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- Supply chain sales forecasting based on lightGBM and LSTM combination model133
The experimental results show that the combined model can forecast supply chain sales with high accuracy, efficiency and interpretability, which can help enterprises to scientifically and reasonably forecast long-term commodity sales.
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- Bitstream-Corrupted JPEG Images are Restorable: Two-stage Compensation and Alignment Framework for Image Restoration15
A robust JPEG decoder is proposed, followed by a two-stage compensation and alignment framework to restore bitstream-corrupted JPEC images, and experimental results and ablation studies demonstrate the superiority of the proposed method.
- A Byte Sequence is Worth an Image: CNN for File Fragment Classification Using Bit Shift and n-Gram Embeddings13
This work proposes Byte2Image, a novel data augmentation technique, to introduce the neglected intra-byte information into file fragments and re-treat them as 2d gray-scale images, which allows us to capture both inter-byte and intra- byte correlations simultaneously through powerful convolutional neural networks (CNNs).
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- Two-way Homogeneity Pursuit for Quantile Network Vector Autoregression6
This work develops a quantile information criterion for the consistent selection of the number of groups and establishes theoretical guarantees for the proposed method, showing that both group memberships and parameter estimators are consistent even when the number of groups is over-specified.
- MindReading: An Ultra-Low-Power Photonic Accelerator for EEG-based Human Intention Recognition5
This paper proposes an ultra-low-power photonic accelerator, MindReading, for human intention recognition by only low bit-width addition and shift operations, which reduces the power consumption and improves the throughput per Watt.
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- A study of the application of self-organizing networks in designing appliances of Internet of Things4
- From Semantics, Scene to Instance-awareness: Distilling Foundation Model for Open-vocabulary Grounded Situation Recognition3
This paper proposes Multimodal Interactive Prompt Distillation (MIPD), a novel framework that distills enriched multimodal knowledge from the foundation model, enabling the student Ov-GSR model to recognize unseen situations and be better aware of rare situations.
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- CL-HOI: Cross-Level Human-Object Interaction Distillation from Vision Large Language Models2
A Cross-Level HOI distillation (CL-HOI) framework is proposed, which distills instance-level HOIs from VLLMs image-level understanding without the need for manual annotations, and demonstrates its efficacy in detecting HOIs without manual labels.
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- From Semantics, Scene to Instance-awareness: Distilling Foundation Model for Grounded Open-vocabulary Situation Recognition1
This paper proposes Multimodal Interactive Prompt Distillation (MIPD), a novel framework that distills enriched multimodal knowledge from the foundation model, enabling the student Ov-GSR model to recognize unseen situations and be better aware of rare situations.
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- HSNet: Heterogeneous Subgraph Network for Single Image Super-resolution–
The Heterogeneous Subgraph Network (HSNet) is proposed, a novel framework that efficiently leverages graph modeling while maintaining computational feasibility, and achieves state-of-the-art performance, effectively balancing reconstruction quality with computational efficiency.
- Research on the Issues and Strategies of Constructing Intelligent Classrooms in Higher Education–
This paper explores strategies for deeply integrating Internet technology with traditional classroom teaching, aiming to create a highly intelligent, personalized, and interactive teaching environment in higher education—intelligent classrooms.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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