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arXiv 2025年

作者： Liu, Baiyang Tong, Kin-Fai Wong, Kai-Kit Chae, Chan-Byoung Wong, Hang College of Big Data and Internet Shenzhen Technology University Shenzhen518118 China State Key Laboratory of Terahertz and Millimeter Waves Department of Electrical Engineering City University of Hong Kong Hong Kong School of Science and Technology Hong Kong Metropolitan University Hong Kong Department of Electronic and Electrical Engineering University College London LondonWC1E7JE United Kingdom Yonsei Frontier Lab Yonsei University Korea Republic of

Interference and scattering, often deemed undesirable, are inevitable in wireless communications, especially when the current mobile networks and upcoming sixth generation (6G) have turned into ultra-dense networks. Current approaches relying on multiple-input multiple-output (MIMO) combined with artificial-intelligence-aided (AI) signal processing have drawbacks of being power-hungry and requiring wide bandwidth that raise scalability concerns. In this article, we take a radical approach and utilize the channel fading phenomenon to our advantage. Specifically, we propose a novel meta-fluid antenna architecture, referred to as the ‘fluid’ antenna system (FAS), that can freely surf on radio wave fluctuations, like ‘fluid’ figuratively speaking, with fine resolution in space to opportunistically avoid interference, eliminating the need for expensive signal processing. Our experimental results demonstrate that under rich scattering conditions, the proposed meta-fluidic architecture is able to exploit the natural ups and downs of radio waves in space for spatial multiplexing. These breakthrough results show that scattering can be desirable not harmful and interference can be dodged not suppressed, fundamentally changing our perception of fading and our understanding on how interference should be managed in wireless communications networks. © 2025, CC0.

关键词： Fading (radio)

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Neural or statistical: An empirical study on language models for Chinese input recommendation on mobile 23rd

Neural or statistical: An empirical study on language models...

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23rd China conference on Information Retrieval, CCIR 2017

作者： Zhang, Hainan Lan, Yanyan Guo, Jiafeng Xu, Jun Cheng, Xueqi CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9783319686981

Chinese input recommendation plays an important role in alleviating human cost in typing Chinese words, especially in the scenario of mobile applications. The fundamental problem is to predict the conditional probability of the next word given the sequence of previous words. Therefore, statistical language models, i.e.n-grams based models, have been extensively used on this task in real application. However, the characteristics of extremely different typing behaviors usually lead to serious sparsity problem, even n-gram with smoothing will fail. A reasonable approach to tackle this problem is to use the recently proposed neural models, such as probabilistic neural language model, recurrent neural network and word2vec. They can leverage more semantically similar words for estimating the probability. However, there is no conclusion on which approach of the two will work better in real application. In this paper, we conduct an extensive empirical study to show the differences between statistical and neural language models. The experimental results show that the two different approach have individual advantages, and a hybrid approach will bring a significant improvement. © Springer International Publishing AG 2017.

关键词： Recurrent neural networks

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Beyond Precision: A Study on Recall of Initial Retrieval with Neural Representations 1

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28th China Conference on Information Retrieval, CCIR 2022

作者： Xiao, Yan Fan, Yixing Zhang, Ruqing Guo, Jiafeng CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

ISBN: (数字)9783031247552

ISBN: (纸本)9783031247545

Vocabulary mismatch is a central problem in information retrieval (IR), i.e., the relevant documents may not contain the same (symbolic) terms of the query. Recently, neural representations have shown great success in capturing semantic relatedness, leading to new possibilities to alleviate the vocabulary mismatch problem in IR. However, most existing efforts in this direction have been devoted to the re-ranking stage. That is to leverage neural representations to help re-rank a set of candidate documents, which are typically obtained from an initial retrieval stage based on some symbolic index and search scheme (e.g., BM25 over the inverted index). This naturally raises a question: if the relevant documents have not been found in the initial retrieval stage due to vocabulary mismatch, there would be no chance to re-rank them to the top positions later. Therefore, in this paper, we study the problem how to employ neural representations to improve the recall of relevant documents in the initial retrieval stage. Specifically, to meet the efficiency requirement of the initial stage, we introduce a neural index for the neural representations of documents, and propose two hybrid search schemes based on both neural and symbolic indices, namely the parallel search scheme and the sequential search scheme. Our experiments show that both hybrid index and search schemes can improve the recall of the initial retrieval stage with small overhead. © 2023, Springer Nature Switzerland AG.

关键词： Semantics

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Academic access data analysis for literature recommendation 23rd

Academic access data analysis for literature recommendation

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23rd China conference on Information Retrieval, CCIR 2017

作者： Fan, Yixing Guo, Jiafeng Lan, Yanyan Xu, Jun Cheng, Xueqi CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9783319686981

Academic reading plays an important role in researchers’ daily life. To alleviate the burden of seeking relevant literature from rapidly growing academic repository, different kinds of recommender systems have been introduced in recent years. However, most existing work focused on adopting traditional recommendation techniques, like content-based filtering or collaborative filtering, in the literature recommendation scenario. Little work has yet been done on analyzing the academic reading behaviors to understand the reading patterns and information needs of real-world academic users, which would be a foundation for improving existing recommender systems or designing new ones. In this paper, we aim to tackle this problem by carrying out empirical analysis over large scale academic access data, which can be viewed as a proxy of academic reading behaviors. We conduct global, group-based and sequence-based analysis to address the following questions: (1) Are there any regularities in users’ academic reading behaviors? (2) Will users with different levels of activeness exhibit different information needs? (3) How to correlate one’s future demands with his/her historical behaviors? By answering these questions, we not only unveil useful patterns and strategies for literature recommendation, but also identify some challenging problems for future development. © Springer International Publishing AG 2017.

关键词： Recommender systems

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Splicer+: Secure Hub Placement and Deadlock-Free Routing for Payment Channel network Scalability

arXiv

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arXiv 2025年

作者： Yang, Lingxiao Dong, Xuewen Wang, Wei Gao, Sheng Qu, Qiang Tian, Wensheng Shen, Yulong The School of Computer Science and Technology Xidian University Xi’an710071 China The Shaanxi Key Laboratory of Network and System Security Xi’an710071 China The Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong University Beijing100044 China The School of Information Central University of Finance and Economics Beijing100081 China The Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China The Huawei Blockchain Lab Huawei Cloud Tech Co. Ltd. Shenzhen518055 China The Nanhu Lab Jiaxing314001 China The Cryptology and Computer Security Laboratory Shanghai Jiao Tong University Shanghai200240 China

Payment channel hub (PCH) is a promising approach for payment channel networks (PCNs) to improve efficiency by deploying robust hubs to steadily process off-chain transactions. However, existing PCHs, often preplaced without considering payment request distribution across PCNs, can lead to load imbalance. PCNs' reliance on source routing, which makes decisions based solely on individual sender requests, can degrade performance by overlooking other requests, thus further impairing scalability. In this paper, we introduce Splicer+, a highly scalable multi-PCH solution based on the trusted execution environment (TEE). We study tradeoffs in communication overhead between participants, transform the original NP-hard PCH placement problem by mixed-integer linear programming, and propose optimal/approximate solutions with load balancing for different PCN scales using supermodular techniques. Considering global PCN states and local directly connected sender requests, we design a deadlock-free routing protocol for PCHs. It dynamically adjusts the payment processing rate across multiple channels and, combined with TEE, ensures high-performance routing with confidential computation. We provide a formal security proof for the Splicer+ protocol in the UC-framework. Extensive evaluations demonstrate the effectiveness of Splicer+, with transaction success ratio (↑51.1%), throughput (↑181.5%), and latency outperforming state-of-the-art PCNs. Copyright © 2025, The Authors. All rights reserved.

关键词： Scalability

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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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Understanding and Improving Neural Ranking Models from a Term Dependence View 15th

Understanding and Improving Neural Ranking Models from a Ter...

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15th Asia Information Retrieval Societies Conference, AIRS 2019

作者： Fan, Yixing Guo, Jiafeng Lan, Yanyan Cheng, Xueqi CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9783030428341

Recently, neural information retrieval (NeuIR) has attracted a lot of interests, where a variety of neural models have been proposed for the core ranking problem. Beyond the continuous refresh of the state-of-the-art neural ranking performance, the community calls for more analysis and understanding of the emerging neural ranking models. In this paper, we attempt to analyze these new models from a traditional view, namely term dependence. Without loss of generality, most existing neural ranking models could be categorized into three categories with respect to their underlying assumption on query term dependence, i.e., independent models, dependent models, and hybrid models. We conduct rigorous empirical experiments over several representative models from these three categories on a benchmark dataset and a large click-through dataset. Interestingly, we find that no single type of model can achieve a consistent win over others on different search queries. An oracle model which can select the right model for each query can obtain significant performance improvement. Based on the analysis we introduce an adaptive strategy for neural ranking models. We hypothesize that the term dependence in a query could be measured through the divergence between its independent and dependent representations. We thus propose a dependence gate based on such divergence representation to softly select neural ranking models for each query accordingly. Experimental results verify the effectiveness of the adaptive strategy. © Springer Nature Switzerland AG 2020.

关键词： Large dataset

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PPO-Based Vehicle Control for Ramp Merging Scheme Assisted by Enhanced C-V2X

arXiv

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arXiv 2025年

作者： Wu, Qiong Ji, Maoxin Fan, Pingyi Wang, Kezhi Cheng, Nan Chen, Wen Letaief, Khaled B. The School of Internet of Things Engineering Jiangnan University Wuxi214122 China The Department of Electronic Engineering State Key laboratory of Space Network and Communications Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China The Department of Computer Science Brunel University Middlesex LondonUB8 3PH United Kingdom The State Key Lab. of ISN School of Telecommunications Engineering Xidian University Xi’an710071 China The Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China Hong Kong

On-ramp merging presents a critical challenge in autonomous driving, as vehicles from merging lanes need to dynamically adjust their positions and speeds while monitoring traffic on the main road to prevent collisions. To address this challenge, we propose a novel merging control scheme based on reinforcement learning, which integrates lateral control mechanisms. This approach ensures the smooth integration of vehicles from the merging lane onto the main road, optimizing both fuel efficiency and passenger comfort. Furthermore, we recognize the impact of vehicle-to-vehicle (V2V) communication on control strategies and introduce an enhanced protocol leveraging Cellular Vehicle-to-Everything (C-V2X) Mode 4. This protocol aims to reduce the Age of Information (AoI) and improve communication reliability. In our simulations, we employ two AoI-based metrics to rigorously assess the protocol's effectiveness in autonomous driving scenarios. By combining the NS3 network simulator with Python, we simulate V2V communication and vehicle control simultaneously. The results demonstrate that the enhanced C-V2X Mode 4 outperforms the standard version, while the proposed control scheme ensures safe and reliable vehicle operation during on-ramp merging. Copyright © 2025, The Authors. All rights reserved.

关键词： Problem oriented languages

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Multi-task representation learning for demographic prediction 38th

Multi-task representation learning for demographic predictio...

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38th European Conference on Information Retrieval Research, ECIR 2016

作者： Wang, Pengfei Guo, Jiafeng Lan, Yanyan Xu, Jun Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Beijing China

ISBN: (纸本)9783319306704

Demographic attributes are important resources for market analysis, which are widely used to characterize different types of users. However, such signals are only available for a small fraction of users due to the difficulty in manual collection process by retailers. Most previous work on this problem explores different types of features and usually predicts different attributes independently. However, manually defined features require professional knowledge and often suffer from under specification. Meanwhile, modeling the tasks separately may lose the ability to leverage the correlations among different attributes. In this paper, we propose a novel Multi-task Representation Learning (MTRL) model to predict users’ demographic attributes. Comparing with the previous methods, our model conveys the following merits: (1) By using a multitask approach to learn the tasks, our model leverages the large amounts of cross-task data, which is helpful to the task with limited data;(2) MTRL uses a supervised way to learn the shared semantic representation across multiple tasks, thus it can obtain a more general and robust representation by considering the constraints among tasks. Experiments are conducted on a real-world retail dataset where three attributes (gender, marital status, and education background) are predicted. The empirical results show that our MTRL model can improve the performance significantly compared with the state-of-the-art baselines. © Springer International Publishing Switzerland 2016.

关键词： Semantics

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Uplink Control Information Based Clustered Access and Discontinuous Reception Management for mMTC

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IEEE Communications Letters 2025年

作者： Wang, Shao-Di Wang, Chang-Long Wang, Hui-Ming Zhou, Feng Leung, Victor C. M. Xidian University Key Laboratory of Electronic Information Countermeasure and Simulation Technology of Ministry of Education Xi’an710071 China Xi’an Jiaotong University School of Information and Communication Engineering Ministry of Education Key Lab for Intelligent Networks and Network Security Xi’an710049 China Shenzhen MSU BIT University Artificial Intelligence Research Institute Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Uplink control information (UCI) and discontinuous reception (DRX) play important roles for massive machine type communication (mMTC). Despite their standalone significance, a conspicuous gap exists in comprehensively exploring the synergy between UCI and DRX concerning both energy conservation and latency reduction for mMTC. In this letter, we propose a UCI-based clustered access and DRX management (UCADM) scheme that strikes a balance between these two critical performance metrics, reducing energy usage while preserving low latency in uplink-downlink interaction for mMTC. Specifically, we present a strictly feasible solution for the proposed UCADM scheme by using the spectral radius and the constraint projection, which can effectively solve DRX switching operations as indicated by scheduling information from UCI. Simulation results confirm the effectiveness of our scheme. © 1997-2012 IEEE.

关键词： Information management

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