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2023 International Conference on Computer Network Security and Software engineering, CNSSE 2023

作者： Zhang, Xinyuan Du, Dehui Software Engineering Institute East China Ormal University Shanghai200062 China Shanghai Key Laboratory of Trnstworthy Computing Shanghai200062 China Intemational Joint Laboratory of Trnstworthy Software of Ministry of Education Shanghai200062 China

ISBN: (数字)9781510666528

ISBN: (纸本)9781510666511

The safety of decision-making in autonomous driving systems (ADSs) is a challenging issue, which is very important for ADS development. As a highly acceptable decision method, Bayesian Net\vork (BN) has attracted more and more attention, therefore, its decision confidence and robustness have also become a focus. Since BN is a form of reasoning based on probability, for the decision results with low confidence (that is, the probability values of several optional decision actions have a small difference), when the data is slightly disturbed, it is likely to change, resulting in serious consequences such as car crash. To generate safe decision-making, we innovatively propose a rational Bayesian network decision-making approach based on BDI model. It helps to improve the decision confidence of the traditional B. BDI model is a well-known theory of inferencing agents' mental states (belief, desire and intention). We use it to guide the decision-making process of Bayesian network. According to the domain knowledge of ADS, we introduce and design rule-based intention inference for the decision agent to build a Bayesian net\vork with BDI-layer. And for other uncertain agents in the environment, we utilize LSTM model to predict their intentions and provide scenario information for the construction of the above network. To sum up, we propose a rational decision-making approach based on Bayesian network guided by BDI model. Our novel approach makes the traditional Bayesian network decision more humanized and improves the confidence of decision results. Finally, we take the lane-change and overtaking scenario as an example to illustrate our approach in detail and demonstrate the effectiveness in improving decision confidence. © 2023 SPIE.

关键词： Decision making

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Generalize Audio Deepfake Algorithm Recognition via Attribution Enhancement

Generalize Audio Deepfake Algorithm Recognition via Attribut...

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

作者： Zhigang Wang Dengpan Ye Jingyang Li Jiacheng Deng Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The development of voice cloning techniques has made forgery audios indistinguishable, posing an urgency to trace their sources. Many existing works focus on improving identification accuracy for audio deepfake algorithm recognition. However, most methods ignore the impact of complex information in audio signals on attribution. In this paper, we propose an audio deepfake attribution enhancement (ADAE) strategy, which aims to magnify the fingerprints of generation styles by removing the speaker information. This is achieved through an information disentangle block with an extra speaker encoder. In addition, we propose the FakeSource dataset, a novel audio deepfake algorithm recognition dataset that contains 25 different voice cloning algorithms, to address the constraint of data scarcity. Experiments on the FakeSource dataset demonstrate that ADAE improves the performance of unseen algorithm detection. We also assess ADAE on a more challenging training-free task which shows competitive performance.

关键词： Deepfakes Signal processing algorithms Cloning Speech recognition Fingerprint recognition Speech enhancement Signal processing Feature extraction Forgery Acoustics

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Auction Design for Value Maximizers with Budget and Return-on-Spend Constraints 19th

Auction Design for Value Maximizers with Budget and Retur...

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19th InternationalConference on Web and Internet Economics, WINE 2023

作者： Lu, Pinyan Xu, Chenyang Zhang, Ruilong Shanghai University of Finance and Economics Shanghai China Ministry of Education Shanghai China Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China Department of Computer Science and Engineering University at Buffalo Amherst United States

ISBN: (纸本)9783031489730

The paper designs revenue-maximizing auction mechanisms for agents who aim to maximize their total obtained values rather than the classical quasi-linear utilities. Several models have been proposed to capture the behaviors of such agents in the literature. In the paper, we consider the model where agents are subject to budget and return-on-spend constraints. The budget constraint of an agent limits the maximum payment she can afford, while the return-on-spend constraint means that the ratio of the total obtained value (return) to the total payment (spend) cannot be lower than the targeted bar set by the agent. The problem was first coined by [5]. In their work, only Bayesian mechanisms were considered. We initiate the study of the problem in the worst-case model and compare the revenue of our mechanisms to an offline optimal solution, the most ambitious benchmark. The paper distinguishes two main auction settings based on the accessibility of agents’ information: fully private and partially private. In the fully private setting, an agent’s valuation, budget, and target bar are all private. We show that if agents are unit-demand, constant approximation mechanisms can be obtained;while for additive agents, there exists a mechanism that achieves a constant approximation ratio under a large market assumption. The partially private setting is the setting considered in the previous work [5] where only the agents’ target bars are private. We show that in this setting, the approximation ratio of the single-item auction can be further improved, and a Ω(1/n) -approximation mechanism can be derived for additive agents. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Budget control

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Decision Mamba: Reinforcement Learning via Hybrid Selective Sequence Modeling 38

Decision Mamba: Reinforcement Learning via Hybrid Selective ...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Huang, Sili Hu, Jifeng Yang, Zhejian Yang, Liwei Luo, Tao Chen, Hechang Sun, Lichao Yang, Bo Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education China School of Artificial Intelligence Jilin University China Institute of High Performance Computing Agency for Science Technology and Research Singapore Lehigh University BethlehemPA United States

Recent works have shown the remarkable superiority of transformer models in reinforcement learning (RL), where the decision-making problem is formulated as sequential generation. Transformer-based agents could emerge with self-improvement in online environments by providing task contexts, such as multiple trajectories, called in-context RL. However, due to the quadratic computation complexity of attention in transformers, current in-context RL methods suffer from huge computational costs as the task horizon increases. In contrast, the Mamba model is renowned for its efficient ability to process long-term dependencies, which provides an opportunity for in-context RL to solve tasks that require long-term memory. To this end, we first implement Decision Mamba (DM) by replacing the backbone of Decision Transformer (DT). Then, we propose a Decision Mamba-Hybrid (DM-H) with the merits of transformers and Mamba in high-quality prediction and long-term memory. Specifically, DM-H first generates high-value sub-goals from long-term memory through the Mamba model. Then, we use sub-goals to prompt the transformer, establishing high-quality predictions. Experimental results demonstrate that DM-H achieves state-of-the-art in long and short-term tasks, such as D4RL, Grid World, and Tmaze benchmarks. Regarding efficiency, the online testing of DM-H in the long-term task is 28× times faster than the transformer-based baselines. © 2024 Neural information processing systems foundation. All rights reserved.

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From Voices to Beats: Enhancing Music Deepfake Detection by Identifying Forgeries in Background

From Voices to Beats: Enhancing Music Deepfake Detection by ...

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

作者： Zhaolin Wei Dengpan Ye Jiacheng Deng Yuhan Lin Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Music deepfake detection is aimed at identifying whether songs are generated by AI. Current methods usually separate vocals from background music for detection, but this could leave residual forgery information in the background. Our study demonstrates for the first time that incorporating background forgery information with vocals can improve detection accuracy. Furthermore, our findings show that using background features can reduce EER by an average of about 2% on existing frameworks. Based on this observation, we propose a novel Hybrid Frontend that captures generalized features from both vocal and background music. The Hybrid Frontend comprises two branches: vocal and background music part. Specifically, the vocal part uses the Sinconv encoder as a deeply embedded feature extractor. The latter captures background variation by fine-tuning the pre-trained model with adapters. Experimental results demonstrate that our method outperforms the vocal-only detection on WildSVDD dataset, achieving an EER of 8.53%, which is 1.3% lower.

关键词： Deepfakes Adaptation models Signal processing Feature extraction Forgery Acoustics Multiple signal classification Data mining Speech processing Artificial intelligence

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A Relay Buffer Prediction Method Based on LSTM in Terminal Cooperative Communication 23

A Relay Buffer Prediction Method Based on LSTM in Terminal C...

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23rd IEEE International Conference on Communication Technology, ICCT 2023

作者： Fei, Jie Zhi, Hui Tie, Kui Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei China School of Electronic and Information Engineering Anhui University Hefei China CO. Ltd Bengbu China

ISBN: (纸本)9798350325959

In terminal cooperative communication, relay assisted communication with buffer has significant advantages in improving system throughput, signal-to-noise ratio, and reducing system outage probability. The existing relay methods with buffer are all fixed buffer method with a fixed size of the buffer and only considering relay wholehearted cooperation and forwarding, that is, all buffer of the relay assists communication. However, when the forwarding task is small and there is a large idle in forwarding buffer, the buffer area cannot be fully utilized. When the forwarding task is large and forwarding buffer is busy, packet loss may occur in the forwarding task. In order to improve the usage efficiency of relay buffer and reduce packet loss, this paper proposes a relay buffer prediction method based on the long short-term memory network to predict the amount of data that the relay needs to forward. According to the prediction, the size of the relay buffer is divided, so that the buffer can be dynamically adjusted on demand. Simulation results show that, compared with the fixed buffer method, the relay buffer prediction method has a better buffer usage efficiency and a lower packet loss rate. © 2023 IEEE.

关键词： Buffer Forecast LSTM Relay

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CAB: Empathetic Dialogue Generation with Cognition, Affection and Behavior 28th

CAB: Empathetic Dialogue Generation with Cognition, Affecti...

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28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Gao, Pan Han, Donghong Zhou, Rui Zhang, Xuejiao Wang, Zikun School of Computer Science and Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University Shenyang China Swinburne University of Technology Hawthorn Australia

ISBN: (纸本)9783031306747

Empathy is an important characteristic to be considered when building a more intelligent and humanized dialogue agent. However, existing methods did not fully comprehend empathy as a complex process involving three aspects: cognition, affection and behavior. In this paper, we propose CAB, a novel framework that takes a comprehensive perspective of cognition, affection and behavior to generate empathetic responses. For cognition, we build paths between critical keywords in the dialogue by leveraging external knowledge. This is because keywords in a dialogue are the core of sentences. Building the logic relationship between keywords, which is overlooked by the majority of existing works, can improve the understanding of keywords and contextual logic, thus enhance the cognitive ability. For affection, we capture the emotional dependencies with dual latent variables that contain both interlocutors’ emotions. The reason is that considering both interlocutors’ emotions simultaneously helps to learn the emotional dependencies. For behavior, we use appropriate dialogue acts to guide the dialogue generation to enhance the empathy expression. Extensive experiments demonstrate that our multi-perspective model outperforms the state-of-the-art models. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Knowledge management

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Dual Space Representation Learning for Skeleton-based Action Recognition

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IEEE Signal Processing Letters 2025年 32卷 2104-2108页

作者： Yang, Yuheng Chen, Haipeng Liu, Zhenguang Hu, Sihao Jiao, Yingying Jilin University College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China Zhejiang University School of Cyber Science and Technology Hangzhou310007 China Georgia Institute of Technology College of Computing GA United States

Skeleton-based action recognition is crucial for machine intelligence. Current methods generally learn from 3D articulated motion sequences in the straightforward Euclidean space. Yet, the vanilla Euclidean space may not be the optimal choice for modeling the intricate correlations among human body joints. This challenge arises from the non-Euclidean nature of human anatomy, where joint correlations often vary non-linearly during movement. To address this, we propose a dual space representation learning method. Specifically, we represent the motion sequences in Hyperbolic space, leveraging its intrinsic properties to capture the non-Euclidean latent anatomy of human motions. We then incorporate the motion features from both Hyperbolic and Euclidean spaces, allowing us to precisely model the non-linear joint correlations while effectively sketching human poses. The proposed method empirically achieves state-of-the-art performance on the NTU RGB+D 60, NTURGB+D 120, and NW-UCLA datasets. © 1994-2012 IEEE.

关键词： Digital arithmetic

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Ant: An Efficient Lossless Compression Algorithm for IoT Time Series Data 21

Ant: An Efficient Lossless Compression Algorithm for IoT Tim...

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big Data and Cloud computing, 16th IEEE International Conference on Social computing and Networking and 13th International Conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Li, Junhui Xu, Guangping Yang, Hongzhang Wu, Yulei Tianjin University of Technology School of Computer Science and Engineering Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China Key Laboratory of Computer Vision and System Ministry of Education China University of Bristol Faculty of Science and Engineering Bristol United Kingdom

ISBN: (纸本)9798350329223

In various applications in Internet of Things like industrial monitoring, large amounts of floating-point time series data are generated at an unprecedented rate. Efficient compression algorithms can effectively reduce the size of data, enhance transmission performance and storage efficiency, and simultaneously lower storage costs. Therefore, there is a need for lightweight and efficient stream compression algorithms. In this paper, we propose a novel lossless floating-point data compression algorithm called Ant. The main idea is to encode double-precision floating-point numbers into integer form, calculate the delta between adjacent values, and then convert the delta into unsigned integers. This encoding method effectively reduces storage costs and improves data compression efficiency. Extensive experiments on real-world datasets demonstrate that our algorithm achieves compression speeds at least as fast as state-of-the-art streaming methods, and a 63% relative improvement in average compression rate. © 2023 IEEE.

关键词： Storage efficiency

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Dual-layer Broadband High-gain Circularly Polarized Patch Antenna with L-shaped Stub

Dual-layer Broadband High-gain Circularly Polarized Patch An...

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2023 IEEE International Workshop on Electromagnetics: Applications and Student Innovation Competition, iWEM 2023

作者： Li, Wen Xue, Wei Li, Yingsong Loh, Tian Hong Chung, Kwok L. Harbin Engineering University College of Information and Communication Engineering Harbin150001 China Anhui University The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei China National Physical Laboratory Electromagnetic and Eletrochemical Technologies Department Middlesex United Kingdom Huizhou University School of Computing Science and Engineering Huizhou516007 China

ISBN: (纸本)9798350336740

This paper introduced a dual-layer broadband high-gain circularly polarized (CP) antenna. The proposed broadband CP antenna consists of the radiating patch and couping structure, which is made up of opening slot patch and L-shaped stub. An axial-ratio bandwidth of 26 % (4.36-5.66 GHz) is shown by this proposed dual-layer CP patch antenna with a simulated -10-dB reflection coefficient bandwidth of 37 % (4.23-6.15 GHz). The broadband characteristics offered by this CP antenna are promising for a wide range of future applications. © 2023 IEEE.

关键词： Microstrip antennas

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