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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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Out-of-Distribution Detection by Principal Component Correspondence

Out-of-Distribution Detection by Principal Component Corresp...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Xiaoyuan Guan Zhiyong Gan Ling Deng Wei Shi Jiankang Chen Shenshen Bu Chunliang Zhao Jianfang Hu Yuren Zhou Wei-Shi Zheng Ruixuan Wang School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing MOE Guangzhou China Network Business Group China Unicom Guangzhou China Department of Network Intelligence Pengcheng Laboratory Shenzhen China School of Data and Science Qingdao University of Science and Technology Qingdao China School of Software Engineering Sun Yat-Sen University Zhuhai China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Out-of-distribution (OOD) detection is vital for the safe application of intelligent systems in real-world scenarios. This paper proposes an enhancement to OOD detection by leveraging the consistency in cognition between two models, both pretrained on in-distribution (ID) data. Specifically, for a given test sample, we first apply Principal Component Analysis (PCA)-based projection on the feature vectors from each model. These obtained feature vectors (with correlation between dimensions decoupled by PCA projection) are then aligned using a multiple linear mapping, which is fitted using the least squares method on the training data. We hypothesize that the regression error for OOD data will be larger than that for ID data, making it a useful metric for OOD detection. Our experimental results demonstrate the effectiveness of this method. When combined with existing robust baselines, our approach achieves state-of-the-art performance in OOD detection.

关键词： Training Measurement Analytical models Training data Vectors data models Intelligent systems Facial features Principal component analysis Least mean squares methods

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Effects of Optimized Operating Parameters on Combustion Characteristics and NO_(x)Emissions of a Burner based on Orthogonal Analysis

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Journal of Thermal Science 2021年第4期30卷 1212-1223页

作者： XU Qian AKKURT Nevzat YANG Gang ZHU Lidong SHI Kejian WANG Kang ZOU Zhenwei LIU Zhihui WANG Jiulong DU Zhiwei School of Energy and Environmental Engineering University of Science and Technology BeijingBeijing 100083China Beijing Key Laboratory for Energy Conservation and Emission Reduction for Metallurgical Industry Beijing 100083China Department of Mechanical Engineering Munzur UniversityTunceli 62000Turkey Department of Big Data Technology and Application Development Computer Network Information CenterChinese Academy of SciencesBeijing 100190China AVIC Xinxiang Aviation Industry(group)CO. LTDHenan 453002China

To optimize the structure of the burner,improve the combustion performance,and reduce the emission of NO_(x),a self-circulating low NO_(x)combustion technology was used to design a new type of flue gas self-circulating low NO_(x)*** on previous research on the numerical model of combustion and the composition of mixed gas on combustion and NO_(x)emissions,the effect of various factors on the ejection coefficient of the flue gas self-circulating structure was analyzed using the orthogonal test method,and the burner operating parameters,such as preheating temperature and excess air coefficient,were deeply studied through the three-dimensional finite element numerical model in this *** results show that the diameter ratio of the nozzle and the length of the cylindrical section of the flue gas self-circulating structure have great influence on its ejection and mixing *** optimal ejection coefficient was ***,the amount of NO_(x)emissions greatly increased from 6.23×10^(-6)(volume fraction)at the preheating temperature 973 K to 3.5×10^(-3)at preheating temperature 1573 *** the excess air coefficient decreased from 1.2 to 1,the maximum combustion temperature decreased from 2036.3 K to 1954.22 K,and the NO_(x)emissions decreased from 352.29×10^(-6)to 159.73×10^(-6).

关键词： preheating temperature excess air coefficient self-circulating flue gas low oxygen combustion low-NO_(x)emissions

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On the Effectiveness of Function-Level Vulnerability Detectors for Inter-Procedural Vulnerabilities

arXiv

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arXiv 2024年

作者： Li, Zhen Wang, Ning Zou, Deqing Li, Yating Zhang, Ruqian Xu, Shouhuai Zhang, Chao Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Department of Computer Science University of Colorado Colorado Springs Colorado SpringsCO United States Institute for Network Sciences and Cyberspace Tsinghua University Beijing China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab China JinYinHu Laboratory Wuhan China

Software vulnerabilities are a major cyber threat and it is important to detect them. One important approach to detecting vulnerabilities is to use deep learning while treating a program function as a whole, known as function-level vulnerability detectors. However, the limitation of this approach is not understood. In this paper, we investigate its limitation in detecting one class of vulnerabilities known as inter-procedural vulnerabilities, where the to-be-patched statements and the vulnerability-triggering statements belong to different functions. For this purpose, we create the first Inter-Procedural Vulnerability dataset (InterPVD) based on C/C++ open-source software, and we propose a tool dubbed VulTrigger for identifying vulnerability-triggering statements across functions. Experimental results show that VulTrigger can effectively identify vulnerability-triggering statements and inter-procedural vulnerabilities. Our findings include: (i) inter-procedural vulnerabilities are prevalent with an average of 2.8 inter-procedural layers;and (ii) function-level vulnerability detectors are much less effective in detecting to-be-patched functions of inter-procedural vulnerabilities than detecting their counterparts of intra-procedural vulnerabilities. Copyright © 2024, The Authors. All rights reserved.

关键词： Open source software

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On the Effectiveness of Function-Level Vulnerability Detectors for Inter-Procedural Vulnerabilities

On the Effectiveness of Function-Level Vulnerability Detecto...

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International Conference on Software engineering (ICSE)

作者： Zhen Li Ning Wang Deqing Zou Yating Li Ruqian Zhang Shouhuai Xu Chao Zhang Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Jin YinHu Laboratory Wuhan China Department of Computer Science University of Colorado Colorado Springs Colorado Springs Colorado USA Institute for Network Sciences and Cyberspace Tsinghua University Beijing China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

Software vulnerabilities are a major cyber threat and it is important to detect them. One important approach to detecting vulnerabilities is to use deep learning while treating a program function as a whole, known as function-level vulnerability detectors. However, the limitation of this approach is not understood. In this paper, we investigate its limitation in detecting one class of vulnerabilities known as inter-procedural vulnerabilities, where the to-be-patched statements and the vulnerability-triggering statements belong to different functions. For this purpose, we create the first Inter -Procedural Vulnerability dataset (InterPVD) based on C/C++ open-source software, and we propose a tool dubbed VulTrigger for identifying vulnerability-triggering statements across functions. Experimental results show that VulTrigger can effectively identify vulnerability-triggering statements and inter-procedural vulnerabilities. Our findings include: (i) inter-procedural vulnerabilities are prevalent with an average of 2.8 inter-procedural layers; and (ii) function-level vulner-ability detectors are much less effective in detecting to-be-patched functions of inter-procedural vulnerabilities than detecting their counterparts of intra-procedural vulnerabilities.

关键词： Deep learning Detectors Open source software Software engineering

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Efficient survivable mapping algorithm for logical topology in IP-over-WDM optical networks against node failure

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Journal of Supercomputing 2023年第5期79卷 5037-5063页

作者： Cheng, Dun-Wei Chang, Jo-Yi Lin, Chen-Yen Lin, Limei Huang, Yanze Thulasiraman, Krishnaiyan Hsieh, Sun-Yuan Department of Computer Science and Information Engineering National Yunlin University of Science and Technology 123 University Road Yunlin64002 Taiwan Department of Computer Science and Information Engineering National Cheng Kung University No. 1 University Road Tainan701 Taiwan College of Mathematics and Informatics and the Key Laboratory of Network Security and Cryptology Fujian Normal University Fujian Fuzhou350117 China Fujian Provincial Key Laboratory of Big Data Mining and Applications School of Mathematics and Physics Fujian University of Technology Fujian Fuzhou350118 China Department of Computer Science University of Oklahoma 660 Parrington Oval NormanOK73019 United States Department of Computer Science and Information Engineering National Chi Nan University No. 1 Daxue Rd Nantou County Puli Township54561 Taiwan

The survivable mapping problem in an IP-over-WDM network with a logical graph and physical graph is the problem of finding a mapping of a logical network in a physical layer so that any failure in physical topology does not break the logical topology’s connection. Determining whether a survivable mapping against failure exists is an NP-complete problem, and therefore many heuristic algorithms have been proposed in the literature. In this paper, a heuristic mapping design strategy is proposed to enable the lightpaths to more efficiently endure a node failure. We individually consider the logical topology by building a Hamiltonian cycle and then deal with the mapping problem for disjoint physical paths. Experimental results demonstrate that the proposed algorithm can provide efficient survivable mapping in IP-over-WDM networks. The simulation shows that the survivability of previous methods (SNF and SMART-Node) is around 60–80%, but our proposed algorithm can build survivable mapping in all kinds of situations. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Heuristic algorithms

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Using Depth-Enhanced Spatial Transformation for Student Gaze Target Estimation in Dual-View Classroom Images

Using Depth-Enhanced Spatial Transformation for Student Gaze...

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

作者： Haonan Miao Peizheng Zhao Yuqi Sun Fang Nan Xiaolong Zhang Yaqiang Wu Feng Tian School of Computer Science and Technology Xi’an Jiaotong University Xi’an China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an China School of Advanced Technology Xi’an Jiaotong-Liverpool University Suzhou China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University Xi’an China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Dual-view gaze target estimation in classroom environments has not been thoroughly explored. Existing methods lack consideration of depth information, primarily focusing on 2D image information and neglecting the latent 3D spatial context, which could lead to suboptimal transformation and cause the gaze cone to intersect with an incorrect object. This paper introduces a novel dual-view gaze target estimation method tailored for classroom settings, leveraging depth-enhanced spatial transformations. By formulating a depth-enhanced 2D space, our method uses depth-enhanced spatial transformation to accurately project students’ gaze cones to the teacher-oriented image. Additionally, we collected a dataset named DVSGE, specifically for student gaze target estimation in dual-view classroom images. Experimental results demonstrate significant performance improvements of 9.8% in AUC and 19.9% in L2-Distance for our method, surpassing existing methods.

关键词： Three-dimensional displays Estimation Focusing Signal processing Acoustics Speech processing

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Ponzi Scheme Detection in Smart Contracts Using Heterogeneous Semantic Graph 6th

Ponzi Scheme Detection in Smart Contracts Using Heterogeneo...

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6th International Conference on Blockchain and Trustworthy Systems, BlockSys 2024

作者： Chen, Wei Jiang, Xinjun Lan, Tian Liu, Leyuan Li, Chengyu Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu610000 China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610000 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu610000 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518000 China

ISBN: (纸本)9789819614103

Behind the booming decentralized finance (DeFi) ecosystem driven by blockchain technology, various financial risks lurking, including money laundering, gambling, Ponzi schemes, and phishing. Due to the decentralization and anonymity of Ethereum, Ponzi schemes can be easily deployed, causing huge economic losses to investors. Existing detection methods based on transaction data are difficult to provide early risk warnings, while detection methods based on smart contract source code and opcodes have insufficient feature fusion at multiple levels. We propose an approach for constructing a multi-level Heterogeneous Semantic Graph (HSG) of smart contracts, and improve the HAN model to detect Ponzi scheme smart contracts based on the Heterogeneous Semantic Graph. The experimental results demonstrate the effectiveness of our approach, achieving an accuracy of 97.21%, a precision of 94.29%, and an F1 score of 90.41%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decentralized finance

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

作者： Libo Qin Fuxuan Wei Qiguang Chen Jingxuan Zhou Shijue Huang Jiasheng Si Wenpeng Lu Wanxiang Che 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)

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

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.

关键词： Large language models Signal processing Benchmark testing Multitasking Filling Acoustics Speech processing Information exchange Standards

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Signal Subspace Tracking for AoA Estimation in ISAC Systems

Signal Subspace Tracking for AoA Estimation in ISAC Systems

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IEEE Conference on Wireless Communications and networking

作者： Shixu Dong Jingbo Zhao Zhaoming Lu J. Andrew Zhang Tianpu Yang Jiayin Deng Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing China School of Electrical and Data Engineering University of Technology Sydney Sydney Australia China Mobile Group Design Institute CO. Ltd Beijing China

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

In indoor integrated sensing and communication (ISAC) systems, angle of arrival (AoA) estimation for moving targets via reflected signals plays an important role in passive sensing. It is a challenging task in the presence of multiple static paths which cause significant interference to AoA estimation for dynamic paths. In this paper, we propose a subspace algorithm that achieves accurate AoA estimation for multiple targets using static communication transceivers in general indoor scenarios. We first use a complex vector to model static paths as a merged static path. Then we design a conditional pseudo-likelihood probability density function (PDF) based on the projection matrix of the signal subspace to bridge the signal subspace with the merged static path and AoAs of dynamic paths. We propose to utilize the Particle Filter to track merged static path and AoAs of dynamic paths via signal subspace. Thereby, the interference caused by static path signals can be eliminated and the accurate AoA estimation results are obtained. Simulation results are provided and validate the proposed algorithm.

关键词： Target tracking Accuracy Heuristic algorithms Simulation Estimation Interference Probability density function

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