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6th International Conference on Artificial Intelligence and Security, ICAIS 2020

作者： Yu, Wenbin Xiong, Zijia Dong, Zangqiang Xu, Yinsong Liu, Wenjie Qu, Zhiguo Liu, Alex X. Jiangsu Engineering Center of Network Monitoring School of Computer and Software Nanjing University of Information Science and Technology NanjingJiangsu210044 China The Department of Computer Science and Application Zhengzhou Institute of Aeronautical Industry Management ZhengzhouHenan450015 China Department of Computer Science and Engineering Michigan State University East LansingMI48824-1226 United States

ISBN: (纸本)9783030578831

In the optical frequency level communication, quantum effect becomes the source of significant communication errors. One alternative to classical detection is orthogonal projection measurement. In this paper, based on the coherent state’s quantum measurement and discrimination, the quantum optimal detection of binary coherent state signal in the presence of thermal noise was studied. Our numerical results showed the error probabilities of quantum detection under the OOK and BPSK modulation, and verified the performance of these two modulation methods in the thermal noise field background. © 2020, Springer Nature Switzerland AG.

关键词： Optical communication

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Interpretable text-to-sql generation with joint optimization 1

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17th International Conference on Web Information Systems and applications, WISA 2020

作者： Zhu, Mingdong Wang, Xianfang Zhang, Yang School of Computer Science and Technology Henan Institute of Technology Xinxiang China Intelligent Industrial Big Data Application Engineering Technology Research Center of Xinxiang Xinxiang China

ISBN: (数字)9783030600297

ISBN: (纸本)9783030600280

The purpose of Text-to-SQL is to obtain the correct answer for a textual question from the database, which can take advantage of advanced database system to provide reliable and efficient response. Existing Text-to-SQL methods generally focus on accuracy by designing complex deep neural network models, and hardly consider interpretability, which is very important for serious applications. To address this, in this paper we propose a novel framework for Interpretable Text-to-SQL Generation (ITSG) with joint optimization, which achieves state-of-the-art accuracy and possesses two-level interpretability at the same time. The framework mainly consists of three layers: a sequence encoder which encodes questions, table headers and significant table contents, an attention-based LSTM layer which generates SQL queries and a reinforcement learning layer which boosts the execution accuracy. Comparing with state-of-the-art methods on benchmark datasets, the experimental results show the effectiveness and interpretability of our ITSG framework. © Springer Nature Switzerland AG 2020.

关键词： Reinforcement learning

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Class-Driven Nonnegative Matrix Factorization with Manifold Regularization for Data Clustering

SSRN

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SSRN 2023年

作者： Li, Huirong Gao, Yuelin Zhou, Yani Liu, Junmin School of Mathematics and Computer Application Shangluo University Shangluo726000 China School of Mathematics and Information Science North Minzu University Yinchuan750021 China School of Health Management Shangluo University Shangluo726000 China Engineering Research Center of Qinling Health Welfare Big Data Universities of Shaanxi Province Shangluo726000 China School of Mathematics and Statistics Xian Jiaotong University Xi’an710049 China

Nonnegative matrix factorization (NMF) is an effective technique to extract the underlying low-dimensional structure of data by utilizing its parts-based representation, which has been widely used in feature extraction and machine learning. However, NMF is an unsupervised learning algorithm without utilizing the discriminative prior information. In this paper, we put forward a new class-driven NMF with manifold regularization (MCDNMF) algorithm, which incorporates both the local manifold regularization and the label information of data into the NMF model. Specifically, MCDNMF not only encodes the local geometrical structure of data space by using the manifold regularization, but also takes the available label information by introducing the class-driven constraint. This class-driven constraint forces the new representations of data points to be more similar within the same class while different between other classes. Therefore, the discriminative abilities of clustering are greatly boosted. Experimental results on several datasets validate the effectiveness of proposedMCDNMF in comparison with the other state-of-the-art methods. © 2023, The Authors. All rights reserved.

关键词： Matrix algebra

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View-Invariant Skeleton-based Action Recognition via Global-Local Contrastive Learning

arXiv

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arXiv 2022年

作者： Bian, Cunling Feng, Wei Meng, Fanbo Wang, Song The School of Computer Science and Technology College of Intelligence and Computing Tianjin University Tianjin300350 China State Administration of Cultural Heritage China The Tianjin Key Laboratory of Cognitive Computing and Application Tianjin University Tianjin300350 China The Institute of International Engineering Tianjin University Tianjin300350 China Department of Computer Science and Engineering University of South Carolina ColumbiaSC29208 United States

Skeleton-based human action recognition has been drawing more interest recently due to its low sensitivity to appearance changes and the accessibility of more skeleton data. However, even the 3D skeletons captured in practice are still sensitive to the viewpoint and direction gave the occlusion of different human-body joints and the errors in human joint localization. Such view variance of skeleton data may significantly affect the performance of action recognition. To address this issue, we propose in this paper a new view-invariant representation learning approach, without any manual action labeling, for skeleton-based human action recognition. Specifically, we leverage the multi-view skeleton data simultaneously taken for the same person in the network training, by maximizing the mutual information between the representations extracted from different views, and then propose a global-local contrastive loss to model the multi-scale co-occurrence relationships in both spatial and temporal domains. Extensive experimental results show that the proposed method is robust to the view difference of the input skeleton data and significantly boosts the performance of unsupervised skeleton-based human action methods, resulting in new state-of-the-art accuracies on two challenging multi-view benchmarks of PKUMMD and NTU RGB+D. Copyright © 2022, The Authors. All rights reserved.

关键词： Benchmarking

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Frequency-Guided Spatial Adaptation for Camouflaged Object Detection

arXiv

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arXiv 2024年

作者： Zhang, Shizhou Kong, Dexuan Xing, Yinghui Lu, Yue Ran, Lingyan Liang, Guoqiang Wang, Hexu Zhang, Yanning National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science Northwestern Polytechnical University Xi’an China The Research & Development Institute Northwestern Polytechnical University Shenzhen China The School of Information and Technology Northwest University Xi’an710127 China Xi’an Key Laboratory of Human-Machine Integration and Control Technology for Intelligent Rehabilitation Xijing University Shaanxi Xi’an710123 China

Camouflaged object detection (COD) aims to segment camouflaged objects which exhibit very similar patterns with the surrounding environment. Recent research works have shown that enhancing the feature representation via the frequency information can greatly alleviate the ambiguity problem between the foreground objects and the background. With the emergence of vision foundation models, like InternImage, Segment Anything Model etc, adapting the pretrained model on COD tasks with a lightweight adapter module shows a novel and promising research direction. Existing adapter modules mainly care about the feature adaptation in the spatial domain. In this paper, we propose a novel frequency-guided spatial adaptation method for COD task. Specifically, we transform the input features of the adapter into frequency domain. By grouping and interacting with frequency components located within non overlapping circles in the spectrogram, different frequency components are dynamically enhanced or weakened, making the intensity of image details and contour features adaptively adjusted. At the same time, the features that are conducive to distinguishing object and background are highlighted, indirectly implying the position and shape of camouflaged object. We conduct extensive experiments on four widely adopted benchmark datasets and the proposed method outperforms 26 state-of-the-art methods with large margins. Code will be released. Copyright © 2024, The Authors. All rights reserved.

关键词： Frequency domain analysis

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A Clustering-Based Routing Protocol Using Path Pattern Discovery Method to Minimize Delay in VANET

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Wireless Communications and Mobile Computing 2023年第1期2023卷

作者： Xie, Xiaoyun Navaei, Yahya Dorostkar Einy, Sajad School of Electronic Information Engineering Gannan University of Science and Technology Jiangxi Ganzhou341000 China Ganzhou Key Laboratory of Cloud Computing and Big Data Jiangxi Ganzhou341000 China Computer and Information Technology Engineering Qazvin Branch Islamic Azad University Qazvin Iran Istanbul Aydin University Department of Application and Research Center for Advanced Studies Istanbul Turkey

In vehicular ad hoc networks (VANETs), vehicle-to-vehicle (V2V) communications can link vehicles to each other, and vehicle-to-infrastructure (V2I) messaging and communications can link roadside infrastructure such as routers. The vehicles in these networks act as relays that transmit critical messages in the network. Due to the high-speed movement of vehicles on the road, real-time messaging and minimizing the delay in sending messages is one of the most important objectives of VANET developers. On the other hand, the high mobility of vehicles causes communication interruptions and decreases the data delivery rate in VANET. To overcome this issue, predicting the path of vehicles can play an important role in sending data from the source to the destination. When an accident occurs on the road, the messages that are sensed by the imbedded sensors in the vehicles need to be sent, and if they are sent by the vehicles that change their route, these messages will not be sent to the destination and the performance of the network will be disturbed. Previous methods in the literature for data transmission in intervehicular networks have focused more on reliability and trust, and little attention has been paid to the prediction of vehicle movement paths in these types of networks. Therefore, for fast and reliable data transmission in VANET, accurate prediction of vehicle movement and creation of movement patterns can be effective in message transmission delay and data delivery rate. In this paper, we present an approach using a combination of cluster-based routing protocols and pattern discovery methods to minimize latency in VANETs. The outline of the proposed method has four modules: primary data collection and analysis, primary data preparation and analysis, pattern extraction and vehicle route discovery, and vehicle clustering and data/information transmission routing. The simulation results show that the proposed method with a delivery rate of 88.56% has significantly i

关键词： Vehicular ad hoc networks

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MuTCELM: An optimal multi-TextCNN-based ensemble learning for text classification

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Heliyon 2024年第19期10卷 e38515页

作者： Agbesi, Victor Kwaku Chen, Wenyu Yussif, Sophyani Banaamwini Ukwuoma, Chiagoziem C. Gu, Yeong Hyeon Al-antari, Mugahed A. School of Computer Science and Engineering University of Electronic Science and Technology of China No. 2006 Xiyuan Ave West Hi-Tech Zone Sichuan Chengdu China Sichuan Engineering Technology Research Center for Industrial Internet Intelligent Monitoring and Application Chengdu University of Technology No. 610059 Sichuan Chengdu China Department of Artificial Intelligence and Data Science College of AI Convergence Daeyang AI Center Sejong University Seoul 05006 South Korea

Feature extraction plays a critical role in text classification, as it converts textual data into numerical representations suitable for machine learning models. A key challenge lies in effectively capturing both semantic and contextual information from text at various levels of granularity while avoiding overfitting. Prior methods have often demonstrated suboptimal performance, largely due to the limitations of the feature extraction techniques employed. To address these challenges, this study introduces Multi-TextCNN, an advanced feature extractor designed to capture essential textual information across multiple levels of granularity. Multi-TextCNN is integrated into a proposed classification model named MuTCELM, which aims to enhance text classification performance. The proposed MuTCELM leverages five distinct sub-classifiers, each designed to capture different linguistic features from the text data. These sub-classifiers are integrated into an ensemble framework, enhancing the overall model performance by combining their complementary strengths. Empirical results indicate that MuTCELM achieves average improvements across all datasets in accuracy, precision, recall, and F1-macro scores by 0.2584, 0.2546, 0.2668, and 0.2612, respectively, demonstrating significant performance gains over baseline models. These findings underscore the effectiveness of Multi-TextCNN in improving model performance relative to other ensemble methods. Further analysis reveals that the non-overlapping confidence intervals between MuTCELM and baseline models indicate statistically significant differences, suggesting that the observed performance improvements of MuTCELM are not attributable to random chance but are indeed statistically meaningful. This evidence indicates the robustness and superiority of MuTCELM across various languages and text classification tasks. © 2024 The Author(s)

关键词： Deep learning Ensemble learning Feature extraction Multi-TextCNN Text classification

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Solving the Traveling Salesperson Problem using Frequency Fitness Assignment

Solving the Traveling Salesperson Problem using Frequency Fi...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Tianyu Liang Zhize Wu Jörg Lässig Daan van den Berg Thomas Weise Institute of Applied Optimization School of Artificial Intelligence and Big Data Hefei University Hefei Anhui China Enterprise Application Development Group Faculty of Electrical Engineering and Computer Science Höchschule Zittau/Gorlitz Görlitz Germany Department of Computer Science Vrije Universiteit Amsterdam Amsterdam The Netherlands Institute of Applied Optimization School of Artificial Intelligence and Big Data Hefei Anhui China

ISBN: (纸本)9781665487696

Metaheuristic optimization is based on the idea that better solutions are preferable to worse ones. Frequency Fitness Assignment (FFA) is a module that can be plugged into most optimization algorithms. It replaces the objective values in the selection step with their encounter frequency during the search so far. The search effort is therefore distributed evenly over the whole range of the objective values. Recently, it was shown that using FFA can significantly improve the performance of algorithms on hard problems such as Trap and Jump functions and the NP-hard MaxSat problem. However, the objective functions of all of these problems have relatively small ranges. This work is the first to explore the impact of FFA on metaheuristics for solving Traveling Salesperson Problem (TSP) instances, whose objective values tend to cover a wider range. We plug FFA into the (1+1) EA to obtain the (1+1) FEA. We perform extensive experiments on 18 instances from TSPLIB using two different unary search operators. We find that the (1+1) FEA does not get stuck in local optima and can solve many more instances to optimality than the (1+1) EA. However, it tends to be slower in reaching good intermediate solutions. Its performance decreases with the problem scale and the number of different possible tour lengths.

关键词： Metaheuristics Search problems Linear programming Computational intelligence Plugs

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Household Garbage Classification: A Transfer Learning Based Method and a Benchmark 1

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11th International Symposium on Parallel Architectures, Algorithms and Programming, PAAP 2020

作者： Cheng, Qian Wu, Zhi-Ze Wu, Zi-Jun Zou, Le Li, Huan-Yi Wang, Xiao-Feng Department of Environmental Engineering Hefei University Hefei Anhui230601 China Anhui Provincial Engineering Laboratory of Big Data Technology Application for Urban Infrastructure Department of Computer Science and Technology Hefei University Hefei230601 China Institute of Applied Optimization School of Artificial Intelligence and Big Data Hefei University Hefei Anhui23060 China

ISBN: (数字)9789811600104

ISBN: (纸本)9789811600098

Household garbage images are highly diverse in color, texture and geometry, which poses significant challenges to garbage classification. Deep convolutional neural network (DCNN) have recently achieved remarkable progress due to their ability to learn high-level feature representations. It usually requires a large number of labelled image data for training a DCNN model. However, there are few public and mature data sets concerned on household garbage images. This severely limits the progress of research and the state of the art is not entirely clear. To address this problem, we introduce a new benchmark data set for household garbage image classification. This data set is called 30 Types of Household Garbage Images (HGI-30), which contains 6′000 images of 30 household garbage types, with complex backgrounds, different resolutions, and complicated variations in sample, pose, illumination and background. The publicly available HGI-30 data set allows researchers to develop more accurate and robust methods for both household garbage image processing and interpretation analysis of household garbage object. We further study the classification problem on this data set and propose a transfer learning based method, also provide a performance analysis, which serves as baseline result on this benchmark. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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Time-varying Characteristics of mmWave Channel based on the Clustered Sparsity Model

Time-varying Characteristics of mmWave Channel based on the ...

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IEEE Conference on Vehicular Technology (VTC)

作者： Lijun Yang Haitao Lu Xinchao Ge Zhixin Sun Pan Cao School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing China RAN System Design Dept. ZTE Corporation Shenzhen China Shenzhen Key Laboratory of 5G RAN Security Technology Research and Application Shenzhen China School of Modern Posts Nanjing University of Posts and Telecommunications Nanjing China School of Physics Engineering and Computer Science University of Hertfordshire Hatfield UK

A better understanding of the mmWave channel coherence time will play a significant role in both communication and sensing. Traditional literatures consider that the channel coherence time in mmWave communication will be very short due to high frequency, which makes the channel estimation more difficult. However, we believe that the propagation characteristics of the mmWave channel will affect its coherence time, i.e., channel sparsity may limit its time variability. In this paper, we investigate the time-varying nature of mmWave channel by analyzing its Doppler spread based on the clustered sparsity model. After the derivation of Doppler spectrum DPS and the simulation analysis based on the two-cluster channel, we found that the maximum angle between clusters and the power distribution between clusters are two main factors limiting the Doppler spread. Finally, the realistic mmWave NYUSIM model is used for Doppler spread comparative analysis with Clarke’s model and find that Doppler spread of NYUSIM channel is far less than that estimated based on Clarke’s model. Thus, it is demonstrated that even if the mmWave frequency is high, the clustered sparsity characteristics makes its channel coherence time not as short as thought intuitively, which is meaningful for mmWave vehicular sensing and communication.

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