AI Communications最新文献

Multi-feature fusion dehazing based on CycleGAN 基于 CycleGAN 的多特征融合去毛刺技术

IF 0.8 4区计算机科学

AI Communications Pub Date : 2024-05-29 DOI: 10.3233/aic-230227

Jingpin Wang, Yuan Ge, Jie Zhao, Chao Han

{"title":"Multi-feature fusion dehazing based on CycleGAN","authors":"Jingpin Wang, Yuan Ge, Jie Zhao, Chao Han","doi":"10.3233/aic-230227","DOIUrl":"https://doi.org/10.3233/aic-230227","url":null,"abstract":"Under the foggy environment, lane line images are obscured by haze, which leads to lower detection accuracy, higher false detection of lane lines. To address the above problems, a multi-layer feature fusion dehazing network based on CycleGAN architecture is proposed. Firstly, the foggy image is enhanced to remove the fog in the image, and then the lane line detection network is used for detection. For the dehazing network, a multi-layer feature fusion module is used in the generator to fuse the features of different coding layers of U-Net to enhance the network’s recovery of information such as details and edges, and a frequency domain channel attention mechanism is added at the key nodes of the network to enhance the network’s attention to different fog concentrations. At the same time, to improve the discriminant effect of the discriminator, the discriminator is extended to a global and local discriminator. The experimental results show that the dehaze effect on Reside and other test data sets is better than the comparison method. The peak signal-to-noise ratio is improved by 2.26 dB compared to the highest GCA-Net algorithm. According to the lane detection of fog images, it is found that the proposed network improves the accuracy of lane detection on foggy days.","PeriodicalId":50835,"journal":{"name":"AI Communications","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141507183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spatio-temporal deep learning framework for pedestrian intention prediction in urban traffic scenes 用于预测城市交通场景中行人意图的时空深度学习框架

IF 0.8 4区计算机科学

AI Communications Pub Date : 2024-05-28 DOI: 10.3233/aic-230053

Monika , Pardeep Singh, Satish Chand

{"title":"Spatio-temporal deep learning framework for pedestrian intention prediction in urban traffic scenes","authors":"Monika , Pardeep Singh, Satish Chand","doi":"10.3233/aic-230053","DOIUrl":"https://doi.org/10.3233/aic-230053","url":null,"abstract":"Pedestrian intent prediction is an essential task for ensuring the safety of pedestrians and vehicles on the road. This task involves predicting whether a pedestrian intends to cross a road or not based on their behavior and surrounding environment. Previous studies have explored feature-based machine learning and vision-based deep learning models for this task but these methods have limitations in capturing the global spatio-temporal context and fusing different features of data effectively. To address these issues, we propose a novel hybrid framework HSTGCN for pedestrian intent prediction that combines spatio-temporal graph convolutional neural networks (STGCN) and long short-term memory (LSTM) networks. The proposed framework utilizes the strengths of both models by fusing multiple features, including skeleton pose, trajectory, height, orientation, and ego-vehicle speed, to predict their intentions accurately. The framework’s performance have been evaluated on the JAAD benchmark dataset and the results show that it outperforms the state-of-the-art methods. The proposed framework has potential applications in developing intelligent transportation systems, autonomous vehicles, and pedestrian safety technologies. The utilization of multiple features can significantly improve the performance of the pedestrian intent prediction task.","PeriodicalId":50835,"journal":{"name":"AI Communications","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141507158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Open-world object detection: A solution based on reselection mechanism and feature disentanglement 开放世界物体检测：基于重新选择机制和特征分解的解决方案

IF 0.8 4区计算机科学

AI Communications Pub Date : 2024-04-25 DOI: 10.3233/aic-230270

Tian Lin, Li Hua, Li Linxuan, Bai Chuanao

引用次数: 0

Residual SwinV2 transformer coordinate attention network for image super resolution 用于图像超分辨率的残差 SwinV2 变换器坐标注意网络

IF 0.8 4区计算机科学

AI Communications Pub Date : 2024-04-09 DOI: 10.3233/aic-230340

Yushi Lei, Zhengwei Zhu, Yilin Qin, Chenyang Zhu, Yanping Zhu

{"title":"Residual SwinV2 transformer coordinate attention network for image super resolution","authors":"Yushi Lei, Zhengwei Zhu, Yilin Qin, Chenyang Zhu, Yanping Zhu","doi":"10.3233/aic-230340","DOIUrl":"https://doi.org/10.3233/aic-230340","url":null,"abstract":"Swin Transformers have been designed and used in various image super-resolution (SR) applications. One of the recent image restoration methods is RSTCANet, which combines Swin Transformer with Channel Attention. However, for some channels of images that may carry less useful information or noise, Channel Attention cannot automatically learn the insignificance of these channels. Instead, it tries to enhance their expression capability by adjusting the weights. It may lead to excessive focus on noise information while neglecting more essential features. In this paper, we propose a new image SR method, RSVTCANet, based on an extension of Swin2SR. Specifically, to effectively gather global information for the channel of images, we modify the Residual SwinV2 Transformer blocks in Swin2SR by introducing the coordinate attention for each two successive SwinV2 Transformer Layers (S2TL) and replacing Multi-head Self-Attention (MSA) with Efficient Multi-head Self-Attention version 2 (EMSAv2) to employ the resulting residual SwinV2 Transformer coordinate attention blocks (RSVTCABs) for feature extraction. Additionally, to improve the generalization of RSVTCANet during training, we apply an optimized RandAugment for data augmentation on the training dataset. Extensive experimental results show that RSVTCANet outperforms the recent image SR method regarding visual quality and measures such as PSNR and SSIM.","PeriodicalId":50835,"journal":{"name":"AI Communications","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140725934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Revisiting block deordering in finite-domain state variable planning 重新审视有限域状态变量规划中的分块去序问题

IF 0.8 4区计算机科学

AI Communications Pub Date : 2024-04-03 DOI: 10.3233/aic-230058

Sabah Binte Noor, Fazlul Hasan Siddiqui

{"title":"Revisiting block deordering in finite-domain state variable planning","authors":"Sabah Binte Noor, Fazlul Hasan Siddiqui","doi":"10.3233/aic-230058","DOIUrl":"https://doi.org/10.3233/aic-230058","url":null,"abstract":"Plan deordering removes unnecessary ordering constraints between actions in a plan, facilitating plan execution flexibility and several other tasks, such as plan reuse, modification, and decomposition. Block deordering is a variant of plan deordering that encapsulates coherent actions into blocks to eliminate further ordering constraints from a partial-order plan (POP) and is useful in many applications (e.g., generating macro-actions and improving the overall plan quality). The existing block deordering strategy is formulated in propositional encodings. Finite-domain state variable encodings (e.g., SAS+ representation), in contrast with propositional encodings, can capture the internal structure and the behavior of state variables of a planning instance through concise constructs such as causal graphs (CGs) and domain transition graphs (DTGs). This work redefines the semantics of block deordering terminologies and related plan deordering concepts in finite domain representation (FDR). Our proposed semantics also resolves some limitations of the existing block semantics and further enhance plan flexibility. In addition, this work exploits block deordering to eliminate redundant actions from a POP. A comparative analysis is also performed on block deordering with various deordering/reordering techniques using explanation-based order generalization (EOG) and MaxSAT. Our experiments on the benchmark problems from International Planning Competitions (IPC) show that our FDR formalism of block deordering significantly improves the plan execution flexibility while maintaining good coverage and execution time.","PeriodicalId":50835,"journal":{"name":"AI Communications","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140751050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Token-modification adversarial attacks for natural language processing: A survey 自然语言处理中的标记修改对抗攻击：调查

IF 0.8 4区计算机科学

AI Communications Pub Date : 2024-04-02 DOI: 10.3233/aic-230279

Tom Roth, Yansong Gao, Alsharif Abuadbba, Surya Nepal, Wei Liu

引用次数: 0

MantaRay-ProM: An efficient process model discovery algorithm MantaRay-ProM：一种高效的流程模型发现算法

IF 0.8 4区计算机科学

AI Communications Pub Date : 2024-04-02 DOI: 10.3233/aic-220219

Shikha Gupta, Sonia Deshmukh, Naveen Kumar

引用次数: 0

Information extraction tool text2alm: From narratives to action language system descriptions and query answering 信息提取工具 text2alm：从叙述到行动语言系统描述和查询回答

IF 0.8 4区计算机科学

AI Communications Pub Date : 2024-03-21 DOI: 10.3233/aic-220194

Yuliya Lierler, Gang Ling, Craig Olson

{"title":"Information extraction tool text2alm: From narratives to action language system descriptions and query answering","authors":"Yuliya Lierler, Gang Ling, Craig Olson","doi":"10.3233/aic-220194","DOIUrl":"https://doi.org/10.3233/aic-220194","url":null,"abstract":"In this work we design an information extraction tool text2alm capable of narrative understanding with a focus on action verbs. This tool uses an action language ALM to perform inferences on complex interactions of events described in narratives. The methodology used to implement the text2alm system was originally outlined by Lierler, Inclezan, and Gelfond (In IWCS 2017 – 12th International Conference on Computational Semantics – Short Papers (2017)) via a manual process of converting a narrative to an ALM model. We refine that theoretical methodology and utilize it in design of the text2alm system. This system relies on a conglomeration of resources and techniques from two distinct fields of artificial intelligence, namely, (i) knowledge representation and reasoning and (ii) natural language processing. The effectiveness of system text2alm is measured by its ability to correctly answer questions from the bAbI tasks published by Facebook Research in 2015. This tool matched or exceeded the performance of state-of-the-art machine learning methods in six of the seven tested tasks. We also illustrate that the text2alm approach generalizes to a broader spectrum of narratives. On the path to creating system text2alm, a semantic role labeler text2drs was designed. Its unique feature is the use of the elements of the fine grained linguistic ontology VerbNet as semantic roles/labels in annotating considered text. This paper provides an accurate account on the details behind the text2alm and text2drs systems.","PeriodicalId":50835,"journal":{"name":"AI Communications","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140196376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IMATSA – an improved and adaptive intelligent optimization algorithm based on tunicate swarm algorithm IMATSA - 基于调谐群算法的改进型自适应智能优化算法

IF 0.8 4区计算机科学

AI Communications Pub Date : 2024-03-21 DOI: 10.3233/aic-220093

Yan Chen, Weizhen Dong, Xiaochun Hu

{"title":"IMATSA – an improved and adaptive intelligent optimization algorithm based on tunicate swarm algorithm","authors":"Yan Chen, Weizhen Dong, Xiaochun Hu","doi":"10.3233/aic-220093","DOIUrl":"https://doi.org/10.3233/aic-220093","url":null,"abstract":"Swarm intelligence optimization algorithm has been proved to perform well in the field of parameter optimization. In order to further improve the performance of intelligent optimization algorithm, this paper proposes an improved and adaptive tunicate swarm algorithm (IMATSA) based on tunicate swarmalgorithm (TSA). IMATSA improves TSA in the following four aspects: population diversity, local search convergence speed, jumping out of local optimal position, and balancing global and local search. Firstly, IMATSA adopts Tent map and quadratic interpolation to initialize population and enhance the diversity. Secondly, IMATSA uses Golden-Sine algorithm to accelerate the convergence of local search. Thirdly, in the process of global development, IMATSA adopts Levy flight and the improved Gauss disturbance method to adaptively improves and coordinates the ability of global development and local search. Then, this paper verifies the performance of IMATSA based on 14 benchmark functions experiment, ablation experiment, parameter optimization experiments of Support Vector Machine (SVM) and Gradient Boosting Decision Tree (GBDT), Wilcoxon signed rank test and image multi-threshold segmentation experiment with the performance metrics are convergence speed, convergence value, significance level P-value, Peak Signal-to-Noise Ratio (PSNR) and Standard Deviation (STD). Experimental results show that IMATSA performs better in three kinds of benchmark functions; each component of IMATSA has a positive effect on the performance; IMATSA performs better in parameter optimization experiments of SVM experiment and GBDT; there is significant difference between IMATSA and other algorithms by Wilcoxon signed rank test; in image segmentation, the performance is directly proportional to the number of thresholds, and compared with other algorithms, IMATSA has better comprehensive performance.","PeriodicalId":50835,"journal":{"name":"AI Communications","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140196416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Formal tools and methods of Artificial Intelligence 人工智能的正式工具和方法

IF 0.8 4区计算机科学

AI Communications Pub Date : 2024-03-18 DOI: 10.3233/aic-249001

Zied Bouraoui, Anaëlle Wilczynski

引用次数: 0