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Bidirectionally Guided Large Language Models for Consumer-Centric Personalized Recommendation

IEEE Transactions on Consumer Electronics 2025年

作者： Yu, Linfang Xiao, Peng Xu, Liqun Kumari, Saru Alenazi, Mohammed J.F. Industrial Research Institute Chengdu610213 China University of Electronic Science and Technology of China Network and Data Security Key Laboratory of Sichuan Province Chengdu610054 China Chengdu University of Technology School of Computer and Network Security Chengdu China Industrial Research Institute Chengdu610213 China Chaudhary Charan Singh University Department of Mathematics Uttar Pradesh Meerut250004 India Department of Computer Engineering Riyadh11451 Saudi Arabia

With the increasing abundance of consumer electronics, consumers are facing the challenge of information overload, making personalized recommendation systems (RSs) crucial for enhancing consumer experience and promoting sales. Recently, LLM-driven RSs have attracted widespread attention. Nevertheless, the application of LLMs in recommendation still encounters challenges: how to effectively integrate user/item content (CT) and collaborative filtering (CF) information;how to effectively process heterogeneous token sequences;and how to efficiently generate recommendations. Therefore, we propose BGLLM4Rec, a bidirectionally guided LLM for recommendation. Specifically, a bidirectionally guided pre-training strategy is proposed to extend user/item ID tokens into the vocabulary of the pre-trained LLM, and leverage both CF LLM and CT LLM to learn their embedding representations. Meanwhile, a heterogeneous-homogeneous (Het-Hom) prompting strategy is proposed to decompose the input sequence into heterogeneous prompt and homogeneous main text parts, performing language modeling solely on the latter to enhance modeling effectiveness and stability. Finally, a recommendation-specific masked prompting fine-tuning strategy is employed to further refine the pre-trained CF LLM, enabling the efficient generation of multiple recommended items without hallucinations. Extensive experiments conducted on three public datasets have demonstrated that BGLLM4Rec exhibits exceptional performance in recommendation tasks. © 1975-2011 IEEE.

关键词： Collaborative filtering

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Hijacking attacks against neural network by analyzing training data 24

Hijacking attacks against neural network by analyzing traini...

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Proceedings of the 33rd USENIX Conference on Security Symposium

作者： Yunjie Ge Qian Wang Huayang Huang Qi Li Cong Wang Chao Shen Lingchen Zhao Peipei Jiang Zheng Fang Shenyi Zhang School of Cyber Science and Engineering Wuhan University Institute of Network Sciences and Cyberspace Tsinghua University BNRist Department of Computer Science City University of Hong Kong School of Cyber Science and Engineering Xi'an Jiaotong University School of Cyber Science and Engineering Wuhan University and Department of Computer Science City University of Hong Kong

ISBN: (纸本)9781939133441

Backdoors and adversarial examples are the two primary threats currently faced by deep neural networks (DNNs). Both attacks attempt to hijack the model behaviors with unintended outputs by introducing (small) perturbations to the inputs. However, neither attack is without limitations in practice. Backdoor attacks, despite the high success rates, often require the strong assumption that the adversary could tamper with the training data or code of the target model, which is not always easy to achieve in reality. Adversarial example attacks, which put relatively weaker assumptions on attackers, often demand high computational resources, yet do not always yield satisfactory success rates when attacking mainstream blackbox models in the real world. These limitations motivate the following research question: can model hijacking be achieved in a simpler way with more satisfactory attack performance and also more reasonable attack assumptions?In this paper, we provide a positive answer with CleanSheet, a new model hijacking attack that obtains the high performance of backdoor attacks without requiring the adversary to temper with the model training process. CleanSheet exploits vulnerabilities in DNNs stemming from the training data. Specifically, our key idea is to treat part of the clean training data of the target model as "poisoned data", and capture the characteristics of these data that are more sensitive to the model (typically called robust features) to construct "triggers". These triggers can be added to any input example to mislead the target model, similar to backdoor attacks. We validate the effectiveness of CleanSheet through extensive experiments on five datasets, 79 normally trained models, 68 pruned models, and 39 defensive models. Results show that CleanSheet exhibits performance comparable to state-of-the-art backdoor attacks, achieving an average attack success rate (ASR) of 97.5% on CIFAR-100 and 92.4% on GTSRB, respectively. Furthermore, CleanSheet consi

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The Distributed Intelligent Collaboration to UAV-Assisted VEC: Joint Position Optimization and Task Scheduling

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IEEE Internet of Things Journal 2025年

作者： Yi, Meng Lee, Vincent C.S. Yang, Peng Li, Peisong Zhang, Yifan Wei, Wei Gao, Honghao Southeast University School of Computer Science and Engineering Nanjing211189 China Monash University Faculty of IT Department of DS and AI ClaytonVIC3800 Australia Southeast University School of Computer Science and Engineering the Key Laboratory of Computer Network and Information Integration Ministry of Education Nanjing211189 China Xidian University Hangzhou Institute of Technology Hangzhou311200 China Southeast University-Monash University Joint Graduate School Suzhou215123 China Xi'an University of Technology School of Computer Science and Engineering Xi'an710048 China Shanghai University School of Computer Engineering and Science Shanghai200444 China

Deploying Unmanned Aerial Vehicles (UAVs) as aerial base stations enhances the coverage and performance of communication networks in Vehicular Edge Computing (VEC) scenarios. However, due to the limited communication range and energy capacity of UAVs, they cannot continuously cover entire areas or sustain long flights. Therefore, achieving full communication coverage of a target area with a minimal number of UAVs and efficient task offloading remains a significant challenge. To address this problem, the UAV-assisted Two-stage Intelligent Collaboration (UTIC) method is proposed in this paper to tackle the joint position optimization and task scheduling issue. Firstly, a UAV-assisted Two-stage Task Scheduling (UTTS) system model is designed to optimize the allocation process. Secondly, an Enhanced Particle Swarm Optimization (E-PSO) algorithm is designed to determine the optimal positions of UAVs, ensuring complete coverage of all mobile vehicles (MVs) with the minimum number of UAVs. Thirdly, Deep Deterministic Policy Gradient (DDPG) method is employed to find the optimal scheduling decisions for MVs, considering energy consumption, delay, and task priorities. Simulation results demonstrate that the proposed UTIC method can achieve nearly 20% reduction in UAV deployment and outperform three other classical reinforcement learning (RL) algorithms in terms of reducing system cost. © 2025 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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A Blockchain-Based Distributed Collaborative Sensing and Spectrum Access Approach for Consumer Electronics

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IEEE Transactions on Consumer Electronics 2025年

作者： Peng, Yuhuai Li, Yuan Guo, Yu Zhang, Dawei Khan, Fazlullah Alturki, Ryan Alshawi, Bandar Northeastern University College of Computer Science and Engineering Shenyang110819 China Zhiyuan Research Institute Xihu District Hangzhou China Zhengzhou University School of Computer and Artificial Intelligence Henan Zhengzhou450001 China University of Nottingham Ningbo China School of Computer Science Faculty of Science and Engineering Zhejiang Ningbo315104 China KKR and KSR Institute of Technology and Sciences Department of Computer Science and Engineering Andhra Pradesh Guntur India Division of Research and Development Lovely Professional University Phagwara India University Centre of Research and Development Chandigarh University Mohali India Umm Al-Qura University Department of Software Engineering College of Computing Makkah Saudi Arabia Umm Al-Qura University College of Computing Department of Computer and Network Engineering Makkah Saudi Arabia

Cognitive radio (CR) provides key technical support for the seamless communication of consumer electronic (CE) devices through distributed cooperative sensing and spectrum access. However, its openness and dynamic nature introduce potential security risks and challenges. Due to the openness of wireless communication, spectrum sensing and access are vulnerable to malicious attacks, and resource-constrained devices cannot effectively implement artificial intelligence (AI)-driven encryption and access control strategies. To address these limitations, this study proposes a blockchain-based distributed collaborative sensing method for CE devices. In the proposed method, after sensing, the smart contracts verify node legitimacy using the ring signature method. The malicious users are rejected by the outlier detection method, which improves the collaborative anti-interference ability. In addition, by using a centralized training and distributed execution architecture, a secure spectrum sensing strategy based on multi-agent reinforcement learning is designed. The proposed strategy ensures that each node makes independent decisions, which enhances the adaptability of the CE ecosystem. The proposed method is verified by extensive simulations, and the results show that it can maintain spectrum awareness of more than 95.72% and an access success rate of 91.19% under malicious attacks. Moreover, communication overhead is significantly reduced by approximately 93.53%. © 1975-2011 IEEE.

关键词： Blockchain

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CroPrompt: Cross-task Interactive Prompting for Zero-shot Spoken Language Understanding

CroPrompt: Cross-task Interactive Prompting for Zero-shot Sp...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Qin, Libo Wei, Fuxuan Chen, Qiguang Zhou, Jingxuan Huang, Shijue Si, Jiasheng Lu, Wenpeng Che, Wanxiang School of Computer Science and Engineering Central South University China Key Laboratory of Data Intelligence and Advanced Computing in Provincial Universities Soochow University China Research Center for Social Computing and Information Retrieval Harbin Institute of Technology China Harbin Institute of Technology Shenzhen China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology Shandong Academy of Sciences China

ISBN: (纸本)9798350368741

Slot filling and intent detection are two highly correlated tasks in spoken language understanding (SLU). Recent SLU research attempts to explore zero-shot prompting techniques in large language models to alleviate the data scarcity problem. Nevertheless, the existing prompting work ignores the cross-task interaction information for SLU, which leads to sub-optimal performance. To solve this problem, we present the pioneering work of Cross-task Interactive Prompting (CroPrompt) for SLU, which enables the model to interactively leverage the information exchange across the correlated tasks in SLU. Additionally, we further introduce a multi-task self-consistency mechanism to mitigate the error propagation caused by the intent information injection. We conduct extensive experiments on the standard SLU benchmark and the results reveal that CroPrompt consistently outperforms the existing prompting approaches. In addition, the multi-task self-consistency mechanism can effectively ease the error propagation issue, thereby enhancing the performance. We hope this work can inspire more research on cross-task prompting for SLU. © 2025 IEEE.

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Trading Computing Power For Reducing Communication Loads: A Semantic Communication Perspective

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IEEE Transactions on Communications 2025年

作者： Zheng, Guangyuan Wen, Miaowen Xu, Lexi Ding, Zhiguo South China University of Technology School of Electronic and Information Engineering Guangzhou510640 China Khalifa University Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates China United Network Communications Corporation Research Institute Beijing100048 China Quzhou324003 China

As a new paradigm focusing on transmitting the meaning of information, semantic communications (SCs) have been revealed significant potential in alleviating network congestion and improving energy efficiency. By extracting a small-size semantic feature from the large-size raw-data, the communication loads can be reduced at a price of more computing power, i.e., the computational resource used in semantic extraction. In this paper, we explore the computation dimension to improve the communication performance in an uplink SC system. The SC-oriented user first compresses its original data via local computing during other users’ transmission time and then transmits it to the base station within the assigned time. To achieve a balanced tradeoff between communications and computing, we formulate an optimization problem to minimize the energy consumption of all users by jointly considering the compression ratio and time allocation. We first propose an efficient general algorithm that can be applied to different SC models. To gain more insights, we then derive the closed-form solutions for two special cases: equal-time allocation and two-user transmission. The obtained analytical results reveal a pronounced energy saving of adopting SC, especially for large transmitted data size, high transmission energy coefficient, scarce time resources, and poor channel conditions. Simulation results reveal that our proposed SC-based scheme achieves excellent performance in terms of saving energy consumption compared to the conventional communication. © 2025 IEEE. All rights reserved.

关键词： Energy utilization

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VN-GT: Optimizing Virtual network Deployment via Game Theory

VN-GT: Optimizing Virtual Network Deployment via Game Theory

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Weijie Wang Yan Wang Guokun Xu Zuxin Chen Siyuan Li Min Yu Weiqing Huang Degang Sun Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Computer Network Information Center Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The static and homogeneous nature of traditional networks presents a significant challenge for our defense efforts. These characteristics enable an experienced attacker to quickly determine our network topology and gather detailed information about the internal hosts through systematic scanning techniques. Implementing a virtual network view can mitigate this by simulating a virtual topology, thereby consuming the attacker’s resources and time. However, deploying a virtual network view reduces network throughput and increase latency. Additionally, an improperly configured virtual network view can waste resources and degrade Quality of Service (QoS). Most existing studies have focused solely on the defender’s perspective, resulting in overly idealistic solutions that are ineffective in real-world scenarios. To address this, we propose VN-GT, a game-theoretic based model that optimizes virtual network deployment by considering both attackers and defenders. We provide a detailed example scenario, analyze the game’s equilibrium, and validate the effectiveness of our method through a real attack and defense experiment.

关键词： Analytical models Systematics network topology Quality of service Organizations Signal processing Throughput Nash equilibrium Topology Speech processing

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DomainDemo: a dataset of domain-sharing activities among different demographic groups on Twitter

arXiv

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arXiv 2025年

作者： Yang, Kai-Cheng Goel, Pranav Quintana-Mathé, Alexi Horgan, Luke McCabe, Stefan D. Grinberg, Nir Joseph, Kenneth Lazer, David Network Science Institute Northeastern University BostonMA United States Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Be’er-Sheva Israel Computer Science and Engineering Department University at Buffalo BuffaloNY United States

Social media play a pivotal role in disseminating web content, particularly during elections, yet our understanding of the association between demographic factors and political discourse online remains limited. Here, we introduce a unique dataset, DomainDemo, linking domains shared on Twitter (X) with the demographic characteristics of associated users, including age, gender, race, political affiliation, and geolocation, from 2011 to 2022. This new resource was derived from a panel of over 1.5 million Twitter users matched against their U.S. voter registration records, facilitating a better understanding of a decade of information flows on one of the most prominent social media platforms and trends in political and public discourse among registered U.S. voters from different sociodemographic groups. By aggregating user demographic information onto the domains, we derive five metrics that provide critical insights into over 129,000 websites. In particular, the localness and partisan audience metrics quantify the domains’ geographical reach and ideological orientation, respectively. These metrics show substantial agreement with existing classifications, suggesting the effectiveness and reliability of DomainDemo’s approach. © 2025, CC BY.

关键词： Tweets

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A 3D Memristive Cubic Map with Dual Discrete Memristors: Design, Implementation, and Application in Image Encryption

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Gao, Suo Iu, Herbert Ho-Ching Erkan, Ugur Simsek, Cemaleddin Toktas, Abdurrahim Cao, Yinghong Wu, Rui Mou, Jun Li, Qi Wang, Chunpeng Dalian Polytechnic University School of Information Science and Engineering Dalian116034 China The University of Western Australia School of Electrical Electronic and Computer Engineering Perth6009 Australia Faculty of Engineering Electrical and Electronics Engineering Respectively Karamanoglu Mehmetbey University Karaman70200 Turkey Ankara University Engineering Faculty Department of Artificial Intelligence and Data Engineering Ankara06830 Turkey Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan250000 China

Discrete chaotic systems based on memristors exhibit excellent dynamical properties and are more straightforward to implement in hardware, making them highly suitable for generating cryptographic keystreams. However, most existing memristor-based chaotic systems rely on a single memristor. This paper introduces a novel discrete chaotic system employing dual memristors, named the 3D memristive cubic map with dual discrete memristors (3D-MCM). The 3D-MCM system demonstrates richer and more intricate dynamical behaviors compared to its single-memristor counterparts, as verified through bifurcation diagrams, Lyapunov exponent spectra, and complexity analyses. Notably, the system exhibits coexisting attractors, substantially enhancing its dynamical complexity. Hardware implementation of the 3D-MCM attractors confirms its feasibility for industrial applications. To illustrate the system's potential in encryption tasks, this study integrates the quaternary-based permutation and dynamic emanating diffusion (QPDED-IE) scheme for image encryption with the *** results demonstrate that the QPDED-IE scheme based on the 3D-MCM exhibits strong diffusion and confusion properties, effectively resisting cryptanalytic attacks. © 1991-2012 IEEE.

关键词： Bifurcation (mathematics)

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TFLAG: Towards Practical APT Detection via Deviation-Aware Learning on Temporal Provenance Graph

arXiv

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arXiv 2025年

作者： Jiang, Wenhan Chai, Tingting Liu, Hongri Wang, Kai Zhang, Hongke School of Computer Science and Technology Harbin Institute of Technology Weihai China Shandong Key Laboratory of Industrial Network Security China School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China

Advanced Persistent Threat (APT) have grown increasingly complex and concealed, posing formidable challenges to existing Intrusion Detection Systems in identifying and mitigating these attacks. Recent studies have incorporated graph learning techniques to extract detailed information from provenance graphs, enabling the detection of attacks with greater granularity. Nevertheless, existing studies have largely overlooked the continuous yet subtle temporal variations in the structure of provenance graphs, which may correspond to surreptitious perturbation anomalies in ongoing APT attacks. Therefore, we introduce TFLAG, an advanced anomaly detection framework that for the first time integrates the structural dynamic extraction capabilities of temporal graph model with the anomaly delineation abilities of deviation networks to pinpoint covert attack activities in provenance graphs. This self-supervised integration framework leverages the graph model to extract neighbor interaction data under continuous temporal changes from historical benign behaviors within provenance graphs, while simultaneously utilizing deviation networks to accurately distinguish authentic attack activities from false positive deviations due to unexpected subtle perturbations. The experimental results indicate that, through a comprehensive design that utilizes both attribute and temporal information, it can accurately identify the time windows associated with APT attack behaviors without prior knowledge (e.g., labeled data samples), demonstrating superior accuracy compared to current state-of-the-art methods in differentiating between attack events and system false positive events. © 2025, CC BY.

关键词： Contrastive Learning

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