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Context Embedding Deep Collaborative Filtering (CEDCF) in the higher education sector

Multimedia Tools and Applications 2024年第38期83卷 85597-85617页

作者： Abakarim, Sana Qassimi, Sara Rakrak, Said Computer Science Department L2IS Laboratory Faculty of Science and Techniques Cadi Ayyad University Marrakesh40000 Morocco

In response to the COVID-19 crisis, higher education institutions increasingly rely on e-learning systems. Indeed, the higher education market has become increasingly competitive with the addition of open education models. However, the abundance of accessible online courses makes it challenging to deliver education that meets student needs. Learners have diverse profiles based on their traits, motivations, prior knowledge, and learning preferences. Recently, much research has given attention to the importance of using the contextual parameters to perform more accurate recommendations. In this context, context-aware recommendation of pedagogical resources can deal with the issue of information overload, cold start problem and meeting the learner’s preferences. This paper describes a context-aware recommender system that harness the learner’s contextual information. Our proposed approach is called Context Embedding Deep Collaborative Filtering (CEDCF), which enriches the learner profile with extracted sentiments from previous interactions. The proposed approach comprises three models, called Generalized Matrix Factorzation (GMF), Multilayer Perceptron (MLP) and Neural Matrix Factorization (NeuMF). The GMF and the MLP are respectively applied to the rating matrix and the contextual parameters. The outputs of these models are then fed into a neural network to perform rating prediction. To put our proposal into shape, we model a real-world application of a merged coursera dataset to recommend courses. The experimental evaluation shows relevant results attesting the efficiency of the proposed approach. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Cold start problem Collaborative filtering Deep learning E-learning Education Higher education Neural networks Recommender system Smart education

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Masked Generative Light Field Prompting for Pixel-Level Structure Segmentations

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Research 2024年第4期2024卷 533-544页

作者： Mianzhao Wang Fan Shi Xu Cheng Shengyong Chen The Engineering Research Center of Learning-Based Intelligent System(Ministry of Education) Tianjin University of TechnologyTianjin 300384China Key Laboratory of Computer Vision and System(Ministry of Education) Tianjin University of TechnologyTianjin 300384China School of Computer Science and Engineering Tianjin University of TechnologyTianjin 300384China

Pixel-level structure segmentations have attracted considerable attention,playing a crucial role in autonomous driving within the metaverse and enhancing comprehension in light field-based machine ***,current light field modeling methods fail to integrate appearance and geometric structural information into a coherent semantic space,thereby limiting the capability of light field transmission for visual *** this paper,we propose a general light field modeling method for pixel-level structure segmentation,comprising a generative light field prompting encoder(LF-GPE)and a prompt-based masked light field pretraining(LF-PMP)*** LF-GPE,serving as a light field backbone,can extract both appearance and geometric structural cues *** aligns these features into a unified visual space,facilitating semantic ***,our LF-PMP,during the pretraining phase,integrates a mixed light field and a multi-view light field *** prioritizes considering the geometric structural properties of the light field,enabling the light field backbone to accumulate a wealth of prior *** evaluate our pretrained LF-GPE on two downstream tasks:light field salient object detection and semantic *** results demonstrate that LF-GPE can effectively learn high-quality light field features and achieve highly competitive performance in pixel-level segmentation tasks.

关键词： prompt backbone integrate

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Latest advances in deep learning-based recommender systems

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International Journal of Reasoning-based Intelligent Systems 2024年第3期16卷 249-266页

作者： Debbah, Amina Lagrini, Samira LRI Laboratory Computer Science Department Badji Mokhtar University P.O. Box 12 Annaba23000 Algeria Labged Laboratory Computer Science Department Badji Mokhtar University P.O. Box 12 Annaba23000 Algeria

Recommender systems (RSs) are prominent tools massively used in different fields of social life, e-commerce, and online platforms. The use of machine learning techniques to build RSs gives good results, but it cannot satisfy all user’s requirements nowadays. The exponential growth of available data integrating social networks, and contextual and temporal properties, greatly complicate the control of user’s request using traditional machine learning and decision support systems, as these techniques fail to handle massive multimedia data sources, and cannot effectively capture nonlinear relationships between users and items. Moreover, they dismiss context, social relationships, and trustworthiness. Currently, deep learning techniques are successfully used in almost artificial intelligence fields including RSs. Latest research proves that deep learning-based RSs yield promising results and outperforms traditional machine learning techniques. This paper provides a comprehensive overview of recent advances in deep learning-based RSs. We deeply analyse the challenges of these systems, and how recent research works address these challenges. Furthermore, we address the strengths and weaknesses of existing approaches in order to offer an exhaustive view for new researchers to develop new ideas when tackling the issue of RSs. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Recommender systems

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Part-aware Surface Slice and Polarization Implicit Function with Regular Discretization for 3D Human Surface Reconstruction

Part-aware Surface Slice and Polarization Implicit Function ...

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2024 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2024

作者： Yang, Yehui Deng, Yong Li, Baoxing Zhao, Xu Shanghai Jiao Tong University Department of Automation Laboratory of Computer Vision Shanghai200240 China

ISBN: (纸本)9798350372601

Detailed 3D human surface reconstruction and editing relies on reasonable and elaborate representations. Currently, representation for 3D human surface can be broadly categorized into mesh-based and function-based approaches. Mesh-based representation offers intuitive visualization of intricate structure but is constrained by resolution limitations. Function-based representation surpasses resolution constraints to achieve higher precision but is spatial redundant for fitting the whole space. To address these challenges, we introduce the Part-aware surface Slice Polarization Implicit Function (PaSP-IF) for 3D human surfaces, aiming at capturing details and reducing redundancy. By establishing polar coordinate systems for slice plane of each body part, we represent the human surface using polar angle and radial distance. Additionally, as for visualization, discretization applied on PaSP-IF can be real-ized by selecting appropriate slice and polar angle resolutions to form the discretized radius matrix. Pixel-level operations can be easily applied on the radius matrix to edit the human surface. Experiments on the THuman2.0 datasets, along with applications in surface editing and clothing transferring, have demonstrated the effectiveness of the proposed representation. © 2024 IEEE.

关键词： Mesh generation

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Self-Supervised Fast Texture Defect Detection Based on Salient Object Detection

Self-Supervised Fast Texture Defect Detection Based on Salie...

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2024 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2024

作者： Wan, Jingyan Zhou, Changsheng Zhao, Xu Shanghai Jiao Tong University Laboratory of Computer Vision Department of Automation Shanghai200240 China

ISBN: (纸本)9798350372601

Texture defect detection is an essential technology in large-scale industrial abnormal detection. Currently, researchers in the defect detection field primarily calculate the anomaly scores of images and perform threshold segmentation to obtain defect detection results. This method generally yields good detection results, but precise boundaries are challenging to obtain due to the similarity of anomaly scores in the edge regions of images. Additionally, there is a growing interest in accelerating defect detection methods to meet real-world application scenarios. To address these challenges, this paper introduces salient object detection methods for texture defect detection. We define the defect regions in defect images as salient regions, effectively capturing the overall image through multiscale learning. This achieves precise localization of defects (salient objects). Moreover, data augmentation is used to artificially synthesize defect images, resolving the salient object detection method's reliance on strong supervised information and achieving self-supervised defect detection. To better distinguish features between normal and salient regions, we incorporate a contrastive learning module aimed at enhancing feature differentiation. The effectiveness of our proposed method is demonstrated by experimental results on the MVTec texture dataset, achieving a high frame rate exceeding 30 FPS on the Nvidia GeForce GTX 2080ti GPU. © 2024 IEEE.

关键词： Contrastive Learning

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Real-Time Industrial Anomaly Detection via Sparse Reconstruction

Real-Time Industrial Anomaly Detection via Sparse Reconstruc...

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2024 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2024

作者： Zhou, Changsheng Wan, Jingyan Zhao, Xu Shanghai Jiao Tong University Laboratory of Computer Vision Department of Automation Shanghai200240 China

ISBN: (纸本)9798350372601

In the field of industrial anomaly detection, the scarcity of anomalous data and labels poses significant challenges, necessitating models that can efficiently detect and localize anomalies with minimal reliance on anomalous training data. Traditional approaches often utilize outlier detection strategies on pre-trained features, which are hampered by the inclusion of redundant and irrelevant information, leading to decreased computational efficiency and diminished performance in real-time applications. Addressing these limitations, this paper introduces a novel defect detection and localization strategy that emphasizes rapid feature reconstruction. Our methodology comprises three key components: (1) a robust pre-trained feature extractor that generates descriptive image features, (2) an innovative feature dictionary developed via dictionary learning to embed features from normal images, and (3) a dynamic feature reconstructor designed for swift reconstruction of test features utilizing the dictionary. This approach enables precise anomaly identification and localization by assessing differences between original and reconstructed features. Rigorous testing on the MVTec AD dataset - a benchmark for real-world industrial anomaly detection - validates the method's superiority, demonstrating substantial improvements in detection speed with minimal impact on accuracy. The findings suggest that this strategy holds significant promise for enhancing the efficiency and reliability of anomaly detection in industrial settings. © 2024 IEEE.

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MMInstruct: a high-quality multi-modal instruction tuning dataset with extensive diversity

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science China(Information sciences) 2024年第12期67卷 36-51页

作者： Yangzhou LIU Yue CAO Zhangwei GAO Weiyun WANG Zhe CHEN Wenhai WANG Hao TIAN Lewei LU Xizhou ZHU Tong LU Yu QIAO Jifeng DAI School of Computer Science Nanjing University School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai AI Laboratory School of Computer Science Fudan University Department of Information Engineering The Chinese University of Hong Kong SenseTime Research Department of Electronic Engineering Tsinghua University

Despite the effectiveness of vision-language supervised fine-tuning in enhancing the performance of vision large language models(VLLMs), existing visual instruction tuning datasets include the following limitations.(1) Instruction annotation quality: despite existing VLLMs exhibiting strong performance,instructions generated by those advanced VLLMs may still suffer from inaccuracies, such as hallucinations.(2) Instructions and image diversity: the limited range of instruction types and the lack of diversity in image data may impact the model's ability to generate diversified and closer to real-world scenarios outputs. To address these challenges, we construct a high-quality, diverse visual instruction tuning dataset MMInstruct,which consists of 973k instructions from 24 domains. There are four instruction types: judgment, multiplechoice, long visual question answering, and short visual question answering. To construct MMInstruct, we propose an instruction generation data engine that leverages GPT-4V, GPT-3.5, and manual correction. Our instruction generation engine enables semi-automatic, low-cost, and multi-domain instruction generation at 1/6 the cost of manual construction. Through extensive experiment validation and ablation experiments,we demonstrate that MMInstruct could significantly improve the performance of VLLMs, e.g., the model fine-tuning on MMInstruct achieves new state-of-the-art performance on 10 out of 12 benchmarks. The code and data shall be available at https://***/yuecao0119/MMInstruct.

关键词： instruction tuning multi-modal multi-domain dataset vision large language model

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Orbit-Orbit Interaction in Spatiotemporal Optical Vortex

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Engineering 2025年第2期45卷 44-51页

作者： Jian Chen Jie Zhao Xi Shen Dewei Mo Cheng-Wei Qiu Qiwen Zhan School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China Shanghai Key Laboratory of Modern Optical System University of Shanghai for Science and TechnologyShanghai 200093China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore

While spin-orbit interaction has been extensively studied,few investigations have reported on the interaction between orbital angular momenta(OAMs).In this work,we study a new type of orbit-orbit coupling between the longitudinal OAM and the transverse OAM carried by a three-dimensional(3D)spatiotemporal optical vortex(STOV)in the process of tight *** 3D STOV possesses orthogonal OAMs in the x-y,t-x,and y-t planes,and is preconditioned to overcome the spatiotemporal astigmatism effect.x,y,and t are the axes in the spatiotemporal *** corresponding focused wavepacket is calculated by employing the Debye diffraction theory,showing that a phase singularity ring is generated by the interactions among the transverse and longitudinal vortices in the highly confined *** Fourier-transform decomposition of the Debye integral is employed to analyze the mechanism of the orbit-orbit *** is the first revelation of coupling between the longitudinal OAM and the transverse OAM,paving the way for potential applications in optical trapping,laser machining,nonlinear light-matter interactions,and more.

关键词： Spatiotemporal optical vortex Orbital angular momentum Momentum interaction Highly confined wavepacket Diffraction

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Energy Efficient Hardware Acceleration of Neural Networks with Power-of-Two Quantisation

Energy Efficient Hardware Acceleration of Neural Networks wi...

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International Conference on computer vision and Graphics, ICCVG 2022

作者： Przewlocka-Rus, Dominika Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland

ISBN: (纸本)9783031220241

Deep neural networks virtually dominate the domain of most modern vision systems, providing high performance at a cost of increased computational complexity. Since for those systems it is often required to operate both in real-time and with minimal energy consumption (e.g., for wearable devices or autonomous vehicles, edge Internet of Things (IoT), sensor networks), various network optimisation techniques are used, e.g., quantisation, pruning, or dedicated lightweight architectures. Due to the logarithmic distribution of weights in neural network layers, a method providing high performance with significant reduction in computational precision (for 4-bit weights and less) is the Power-of-Two (PoT) quantisation (and therefore also with a logarithmic distribution). This method introduces additional possibilities of replacing the typical for neural networks Multiply and ACcumulate (MAC—performing, e.g., convolution operations) units, with more energy-efficient Bitshift and ACcumulate (BAC). In this paper, we show that a hardware neural network accelerator with PoT weights implemented on the Zynq UltraScale + MPSoC ZCU104 SoC FPGA can be at least 1.4x more energy efficient than the uniform quantisation version. To further reduce the actual power requirement by omitting part of the computation for zero weights, we also propose a new pruning method adapted to logarithmic quantisation. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： System-on-chip

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Traffic Sign Classification Using Deep and Quantum Neural Networks

Traffic Sign Classification Using Deep and Quantum Neural Ne...

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International Conference on computer vision and Graphics, ICCVG 2022

作者： Kuros, Sylwia Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland

ISBN: (纸本)9783031220241

Quantum Neural Networks (QNNs) are an emerging technology that can be used in many applications including computer vision. In this paper, we presented a traffic sign classification system implemented using a hybrid quantum-classical convolutional neural network. Experiments on the German Traffic Sign Recognition Benchmark dataset indicate that currently QNN do not outperform classical DCNN (Deep Convolutuional Neural Networks), yet still provide an accuracy of over 90% and are a definitely promising solution for advanced computer vision. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Traffic signs

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