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Efficient Distributed Skyline Queries for Mobile Applications

计算机科学技术学报（英文版） 2010年第3期25卷 523-536页

作者： Ying-Yuan Xiao Yue-Guo Chen Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin 300191China Key Laboratory of Computer Vision and System Tianjin University of TechnologyTianjin 300191China School of Computing National University of Singapore117417Singapore Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of ChinaBeijing 100872China

In this paper,we consider skyline queries in a mobile and distributed environment,where data objects are distributed in some sites(database servers)which are interconnected through a high-speed wired network,and queries are issued by mobile units(laptop,cell phone,etc.)which access the data objects of database servers by wireless *** inherent properties of mobile computing environment such as mobility,limited wireless bandwidth,frequent disconnection,make skyline queries more *** show how to efficiently perform distributed skyline queries in a mobile environment and propose a skyline query processing approach,called efficient distributed skyline based on mobile computing(EDS-MC).In EDS-MC,a distributed skyline query is decomposed into five processing phases and each phase is elaborately designed in order to reduce the network communication,network delay and query response *** conduct extensive experiments in a simulated mobile database system,and the experimental results demonstrate the superiority of EDS-MC over other skyline query processing techniques on mobile computing.

关键词： skyline distributed query processing mobile database system

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Wireless Hallucination in Generative AI-enabled Communications: Concepts, Issues, and Solutions

arXiv

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arXiv 2025年

作者： Wang, Xudong Wang, Jiacheng Feng, Lei Niyato, Dusit Zhang, Ruichen Kang, Jiawen Xiong, Zehui Du, Hongyang Mao, Shiwen State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China College of Computing and Data Science Nanyang Technological University Singapore School of Automation Guangdong University of Technology Guangzhou510006 China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Department of Electrical and Electronic Engineering University of Hong Kong Pok Fu Lam Hong Kong Department of Electrical and Computer Engineering Auburn University Auburn United States

Generative AI (GenAI) is driving the intelligence of wireless communications. Due to data limitations, random generation, and dynamic environments, GenAI may generate channel information or optimization strategies that violate physical laws or deviate from actual real-world requirements. We refer to this phenomenon as wireless hallucination, which results in invalid channel information, spectrum wastage, and low communication reliability but remains underexplored. To address this gap, this article provides a comprehensive concept of wireless hallucinations in GenAI-driven communications, focusing on hallucination mitigation. Specifically, we first introduce the fundamental, analyze its causes based on the GenAI workflow, and propose mitigation solutions at the data, model, and post-generation levels. Then, we systematically examines representative hallucination scenarios in GenAI-enabled communications and their corresponding solutions. Finally, we propose a novel integrated mitigation solution for GenAI-based channel estimation. At the data level, we establish a channel estimation hallucination dataset and employ generative adversarial networks (GANs)-based data augmentation. Additionally, we incorporate attention mechanisms and large language models (LLMs) to enhance both training and inference performance. Experimental results demonstrate that the proposed hybrid solutions reduce the normalized mean square error (NMSE) by 0.19, effectively reducing wireless hallucinations. Copyright © 2025, The Authors. All rights reserved.

关键词： Generative adversarial networks

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LMP-GAN: Out-Of-Distribution Detection For Non-Control data Malware Attacks

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年 PP卷 PP页

作者： Wood, David Kapp, David Kebede, Temesgen Hirakawa, Keigo Wuhan University School of Computer Science China Wuhan University National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering China Zhongguancun Academy China Wuhan University State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing China Sun Yat-sen University School of Geography and Planning China Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates Chongqing University College of Computer Science China The University of Tokyo Japan RIKEN Center for Advanced Intelligence Project Japan Intelligent Science & Technology Academy Limited CASIC China iFlytek Company Ltd. National Engineering Research Center of Speech and Language Information Processing China Nanyang Technological University College of Computing & Data Science Singapore Henan Academy of Sciences Aerospace Information Research Institute China

Anomaly detection is a common application of machine learning. Out-of-distribution (OOD) detection in particular is a semi-supervised anomaly detection technique where the detection method is trained only on the inlier (in-distribution) samples - unlike the fully supervised variant, the distribution of the outlier samples are never explicitly modeled in OOD detection tasks. In this work, we design a novel GAN-based OOD detection network specifically designed to protect a cyber-physical signal systems from novel Trojan malware called non-control data (NCD) attack that evades conventional malware detection techniques. Inspired in part by the classical locally most powerful (LMP) test in statistical inferences, the proposed LMP-GAN trains the OOD detector (discriminator) by generating OOD samples that are aimed at making maximal alteration to the inlier samples while evading detection. We experimentally compare the results to the state-of-the-art anomaly detection methods to demonstrate the benefits and the appropriateness of the LMP-GAN OOD detector. © 2025 IEEE.

关键词： Malware

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The Classification of Motor Imagery EEG Signals Based on the Time-Frequency-Spatial Feature 10

The Classification of Motor Imagery EEG Signals Based on the...

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10th IEEE data Driven Control and Learning Systems Conference, DDCLS 2021

作者： Deng, Xin Zhang, Boxian Liu, Ke Wang, Jin Yang, Pengfei Hu, Chengxin Key Laboratory of Data Engineering and Visual Computing College of Computer Science and Technology Chongqing University of Posts and Telecommunication Chongqing400065 China

ISBN: (纸本)9781665424233

The effective features of the motor imagery (MI) electroencephalogram (EEG) signals plays a significant role to improve the classification accuracy for the brain-computer interface (BCI) system. Some traditional methods usually extract the frequency or spatial features without considering the related information between different channels that would affect the classification performance. This paper proposes a new method for feature extraction of EEG signals based on the fusion of time-frequency and spatial features. At the beginning, the common spatial pattern (CSP) algorithm is adopted to extract the spatial features. Then the discrete wavelet transform (DWT) and the wavelet packet decomposition (WPD) are used to extract the μ rhythm of the motor imagery EEG signals as the time-frequency features. After that, by combining the spatial and time-frequency features, the time-frequency-spatial feature is formed. Based on different kinds of features, the experimental data are classified by using the support vector machine (SVM), as well as the sparse representation classification (SRC) algorithm with the elastomeric network (EN) and L1 norm, respectively. The experimental results show that the SRC with EN has a better performance on either the time-frequency feature or spatial feature than the SRC with L1 norm does. In contrast, the SVM and the SRC with Ll norm perform better than the SRC with EN based on the time-frequency-spatial feature. The study concludes that the time-frequency-spatial feature cooperating with the certain classifiers can achieve the good classification effect for the MI EEG signals, which not only reduces the operation time but also improves the classification accuracy. © 2021 IEEE.

关键词： Support vector machines

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Balanced Multi-view Clustering

arXiv

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arXiv 2025年

作者： Li, Zhenglai Wang, Jun Tang, Chang Zhu, Xinzhong Zhang, Wei Liu, Xinwang Faculty of data science City University of Macau 999078 China School of computer National University of Defense Technology Changsha410073 China School of computer China University of Geosciences Wuhan430074 China College of Mathematics Physics and Information Engineering Zhejiang Normal University Jinhua China Research Institute of Ningbo Cixing Co. Ltd Ningbo China Qilu University of Technology Shandong Academy of Sciences China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan250000 China

Multi-view clustering (MvC) aims to integrate information from different views to enhance the capability of the model in capturing the underlying data structures. The widely used joint training paradigm in MvC is potentially not fully leverage the multi-view information, since the imbalanced and under-optimized view-specific features caused by the uniform learning objective for all views. For instance, particular views with more discriminative information could dominate the learning process in the joint training paradigm, leading to other views being under-optimized. To alleviate this issue, we first analyze the imbalanced phenomenon in the joint-training paradigm of multi-view clustering from the perspective of gradient descent for each view-specific feature extractor. Then, we propose a novel balanced multi-view clustering (BMvC) method, which introduces a view-specific contrastive regularization (VCR) to modulate the optimization of each view. Concretely, VCR preserves the sample similarities captured from the joint features and view-specific ones into the clustering distributions corresponding to view-specific features to enhance the learning process of view-specific feature extractors. Additionally, a theoretical analysis is provided to illustrate that VCR adaptively modulates the magnitudes of gradients for updating the parameters of view-specific feature extractors to achieve a balanced multi-view learning procedure. In such a manner, BMvC achieves a better trade-off between the exploitation of view-specific patterns and the exploration of view-invariance patterns to fully learn the multi-view information for the clustering task. Finally, a set of experiments are conducted to verify the superiority of the proposed method compared with state-of-the-art approaches on eight benchmark MvC datasets. The demo code of this work is publicly available at https://***/guanyuezhen/BMvC. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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Short-Term Traffic Flow Prediction Based on Ensemble Machine Learning Strategies 10

Short-Term Traffic Flow Prediction Based on Ensemble Machine...

引用

10th IEEE data Driven Control and Learning Systems Conference, DDCLS 2021

作者： Zeng, Ximu Wang, Yixiong Deng, Xin Wang, Jin Key Laboratory of Data Engineering and Visual Computing College of Computer Science and Technology Chongqing University of Posts and Telecommunication Chongqing400065 China

ISBN: (纸本)9781665424233

In order to solve the problem of traffic congestion, many city governments have begun to develop intelligent transportation systems. As a research hotspot in the field of intelligent transportation, the short-term traffic flow prediction is of great significance to traffic diversion and route planning. In the recent big data era, the machine learning (ML) algorithms have been applied to mining deep information in the data. However, the performance of a single ML model is usually not good. The ensemble learning can improve accuracy in most cases. In this paper, we propose a new short-term traffic flow prediction model. This paper regards the traffic prediction problem as a regression prediction problem, rather than a time series forecasting problem. The proposed model combines the prediction results of the XGBoost, LightGBM and CatBoost models through the ensemble machine learning strategy. With the experiments on real traffic data in Xi'an city, this paper has obtained the comparison of predictive performance of the models. Compared with single models such as the ARIMA, LSTM, XGBoost, LightGBM and CatBoost models, the results show the proposed model is more accurate and more suitable for short-time traffic status prediction. key Words: traffic prediction, ensemble learning, XGBoost, LightGBM, CatBoost © 2021 IEEE.

关键词： Traffic congestion

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Blockchain-assisted Puncturable Signcryption for Cloud and Fog-Hosted Industrial Cyber-Physical Systems

引用

IEEE Network 2025年

作者： Ma, Qingru Shen, Jian Pandi, Vijayakumar Gupta, Brij B. Arya, Varsha Mahalinghum, Maheswari Zhejiang Sci-Tech University School of Computer Science and Technology School of Artificial Intelligence Hangzhou310018 China Zhejiang Sci-Tech University School of Information Science and Engineering School of Cyber Science and Technology Zhejiang Key Laboratory of Digital Fashion and Data Governance Zhejiang Provincial International Cooperation Base for Science and Technology on Cloud Computing Security and Data Aggregation Hangzhou310018 China Department of Computer Science and Engineering Tamil Nadu Tiruchirappalli620009 India Asia University Department of Computer Science and Information Engineering Taichung413 Taiwan China Medical University Hospital China Medical University Department of Medical Research Taichung Taiwan Pune412115 India School of Cybersecurity Korea University Seoul Korea Republic of Kyung Hee University Seoul02447 Korea Republic of Lebanese American University Beirut1102 Lebanon Center for Interdisciplinary Research Dehradun India Sathyabama Institute of Science and Technology School of Computing Department of Computer Science and Engineering Tamil Nadu Chennai India

The widespread adoption of emerging technologies, such as interconnected sensors and advanced automation systems, has led to rapid advancements in smart industrial environments. While industrial cyber-physical systems (ICPSs) bring efficiency and innovation to industrial processes, the security risks are also of great concern. The existing work provides many methods for data security and authentication for ICPSs. However, since the authentication status of ICPSs is changeable, the up-to-date methods can hardly meet the requirements for rapid authentication and secure data transmission. What’s more, largescale message revocation incurs significant communication and computational costs. This paper proposes a blockchain-assisted identity-based puncturable signcryption (BC-IBPSC) scheme for cloud and fog-hosted ICPSs. First, based on the concepts of puncturable encryption and signature, a novel identity-based puncturable signcryption method is presented to achieve efficient verifiable data encryption. Second, the blockchain is utilized to record all valid and punctured messages, to achieve evidence storage and traceability. The results of the analysis indicate that the scheme ensures security and operates efficiently. In addition, experiments show that the size of the signcrypted files does not affect the value of Gas consumption on the blockchain when uploading proofs. © 2025 IEEE. All rights reserved.

关键词： Authentication

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Time-aware bike flow prediction framework with dynamic edge fusion and memory integration

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Knowledge-Based Systems 2025年 322卷

作者： M.Rajeh, Taha Li, Tianrui Luo, Zhpeng Javed, Muhammad Hafeez Alhaek, Fares School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu 611756 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Chengdu 611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu 611756 China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu 611756 China Stirling College Chengdu University Chengdu 610106 China Computing Science Faculty of Natural Sciences University of Stirling Stirling FK9 4LA United Kingdom

Bike-Sharing Systems (BSSs) have become a popular solution to urban sustainability concerns, providing affordable and environmentally friendly transportation. However, modeling and predicting shared-bike flow patterns are essential for developing intelligent transportation systems and facilitating city management. Despite the existence of several inflow and outflow prediction models, certain unresolved issues remain, including the inability to differentiate and capture heterogeneous dynamic relationships within daily flows, and the suboptimal utilization of historical flow data for forecasting. To address these limitations, we propose MVRC (Multi-View Relational Framework based on Clustering), a novel deep-learning framework for precise inflow and outflow prediction. The MVRC framework models three types of heterogeneous dynamic flow relationships during different periods of the day. Initially, we introduce a novel clustering method to group nearby stations exhibiting similar patterns, followed by creating a dynamic memory module to store and update edge attributes based on the time of day. Furthermore, the dynamic edge fusion mechanism effectively integrates heterogeneous, time-evolving flow features, capturing their varying impacts. To further enhance the model's capabilities, an offline component that leverages historical data is incorporated, improving performance during testing. Extensive experiments conducted on real datasets from New York City and Washington, D.C., demonstrate the superior prediction efficiency and framework effectiveness of the MVRC framework compared to existing state-of-the-art techniques. These results highlight the practicality and efficacy of MVRC in terms of prediction accuracy and stability. © 2025

关键词： Attention Bike flow Clustering data fusion Flow prediction Graph convolutional networks Urban computing

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Federated Digital Twin Construction via Distributed Sensing: A Game-Theoretic Online Optimization with Overlapping Coalitions

arXiv

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arXiv 2025年

作者： Chen, Ruoyang Yi, Changyan Zhou, Fuhui Kang, Jiawen Wu, Yuan Niyato, Dusit College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China College of Electronic and Information Engineering Nanjing University of Astronautics and Aeronautics Nanjing210016 China School of Automation Guangdong University of Technology Guangzhou510006 China State Key Laboratory of Internet of Things for Smart City University of Macau China Department of Computer and Information Science University of Macau China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

In this paper, we propose a novel federated framework for constructing the digital twin (DT) model, referring to a living and self-evolving visualization model empowered by artificial intelligence, enabled by distributed sensing under edge-cloud collaboration. In this framework, the DT model to be built at the cloud is regarded as a global one being split into and integrating from multiple functional components, i.e., partial-DTs, created at various edge servers (ESs) using feature data collected by associated sensors. Considering time-varying DT evolutions and heterogeneities among partial-DTs, we formulate an online problem that jointly and dynamically optimizes partial-DT assignments from the cloud to ESs, ES-sensor associations for partial-DT creation, and as well as computation and communication resource allocations for global-DT integration. The problem aims to maximize the constructed DT’s model quality while minimizing all induced costs, including energy consumption and configuration costs, in long runs. To this end, we first transform the original problem into an equivalent hierarchical game with an upper-layer two-sided matching game and a lower-layer overlapping coalition formation game. After analyzing these games in detail, we apply the Gale-Shapley algorithm and particularly develop a switch rules-based overlapping coalition formation algorithm to obtain short-term equilibria of upper-layer and lower-layer subgames, respectively. Then, we design a deep reinforcement learning-based solution, called DMO, to extend the result into a long-term equilibrium of the hierarchical game, thereby producing the solution to the original problem. Simulations show the effectiveness of the introduced framework, and demonstrate the superiority of the proposed solution over counterparts. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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Improved lattice-based CCA2-secure PKE in the standard model

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Science China(Information Sciences) 2020年第8期63卷 147-168页

作者： Jiang ZHANG Yu YU Shuqin FAN Zhenfeng ZHANG State Key Laboratory of Cryptology Guangdong Provincial Key Laboratory of Data Security and Privacy Protection Jinan University Department of Computer Science and Engineering Shanghai Jiao Tong University Trusted Computing and Information Assurance Laboratory Institute of SoftwareChinese Academy of Sciences

Based on the identity-based encryption(IBE) from lattices by Agrawal et al.(Eurocrypt’10),Micciancio and Peikert(Eurocrypt’12) presented a CCA1-secure public-key encryption(PKE), which has the best known efficiency in the standard model and can be used to obtain a CCA2-secure PKE from lattices by using the generic BCHK transform(SIAM J Comput, 2006) with a cost of introducing extra overheads to both computation and storage for the use of other primitives such as signatures and commitments. In this paper, we propose a more efficient standard model CCA2-secure PKE from lattices by carefully combining a different message encoding(which encodes the message into the most significant bits of the LWE’s "secret term") with several nice algebraic properties of the tag-based lattice trapdoor and the LWE problem(such as unique witness and additive homomorphism). Compared to the best known lattice-based CCA1-secure PKE in the standard model due to Micciancio and Peikert(Eurocrypt’12), we not only directly achieve the CCA2-security without using any generic transform(and thus do not use signatures or commitments), but also reduce the noise parameter roughly by a factor of 3. This improvement makes our CCA2-secure PKE more efficient in terms of both computation and storage. In particular, when encrypting a 256-bit(respectively,512-bit) message at 128-bit(respectively, 256-bit) security, the ciphertext size of our CCA2-secure PKE is even 33%–44%(respectively, 36%–46%) smaller than that of their CCA1-secure PKE.

关键词： lattice public-key encryption chosen ciphertext security standard model

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