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A Neural Network Framework for Classifying Educational Content Aligned with Learning Outcomes

Procedia computer science 2025年 257卷 529-536页

作者： Adel Alkhalil Department of Software Engineering College of Computer Science and Engineering University of Ha’il Ha’il 81481 Saudi Arabia

Rapid growth of digital educational content necessitates efficient and accurate methods for organizing and mapping resources to ensure well-alignment with targeted learning outcomes, academic standards, and competency frameworks. Traditional text classification approaches, including rule-based and classical machine learning techniques, often fail to address the semantic diversity and scalability demands of modern educational systems. This study investigates the application of neural networks for text classification to automate the mapping of educational content into predefined categories. Leveraging state-of-the-art architectures such as Long Short-Term Memory (LSTM) networks, and transformers like BERT, we present an architecture of a systematic Classification of educational materials. We discuss the implications of this work for adaptive learning environments, emphasizing the potential of neural networks to enhance the efficiency and scalability of content mapping. This study contributes to the growing body of research in artificial intelligence for education and sets the stage for further exploration into multilingual and domain-specific content classification methods.

关键词： Neural Networks Text Classification Educational Content Curriculum Mapping Transformer Models Learning Outcomes

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Lift: Exploiting Hybrid Stacked Memory for Energy-Efficient Processing of Graph Convolutional Networks 23

Lift: Exploiting Hybrid Stacked Memory for Energy-Efficient ...

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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Jiaxian Chen Zhaoyu Zhong Kaoyi Sun Chenlin Ma Rui Mao Yi Wang College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9798350323481

Graph Convolutional Networks (GCNs) are powerful learning approaches for graph-structured data. GCNs are both computing- and memory-intensive. The emerging 3D-stacked computation-in-memory (CIM) architecture provides a promising solution to process GCNs efficiently. The CIM architecture can provide near-data computing, thereby reducing data movement between computing logic and memory. However, previous works do not fully exploit the CIM architecture in both dataflow and mapping, leading to significant energy *** paper presents Lift, an energy-efficient GCN accelerator based on 3D CIM architecture using software and hardware co-design. At the hardware level, Lift introduces a hybrid architecture to process vertices with different characteristics. Lift adopts near-bank processing units with a push-based dataflow to process vertices with strong re-usability. A dedicated unit is introduced to reduce massive data movement caused by high-degree vertices. At the software level, Lift adopts a hybrid mapping to further exploit data locality and fully utilize the hybrid computing resources. The experimental results show that the proposed scheme can significantly reduce data movement and energy consumption compared with representative schemes.

关键词： 3D-stacked memory

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Dealing with Noisy Data in Federated Learning: An Incentive Mechanism with Flexible Pricing 25

Dealing with Noisy Data in Federated Learning: An Incentive ...

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Proceedings of the ACM on Web Conference 2025

作者： Hengzhi Wang Haoran Chen Minghe Ma Laizhong Cui College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9798400712746

Federated Learning (FL) has emerged as a promising training framework that enables a server to effectively train a global model by coordinating multiple devices, i.e., clients, without sharing their raw data. Keeping data locally can ensure data privacy, but also makes the server difficult to assess data quality, leading to the noisy data issue. Specifically, for any given training task, only a portion of each client's data is relevant and beneficial, while the rest may be redundant or noisy. Training with excessive noisy data can degrade performance. Motivated by this, we investigate the limitations of existing studies and develop an incentive mechanism with flexible pricing tailored for noisy data settings. The insight lies in mitigating the impact of noisy data by selecting appropriate clients and incentivizing them to clean their data spontaneously. Further, both rigorous theoretical analysis and extensive simulations compared with state-of-the-art methods have been well-conducted to validate the effectiveness of the proposed mechanism.

关键词： federated learning

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Heterogeneous Graph Transfer Learning for Category-aware Cross-Domain Sequential Recommendation 25

Heterogeneous Graph Transfer Learning for Category-aware Cro...

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Proceedings of the ACM on Web Conference 2025

作者： Zitao Xu Xiaoqing Chen Weike Pan Zhong Ming College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9798400712746

Cross-domain sequential recommendation (CDSR) is proposed to alleviate the data sparsity issue while capturing users' sequential preferences. However, most existing methods do not explore the item transition patterns across different domains and can also not be applied to a multi-domain ***, previous methods rely on overlapping users as bridges to transfer knowledge, which struggles to capture the complex associations across domains without sufficient overlapping users. In this paper, we introduce item attributes into CDSR, and propose a heterogeneous graph transfer learning method to address these ***, we construct a cross-domain heterogeneous graph to allow the association of user, item, and category nodes from different domains,and enhance the flexibility of the model by enabling message propagation between more nodes through edge expansion based on the semantic similarity and co-occurrence *** addition, we devise meta-paths from different perspectives for nodes at item, user and category levels to guide information aggregation, which can transfer knowledge across domains and reduce the reliance on the number of overlapping *** further design attention modules to capture users' dynamic preferences from the item sequences they have interacted with in each domain, and explore the transition patterns within category sequences which reflect users' coarse-grained ***, we perform knowledge transfer across different domains, and predict the most likely items that users will interact with in each domain. Extensive empirical studies on three real-world datasets indicate that our HGTL significantly outperforms the state-of-the-art baselines in all cases.

关键词： cross-domain recommendation

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Research on Chip Special Layout Recognition Technology 24

Research on Chip Special Layout Recognition Technology

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Proceedings of the 2024 4th International Conference on Big Data, Artificial Intelligence and Risk Management

作者： Jimao Jia Software Engineering in College of Computer Science College of Software Inner Mongolia University Hohhot Inner Mongolia China

ISBN: (纸本)9798400709753

Chip analysis is an important means of chip research, and with the progress of the chip process, the traditional means are subject to great challenges. For this reason, the research and application of chip automation and intelligent technology is an important topic. In this paper, for the chip analysis in the layout presents special process holes, missing effective elements, poor image quality and other characteristics, the existing recognition algorithms have been applied to the difficulties of bad results, proposed deep learning-based improvement algorithms, basically solves the layout of the large holes, hollow holes, dark holes, and other special elements of the problem, the recognition effect reaches the expected.

关键词： chip analysis

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Adversarial Knowledge Transfer for Black-Box Model Inversion Attack

Adversarial Knowledge Transfer for Black-Box Model Inversion...

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

作者： Xinhao Liu Zetao Lin Yingzhao Jiang Qiao Yan College of Computer Science & Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recent advancements in model inversion attacks have raised privacy concerns, exploiting access to models to reconstruct private training data from inputs and outputs. These attacks are categorized as white-box, black-box, or label-only based on access level. We propose a new black-box model inversion attack, Label-Controlled Adversarial Knowledge Transfer (L-AdKT). L-AdKT leverages adversarial training with a Generative Adversarial Network (GAN) and a substitute model to extract knowledge from the target model. The substitute model minimizes discrepancies with the target model while guiding the generator to produce realistic samples. This approach enables white-box techniques to be applied in black-box settings. Experiments show that L-AdKT outperforms state-of-the-art black-box attacks by over 20% across benchmarks and remains robust against various defense mechanisms.

关键词： Training Closed box Training data Signal processing Generative adversarial networks Data models Speech processing Knowledge transfer Glass box Resilience

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Human Action Recognition in Multi-Level Convolutional Temporal Attention Network

Human Action Recognition in Multi-Level Convolutional Tempor...

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

作者： Qian Huang ZhongQi Chen Chang Li WeiWen Qian College of Computer Science and Software Engineering Hohai University NanJing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Human Action Recognition (HAR) has widespread applications in areas such as human-computer interaction, elderly care, and home healthcare. However, current sensor-based HAR faces challenges of low fine-grained recognition performance and difficulty in distinguishing similar actions. To solve this problem, this paper proposes a model based on Multilevel Convolutional Time Series Attention Network (MCTSANet). By Multi-ResCNN to pay attention to different levels of features and using Time Series Attention (TSA) to pay attention to the more important data in the channel, so as to improve the ability of confusable action recognition. Experiments on three public datasets show that the proposed method outperforms state-of-the-art sensor-based HAR approaches.

关键词： Human computer interaction Convolution Time series analysis Noise Speech recognition Medical services Human activity recognition Speech processing Older adults Faces

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Spatially Compact Dense Block Mining in Spatial Tensors 25

Spatially Compact Dense Block Mining in Spatial Tensors

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Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1

作者： Weike Tang Dingming Wu Tsz Nam Chan Kezhong Lu College of Computer Science & Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9798400712456

Spatial tensors have been extensively used in a wide range of applications, including remote sensing, geospatial information systems, conservation planning, and urban planning. We study the problem of Spatially Compact Dense (SCD) block mining in a spatial tensor, which targets for discovering dense blocks that cover small spatial regions. However, most of existing dense block mining (DBM) algorithms cannot solve the SCD-block mining problem since they only focus on maximizing the density of candidate blocks, so that the discovered blocks are spatially loose, i.e., covering large spatial regions. Therefore, we first formulate the problem of mining top-k Spatially Compact Dense blocks (SCD-blocks) in spatial tensors, which ranks SCD-blocks based on a new scoring function that takes both the density value and the spatial coverage into account. Then, we adopt a filter-refinement framework that first generates candidate SCD-blocks with good scores in the filtering phase and then uses the traditional DBM algorithm to further maximize the density values of the candidates in the refinement phase. Due to the NP-hardness of the problem, we develop two types of solutions in the filtering phase, namely the top-down solution and the bottom-up solution, which can find good candidate SCD-blocks by approximately solving the new scoring function. The evaluations on four real datasets verify that compared with the dense blocks returned by existing DBM algorithms, the proposed solutions are able to find SCD-blocks with comparable density values and significantly smaller spatial coverage.

关键词： dense block

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MPNAS: Multimodal Sentiment Analysis Pruning via Neural Architecture Search

MPNAS: Multimodal Sentiment Analysis Pruning via Neural Arch...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Binyan Ren, Ao Zhang, Zihao Duan, Moming Liu, Duo Tan, Yujuan Zhong, Kan College of Computer Science Chongqing University Chongqing China School of Big Data Software & Engineering Chongqing University Chongqing China Institute of Data Science National University of Singapore Singapore

ISBN: (纸本)9798350368741

With the rapid development of social media, sentiment analysis from multimodal posts has garnered significant attention in recent years. However, the substantial size of these models impedes their deployment on resource-constrained embedded devices. Although pruning has been extensively studied to reduce the size of unimodal models, specific challenges remain for Multimodal Sentiment Analysis (MSA) models. First, existing techniques prune fixed original models into sparse models, while our findings indicate that different model architectures of identical size yield varying performance outcomes. Second, prior studies fail to explore the unique characteristics of MSA models, resulting in suboptimal pruning performance. To address these challenges, we propose MPNAS, a unified pruning framework via Neural Architecture Search (NAS) for MSA models. Specifically, we formulate pruning as a NAS problem and analyze MSA model characteristics to guide the subnet search. We conduct an initial coarse-grained NAS on the original model, expanding the search space slightly to identify suitable subnets that enhance pruning rates and accuracy. Subsequently, we refine coarse-grained subnets in a fine-grained NAS stage, where MSA model characteristics guide the search process. Extensive experiments on three representative datasets demonstrate the superiority of our approach over existing methods. © 2025 IEEE.

关键词： Multimodal Compression NAS

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A Paradigm Shift to Causal Model-Driven Decision-Making With Generative AI 13th

A Paradigm Shift to Causal Model-Driven Decision-Making Wit...

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13th International Conference on AI and Multimodal Services, AIMS 2024, Held as Part of the Services Conference Federation, SCF 2024

作者： He, Sheng Ning, Yishuang Zhang, Liang-Jie Lei, Kai National Engineering Research Center for Supporting Software of Enterprise Internet Services Shenzhen518057 China Kingdee Research Kingdee International Software Group Co. Ltd. Shenzhen518057 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Peking University Shenzhen518055 China

ISBN: (纸本)9783031776809

In recent years, the rise of big data has popularized data-driven decision-making. However, the interpretability shortcomings of artificial intelligence (AI) models limit their reliability for critical decisions. This paper proposes a paradigm shift from conventional data-driven to causal model-driven decision-making, leveraging advancements in causal inference and large language models (LLMs). By applying the capabilities of generative AI, this paradigm shift enables the fusion of extensive domain knowledge, facilitating the development of causal models that capture the complexity of real-world systems and problems. The model-driven approach provides a better understanding of the causal mechanisms, relationships, and dynamics compared to correlational data-driven methods. Moreover, we introduce the concept of a composable business model based on modular causal components. We present a methodology for constructing robust causal model-driven decision frameworks, emphasizing the comprehensive utilization of generative AI to incorporate domain knowledge with causal inferences. Through an in-depth analysis of case studies across multiple domains, this model-driven approach empirically showcases its potential to improve decision quality, optimize resource allocation, and enhance process efficiency. Additionally, we critically discuss future research directions and challenges in the evolutionary trajectory of causal model-driven decision-making. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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