Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence最新文献_第6页

Electrophysiological Brain Source Imaging via Combinatorial Search with Provable Optimality 基于可证明最优性组合搜索的脑电生理源成像

Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence Pub Date : 2023-06-26 DOI: 10.1609/aaai.v37i10.26471

Guihong Wan, Meng Jiao, Xinglong Ju, Yu Zhang, H. Schweitzer, Feng Liu

{"title":"Electrophysiological Brain Source Imaging via Combinatorial Search with Provable Optimality","authors":"Guihong Wan, Meng Jiao, Xinglong Ju, Yu Zhang, H. Schweitzer, Feng Liu","doi":"10.1609/aaai.v37i10.26471","DOIUrl":"https://doi.org/10.1609/aaai.v37i10.26471","url":null,"abstract":"Electrophysiological Source Imaging (ESI) refers to reconstructing the underlying brain source activation from non-invasive Electroencephalography (EEG) and Magnetoencephalography (MEG) measurements on the scalp. Estimating the source locations and their extents is a fundamental tool in clinical and neuroscience applications. However, the estimation is challenging because of the ill-posedness and high coherence in the leadfield matrix as well as the noise in the EEG/MEG data. In this work, we proposed a combinatorial search framework to address the ESI problem with a provable optimality guarantee. Specifically, by exploiting the graph neighborhood information in the brain source space, we converted the ESI problem into a graph search problem and designed a combinatorial search algorithm under the framework of A* to solve it. The proposed algorithm is guaranteed to give an optimal solution to the ESI problem. Experimental results on both synthetic data and real epilepsy EEG data demonstrated that the proposed algorithm could faithfully reconstruct the source activation in the brain.","PeriodicalId":74506,"journal":{"name":"Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence","volume":"62 1","pages":"12491-12499"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78339379","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

Generating Transferable 3D Adversarial Point Cloud via Random Perturbation Factorization 随机扰动分解生成可转移的三维对抗点云

Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence Pub Date : 2023-06-26 DOI: 10.1609/aaai.v37i1.25154

Bangyan He, J. Liu, Yiming Li, Siyuan Liang, Jingzhi Li, Xiaojun Jia, Xiaochun Cao

{"title":"Generating Transferable 3D Adversarial Point Cloud via Random Perturbation Factorization","authors":"Bangyan He, J. Liu, Yiming Li, Siyuan Liang, Jingzhi Li, Xiaojun Jia, Xiaochun Cao","doi":"10.1609/aaai.v37i1.25154","DOIUrl":"https://doi.org/10.1609/aaai.v37i1.25154","url":null,"abstract":"Recent studies have demonstrated that existing deep neural networks (DNNs) on 3D point clouds are vulnerable to adversarial examples, especially under the white-box settings where the adversaries have access to model parameters. However, adversarial 3D point clouds generated by existing white-box methods have limited transferability across different DNN architectures. They have only minor threats in real-world scenarios under the black-box settings where the adversaries can only query the deployed victim model. In this paper, we revisit the transferability of adversarial 3D point clouds. We observe that an adversarial perturbation can be randomly factorized into two sub-perturbations, which are also likely to be adversarial perturbations. It motivates us to consider the effects of the perturbation and its sub-perturbations simultaneously to increase the transferability for sub-perturbations also contain helpful information. In this paper, we propose a simple yet effective attack method to generate more transferable adversarial 3D point clouds. Specifically, rather than simply optimizing the loss of perturbation alone, we combine it with its random factorization. We conduct experiments on benchmark dataset, verifying our method's effectiveness in increasing transferability while preserving high efficiency.","PeriodicalId":74506,"journal":{"name":"Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence","volume":"2 1","pages":"764-772"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78477112","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: 6

Ultrafast Euclidean Shortest Path Computation Using Hub Labeling 基于轮毂标记的超快速欧氏最短路径计算

Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence Pub Date : 2023-06-26 DOI: 10.1609/aaai.v37i10.26463

Jinchun Du, Bojie Shen, M. A. Cheema

引用次数: 2

Structurally Restricted Fragments of Numeric Planning - a Complexity Analysis 数字规划的结构限制片段-复杂性分析

Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence Pub Date : 2023-06-26 DOI: 10.1609/aaai.v37i10.26428

Alexander Shleyfman, Daniel Gnad, P. Jonsson

{"title":"Structurally Restricted Fragments of Numeric Planning - a Complexity Analysis","authors":"Alexander Shleyfman, Daniel Gnad, P. Jonsson","doi":"10.1609/aaai.v37i10.26428","DOIUrl":"https://doi.org/10.1609/aaai.v37i10.26428","url":null,"abstract":"Numeric planning is known to be undecidable even under severe restrictions. Prior work has investigated the decidability boundaries by restricting the expressiveness of the planning formalism in terms of the numeric functions allowed in conditions and effects. We study a well-known restricted form of Hoffmann's simple numeric planning, which is undecidable. We analyze the complexity by imposing restrictions on the causal structure, exploiting a novel method for bounding variable domain sizes. First, we show that plan existence for tasks where all numeric variables are root nodes in the causal graph is in PSPACE.\u0000Second, we show that for tasks with only numeric leaf variables the problem is decidable, and that it is in PSPACE if the propositional state space has a fixed size. Our work lays a strong foundation for future investigations of structurally more complex tasks. From a practical perspective, our method allows to employ heuristics and methods that are geared towards finite variable domains (such as pattern database heuristics or decoupled search) to solve non-trivial families of numeric planning problems.","PeriodicalId":74506,"journal":{"name":"Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence","volume":"163 1","pages":"12112-12119"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76634971","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

Self-Supervised Logic Induction for Explainable Fuzzy Temporal Commonsense Reasoning 可解释模糊时间常识推理的自监督逻辑归纳

Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence Pub Date : 2023-06-26 DOI: 10.1609/aaai.v37i11.26481

Bibo Cai, Xiao Ding, Zhouhao Sun, Bing Qin, Ting Liu, Baojun Wang, Lifeng Shang

{"title":"Self-Supervised Logic Induction for Explainable Fuzzy Temporal Commonsense Reasoning","authors":"Bibo Cai, Xiao Ding, Zhouhao Sun, Bing Qin, Ting Liu, Baojun Wang, Lifeng Shang","doi":"10.1609/aaai.v37i11.26481","DOIUrl":"https://doi.org/10.1609/aaai.v37i11.26481","url":null,"abstract":"Understanding temporal commonsense concepts, such as times of occurrence and durations is crucial for event-centric language understanding. Reasoning about such temporal concepts in a complex context requires reasoning over both the stated context and the world knowledge that underlines it. A recent study shows massive pre-trained LM still struggle with such temporal reasoning under complex contexts (e.g., dialog) because they only implicitly encode the relevant contexts and fail to explicitly uncover the underlying logical compositions for complex inference, thus may not be robust enough. In this work, we propose to augment LMs with the temporal logic induction ability, which frames the temporal reasoning by defining three modular components: temporal dependency inducer and temporal concept defuzzifier and logic validator. The former two components disentangle the explicit/implicit dependency between temporal concepts across context (before, after, ...) and the specific meaning of fuzzy temporal concepts, respectively, while the validator combines the intermediate reasoning clues for robust contextual reasoning about the temporal concepts. Extensive experimental results on TIMEDIAL, a challenging dataset for temporal reasoning over dialog, show that our method, Logic Induction Enhanced Contextualized TEmporal Reasoning (LECTER), can yield great improvements over the traditional language model for temporal reasoning.","PeriodicalId":74506,"journal":{"name":"Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence","volume":"1 1","pages":"12580-12588"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77174303","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: 2

A Fair Incentive Scheme for Community Health Workers 社区卫生工作者公平奖励计划

Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence Pub Date : 2023-06-26 DOI: 10.1609/aaai.v37i12.26653

Avinandan Bose, Tracey Li, Arunesh Sinha, Tien Mai

{"title":"A Fair Incentive Scheme for Community Health Workers","authors":"Avinandan Bose, Tracey Li, Arunesh Sinha, Tien Mai","doi":"10.1609/aaai.v37i12.26653","DOIUrl":"https://doi.org/10.1609/aaai.v37i12.26653","url":null,"abstract":"Community health workers (CHWs) play a crucial role in\u0000the last mile delivery of essential health services to underserved\u0000populations in low-income countries. Many nongovernmental\u0000organizations (NGOs) provide training and\u0000support to enable CHWs to deliver health services to their\u0000communities, with no charge to the recipients of the services.\u0000This includes monetary compensation for the work that\u0000CHWs perform, which is broken down into a series of well defined\u0000tasks. In this work, we partner with a NGO D-Tree\u0000International to design a fair monetary compensation scheme\u0000for tasks performed by CHWs in the semi-autonomous region\u0000of Zanzibar in Tanzania, Africa. In consultation with\u0000stakeholders, we interpret fairness as the equal opportunity\u0000to earn, which means that each CHW has the opportunity to\u0000earn roughly the same total payment over a given T month\u0000period, if the CHW reacts to the incentive scheme almost rationally.\u0000We model this problem as a reward design problem\u0000for a Markov Decision Process (MDP) formulation for the\u0000CHWs’ earning. There is a need for the mechanism to be\u0000simple so that it is understood by the CHWs, thus, we explore\u0000linear and piecewise linear rewards in the CHWs’ measured\u0000units of work. We solve this design problem via a novel\u0000policy-reward gradient result. Our experiments using two real\u0000world parameters from the ground provide evidence of reasonable\u0000incentive output by our scheme.","PeriodicalId":74506,"journal":{"name":"Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence","volume":"46 1","pages":"14127-14135"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77176073","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

Iteratively Enhanced Semidefinite Relaxations for Efficient Neural Network Verification 高效神经网络验证的迭代增强半定松弛

Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence Pub Date : 2023-06-26 DOI: 10.1609/aaai.v37i12.26744

Jianglin Lan, Yang Zheng, A. Lomuscio

引用次数: 0

Exploring Hypergraph of Earnings Call for Risk Prediction (Student Abstract) 探讨盈余呼叫超图的风险预测(学生摘要)

Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence Pub Date : 2023-06-26 DOI: 10.1609/aaai.v37i13.26973

Yi He, Wenxin Tai, Fan Zhou, Yi Yang

引用次数: 0

Hierarchical ConViT with Attention-Based Relational Reasoner for Visual Analogical Reasoning 基于注意的关系推理的层次卷积视觉类比推理

Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence Pub Date : 2023-06-26 DOI: 10.1609/aaai.v37i1.25072

Wentao He, Jialu Zhang, Jianfeng Ren, Ruibin Bai, Xudong Jiang

{"title":"Hierarchical ConViT with Attention-Based Relational Reasoner for Visual Analogical Reasoning","authors":"Wentao He, Jialu Zhang, Jianfeng Ren, Ruibin Bai, Xudong Jiang","doi":"10.1609/aaai.v37i1.25072","DOIUrl":"https://doi.org/10.1609/aaai.v37i1.25072","url":null,"abstract":"Raven’s Progressive Matrices (RPMs) have been widely used to evaluate the visual reasoning ability of humans. To tackle the challenges of visual perception and logic reasoning on RPMs, we propose a Hierarchical ConViT with Attention-based Relational Reasoner (HCV-ARR). Traditional solution methods often apply relatively shallow convolution networks to visually perceive shape patterns in RPM images, which may not fully model the long-range dependencies of complex pattern combinations in RPMs. The proposed ConViT consists of a convolutional block to capture the low-level attributes of visual patterns, and a transformer block to capture the high-level image semantics such as pattern formations. Furthermore, the proposed hierarchical ConViT captures visual features from multiple receptive fields, where the shallow layers focus on the image fine details while the deeper layers focus on the image semantics. To better model the underlying reasoning rules embedded in RPM images, an Attention-based Relational Reasoner (ARR) is proposed to establish the underlying relations among images. The proposed ARR well exploits the hidden relations among question images through the developed element-wise attentive reasoner. Experimental results on three RPM datasets demonstrate that the proposed HCV-ARR achieves a significant performance gain compared with the state-of-the-art models. The source code is available at: https://github.com/wentaoheunnc/HCV-ARR.","PeriodicalId":74506,"journal":{"name":"Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence","volume":"15 1","pages":"22-30"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82427960","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: 7

Securing Lifelines: Safe Delivery of Critical Services in Areas with Volatile Security Situation via a Stackelberg Game Approach 确保生命线:通过Stackelberg博弈方法在安全形势不稳定的地区安全提供关键服务

Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence Pub Date : 2023-06-26 DOI: 10.1609/aaai.v37i5.25720

Tien Mai, Arunesh Sinha

引用次数: 0