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arXiv 2024年

作者： Lin, Kun-Yu Ding, Henghui Zhou, Jiaming Tang, Yu-Ming Peng, Yi-Xing Zhao, Zhilin Loy, Chen Change Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China S-Lab Nanyang Technological University Singapore Institute of Big Data Fudan University China AI Thrust Hong Kong University of Science and Technology Guangzhou China Data Science Lab School of Computing & DataX Research Centre Macquarie University Australia

Building upon the impressive success of CLIP (Contrastive Language-Image Pretraining), recent pioneer works have proposed to adapt the powerful CLIP to video data, leading to efficient and effective video learners for open-vocabulary action recognition. Inspired by that humans perform actions in diverse environments, our work delves into an intriguing question: Can CLIP-based video learners effectively generalize to video domains they have not encountered during training? To answer this, we establish a CROSS-domain Open-Vocabulary Action recognition benchmark named XOV-Action, and conduct a comprehensive evaluation of five state-of-the-art CLIP-based video learners under various types of domain gaps. The evaluation demonstrates that previous methods exhibit limited action recognition performance in unseen video domains, revealing potential challenges of the cross-domain open-vocabulary action recognition task. In this paper, we focus on one critical challenge of the task, namely scene bias, and accordingly contribute a novel scene-aware video-text alignment method. Our key idea is to distinguish video representations apart from scene-encoded text representations, aiming to learn scene-agnostic video representations for recognizing actions across domains. Extensive experiments demonstrate the effectiveness of our method. The benchmark and code will be available at https://***/KunyuLin/XOV-Action/. Copyright © 2024, The Authors. All rights reserved.

关键词： Video recording

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A Novel Effectiveness Assessment Framework for Neoadjuvant Chemoradiotherapy of Locally Advanced Rectal Cancer Based on Multi-modal Intelligence

A Novel Effectiveness Assessment Framework for Neoadjuvant C...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiao Tian Dong Sui Weifeng Liu Maozu Guo Gongning Luo Kuanquan Wang Beijing Key Laboratory for Intelligent Processing Methods of Architectural Big Data Beijing China School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing China Perceptual Computing Research Center Harbin Institute of Technology Harbin China

ISBN: (数字)9798350337488

ISBN: (纸本)9798350337495

Neoadjuvant chemoradiotherapy (nCRT) is the stan-dard treatment for locally advanced rectal cancer (LARC). With the development of artificial intelligence, an increasing number of studies have begun to explore its application in cancer treatment prediction. However, the prior methods exhibit considerable variability even with slight modifications to the input data, which could potentially undermine the reliability of the results. In this paper, we proposed RP-Net, a novel multi-modal fusion-based framework that combines feature information from magnetic resonance imaging (MRI) and whole slide images (WSI), establishing a relationship to map the therapeutic effectiveness of nCRT for LARC. We investigated the relationship of the tumour region and its periphery tissues, and demonstrated the validity of the proposed framework that involving 11 different combinations of modalities. The experimental results revealed that it has achieved higher prediction accuracy compared to the four intra-categories single-modal combinations and outperformed the two intra-categories multi-modal combinations. When compared to the other four inter-categories multi-modal combinations, the fusion features get accuracy of 2 % ~ 6% improvement respectively.

关键词： Protocols Magnetic resonance imaging Decision making Cancer treatment Reliability Bioinformatics Artificial intelligence

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MFCLIP: Multi-modal Fine-grained CLIP for Generalizable Diffusion Face Forgery Detection

arXiv

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arXiv 2024年

作者： Zhang, Yaning Wang, Tianyi Yu, Zitong Gao, Zan Shen, Linlin Chen, Shengyong Jinan250353 China Nanyang Technological University 50 Nanyang Ave Block N 4 639798 Singapore School of Computing and Information Technology Great Bay University Dongguan523000 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Jinan250014 China The Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin300384 China Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518129 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China

The rapid development of photo-realistic face generation methods has raised significant concerns in society and academia, highlighting the urgent need for robust and generalizable face forgery detection (FFD) techniques. Although existing approaches mainly capture face forgery patterns using image modality, other modalities like fine-grained noises and texts are not fully explored, which limits the generalization capability of the model. In addition, most FFD methods tend to identify facial images generated by GAN, but struggle to detect unseen diffusion-synthesized ones. To address the limitations, we aim to leverage the cutting-edge foundation model, contrastive language-image pre-training (CLIP), to achieve generalizable diffusion face forgery detection (DFFD). In this paper, we propose a novel multi-modal fine-grained CLIP (MFCLIP) model, which mines comprehensive and fine-grained forgery traces across image-noise modalities via language-guided face forgery representation learning, to facilitate the advancement of DFFD. Specifically, we devise a fine-grained language encoder (FLE) that extracts fine global language features from hierarchical text prompts. We design a multi-modal vision encoder (MVE) to capture global image forgery embeddings as well as fine-grained noise forgery patterns extracted from the richest patch, and integrate them to mine general visual forgery traces. Moreover, we build an innovative plug-and-play sample pair attention (SPA) method to emphasize relevant negative pairs and suppress irrelevant ones, allowing cross-modality sample pairs to conduct more flexible alignment. Extensive experiments and visualizations show that our model outperforms the state of the arts on different settings like cross-generator, cross-forgery, and cross-dataset evaluations. Copyright © 2024, The Authors. All rights reserved.

关键词： Alignment

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Generative AI Enabled Matching for 6G Multiple Access

arXiv

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arXiv 2024年

作者： Wang, Xudong Du, Hongyang Niyato, Dusit Zhou, Lijie Feng, Lei Yang, Zhixiang Zhou, Fanqin Li, Wenjing The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical and Electronic Engineering University of Hong Kong Pok Fu Lam Hong Kong The College of Computing and Data Science Nanyang Technological University Singapore

In wireless networks, applying deep learning models to solve matching problems between different entities has become a mainstream and effective approach. However, the complex network topology in 6G multiple access presents significant challenges for the real-time performance and stability of matching generation. Generative artificial intelligence (GenAI) has demonstrated strong capabilities in graph feature extraction, exploration, and generation, offering potential for graph-structured matching generation. In this paper, we propose a GenAI-enabled matching generation framework to support 6G multiple access. Specifically, we first summarize the classical matching theory, discuss common GenAI models and applications from the perspective of matching generation. Then, we propose a framework based on generative diffusion models (GDMs) that iteratively denoises toward reward maximization to generate a matching strategy that meets specific requirements. Experimental results show that, compared to decision-based AI approaches, our framework can generate more effective matching strategies based on given conditions and predefined rewards, helping to solve complex problems in 6G multiple access, such as task allocation. © 2024, CC BY.

关键词： Generative adversarial networks

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Location and Lightpath Selection in Multi-Domain Optical data Center Networks

Location and Lightpath Selection in Multi-Domain Optical Dat...

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Mobile Internet, Cloud computing and Information Security (MICCIS), International Conference on

作者： Changle Xu Xu Zhang Chuan Feng Xiaoxue Gong School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China Institute of Intelligent Communication and Network Security Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350389906

ISBN: (纸本)9798350389913

In the era of booming fifth-generation fixed networks (F5G), the service demands for high bandwidth continue to grow. Meanwhile, in multi-domain network scenarios, service requests are vulnerable to physical layer attacks initiated by malicious clients. In this paper, a risk-aware heuristic algorithm is proposed to address the problem of lightpath provisioning in multi-domain elastic optical data center networks (EODCNs). Simulation results show that the blocking probability of the proposed algorithm is lower than the comparison algorithm, 8.2% and 8.8% lower in NSFNET topology and US-Backbone topology, respectively.

关键词： Integrated optics Multiplexing data centers Network topology Optical design Simulation Optical fiber networks

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Hyperbolic Neural Network Based Preselection for Expensive Multi-Objective Optimization

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Li, Bingdong Yang, Yanting Hong, Wenjing Yang, Peng Zhou, Aimin Shanghai Institute of AI for Education the School of Computer Science and Technology and the Shanghai Frontiers Science Center of Molecule Intelligent Syntheses East China Normal University Shanghai China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science and Engineering Guangdong Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology Shenzhen China

A series of surrogate-assisted evolutionary algorithms (SAEAs) have been proposed for expensive multi-objective optimization problems (EMOPs), building cheap surrogate models to replace the expensive real function evaluations. However, the search efficiency of these SAEAs is not yet satisfactory. More efforts are needed to further exploit useful information from the real function evaluations in order to better guide the search process. Facing this challenge, this paper proposes a Hyperbolic Neural Network (HNN) based preselection operator to accelerate the optimization process based on limited evaluated solutions. First, the preselection task is modeled as a multi-label classification problem where solutions are classified into different layers (ordinal categories) through -relaxed objective aggregation. Second, in order to resemble the hierarchical structure of candidate solutions, a hyperbolic neural network is applied to tackle the multi-label classification problem. The reason for using HNN is that hyperbolic spaces more closely resemble hierarchical structures than Euclidean spaces. Moreover, to alleviate the data deficiency issue, a data augmentation strategy is employed for training the HNN. In order to evaluate its performance, the proposed HNN-based preselection operator is embedded into two surrogate-assisted evolutionary algorithms. Experimental results on two benchmark test suites and three real-world problems with up to 11 objectives and 150 decision variables involving seven state-of-the-art algorithms demonstrate the effectiveness of the proposed method. IEEE

关键词： Multiobjective optimization

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Segment Routing based In-Band Network Telemetry in IPv6 over Optical Networks

Segment Routing based In-Band Network Telemetry in IPv6 over...

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Mobile Internet, Cloud computing and Information Security (MICCIS), International Conference on

作者： Wei Liu Xu Zhang Chuan Feng Xiaoxue Gong School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China Institute of Intelligent Communication and Network Security Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350389906

ISBN: (纸本)9798350389913

With the rise of automated network operations, network telemetry has become increasingly important. Optical networks play a key role in modern communication systems because of their high bandwidth, low latency, and low loss. To better manage and monitor Internet Protocol version 6 (IPv6) over optical networks, programmable data planes (PDP) are used to achieve flexible data collection. Meanwhile, to solve the scalability problem of the control plane in the software-defined network (SDN), segment routing over IPv6 (SRv6) is introduced. By programming the source node on the path, SRv6 wisely instructs packets to collect the appropriate telemetry data at the segment nodes to balance the trade-off between bandwidth usage and monitoring coverage. To efficiently achieve the observability of IPv6 over optical networks, we model the system and propose an SRv6-based in-band network telemetry (INT) algorithm. Simulation results verify the performance of our algorithm.

关键词： Protocols Simulation Bandwidth Optical fiber networks Routing Universal Serial Bus Topology

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A Survivable Routing Algorithm with Hybrid Protection in Elastic Optical Networks

A Survivable Routing Algorithm with Hybrid Protection in Ela...

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Mobile Internet, Cloud computing and Information Security (MICCIS), International Conference on

作者： Hankun Zeng Xu Zhang Chuan Feng Xiaoxue Gong School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China Institute of Intelligent Communication and Network Security Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350389906

ISBN: (纸本)9798350389913

Spectral efficiency is a crucial factor in the survivability of multi-class traffic in elastic optical networks (EONs). In this paper, we propose a spectrum-efficient hybrid protection scheme, namely dedicated path protection (DPP) and shared backup path protection (SBPP), to address the survivability issue in EONs. Meanwhile, we design a survivable routing, modulation level, and spectrum assignment (RMLSA) algorithm to achieve hybrid protection in EONs. The simulation results demonstrate that the proposed algorithm has a lower blocking rate and Spectrum occupation ratio.

关键词： Time-frequency analysis Spectral efficiency Simulation Modulation Information security Optical fiber networks Routing

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Ppt4j: Patch Presence Test for Java Binaries

Ppt4j: Patch Presence Test for Java Binaries

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International Conference on Software engineering (ICSE)

作者： Zhiyuan Pan Xing Hu Xin Xia Xian Zhan David Lo Xiaohu Yang The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Software Engineering Application Technology Lab Huawei Hangzhou China The Hong Kong Polytechnic University Hong Kong China School of Computing and Information Systems Singapore Management University Singapore

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

The number of vulnerabilities reported in open source software has increased substantially in recent years. Security patches provide the necessary measures to protect software from attacks and vulnerabilities. In practice, it is difficult to identify whether patches have been integrated into software, especially if we only have binary files. Therefore, the ability to test whether a patch is applied to the target binary, a.k.a. patch presence test, is crucial for practitioners. However, it is challenging to obtain accurate semantic information from patches, which could lead to incorrect results. In this paper, we propose a new patch presence test framework named Ppt4j (Patch Presence Test forJava Binaries). Ppt4j is designed for open-source Java libraries. It takes Java binaries (i.e. bytecode files) as input, extracts semantic information from patches, and uses feature-based techniques to identify patch lines in the binaries. To evaluate the effectiveness of our proposed approach Ppt4j, we construct a dataset with binaries that include 110 vulnerabilities. The results show that Ppt4j achieves an F1 score of 98.5% with reasonable efficiency, improving the baseline by 14.2%. Furthermore, we conduct an in-the-wild evaluation of Ppt4j on JetBrains IntelliJ IDEA. The results suggest that a third-party library included in the software is not patched for two CVEs, and we have reported this potential security problem to the vendor.

关键词： Java Semantics Feature extraction Libraries Security Software measurement data mining

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Semantic Pre-Extraction for Energy-Efficient AoI Minimization in UAV-Assisted Wireless Networks

Semantic Pre-Extraction for Energy-Efficient AoI Minimizatio...

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IEEE Conference on Wireless Communications and Networking

作者： Yusi Long Gary C.F. Lee Lanhua Li Shimin Gong Sumei Sun Dusit Niyato School of Intelligent Systems Engineering Shenzhen Campus of Sun Yat-sen University China Guangdong Provincial Key Laboratory of Fire Science and Intelligent Emergency Technology China Institute for Infocomm Research (IR) A*STAR Singapore College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

This paper investigates an unmanned aerial vehicle (UAV)-assisted semantic communication network. The energy-limited ground users (GUs) provide semantic services to periodically generated raw data and a UAV relays the extracted semantic information to a base station (BS). Semantic extraction enhances data responsiveness and reduces the age-of-information (AoI) by transmitting only the most essential information. However, more complex semantic extraction increases energy consumption, making it easier for the GUs to deplete their energy. Therefore, we introduce a novel energy-efficient AoI (EAoI) metric to capture both information freshness and energy consumption of the GUs. We formulate a time-averaged EAoI minimization problem by jointly optimizing the GUs' scheduling, pre-extraction strategy, semantic control, computing resource allocation, and the UAV's trajectory. We further propose a semantic-aware joint pre-extraction and trajectory planning (Sem-JPT) algorithm to decompose the complex optimization problem into three subproblems, which are solved by a series of approximation methods. Simulation results demonstrate that semantic communication can reduce the overall EAoI by more than 18% compared with conventional bit-based communication. Moreover, the proposed Sem-JPT algorithm can maintain information freshness and prolong the GUs' lifetimes, outperforming existing baselines.

关键词： Energy consumption Trajectory planning Processor scheduling Wireless networks Semantic communication Minimization Information age Energy efficiency Trajectory data mining

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