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12th IEEE International Conference on Identification, information and Knowledge in the Internet of Things, IIKI 2024

作者： Du, Mingsen Wang, Yunxia Wei, Yanxuan Wu, Xiaona Ji, Cun Shandong Normal University School of Information Science and Engineering Jinan China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan China

ISBN: (纸本)9798331510633

Time series classification has gained significant attention in the data mining field in recent years. Despite the proposal of numerous methods over the past decades, most of these approaches focus on feature extraction in one-dimensional space, which often results in lower classification accuracy. To address this issue, this paper proposes a Time Series Classification method based on U-Net with 2D transformation (TSC-Unet). This method first transforms univariate time series into 2D space and augments the data, effectively enhancing the diversity of the dataset. Subsequently, features are extracted from the 2D space using a fully convolutional network, fully exploiting multi-scale feature information. U-Netwith skip connections, effectively retains and transmits high-resolution features, significantly reducing information loss and improving segmentation performance. Additionally, the ability to learn multi-scale features enables the model to capture details at different levels, thereby enhancing its understanding of complex data. Experimental results demonstrate that TSC-Unet achieves high classification accuracy on the UCR datasets, validating the effectiveness and superiority of the proposed method. © 2024 IEEE.

关键词： Time series

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MASS FLOW DETECTION technology FOR SEED AND FERTILISER PARTICLES AND ITS APPLICATION IN UAV-BASED SPREADING

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INMATEH - Agricultural Engineering 2025年第1期75卷 584-599页

作者： Wang, Yanshuang Song, Cancan Gao, Yongqi Lan, Yubin Wang, Guobin Ming, Rui College of Agricultural Engineering and Food Science Shandong University of Technology Zibo China Shandong University of Technology Sub-center of National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology Zibo China Research of Institute of Ecological Unmanned Farm Shandong University of Technology Zibo China School of Computer and Big Data Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou China

The processes of sowing and fertiliser application represent a significant aspect of agricultural production. In order to achieve efficient and precise seeding and fertiliser application, mass flow detection of seed and fertiliser particles can facilitate real-time monitoring and precise decision-making for intelligent seeding and fertiliser application. However, the diversity of seed and fertiliser particle types and particle flow modes presents a challenge for existing detection methods, particularly in meeting the varying operational requirements, especially for particle mass flow detection in low-altitude and high-speed sowing operations of agricultural drones. In such cases, the overlap and flow rate will have a significant impact on the detection results due to the large displacement and the continuous high-throughput dense phase of the particle flow. This paper provides a summary of the existing seed and fertiliser particle mass flow detection techniques and their underlying working principles. It compares the direct detection based on mass with the indirect detection methods based on velocity and concentration, and analyses their respective advantages, disadvantages and applicability. It also explores the possibility of optimising the existing detection methods for the specific needs of agricultural UAVs and considers the potential introduction of cutting-edge science and technology in order to develop an efficient, accurate and convenient detection system to meet the growing market demand. © (2025), (INMA Bucharest). All Rights Reserved.

关键词： Precision agriculture

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Towards Efficient On-Chip Learning for Spiking Neural Networks Accelerator with Surrogate Gradient

Towards Efficient On-Chip Learning for Spiking Neural Networ...

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2023 IEEE International Conference on Integrated Circuits, Technologies and Applications, ICTA 2023

作者： Huang, Kexin Liu, Wei Liu, Yue Xiao, Shanlin Yu, Zhiyi School of Microelectronics Science and Technology Sun Yat-sen University China Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems China

ISBN: (纸本)9798350344288

Spiking Neural Networks (SNNs) have advantages in low power consumption, however, there is a problem of non-differentiability in the backpropagation (BP) of SNN. In this work, we propose a high-performance, low-cost SNN accelerator supports on-chip learning and utilizes a surrogate gradient (SG) for supervised learning, addressing the non-differentiability issue. The design is implemented on the VC707 FPGA, operating at a clock frequency of 115 MHz with a power consumption of 798 mW, achieving an accuracy of 95.49% on the MNIST dataset. The training and inference speeds are 1183 frame/s and 4163 frame/s. The training energy is 0.67 mJ/img and the inference energy is 0.21 mJ/img, reducing the energy consumption by 10-40 times compared to state-of-the-art (SOTA) unsupervised works. © 2023 IEEE.

关键词： Neural networks

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Leveraging Knowledge Distillation for Improved Event Extraction in QA Models 6

Leveraging Knowledge Distillation for Improved Event Extract...

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6th International Conference on Frontier Technologies of information and computer, ICFTIC 2024

作者： Zhou, Peiyao Hu, Zhikui Zi, Kangli Zhang, Dawei School of Computer Science Jiangsu University of Science and Technology Zhenjiang China Institute of Computing Technology Chinese Academy of Sciences Key Laboratory of Intelligent Information Processing Beijing China

ISBN: (纸本)9798331541750

Event extraction is an important task in natural language processing, and it is widely utilized in intelligence domains such as business and military for information extraction. Recently, many works have successfully transformed document-level event extraction into Question-answering (QA) tasks with remarkable results. This approach embeds event argument information within the questions, introducing prior knowledge into the process. Jin et al. highlighted the importance of high-quality QA pairs for practical QA tasks, emphasizing that constructing these pairs remains a key challenge[1]. In this study, we propose a Prompt-based question generation method to automatically generate questions containing event arguments, which converts event extraction into a QA task. We introduce an event ontology-based information retrieval module to enhance answer accuracy and select the most relevant document segments as input text. Additionally, we employ knowledge distillation to build the QA model, transferring knowledge from a large pre-trained model to a more compact and efficient one, improving the student model's performance. Experiments show that our model performs strongly in mainstream benchmarks, with 4.8 improvements on WikiEvents. © 2024 IEEE.

关键词： Question answering

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Unsupervised Nonlinear Adaptive Manifold Learning for Global and Local information

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Tsinghua Science and technology 2021年第2期26卷 163-171页

作者： Jiajun Gao Fanzhang Li Bangjun Wang Helan Liang School of Computer Science and Technology Soochow UniversitySuzhou 215006China Joint International Research Laboratory of Machine Learning and Neuromorphic Computing Provincial Key Laboratory for Computer Information Processing TechnologySoochow UniversitySuzhou 215006China

In this paper,we propose an Unsupervised Nonlinear Adaptive Manifold Learning method(UNAML)that considers both global and local *** this approach,we apply unlabeled training samples to study nonlinear manifold features,while considering global pairwise distances and maintaining local topology *** method aims at minimizing global pairwise data distance errors as well as local structural *** order to enable our UNAML to be more efficient and to extract manifold features from the external source of new data,we add a feature approximate error that can be used to learn a linear ***,we add a feature approximate error that can be used to learn a linear *** addition,we use a method of adaptive neighbor selection to calculate local structural *** paper uses the kernel matrix method to optimize the original *** algorithm proves to be more effective when compared with the experimental results of other feature extraction methods on real face-data sets and object data sets.

关键词： unsupervised manifold learning global and local information,adaptive neighbor selection method kernel matrix

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Towards Energy-Efficient Asynchronous Circuit Design with Flip-Flop-to-Latch Replacement

Towards Energy-Efficient Asynchronous Circuit Design with Fl...

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2023 IEEE International Conference on Integrated Circuits, Technologies and Applications, ICTA 2023

作者： Xu, Zeyang Xiao, Shanlin Zhou, Lingfeng Wang, Bohan Yu, Zhiyi School of Microelectronics Science and Technology Sun Yat-sen University China Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems China

ISBN: (纸本)9798350344288

With data flow controlled by handshake templates, asynchronous circuits have the potential for low power consumption and high performance. However, the control of the handshake signal introduces additional hardware overhead. We utilize latch to further reduce the power consumption, then propose an efficient design methodology for a latch-based asynchronous pipeline and timing constraints method with conventional EDA tools. An asynchronous RISC-V processor was designed and thoroughly compared with its synchronous counterpart and the popular RISC-V cores to validate the effectiveness of the proposed methodology. Experimental results demonstrate a 3.5% area optimization and a 14.9% reduction in power consumption compared with the synchronous counterpart. © 2023 IEEE.

关键词： Pipelines

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Research on Collaborative Filtering Algorithm of E-commerce Based on Flink 13

Research on Collaborative Filtering Algorithm of E-commerce ...

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13th International Conference on Intelligent Control and information processing, ICICIP 2025

作者： Zheng, Haohao Luo, Xiaonan Zhu, Xiaoshu School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9798331516147

In online shopping, a person's interest in a product is closely related to whether they will purchase it Analyzing people's interest in various products on time, along with product recommendations, can increase purchase rates. However, in many cases, We only know user implicit feedback, such as browsing or favoriting items, and cannot directly access user explicit feedback, such as product ratings. Moreover, users have limited exposure to products, resulting in a sparse user- item matrix. The traditional Alternating Least Squares (ALS) algorithm can partially address the issue of matrix sparsity, but its accuracy in recommending products based on implicit feedback alone is insufficient This paper proposes an improved ALS algorithm that converts implicit feedback into explicit ratings. We calculate the browsing frequency of a product based on user browsing counts. For a given product, we represent user preference by summing the browsing frequencies of all products with browsing frequencies less than or equal to that of the product. Then, by multiplying the preference level by the product's maximum rating, we derive a product rating, achieving a transformation from implicit feedback to explicit scores. Additionally, traditional collaborative filtering algorithms often use offline data, which may not align with user current preferences. To improve the algorithm's accuracy, we also utilize Flink for real-time data processing, allowing us to capture the most recent feedback data. This study tests the approach using the UserBehavior dataset from the publicly available Tianchi dataset. Experimental results indicate improvements in both Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). © 2025 IEEE.

关键词： Collaborative filtering

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Research on Emotional Analysis of Tibetan Short Text Based on Fusion Sentiment Lexicon 1

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1st International Artificial Intelligence Conference, IAIC 2023

作者： Ji, Wan Ma Dao Rang, An Jian Cai Cuo, Jan Yang Jia, Gong Bao Cuo, De Ji Gong, Zan La Qin, Yang School of Computer Science Qinghai University for Minzu Xining810007 China State Key Laboratory of Intelligent Information Processing and Application of Tibetan Language Jointly Constructed By the Ministry of Provincial Affairs and the Ministry of Education of China Xining China Qinghai Key Laboratory of Tibetan Information Processing and Machine Translation Item No. 2021-Z-001 Xining China

ISBN: (数字)9789819712779

ISBN: (纸本)9789819712762

Short-text sentiment analysis plays an integral role in predicting sentiment polarity. The current Tibetan short-text sentiment analysis model applies deep neural networks to learn some local grammatical structure information but ignores known sentiment lexicon information in modeling. Aiming at the above problems, this paper proposes a method for sentiment analysis of Tibetan short-text that incorporates sentiment lexicon information. The method firstly uses the word features extracted by Word2vec and Glove to fuse with the sentiment dictionary information, and inputs them into CNN & BiLSTM networks to extract text context features;secondly, adjusts the weights of the sentiment features through the self-attention mechanism;and finally, uses Softmax to infer the sentiment categories. The experimental results show that the accuracy of this method can reach 93.18% on the self-built sentiment analysis corpus. In addition, compared with the methods of extracting syllable features and word features, the accuracy of this paper's method can be improved by 4.92 and 3.43% respectively, which proves the effectiveness of the fused sentiment lexicon. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Sentiment analysis

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HairDiffusion: Vivid Multi-Colored Hair Editing via Latent Diffusion 38

HairDiffusion: Vivid Multi-Colored Hair Editing via Latent D...

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38th Conference on Neural information processing Systems, NeurIPS 2024

作者： Zeng, Yu Zhang, Yang Liu, Jiachen Shen, Linlin Deng, Kaijun He, Weizhao Wang, Jinbao Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g., identity, background, cloth). Many existing methods are based on StyleGAN to address this task. However, due to the limited spatial distribution of StyleGAN, it struggles with multiple hair color editing and facial preservation. Considering the advancements in diffusion models, we utilize Latent Diffusion Models (LDMs) for hairstyle editing. Our approach introduces Multi-stage Hairstyle Blend (MHB), effectively separating control of hair color and hairstyle in diffusion latent space. Additionally, we train a warping module to align the hair color with the target region. To further enhance multi-color hairstyle editing, we fine-tuned a CLIP model using a multi-color hairstyle dataset. Our method not only tackles the complexity of multi-color hairstyles but also addresses the challenge of preserving original colors during diffusion editing. Extensive experiments showcase the superiority of our method in editing multi-color hairstyles while preserving facial attributes given textual descriptions and reference images. © 2024 Neural information processing systems foundation. All rights reserved.

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Fast Multivariate Time Series Anomaly Detection Based on Matrix Completion

Fast Multivariate Time Series Anomaly Detection Based on Mat...

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2023 International Conference on Frontiers of Robotics and Software Engineering, FRSE 2023

作者： He, Shiming Yi, Tongzhijian Yang, Bo Guo, Chenxi Changsha University of Science & Technology School of Computer and Communication Engineering The Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha China

ISBN: (纸本)9798350301113

Increasingly complex systems contain large numbers of devices that generate great number of multivariate time series that are monitored and recorded. For anomaly detection of these complex time series, deep learning technology is often used to build a learning model for anomaly detection. The method based on deep learning usually takes a long time to train data in practice, and the online service system is deployed or changed very frequently, which makes the online service system need to frequently spend a lot of time updating the anomaly detection model. To overcome this challenge, this paper proposes a fast multivariate time series anomaly detection (FMAD) approach based on matrix completion. After sampling the primitive low-rank matrix, FMAD uses matrix completion to reconstruct the original low-rank matrix to detect anomalies in multivariate time series. We conducted related experimental analysis and discussion. The experimental results on three real data sets show that FMAD has improved the problem of too long model training time and improved the detection efficiency. © 2023 IEEE.

关键词： Anomaly detection

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