low-level epidemic state but still spreading in society. To timely cut off the transmission chain, early screening for COVID-19 is crucial. However, adding extra network modules during training may lead to module stacking errors and increased complexity. In this paper, we propose an optimization method based on residual stage networks (ResStage) and polarized self-Attention mechanism to improve the detection ability of the model. This method provides better paths for information propagation across different layers of the network and extraction more refined and high-quality features. Results show that the fusion model based on ResStage and polarized self-Attention can improve the detection accuracy of COVID-19 without increasing excessive parameters, achieving an AUC score of 86.53%. Moreover, this paper also subdivides the training tasks, including COVID-19 positive patients, COVID-19 positive patients with coughing, healthy individuals with coughing, asthma patients with coughing, and healthy asymptomatic individuals. These different tasks help to evaluate the performance of model and provide more references for practical applications. © 2023 IEEE.

关键词： COVID-19

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MetricGAN+ Speech Enhancement Model Combining Multi-scale and Multi-resolution Feature Network and Joint Perception Loss 4

MetricGAN+ Speech Enhancement Model Combining Multi-scale an...

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4th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2023

作者： Guo, Chuangjian Huang, Zhihua Li, Hui Song, Jingyi Xinjiang University Key Laboratory of Signal Detection and Processing in Xinjiang Uygur Autonomous Region Urumchi China

ISBN: (纸本)9798350324303

Recently, generative adversarial networks have been widely used in speech enhancement. However, some existing speech enhancement methods only aim to optimize a single perceptual metric, which may cause other perceptual metrics to be suboptimal. In this paper, we propose a joint perceptual loss function to guide the training of speech enhancement models. In addition, we use a multi-scale and multi-resolution feature extraction network in the discriminator of MetriGAN+model, so that the model can make full use of high-level context information and low-level time-frequency features, so as to improve the prediction performance of the discriminator and guide the training of the generator better. The evaluation results on the VoiceBank-DEMAND dataset show that the proposed model can effectively improve speech quality and intelligibility, and can improve the PESQ score by 0.19 compared to the previous MetricGAN+. © 2023 IEEE.

关键词： Speech enhancement

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A Bit-Filp Joint Algorithm of Polar-SCMA Systems for mMTC

A Bit-Filp Joint Algorithm of Polar-SCMA Systems for mMTC

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2023 IEEE IAS Global Conference on Emerging Technologies, GlobConET 2023

作者： Qiao, Wei Ge, Wenping Zhang, Shiwei Sun, Yin Xinjiang University Key Laboratory of Signal Detection and Processing College of Information Science and Engineering Urumqi China

ISBN: (纸本)9798350331790

The conflict between limited spectrum resources and the need for massive machine-type communications (mMTC) is becoming more significant. To address these challenges, Sparse Code Multiple Access (SCMA) has emerged as a promising solution for mMTC in 5G. SCMA allows many users to share time and frequency resources, increasing spectral efficiency and receiving increased attention in the mMTC domain. As a control channel coding scheme in 5G communication systems, polar codes (PC) offer dependable data transmission capabilities. This paper presents a joint algorithm for an uplink PC-SCMA receiver that yields a reasonable block error rate (BLER) performance and an overload rate of 150% for mMTC. Generally, the previous error bit leads to much decoding error propagation in the following information bits for the iterative algorithm. We propose a bit-flip-assisted joint iterative detection decoding (BF-JIDD) algorithm for the error propagation problem in JIDD. Based on the simulation results, it can be concluded that the BF-JIDD algorithm outperforms the JIDD algorithm in terms of BLER performance. © 2023 IEEE.

关键词： Spectrum efficiency

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The detection of Fraudulent Smart Contracts Based on ECA-EfficientNet and Data Enhancement

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Computers, Materials & Continua 2023年第12期77卷 4073-4087页

作者： Xuanchen Zhou Wenzhong Yang Liejun Wang Fuyuan Wei KeZiErBieKe HaiLaTi Yuanyuan Liao College of Software Xinjiang UniversityUrumqi830000China Key Laboratory of Signal Detection and Processing in Xinjiang Uygur Autonomous Region Urumqi830000China Key Laboratory of Multilingual Information Technology in Xinjiang Uygur Autonomous Region Urumqi830000China

With the increasing popularity of Ethereum,smart contracts have become a prime target for fraudulent activities such as Ponzi,honeypot,gambling,and phishing *** some researchers have studied intelligent fraud detection,most research has focused on identifying Ponzi contracts,with little attention given to detecting and preventing gambling or phishing *** are three main issues with current ***,there exists a severe data imbalance between fraudulent and non-fraudulent ***,the existing detection methods rely on diverse raw features that may not generalize well in identifying various classes of fraudulent ***,most prior studies have used contract source code as raw features,but many smart contracts only exist in *** address these issues,we propose a fraud detection method that utilizes Efficient Channel Attention EfficientNet(ECA-EfficientNet)and data *** method begins by converting bytecode into Red Green Blue(RGB)three-channel images and then applying channel exchange data *** then use the enhanced ECA-EfficientNet approach to classify fraudulent smart contract RGB *** proposed method achieves high F1-score and Recall on both publicly available Ponzi datasets and self-built multi-classification datasets that include Ponzi,honeypot,gambling,and phishing smart *** results of the experiments demonstrate that our model outperforms current methods and their variants in Ponzi contract *** research addresses a significant problem in smart contract security and offers an effective and efficient solution for detecting fraudulent contracts.

关键词： Fraud detection smart contract ECA-EfficientNet Ethereum

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Change detection based on optimized guided filtering in wide-field video image under low illumination

Change detection based on optimized guided filtering in wide...

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2024 International Conference on Image processing and Artificial Intelligence, ICIPAl 2024

作者： Xie, Chengyou Jia, Zhenhong Key Laboratory of Signal Detection and Processing Xinjiang Uygur Autonomous Region Xinjiang University Urumqi830046 China

ISBN: (纸本)9781510681514

To effectively and accurately detect regions with subtle changes in wide-field videos captured by eagle-eye equipment in low-light environments. A two-level change detection (CD) algorithm based on guided filter fusion is proposed to achieve better separation in complicated scenarios. First, the difference images (DIs) is obtained through the difference operator and the extreme minimum ratio operator. At the same time, using Principal Component Analysis (PCA) fuse the global brightness component and the V component are extracted from images to obtain the optimized brightness map. Then, the saliency map is obtained from the optimized brightness map based on the maximum salient pixel and spatial continuity characteristics as a guide for DIs fusion. Finally, a two-level method is proposed to combine the threshold method and clustering algorithm to further suppress the noise and DI normalization. Then, using the K-means algorithm to to generate the final binary change map. Experimental results show that the proposed algorithm can accurately and effectively detect changes in Eagle Eye surveillance video images in low-light environments. Compared with other algorithms, this algorithm reduces the running time and effectively minimizes the interference of light sources and dark spot noise in low-light environments. © 2024 SPIE.

关键词： Luminance

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Simulation analysis of ground-penetrating radar in seasonal freezing soil area with temperature-varying media 20

Simulation analysis of ground-penetrating radar in seasonal ...

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20th International Conference on Ground Penetrating Radar, GPR, 2024

作者： Lu, Guojiang Chen, Qinghua Zhang, Xianwu Key Laboratory of Signal Detection and Processing College of Information Science and Engineering Xinjiang University Urumqi830000 China

Affected by the seasonal freezing area climate, the shallow engineering geological electromagnetic environment exhibits seasonal variations, affecting the propagation process of electromagnetic waves in shallow geological soil. This brings excellent difficulties in accurately identifying the detection target. In this paper, Kaba-he, the seasonal freezing area of Xinjiang, is taken as an example to build a model of the dielectric constant of the temperature variable soil medium in different seasons, and utilizing the high-order O(2,4) FDTD algorithm implemented with MATLAB software. This experiment provides the propagation velocity of electromagnetic waves in shallow subgrade soil of Kaba-he during different seasons and, based on numerical simulation results, gives the calculated depth of the underground targets buried in other seasons. This research can provide theoretical guidance for the application of GPR in Kaba-he. © Published under licence by IOP Publishing Ltd.

关键词： MATLAB

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Fake News detection Based on Cross-modal Co-attention 4

Fake News Detection Based on Cross-modal Co-attention

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4th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2023

作者： Gu, Yadong Ablimit, Mijit Hamdulla, Askar Xinjiang University Xinjiang Key Laboratory of Signal Detection and Processing School of Information Science and Engineering Urumqi China

ISBN: (纸本)9798350324303

The spread of rumors has brought many negative impacts to society. Nowadays, rumors on social media platforms often exist in the form of both images and text. In response to this, many methods for multimodal rumor detection have been proposed, but most of these methods only utilize extracted text features and image features for simple fusion, without considering the connections between modalities. In this study, we employ ConvNet to extract image features, BERT to extract text features, and take into account the complementarity between modalities. We use a co-attention module to enhance the extracted features of individual modalities, aiming to achieve the interaction of modality features and the complementary nature between modality information. The obtained features are then concatenated to achieve the fusion of image and text features. The multimodal fusion model used in this study was experimented on Twitter and Weibo, achieving accuracy rates of 81.20% and 88.15% respectively. © 2023 IEEE.

关键词： Feature extraction

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Pipeline Chain-of-Thought: A Prompt Method for Large Language Model Relation Extraction 27

Pipeline Chain-of-Thought: A Prompt Method for Large Languag...

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27th International Conference on Asian Language processing, IALP 2023

作者： Zhao, Hangtian Yilahun, Hakiz Hamdulla, Askar School of Computer Science and Technology XinJiang University Xinjiang Key Laboratory of Signal Detection and Processing Urumqi China

ISBN: (纸本)9798350330786

The development of language models has been influencing approaches to relation extraction (RE) problems. Although large language models (LLMs) have demonstrated breakthrough potential in certain aspects, they are still in the exploratory stage for RE tasks. Currently, the mainstream approach to improving the RE performance of LLMs is through prompt fine-tuning, but most methods require providing entity information in the prompt, which effectively only allows the LLMs to perform relationship classification tasks. We propose the Pipeline Chain-of-Thought (Pipeline-COT), which breaks down the RE task into steps and transforms it into reasoning tasks that have flat scaling curves, thereby enabling the use of Chain-of-Thought (COT) to enhance model inference. In addition, our method utilizes n-shot samples to provide signals for the Bayesian inference of the model by prompting the LLMs to focus on specific concepts to generate answers. We evaluated pipeline-COT on the Chinese dataset DuIE2.0, and compared with baseline methods that require including entity information in the prompt, our method still shows competitive performance. © 2023 IEEE.

关键词： Pipelines

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