检索结果-内蒙古大学图书馆

A new online field feature selection algorithm based on streaming data

Journal of Ambient Intelligence and Humanized Computing 2024年第2期15卷 1365-1377页

作者： Zhang, Zhenjiang Song, Fuxing Zhang, Peng Chao, Han-Chieh Zhao, Yingsi School of Software Engineering Beijing Jiaotong University Beijing100044 China Department of Electronic and Information Engineering Key Laboratory of Communication and Information Systems Beijing Municipal Commission of Education Beijing Jiaotong University Beijing100044 China School of Information Science and Engineering Fujian University of Technology Fuzhou350118 China School of Mathematics and Computer Science Wuhan Polytechnic University Wuhan430023 China Department of Electrical Engineering National Dong Hwa University Hualien97401 Taiwan School of Economics and Management Beijing Jiaotong University Beijing100044 China

The rapid development of Internet technology derived out a massive network text data. Therefore, how to classify the massive text data efficiently has important theoretical significance and application value. In order to acquire accurate classification results, the process has been divided into two parts. In terms of text representation, this paper proposes an online field feature selection algorithm (OFFS algorithm) based on streaming data, which solves the problems of low efficiency and memory consumption of traditional feature selection algorithms. With improvements in the vector space model, the new algorithm can select the real-time feature of the data and quickly generate text vector. In the aspect of classifier design, an OFFS-BP neural network text classifier based on BP neutral network and OFFS algorithm is designed. It adapts to the distributed parallel computing, reduces the training time and balances the computation efficiency and classification accuracy. Finally based on the Spark platform, the OFFS-BP neural network classifier is implemented. The experimental results show that the OFFS-BP neural network classifier is more suitable for big data environment with less computation time and higher classification efficiency. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： Neural networks

来源：评论

学校读者我要写书评

暂无评论

Anomaly Detection via Word Embeddings and Neural Networks 3

Anomaly Detection via Word Embeddings and Neural Networks

引用

3rd International Symposium on computer Technology and Information Science, ISCTIS 2023

作者： Dong, Junchao Pan, Fang Hu, Xiaoli Wang, Wenyan School of Computer Engineering Guilin University of Electronic Technology Beihai China Teachers College for Vocational and Technical Education Guangxi Normal University Guilin China Guilin University of Electronic Technology Guangxi Key Laboratory of Trusted Software Guilin China School of Computer Sciences Universiti Sains Malaysia Gelugor Malaysia

ISBN: (纸本)9798350325386

The price in the stock market is the result of the willingness of buyers and sellers to agree on a bid. Therefore, the most effective influences on stock prices are the investors directly involved in the stock transaction. On the contrary, the volatility of stock prices can also indirectly reflect the type of transactions at the current moment. Using this correspondence, the paper adopts the Skip-gram word embedding method in natural language processing to train the embeddings of various transaction types that may occur in the transaction process, and obtain the dense vector representation corresponding to the transaction types. After conducting experiments on multiple real stock datasets, the obtained experimental results show that the model trained with Skip-gram embedding has better prediction convergence effect. In addition, when the embedded results are used for abnormal stock price fluctuation detection task, they are more stable and obtain higher returns than the random embedding results or the non-embedding results. © 2023 IEEE.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

Bilinear Graph Neural Network-Enhanced Web Services Classification 23

Bilinear Graph Neural Network-Enhanced Web Services Classifi...

引用

23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data Science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Zhang, Lulu Cao, Buqing Peng, Mi Qing, Yueying Kang, Guosheng Liu, Jianxun Fletcher, Kenneth K. School of Computer Science and Engineering Hunan University of Science and Technology Hunan Key Laboratory of Service Computing and New Software Service Technology Xiangtan China University of Massachusetts Boston Department of Computer Science Boston United States

ISBN: (纸本)9781665494571

With the growing number of Web services, classifying Web services accurately and efficiently has become a challenging problem. Effective service classification is conducive to improving the quality of service discovery and the efficiency of service composition. Existing graph neural network methods represent the target service node by a weighted sum of the features of the neighbor service nodes, and have achieved remarkable results in service classification. However, these methods ignore the impact of possible interactions between neighbor service nodes on the target service node. To address this problem, we propose a Web services classification method based on bilinear graph neural network. Our proposed method can model the bilinear interactions between neighbor service nodes and effectively improve the accuracy of service classification. First, our proposed model uses the Word2Vec model to extract the latent semantic vectors from service description documents. We then construct the service relationship network according to tags and shared annotation relationships of Web services. Next, a bilinear aggregator is used to model the pairwise interactions between neighbor service nodes, highlighting the local common attributes. Finally, the bilinear aggregator is combined with the weighted sum aggregator to construct a bilinear graph neural network, which can learn more comprehensive representations of services. The experimental results on the real dataset from ProgrammableWeb show that the classification accuracy of the proposed method is greatly improved compared with DeepWalk, Node2Vec, GCN, and GAT. © 2021 IEEE.

关键词： Smart cities Annotations Mashups Semantics Quality of service Feature extraction Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Adaptive information fusion network for multi-modal personality recognition

Adaptive information fusion network for multi-modal personal...

引用

作者： Bao, Yongtang Liu, Xiang Qi, Yue Liu, Ruijun Li, Haojie School of Computer Science and Engineering Shandong University of Science and Technology Qingdao China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Virtual Reality Research Institute Beihang University Qingdao Research Institute Qingdao China School of Software Beihang University Beijing China

Personality recognition is of great significance in deepening the understanding of social relations. While personality recognition methods have made significant strides in recent years, the challenge of heterogeneity between modalities during feature fusion still needs to be solved. This paper introduces an adaptive multi-modal information fusion network (AMIF-Net) capable of concurrently processing video, audio, and text data. First, utilizing the AMIF-Net encoder, we process the extracted audio and video features separately, effectively capturing long-term data relationships. Then, adding adaptive elements in the fusion network can alleviate the problem of heterogeneity between modes. Lastly, we concatenate audio-video and text features into a regression network to obtain Big Five personality trait scores. Furthermore, we introduce a novel loss function to address the problem of training inaccuracies, taking advantage of its unique property of exhibiting a peak at the critical mean. Our tests on the ChaLearn First Impressions V2 multi-modal dataset show partial performance surpassing state-of-the-art networks. © 2024 John Wiley & Sons Ltd.

关键词： Information fusion

来源：评论

学校读者我要写书评

暂无评论

ROTATION HAS TWO SIDES: EVALUATING DATA AUGMENTATION FOR DEEP ONE-CLASS CLASSIFICATION 12

ROTATION HAS TWO SIDES: EVALUATING DATA AUGMENTATION FOR DEE...

引用

12th International Conference on Learning Representations, ICLR 2024

作者： Wang, Guodong Wang, Yunhong Bao, Xiuguo Huang, Di State Key Laboratory of Software Development Environment Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China Natl. Comp. Net. Emer. Resp. Tech. Team/Coord. Ctr. of China Beijing China

One-class classification (OCC) involves predicting whether a new data is normal or anomalous based solely on the data from a single class during training. Various attempts have been made to learn suitable representations for OCC within a self-supervised framework. Notably, discriminative methods that use geometric visual transformations, such as rotation, to generate pseudo-anomaly samples have exhibited impressive detection performance. Although rotation is commonly viewed as a distribution-shifting transformation and is widely used in the literature, the cause of its effectiveness remains a mystery. In this study, we are the first to make a surprising observation: there exists a strong linear relationship (Pearson's Correlation, r > 0.9) between the accuracy of rotation prediction and the performance of OCC. This suggests that a classifier that effectively distinguishes different rotations is more likely to excel in OCC, and vice versa. The root cause of this phenomenon can be attributed to the transformation bias in the dataset, where representations learned from transformations already present in the dataset tend to be less effective, making it essential to accurately estimate the transformation distribution before utilizing pretext tasks involving these transformations for reliable self-supervised representation learning. To the end, we propose a novel two-stage method to estimate the transformation distribution within the dataset. In the first stage, we learn general representations through standard contrastive pre-training. In the second stage, we select potentially semantics-preserving samples from the entire augmented dataset, which includes all rotations, by employing density matching with the provided reference distribution. By sorting samples based on semantics-preserving versus shifting transformations, we achieve improved performance on OCC benchmarks. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Rotation

来源：评论

学校读者我要写书评

暂无评论

Probabilistic-Search and Neighbor-Density Based Doppler-Shift Acquisition in Space Communications

引用

IEEE Transactions on Communications 2025年

作者： Zhang, Zhaowei Du, Shuai Liu, Lin Liu, Hui Zuo, Jiakuo Miao, Wang Zhao, Haitao Nanjing University of Posts and Telecommunications School of Internet of Things China Nanjing University State Key Laboratory for Novel Software Technology China Jinling Institute of Technology School of Software Engineering China University of Exeter Department of Computer Science United Kingdom

In space communications, the signal's long-distance transmission between a flying transmitter-receiver pair encounters two obstacles: a huge path-loss and a high-speed movement. The huge path-loss results in a low signal-to-noise ratio (SNR) and the high-speed movement brings a dynamic Doppler-shift, thus posing a great challenge for Doppler-shift acquisition. Under the low SNR, the Doppler-shift acquisition has to accumulate many symbols in a long-time period. However, during this period, the dynamic Doppler-shift disperses all these symbols' total energy over a wide range, thus causing the energy dispersion problem. To address this problem, we propose a probabilistic-search and neighbor-density (PSND) scheme, where the probabilistic-search considers the element's amplitude and sacrifices some redundant signal elements for a narrower search-range, while the neighbordensity sums the signal element's all neighbors for a larger signal energy. The PSND scheme includes two algorithms: the Generic PSND and Iterative PSND. The Generic PSND algorithm first selects some search-elements with their probabilities to construct a probabilistic-search-range, then selects some density-elements with their neighbor-elements to derive the neighbor-densities, and finally searches the largest neighbor-density to obtain the acquisition result. Built upon the Generic PSND algorithm above, the Iterative PSND algorithm iteratively updates the searchelements to further strengthen the neighbor-density in a narrower probabilistic-search-range. Moreover, the simulations results have demonstrated our PSND scheme's higher acquisition probability, as compared with the existing schemes. © 1972-2012 IEEE.

关键词： Signal to noise ratio

来源：评论

学校读者我要写书评

暂无评论

Data Reconstruction from Gradient Updates in Federated Learning 4th

Data Reconstruction from Gradient Updates in Federated Lea...

引用

4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Zhang, Xiaoxue Li, Junhao Zhang, Jianjie Yan, Jijie Zhu, Enmin Chen, Kongyang School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China Institute of Artificial Intelligence and Blockchain Guangzhou University Guangzhou China Pazhou Lab Guangzhou China Jiangsu Key Laboratory of Media Design and Software Technology Jiangnan University Wuxi China

ISBN: (纸本)9783031200953

Federated learning has become an emerging technology to protect data privacy in the distributed learning area, by keeping each client user’s data locally. However, recent work shows that client users’ data might still be stolen (or reconstructed) directly from gradient updates. After exploring the attack and defense techniques of these data reconstruction methods, we discover that the attacker cannot steal the victim’s data unless it has prior knowledge about the victim’s data size. Thus, the attacker can hardly reconstruct any useful information without these prior knowledge. In this paper, we provide a novel data reconstruction method to obtain a high-dimensional compressed data from the gradient updates, without these prior knowledge. Experiment results show that our reconstructed data can be used to attack the model, with high attack accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Data privacy

来源：评论

学校读者我要写书评

暂无评论

A Privacy-Preserving Decision Tree Evaluation Scheme for Multiple Wearable Devices

A Privacy-Preserving Decision Tree Evaluation Scheme for Mul...

引用

International Conference on computer Supported Cooperative Work in Design

作者： Yuanshun Huang Guihua Duan State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Computer Science and Engineering Central South University Changsha China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Privacy-Preserving Decision Tree (PPDT) evaluation is a typical algorithm in Machine Learning as a Service scenario. For services like health analysis, PPDT evaluation by fusing private data from multiple Wearable Devices is necessary. Homomorphic Encryption (HE) implements PPDT evaluation without revealing locally sensitive data. However, existing HE schemes need better scalability and are incompatible with clients such as Wearable Devices, which have limited computing power, bandwidth, memory, and storage. This paper proposed a noninteractive PPDT evaluation scheme for multiple Wearable Devices. We apply transciphering technology and an efficient Fully Homomorphic Encryption (FHE) cryptosystem to optimize overall performance. Simultaneously, a Computing Server and a Trusted Third Party (TTP) are adopted to perform all the homomorphic operations on the server side, leaving clients to encrypt and decrypt local data using stream ciphers. Then, we implement an instance of our scheme based on FiLIP stream cipher, a homomorphic comparison algorithm, and a homomorphic traverse algorithm. The experimental results show that the various overheads on the client side are relatively lower.

关键词： Performance evaluation Ciphers Privacy Machine learning algorithms Federated learning Scalability Servers

来源：评论

学校读者我要写书评

暂无评论

CNN channel pruning via gradient integration and knowledge distillation

CNN channel pruning via gradient integration and knowledge d...

引用

International Conference on Signal Processing Proceedings (ICSP)

作者： Suyun Lian Yang Zhao Jiajian Cai Jihong Pei The Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

With the advancement of artificial intelligence technology, intelligent applications have permeated all aspects of human life, including automatic driving, intelligent security, and face recognition. Convolutional neural networks (CNNs) are essential models for AI, but their complexity increases with more available data. The complex models are unsuitable for edge devices like wearables and smartphones due to high memory and computational requirements. In this paper, the compact model is achieved by pruning some redundant output channels. To effectively measure the validity of the output channels, this paper proposes a channel pruning algorithm based on gradient integration, which combines the gradient of the channel with the model output and the channel’s values. This pruning could destroy model accuracy, so knowledge distillation is introduced during fine-tuning to leverage the original model’s reasoning ability. Experiments show that our approach improves accuracy by 0.51% while reducing parameters by 30.1% and FLOPs by 26% in the VGG16 model.

关键词： Training Accuracy Computational modeling Signal processing algorithms Signal processing Cognition Convolutional neural networks Security Wearable devices Smart phones

来源：评论

学校读者我要写书评

暂无评论

ATA-Cache: Contention Mitigation for GPU Shared L1 Cache with Aggregated Tag Array

arXiv

引用

arXiv 2023年

作者： Xu, Xiangrong Wang, Liang Xiao, Limin Liu, Lei Xie, Xilong Han, Meng Liu, Hao State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing100191 China

GPU shared L1 cache is a promising architecture while still suffering from high resource contentions. We present a GPU shared L1 cache architecture with an aggregated tag array that minimizes the L1 cache contentions and takes full advantage of inter-core locality. The key idea is to decouple and aggregate the tag arrays of multiple L1 caches so that the cache requests can be compared with all tag arrays in parallel to probe the replicated data in other caches. The GPU caches are only accessed by other GPU cores when replicated data exists, filtering out unnecessary cache accesses that cause high resource contentions. The experimental results show that GPU IPC can be improved by 12% on average for applications with a high inter-core locality. Copyright © 2023, The Authors. All rights reserved.

关键词： Graphics processing unit

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：