International Joint Conference on Artificial Intelligence最新文献_第2页

Moral Planning Agents with LTL Values 具有LTL价值观的道德规划主体

International Joint Conference on Artificial Intelligence Pub Date : 2023-08-01 DOI: 10.24963/ijcai.2023/47

Umberto Grandi, E. Lorini, Timothy Parker

引用次数: 0

DFVSR: Directional Frequency Video Super-Resolution via Asymmetric and Enhancement Alignment Network 基于非对称和增强对准网络的定向频率视频超分辨率

International Joint Conference on Artificial Intelligence Pub Date : 2023-08-01 DOI: 10.24963/ijcai.2023/76

Shuting Dong, Feng Lu, Zhe Wu, Chun Yuan

{"title":"DFVSR: Directional Frequency Video Super-Resolution via Asymmetric and Enhancement Alignment Network","authors":"Shuting Dong, Feng Lu, Zhe Wu, Chun Yuan","doi":"10.24963/ijcai.2023/76","DOIUrl":"https://doi.org/10.24963/ijcai.2023/76","url":null,"abstract":"Recently, techniques utilizing frequency-based methods have gained significant attention, as they exhibit exceptional restoration capabilities for detail and structure in video super-resolution tasks. However, most of these frequency-based methods mainly have three major limitations: 1) insufficient exploration of object motion information, 2) inadequate enhancement for high-fidelity regions, and 3) loss of spatial information during convolution. In this paper, we propose a novel network, Directional Frequency Video Super-Resolution (DFVSR), to address these limitations. Specifically, we reconsider object motion from a new perspective and propose Directional Frequency Representation (DFR), which not only borrows the property of frequency representation of detail and structure information but also contains the direction information of the object motion that is extremely significant in videos. Based on this representation, we propose a Directional Frequency-Enhanced Alignment (DFEA) to use double enhancements of task-related information for ensuring the retention of high-fidelity frequency regions to generate the high-quality alignment feature. Furthermore, we design a novel Asymmetrical U-shaped network architecture to progressively fuse these alignment features and output the final output. This architecture enables the intercommunication of the same level of resolution in the encoder and decoder to achieve the supplement of spatial information. Powered by the above designs, our method achieves superior performance over state-of-the-art models on both quantitative and qualitative evaluations.","PeriodicalId":394530,"journal":{"name":"International Joint Conference on Artificial Intelligence","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115249546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Action Space Reduction for Planning Domains 规划域的行动空间缩减

International Joint Conference on Artificial Intelligence Pub Date : 2023-08-01 DOI: 10.24963/ijcai.2023/599

Harsha Kokel, Junkyu Lee, Michael Katz, Shirin Sohrabi, Kavitha Srinivas

引用次数: 0

Towards Semantics- and Domain-Aware Adversarial Attacks 面向语义和领域感知的对抗性攻击

International Joint Conference on Artificial Intelligence Pub Date : 2023-08-01 DOI: 10.24963/ijcai.2023/60

Jianping Zhang, Yung-Chieh Huang, Weibin Wu, Michael R. Lyu

{"title":"Towards Semantics- and Domain-Aware Adversarial Attacks","authors":"Jianping Zhang, Yung-Chieh Huang, Weibin Wu, Michael R. Lyu","doi":"10.24963/ijcai.2023/60","DOIUrl":"https://doi.org/10.24963/ijcai.2023/60","url":null,"abstract":"Language models are known to be vulnerable to textual adversarial attacks, which add human-imperceptible perturbations to the input to mislead DNNs. It is thus imperative to devise effective attack algorithms to identify the deficiencies of DNNs before real-world deployment. However, existing word-level attacks have two major deficiencies: (1) They may change the semantics of the original sentence. (2) The generated adversarial sample can appear unnatural to humans due to the introduction of out-of-domain substitute words. In this paper, to address such drawbacks, we propose a semantics- and domain-aware word-level attack method. Specifically, we greedily replace the important words in a sentence with the ones suggested by a language model. The language model is trained to be semantics- and domain-aware via contrastive learning and in-domain pre-training. Furthermore, to balance the quality of adversarial examples and the attack success rate, we propose an iterative updating framework to optimize the contrastive learning loss and the in-domain pre-training loss in circular order. Comprehensive experimental comparisons confirm the superiority of our approach. Notably, compared with state-of-the-art benchmarks, our strategy can achieve over 3% improvement in attack success rates and 9.8% improvement in the quality of adversarial examples.","PeriodicalId":394530,"journal":{"name":"International Joint Conference on Artificial Intelligence","volume":"271 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116422536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unsupervised and Few-Shot Parsing from Pretrained Language Models (Extended Abstract) 基于预训练语言模型的无监督少镜头解析(扩展摘要)

International Joint Conference on Artificial Intelligence Pub Date : 2023-08-01 DOI: 10.24963/ijcai.2023/797

Zhiyuan Zeng, Deyi Xiong

引用次数: 0

Acoustic NLOS Imaging with Cross Modal Knowledge Distillation 基于交叉模态知识蒸馏的声学NLOS成像

International Joint Conference on Artificial Intelligence Pub Date : 2023-08-01 DOI: 10.24963/ijcai.2023/156

Ui-Hyeon Shin, Seungwoo Jang, Kwangsu Kim

引用次数: 0

Game-theoretic Mechanisms for Eliciting Accurate Information 获取准确信息的博弈论机制

International Joint Conference on Artificial Intelligence Pub Date : 2023-08-01 DOI: 10.24963/ijcai.2023/740

B. Faltings

引用次数: 1

Boosting Decision-Based Black-Box Adversarial Attack with Gradient Priors 基于梯度先验增强决策的黑盒对抗攻击

International Joint Conference on Artificial Intelligence Pub Date : 2023-08-01 DOI: 10.24963/ijcai.2023/133

Han Liu, Xingshuo Huang, Xiaotong Zhang, Qimai Li, Fenglong Ma, Wen Wang, Hongyang Chen, Hong Yu, Xianchao Zhang

{"title":"Boosting Decision-Based Black-Box Adversarial Attack with Gradient Priors","authors":"Han Liu, Xingshuo Huang, Xiaotong Zhang, Qimai Li, Fenglong Ma, Wen Wang, Hongyang Chen, Hong Yu, Xianchao Zhang","doi":"10.24963/ijcai.2023/133","DOIUrl":"https://doi.org/10.24963/ijcai.2023/133","url":null,"abstract":"Decision-based methods have shown to be effective in black-box adversarial attacks, as they can obtain satisfactory performance and only require to access the final model prediction. Gradient estimation is a critical step in black-box adversarial attacks, as it will directly affect the query efficiency. Recent works have attempted to utilize gradient priors to facilitate score-based methods to obtain better results. However, these gradient priors still suffer from the edge gradient discrepancy issue and the successive iteration gradient direction issue, thus are difficult to simply extend to decision-based methods. In this paper, we propose a novel Decision-based Black-box Attack framework with Gradient Priors (DBA-GP), which seamlessly integrates the data-dependent gradient prior and time-dependent prior into the gradient estimation procedure. First, by leveraging the joint bilateral filter to deal with each random perturbation, DBA-GP can guarantee that the generated perturbations in edge locations are hardly smoothed, i.e., alleviating the edge gradient discrepancy, thus remaining the characteristics of the original image as much as possible. Second, by utilizing a new gradient updating strategy to automatically adjust the successive iteration gradient direction, DBA-GP can accelerate the convergence speed, thus improving the query efficiency. Extensive experiments have demonstrated that the proposed method outperforms other strong baselines significantly.","PeriodicalId":394530,"journal":{"name":"International Joint Conference on Artificial Intelligence","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122090117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Customized Positional Encoding to Combine Static and Time-varying Data in Robust Representation Learning for Crop Yield Prediction 结合静态和时变数据的自定义位置编码鲁棒表示学习用于作物产量预测

International Joint Conference on Artificial Intelligence Pub Date : 2023-08-01 DOI: 10.24963/ijcai.2023/676

Qinqing Liu, Fei Dou, Meijian Yang, Ezana Amdework, Guiling Wang, J. Bi

{"title":"Customized Positional Encoding to Combine Static and Time-varying Data in Robust Representation Learning for Crop Yield Prediction","authors":"Qinqing Liu, Fei Dou, Meijian Yang, Ezana Amdework, Guiling Wang, J. Bi","doi":"10.24963/ijcai.2023/676","DOIUrl":"https://doi.org/10.24963/ijcai.2023/676","url":null,"abstract":"Accurate prediction of crop yield under the conditions of climate change is crucial to ensure food security. Transformers have shown remarkable success in modeling sequential data and hold the potential for improving crop yield prediction. To understand how weather and meteorological sequence variables affect crop yield, the positional encoding used in Transformers is typically shared across different sample sequences. We argue that it is necessary and beneficial to differentiate the positional encoding for distinct samples based on time-invariant properties of the sequences. Particularly, the sequence variables influencing crop yield vary according to static variables such as geographical locations. Sample data from southern areas may benefit from more tailored positional encoding different from that for northern areas. We propose a novel transformer based architecture for accurate and robust crop yield prediction, by introducing a Customized Positional Encoding (CPE) that encodes a sequence adaptively according to static information associated with the sequence. Empirical studies demonstrate the effectiveness of the proposed novel architecture and show that partially lin-\u0000\u0000earized attention better captures the bias introduced by side information than softmax re-weighting. The resultant crop yield prediction model is robust to climate change, with mean-absolute-error reduced by up to 26% compared to the best baseline model in extreme drought years.","PeriodicalId":394530,"journal":{"name":"International Joint Conference on Artificial Intelligence","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122106970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adversarial Behavior Exclusion for Safe Reinforcement Learning 安全强化学习的对抗行为排斥

International Joint Conference on Artificial Intelligence Pub Date : 2023-08-01 DOI: 10.24963/ijcai.2023/54

Md Asifur Rahman, Tongtong Liu, Sarra M. Alqahtani

{"title":"Adversarial Behavior Exclusion for Safe Reinforcement Learning","authors":"Md Asifur Rahman, Tongtong Liu, Sarra M. Alqahtani","doi":"10.24963/ijcai.2023/54","DOIUrl":"https://doi.org/10.24963/ijcai.2023/54","url":null,"abstract":"Learning by exploration makes reinforcement learning (RL) potentially attractive for many real-world applications. However, this learning process makes RL inherently too vulnerable to be used in real-world applications where safety is of utmost importance. Most prior studies consider exploration at odds with safety and thereby restrict it using either joint optimization of task and safety or imposing constraints for safe exploration. This paper migrates from the current convention to using exploration as a key to safety by learning safety as a robust behavior that completely excludes any behavioral pattern responsible for safety violations. Adversarial Behavior Exclusion for Safe RL (AdvEx-RL) learns a behavioral representation of the agent's safety violations by approximating an optimal adversary utilizing exploration and later uses this representation to learn a separate safety policy that excludes those unsafe behaviors. In addition, AdvEx-RL ensures safety in a task-agnostic manner by acting as a safety firewall and therefore can be integrated with any RL task policy. We demonstrate the robustness of AdvEx-RL via comprehensive experiments in standard constrained Markov decision processes (CMDP) environments under 2 white-box action space perturbations as well as with changes in environment dynamics against 7 baselines. Consistently, AdvEx-RL outperforms the baselines by achieving an average safety performance of over 75% in the continuous action space with 10 times more variations in the testing environment dynamics. By using a standalone safety policy independent of conflicting objectives, AdvEx-RL also paves the way for interpretable safety behavior analysis as we show in our user study.","PeriodicalId":394530,"journal":{"name":"International Joint Conference on Artificial Intelligence","volume":"267 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122176768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0