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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Jiang, Kai Huang, Jiaxing Xie, Weiying Lei, Jie Li, Yunsong Shao, Ling Lu, Shijian State Key Laboratory of Integrated Services Networks Xidian University Xi'an710071 China S-Lab School of Computer Science and Engineering Nanyang Technological University Singapore School of Electrical and Data Engineering The University of Technology Sydney Australia UCAS-Terminus AI Lab University of Chinese Academy of Sciences China

Large-vocabulary object detectors (LVDs) aim to detect objects of many categories, which learn super objectness features and can locate objects accurately while applied to various downstream data. However, LVDs often struggle in recognizing the located objects due to domain discrepancy in data distribution and object vocabulary. At the other end, recent vision-language foundation models such as CLIP demonstrate superior open-vocabulary recognition capability. This paper presents KGD, a Knowledge Graph Distillation technique that exploits the implicit knowledge graphs (KG) in CLIP for effectively adapting LVDs to various downstream domains. KGD consists of two consecutive stages: 1) KG extraction that employs CLIP to encode downstream domain data as nodes and their feature distances as edges, constructing KG that inherits the rich semantic relations in CLIP explicitly;and 2) KG encapsulation that transfers the extracted KG into LVDs to enable accurate cross-domain object classification. In addition, KGD can extract both visual and textual KG independently, providing complementary vision and language knowledge for object localization and object classification in detection tasks over various downstream domains. Experiments over multiple widely adopted detection benchmarks show that KGD outperforms the state-of-the-art consistently by large margins. © 2024 Neural information processing systems foundation. All rights reserved.

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Visual autoregressive modeling: scalable image generation via next-scale prediction 24

Visual autoregressive modeling: scalable image generation vi...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： keyu Tian Yi Jiang Zehuan Yuan Bingyue Peng Liwei Wang Center for Data Science Peking University and Bytedance Inc. Bytedance Inc. Center for Data Science Peking University and State Key Lab of General Artificial Intelligence School of Intelligence Science and Technology Peking University

ISBN: (纸本)9798331314385

We present Visual AutoRegressive modeling (VAR), a new generation paradigm that redefines the autoregressive learning on images as coarse-to-fine "next-scale prediction" or "next-resolution prediction", diverging from the standard raster-scan "next-token prediction". This simple, intuitive methodology allows autoregressive (AR) transformers to learn visual distributions fast and can generalize well: VAR, for the first time, makes GPT-style AR models surpass diffusion transformers in image generation. On ImageNet 256×256 benchmark, VAR significantly improve AR baseline by improving Fréchet inception distance (FID) from 18.65 to 1.73, inception score (IS) from 80.4 to 350.2, with 20× faster inference speed. It is also empirically verified that VAR outperforms the Diffusion Transformer (DiT) in multiple dimensions including image quality, inference speed, data efficiency, and scalability. Scaling up VAR models exhibits clear power-law scaling laws similar to those observed in LLMs, with linear correlation coefficients near -0.998 as solid evidence. VAR further showcases zero-shot generalization ability in downstream tasks including image in-painting, out-painting, and editing. These results suggest VAR has initially emulated the two important properties of LLMs: Scaling Laws and zero-shot generalization. We have released all models and codes to promote the exploration of AR/VAR models for visual generation and unified learning.

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Teaching resource recommendation for OBE based on kernel canonical correlation analysis 12

Teaching resource recommendation for OBE based on kernel can...

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12th International Conference on Information technology in Medicine and Education, ITME 2022

作者： Changqian, Wu Shaozi, Li Yaojin, Lin Xiehua, Yu MinNan Science and Technology Key Laboratory of Big Data and Artificial Intelligence University José Rizal University University of Fujian Province Quanzhou China Xiamen University School of Informatics Xiamen China Minnan Normal University School of Computer Science Lab of Data Science and Intelligence Application Zhangzhou China MinNan Science and Technology University Key Laboratory of Big Data and Artificial Intelligence University of Fujian Province Quanzhou China

ISBN: (纸本)9798350310153

data mining is used to extract information from OBE teaching resources, then the teaching resources and user personality database are established to improve the effectiveness of resources recommendation on the online learning platform. The properties of teaching resources and user personality traits are analyzed using the kernel canonical correlation analysis algorithm, and teaching materials with a high correlation coefficient are chosen to recommend to users. The simulation results demonstrate that this proposed method has higher accuracy and more accurate recommended resources when compared to the widely used teaching resource recommendation techniques. © 2022 IEEE.

关键词： data mining

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Semantic Structure Enhanced Event Causality Identification

arXiv

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arXiv 2023年

作者： Hu, Zhilei Li, Zixuan Jin, Xiaolong Bai, Long Guan, Saiping Guo, Jiafeng Cheng, Xueqi School of Computer Science and Technology University of Chinese Academy of Sciences China CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China

Event Causality Identification (ECI) aims to identify causal relations between events in unstructured texts. This is a very challenging task, because causal relations are usually expressed by implicit associations between events. Existing methods usually capture such associations by directly modeling the texts with pre-trained language models, which underestimate two kinds of semantic structures vital to the ECI task, namely, event-centric structure and event-associated structure. The former includes important semantic elements related to the events to describe them more precisely, while the latter contains semantic paths between two events to provide possible supports for ECI. In this paper, we study the implicit associations between events by modeling the above explicit semantic structures, and propose a Semantic Structure Integration model (SemSIn). It utilizes a GNN-based event aggregator to integrate the event-centric structure information, and employs an LSTM-based path aggregator to capture the event-associated structure information between two events. Experimental results on three widely used datasets show that SemSIn achieves significant improvements over baseline methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Modeling languages

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FIRE: combining multi-stage filtering with taint analysis for scalable recurring vulnerability detection 24

FIRE: combining multi-stage filtering with taint analysis fo...

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Proceedings of the 33rd USENIX Conference on Security Symposium

作者： Siyue Feng Yueming Wu Wenjie Xue Sikui Pan Deqing Zou Yang Liu 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 and School of Cyber Science and Engineering Huazhong University of Science and Technology China Nanyang Technological University Singapore 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 and School of Cyber Science and Engineering Huazhong University of Science and Technology China and Jinyinhu Laboratory 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 and School of Computer Science and Technology Huazhong University of Science and Technology China

ISBN: (纸本)9781939133441

With the continuous development of software open-sourcing, the reuse of open-source software has led to a significant increase in the occurrence of recurring vulnerabilities. These vulnerabilities often arise through the practice of copying and pasting existing vulnerabilities. Many methods have been proposed for detecting recurring vulnerabilities, but they often struggle to ensure both high efficiency and consideration of semantic information about vulnerabilities and patches. In this paper, we introduce FIRE, a scalable method for large-scale recurring vulnerability detection. It utilizes multi-stage filtering and differential taint paths to achieve precise clone vulnerability scanning at an extensive scale. In our evaluation across ten open-source software projects, FIRE demonstrates a precision of 90.0% in detecting 298 recurring vulnerabilities out of 385 ground truth instance. This surpasses the performance of existing advanced recurring vulnerability detection tools, detecting 31.4% more vulnerabilities than VUDDY and 47.0% more than MOVERY. When detecting vulnerabilities in large-scale software, FIRE outperforms MOVERY by saving about twice the time, enabling the scanning of recurring vulnerabilities on an ultra-large scale.

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An Efficient and Privacy-Preserving data Aggregation Scheme Supporting Arbitrary Statistical Functions in IoT

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China Communications 2022年第6期19卷 91-104页

作者： Haihui Liu Jianwei Chen Liwei Lin Ayong Ye Chuan Huang College of Computer and Cyber Security Fujian Normal UniversityFuzhou 350117China Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Normal UniversityFuzhou 350117China School of Computer Science and Mathematics Fujian University of TechnologyFuzhou 350118China Digital Fujian Institute of Big Data Security Technology Fujian Normal UniversityFuzhou 350117China

The Internet of Things(IoT)has profoundly impacted our lives and has greatly revolutionized our *** terminal devices in an IoT data aggregation application sense real-time data for the remote cloud server to achieve intelligent ***,the high frequency of collecting user data will raise people concerns about personal *** recent years,many privacy-preserving data aggregation schemes have been ***,most existing schemes cannot support either arbitrary aggregation functions,or dynamic user group management,or fault *** this paper,we propose an efficient and privacy-preserving data aggregation *** the scheme,we design a lightweight encryption method to protect the user privacy by using a ring topology and a random location *** this basis,the proposed scheme supports not only arbitrary aggregation functions,but also flexible dynamic user ***,the scheme achieves faulttolerant capabilities by utilizing a future data buffering *** analysis reveals that the scheme can achieve the desired security properties,and experimental evaluation results show the scheme's efficiency in terms of computational and communication overhead.

关键词： Internet of Things data aggregation privacy protection arbitrary aggregation functions

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Object Detection Algorithm Based on Bimodal Feature Alignment 7th

Object Detection Algorithm Based on Bimodal Feature Alignme...

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7th Asian Conference on Pattern Recognition, ACPR 2023

作者： Sun, Ying Hou, Zhiqiang Yang, Chen Ma, Sugang Fan, Jiulun School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an710121 China Key Laboratory of Network Data Analysis and Intelligent Processing of Shaanxi Province Xi’an710121 China School of Communications and Information Engineering Xi’an University of Posts and Telecommunications Xi’an710121 China

ISBN: (纸本)9783031476334

A dual-modal feature alignment based object detection algorithm is proposed for the full fusion of visible and infrared image features. First, we propose a two stream detection model. The algorithm supports simultaneous input of visible and infrared image pairs. Secondly, a gated fusion network is designed, consisting of a dual-modal feature alignment module and a feature fusion module. Medium-term fusion is used, which will be used as the middle layer of the dual-stream backbone network. In particular, the dual-mode feature alignment module extracts detailed information of the dual-mode aligned features by computing a multi-scale dual-mode aligned feature vector. The feature fusion module recalibrates the bimodal fused features and then multiplies them with the bimodal aligned features to achieve cross-modal fusion with joint enhancement of the lower and higher level features. We validate the performance of the proposed algorithm using both the publicly available KAIST pedestrian dataset and a self-built GIR dataset. On the KAIST dataset, the algorithm achieves an accuracy of 77.1%, which is 17.3% and 5.6% better than the accuracy of the benchmark algorithm YOLOv5-s for detecting visible and infrared images alone;on the self-built GIR dataset, the detection accuracy is 91%, which is 1.2% and 14.2% better than the benchmark algorithm for detecting visible and infrared images alone respectively. And the speed meets the real time requirements. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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E-MAEL: Efficient Multi-Cloud data Auditing With Error Localization for IoT Consumer Devices in Metaverse

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IEEE Transactions on Consumer Electronics 2025年

作者： Liu, Mingxi Fan, Kuan Lu, Ning Shi, Wenbo Alenazi, Mohammed J.F Chen, Chien-Ming Kumari, Saru Northeastern University at Qinhuangdao College of Computer Science and Engineering Northeastern University Hebei Key Laboratory of Marine Perception Network and Data Processing Shenyang China Hebei Key Laboratory of Marine Perception Network and Data Processing Qinhuangdao China Northeastern University College of Computer Science and Engineering Shenyang China Northeastern University at Qinhuangdao sHebei Key Laboratory of Marine Perception Network and Data Processing Qinhuangdao China Department of Computer Engineering Riyadh11451 Saudi Arabia Nanjing University of Information Science and Technology School of Artificial Intelligence Nanjing China Chaudhary Charan Singh University Department of Mathematics Uttar Pradesh Meerut India

By integrating multi-cloud (MC) storage with the "edge-to-cloud" architecture of consumer IoT, the metaverse paradigm achieves enhanced efficiency in real-time data collection and interactive virtual feedback. However, data stored across multiple clouds is vulnerable to tampering and theft due to the inherent insecurity of cloud computing, and cloud service providers, driven by economic interests, may conceal data losses. Existing data integrity auditing techniques fail to ensure prompt and reliable verification for distributed data in the MC-enabled metaverse paradigm, limiting their effectiveness in eliminating erroneous data swiftly. To address these challenges, we propose E-MAEL (Efficient Multi-cloud data Auditing with Error Localization) for IoT devices in the metaverse. We design an MC self-auditing framework based on blockchain to eliminate audit delays caused by third-party auditors. A variable-length auditing protocol based on Schnorr is introduced, reducing computation overhead by up to 21% compared to schemes relying on bilinear pairing and RSA assumptions. We develop a single-round error localization strategy based on symmetric unbalanced block design, enabling efficient identification and removal of erroneous data while resisting collusion attacks. A dynamic reputation update strategy using convex functions is proposed, providing robust reputation management for MCs and effectively penalizing audit errors. Theoretical security analyses and experimental results demonstrate the effectiveness of EMAEL in improving computational efficiency and enhancing reliability in MC auditing systems. © 1975-2011 IEEE.

关键词： Cloud storage

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A Unified Generative Retriever for Knowledge-Intensive Language Tasks via Prompt Learning

arXiv

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arXiv 2023年

作者： Chen, Jiangui Zhang, Ruqing Guo, Jiafeng de Rijke, Maarten Liu, Yiqun Fan, Yixing Cheng, Xueqi CAS Key Lab of Network Data Science and Technology ICT CAS University of Chinese Academy of Sciences Beijing China University of Amsterdam Amsterdam Netherlands Department of Computer Science and Technology Tsinghua University Beijing China

Knowledge-intensive language tasks (KILTs) benefit from retrieving high-quality relevant contexts from large external knowledge corpora. Learning task-specific retrievers that return relevant contexts at an appropriate level of semantic granularity, such as a document retriever, passage retriever, sentence retriever, and entity retriever, may help to achieve better performance on the end-to-end task. But a task-specific retriever usually has poor generalization ability to new domains and tasks, and it may be costly to deploy a variety of specialised retrievers in practice. We propose a unified generative retriever (UGR) that combines task-specific effectiveness with robust performance over different retrieval tasks in KILTs. To achieve this goal, we make two major contributions: (i) To unify different retrieval tasks into a single generative form, we introduce an n-gram-based identifier for relevant contexts at different levels of granularity in KILTs. And (ii) to address different retrieval tasks with a single model, we employ a prompt learning strategy and investigate three methods to design prompt tokens for each task. In this way, the proposed UGR model can not only share common knowledge across tasks for better generalization, but also perform different retrieval tasks effectively by distinguishing task-specific characteristics. We train UGR on a heterogeneous set of retrieval corpora with well-designed prompts in a supervised and multi-task fashion. Experimental results on the KILT benchmark demonstrate the effectiveness of UGR on in-domain datasets, out-of-domain datasets, and unseen tasks.1 Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Research on Domain Event Extraction Methods Based on Pre-Trained Model

Research on Domain Event Extraction Methods Based on Pre-Tra...

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2022 International Conference on Signal Processing, Computer networks, and Communications, SPCNC 2022

作者： Meng, Fanshen Zhang, Yonggang Liu, Xuhong Liu, Xiulei Yu, Junyang Institute of Data Science and Information Analysis Beijing Information Science & Technology University Beijing100029 China Center for Information Research Academy of Military Sciences Beijing100142 China Beijing Key Laboratory of Network Culture and Digital Communication Beijing Information Science & Technology University Beijing100029 China Beijing Advanced Innovation Center of Materials Genome Engineering Beijing Information Science & Technology University Beijing100029 China School of Software Henan University Henan 475001 China

ISBN: (纸本)9781510664616

One of the important research directions in information extraction is event extraction(EE). It aims at recognizing event types and event arguments from natural language texts, which is an important technical basis for artificial intelligence application that serves for the information work in business, science and technology, military and other fields. Currently, data annotation samples of the relevant field based on encyclopedia and news data are relatively rare and lack relevant datasets. Therefore, there are only a few public research on the event extraction in the relevant intelligence field. By integrating universal information extraction, the event extraction method which use the pre-trained model for the relevant intelligence field can handle the problem of rare data annotation samples for the event extraction in the relevant field. By expanding training samples automatically, the context information of encyclopedia and news data is learnt effectively to extract relevant events from encyclopedia and news data. Compared with other event extraction methods, using precision, recall and F1 value as assessment indicators, in event recognition tasks, the value of F1 enhances 1.26%, and in argument recognition tasks, the value of F1 enhances 1.58%. The method can significantly boost the extraction performance in small samples shown in the experimental results. © 2023 SPIE.

关键词： Information retrieval

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