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A Hybrid Recommendation Algorithm with Co-embedded Item Attributes and Ratings 4

A Hybrid Recommendation Algorithm with Co-embedded Item Attr...

引用

4th International Conference on Applied Machine Learning, ICAML 2022

作者： Xin, Gaowei Qin, Jiwei Zheng, Jiong School of Information Science and Engineering Xinjiang University Urumqi830046 China Key Laboratory of Signal Detection and Processing Xinjiang Uygur Autonomous Region Xinjiang University Urumqi830046 China

ISBN: (纸本)9781665462655

In today's recommendation algorithm field, attribute feature information of users or items is usually introduced as auxiliary information to alleviate the sparsity problem, which can achieve good recommendation results to some extent. To make better use of the auxiliary information for data compression and dimensionality reduction, autoencoder models are usually employed. However, existing autoencoder-based models require the same input and output dimensions, resulting in data information loss and poor autoencoder scalability, which ultimately leads to low recommendation line performance. To cope with this problem, we propose a hybrid collaborative recommendation algorithm with co-embedded item attributes and ratings (HCR-item). By one-hot encoding the discrete item attribute features and then fusing the rating features with the item attribute features as the input of the autoencoder, the autoencoder completes the data compression and reconstructs the rating interaction prediction matrix. The algorithm proposed in this paper integrates item attribute features and rating features together, flexibly uses different feature information, extends the feature space, and solves the problem of low recommendation performance caused by sparsity. The algorithm has been extensively experimented on several real datasets and compared with other algorithms, and the results show that HCR-item has better recommendation performance. © 2022 IEEE.

关键词： Recommender systems

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Research on Shaking Video Image Stabilization Algorithm for Moving Object detection

Research on Shaking Video Image Stabilization Algorithm for ...

引用

Algorithm, Image processing and Machine Vision (AIPMV), International Conference on

作者： Chuangchuang Wang Zhenhong Jia Xiaohui Huang Sensen Song Jiajia Wang Gang Zhou Fei Shi Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi China

ISBN: (数字)9798350390254

ISBN: (纸本)9798350390261

Video captured by surveillance equipment will jitter due to the shaking of the equipment, this jitter will affect the detection results of moving target detection algorithms that rely on stable video frames. This paper proposes an improved ORB algorithm to solve the video jitter problem, so that the moving target detection algorithm can accurately detect moving targets in jittery videos. First, wavelet transform is used to locate the high-frequency area of the image, and then feature extraction is performed on the area, which improves the efficiency of ORB feature extraction. And use Boosted Efficient Binary Local Image Descriptor (BEBLID) to replace the descriptors of directional FAST and rotation BRIEF (ORB) to improve matching accuracy. In the feature matching stage, a neighborhood query method is proposed to replace the global search of the traditional BFMatcher, which improves the matching speed. Finally, Progressive Sample Consistency (PROSAC) is employed to ensure accurate matching of point pairs, resulting in a motion matrix for video stabilization. Finally, a Gaussian mixture model with adaptive distribution numbers is combined to quickly detect moving targets. Comparative experiments with scale-invariant feature transform(SIFT), Accelerated-KAZE(AKAZE), ORB, Qtree _ ORB and SIRB prove the superior accuracy and speed of this algorithm.

关键词： Wavelet transforms Accuracy Surveillance Search methods Object detection Jitter Streaming media Feature extraction Real-time systems Gaussian mixture model

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A Lightweight Speech Emotion Recognition Model with Bias-Focal Loss

A Lightweight Speech Emotion Recognition Model with Bias-Foc...

引用

IEEE International Conference on Information Communication and signal processing (ICICSP)

作者： Shijing Hou Huamin Yang Ying Hu Key Laboratory of signal detection and processing Xinjiang University Urumqi China

A major challenge in speech emotion recognition (SER) is how to build a lightweight model with limited training data for applying on the devices with limited in resources. In this paper, we propose a lightweight speech emotion recognition (SER) model with Bias-Focal loss function, where a Dynamic Separable Convolution (DySC) block is designed to extract more fine-grained emotional features and makes the model smaller. We propose a Bias-Focal loss to address the issue of the inconsistent of training samples, while focusing on data points with high feature diversity during the training phase. Experimental results show that our proposed lightweight model is the smallest comparing with other methods, while the number of parameters in our proposed model is 0.85M. Meanwhile, our proposed model achieves the best performance that the score of Unweighted Accuracy (UA) is 75.01 %, and that of Weighted Accuracy (WA) and F1-score are 74.05% and 74.29 % on the IEMOCAP (scripted+improvised)dataset.

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Beyond the Snowfall: Enhancing Snowy Day Object detection Through Progressive Restoration and Multi-Feature Fusion

Beyond the Snowfall: Enhancing Snowy Day Object Detection Th...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Zhong Wang Gang Zhou Jing Ma Tianhao Xue Zhenhong Jia Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi China

In the field of computer vision, object detection is a prominent and challenging task. Despite the favorable performance of deep learning-based object detection techniques on clear images, it fails in inclement weather conditions like snow because of image degradation. Recent efforts have explored using image restoration methods to enhance degraded images before object detection. However, direct restoration can sometimes cause new disturbances, impeding detection performance improvements. To address this issue, we propose a joint framework that connects the iterative desnow module and detection module in an end-to-end manner. Specially, we design an Advantage Union structure for multi-feature fusion, which effectively combines original, intermediate, and restored features, reducing potential information loss from restoration. Experimental results show that our method achieves higher accuracy compared to the recent state-of-the-art methods in both synthetic dataset and real-to-world snowy images.

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Coarse-to-fine change detection in low-illumination video sequences via classifier trained on ResNet18

Coarse-to-fine change detection in low-illumination video se...

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

作者： Lin, Jiajun Jia, Zhenhong School of Computer Science and Technology Xinjiang University Urumqi830046 China Key Laboratory of Signal Detection and Processing Xinjiang Uygur Autonomous Region Xinjiang University Urumqi830046 China

ISBN: (纸本)9781510681514

In low-illumination environments, the contrast between targets and the background sharply decreases, and imaging sensors introduce complex random noise while capturing more light, leading to severe distortion in the target features of visible light images. Under these conditions, target feature-based detection algorithms face significant challenges. In this paper, we propose a coarse-to-fine change detection algorithm that detects targets in low-illumination environments by focusing on the change features generated by the targets. First, a binary classifier based on ResNet18 is trained with a specially curated dataset of targets and noise. Second, this classifier is applied to identify potential change objects in test data, preserve targets, and extract corresponding bi-temporal local regions of interest. Third, novel difference feature extraction operators are employed to generate local difference images. Next, a Laplacian-of-Gaussian-based graph cut algorithm is used to perform binary segmentation, distinguishing foreground from background in the images. We validated the feasibility of this algorithm in three challenging night scenes. Compared with the current state-of-the-art unsupervised change detection algorithms, the proposed algorithm shows overall better detection performance. © 2024 SPIE.

关键词： Change detection

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RVDNet: A Two-Stage Network for Real-World Video Desnowing with Domain Adaptation

RVDNet: A Two-Stage Network for Real-World Video Desnowing w...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Tianhao Xue Gang Zhou Runlin He Zhong Wang Juan Chen Zhenhong Jia Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi China

Video snow removal is an important task in computer vision, as the snowflakes in videos reduce visibility and negatively affect the performance of outdoor visual systems. However, due to the complexity of real snowy scenarios, it is difficult to apply existing supervised learning-based methods to process real-world snowy videos. In this paper, we propose a novel two-stage video desnow network for the real world, called RVDNet. The first stage of RVDNet utilizes Spatial Feature Extraction Modules (SFEM) to extract the spatial features of the input frames. In the second stage, we design Spatial-Temporal Desnowing Modules (STDM) to remove snowflakes via spatio-temporal learning. Furthermore, we introduce the unsupervised domain adaptation module, which is embedded for aligning the feature space of real and synthetic data in the spatial and spatio-temporal domains, respectively. Experiments on the proposed SnowScape dataset prove that our method has superior desnow performance not only on synthetic data, but also in the real world.

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Unsupervised wide field-of-view video image change detection

Unsupervised wide field-of-view video image change detection

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

作者： Liu, Rui Jia, Zhenhong Huang, Xiaohui Wang, Jiajia Zhou, Gang Shi, Fei School of Computer Science and Technology Xinjiang University Urumqi830046 China Key Laboratory of Signal Detection and Processing Xinjiang Uygur Autonomous Region Xinjiang University Urumqi830046 China

ISBN: (纸本)9781510681514

Video surveillance requires simultaneous monitoring of multiple areas. Consequently, real-time automatic change detection of the monitored areas becomes very important. In the context of wide field-of-view conditions, the combination of a wide field-of-view, intricate environmental factors, and a substantial presence of random noise can lead to the degradation of visual fidelity and a diminished signal-to-noise ratio in the video images acquired through the image sensor. As a consequence, the task of detecting subtle changes becomes challenging for the surveillance system. To address the above problems, we have proposed a change detection method that leverages improved difference images and super fast and robust fuzzy c-means with constraints clustering. Initially, we employ an improved log-ratio operator and an improved mean-ratio operator to generate two distinct difference images. Subsequently, the wavelet fusion algorithm is applied to merge these two difference images, effectively integrating their distinctive features and producing a fused difference image with differentiability. Then, the new difference image is subjected to soft threshold primary classification and a cumulative distribution function normalization to obtain the difference image after primary classification. Finally, the super fast and robust fuzzy c-means with constraints clustering algorithm is employed for the ultimate classification, enabling the separation of changed and unchanged areas within the image. © 2024 SPIE.

关键词： Clustering algorithms

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Low-Resource Speech keyword Search Based on Residual Neural Network 16th

Low-Resource Speech Keyword Search Based on Residual Neural ...

引用

16th Chinese Conference on Biometric Recognition, CCBR 2022

作者： Wang, Dafei Huang, Zhihua Li, Hui Liu, Wenchen School of Information Science and Engineering Xinjiang University Urumqi830017 China Key Laboratory of Signal Detection and Processing in Xinjiang Uygur Autonomous Region Xinjiang University Urumqi830017 China

ISBN: (纸本)9783031202322

Low-resource speech keyword search is usually based on DTW template matching. However, this method has low precision and is sensitive to speaker or recording environment changes. This paper proposes a low-resource speech keyword search method based on image recognition. Bottleneck features are extract through a pre-trained model based on multi-language to generate similarity matrix images. ResNet is used to classify similarity matrix images. According to the image classification result, it is determined whether the speech keyword appears. This model does not require prior language knowledge and abundant corpus resources, so it is suitable for low-resource speech keyword search. We validate the performance of the model on different datasets. On SWS 2013, the model recall, accuracy, and F1 increased by 20.83%, 17.59%, and 19.18%, respectively. On QUESST 2014, they increased by 24.44%, 24.96%, and 24.70%, respectively. Experimental results show that the propose method is effective. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Template matching

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Unsupervised Domain Adaptive Learning for Image Desnowing with Real-World Data

Unsupervised Domain Adaptive Learning for Image Desnowing wi...

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IEEE International Conference on Image processing

作者： Jingxu Ren Gang Zhou Yusen Zhu Yangxin Liu Juan Chen Zhenhong Jia Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi China

Snow images usually contain snow grains, snow streaks, and mist, which greatly affect the visibility of images. Currently, supervised learning with synthetic data often faces limitations when it comes to handling real-world snow images. To address this crucial issue, this work proposes an unsupervised domain adaptation image snow removal framework. The framework improves the performance on real-world images by learning a domain classifier in adversarial training manner. Additionally, considering the diversity of snowflake shapes and sizes in real-world snow images, we design a multiple-kernel dilated convolution module. Extensive experiments on three representative datasets have validated that our model can achieve better results than existing desnowing methods. More importantly, experiments on real datasets show that the proposed method obtains state-of-the-art performance in real-world desnowing.

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Tor encrypted traffic recognition and classification technology based on flow information graph and deep learning

Tor encrypted traffic recognition and classification technol...

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

作者： Zhang, Yu Jia, Zhenhong Huang, Xiaohui Wang, Jiajia Zhou, Gang Shi, Fei Lu, Changwu School of Computer Science and Technology Xinjiang University Urumqi830046 China Key Laboratory of Signal Detection and Processing Xinjiang University Xinjiang Uygur Autonomous Region Urumqi830046 China

ISBN: (纸本)9781510681514

Internet traffic analysis is the core approach to network management and security. In the rapidly changing environment of encrypted traffic, traditional plaintext-based analysis methods have become obsolete. Although there are currently some methods for analysing encrypted traffic, they overlook the inherent logic and hierarchy of different encrypted traffic analysis requirements and lack research into the essential characteristics of encrypted *** article proposes a framework FM-ENet based on Graph Neural Network and Deep learning for Tor encrypted traffic classification to meet the practical needs of network management and security monitoring.A Flow Information Graph has been designed on the basis of Graph Neural Network, which can effectively alleviate the problem of concept *** the area of deep neural networks, we have developed an end-to-end multi-level spatio-temporal feature fusion enhanced network module ST-FENet, which has the advantage of automatically learning the non-linear relationship between input and output data without the need for manual feature extraction,this structure can complement each other in classification performance and solve the problem of low classifier efficiency. We compared FM-ENet to current popular methods using the public ISCX-Tor dataset. FM-ENet can achieve higher performance while saving costs, with a 9% improvement in accuracy compared to FlowPrint and a 4.4%, 4.5% and 4.4% improvement in PR, RC and F1 indicators compared to TSCRNN. © 2024 SPIE.

关键词： Network security

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