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":"73 1","pages":""},"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":"32 1","pages":""},"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

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":"23 1","pages":""},"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":"131 1","pages":""},"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

Temporally-aware node embeddings for evolving networks topologies 针对不断演变的网络拓扑结构的时间感知节点嵌入

IF 0.8 4区计算机科学

AI Communications Pub Date : 2024-01-02 DOI: 10.3233/aic-230028

K. B. Enes, Matheus Nunes, Fabricio Murai, Gisele L. Pappa

{"title":"Temporally-aware node embeddings for evolving networks topologies","authors":"K. B. Enes, Matheus Nunes, Fabricio Murai, Gisele L. Pappa","doi":"10.3233/aic-230028","DOIUrl":"https://doi.org/10.3233/aic-230028","url":null,"abstract":"Static node embedding algorithms applied to snapshots of real-world applications graphs are unable to capture their evolving process. As a result, the absence of information about the dynamics in these node representations can harm the accuracy and increase processing time of machine learning tasks related to these applications. Aiming at fill the gap regarding the inability of static methods to capture evolving processes on dynamic networks, we propose a biased random walk method named Evolving Node Embedding (EVNE). EVNE leverages the sequential relationship of graph snapshots by incorporating historic information when generating embeddings for the next snapshot. It learns node representations through a neural network, but differs from existing methods as it: (i) incorporates previously run walks at each step; (ii) starts the optimization of the current embedding from the parameters obtained in the previous iteration; and (iii) uses two time-varying parameters to regulate the behavior of the biased random walks over the process of graph exploration. Through a wide set of experiments we show that our approach generates better embeddings, outperforming baselines by up to 20% in a downstream node classification task. EVNE’s embeddings achieve better performance than others, based on experiments with four classifiers and five datasets. In addition, we present seven variations of our model to show the impact of each of EVNE’s mechanisms.","PeriodicalId":50835,"journal":{"name":"AI Communications","volume":"135 15","pages":""},"PeriodicalIF":0.8,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139453103","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

Evolutionary multi-objective physics-informed neural networks: The MOPINNs approach 进化多目标物理信息神经网络：MOPINNs 方法

IF 0.8 4区计算机科学

AI Communications Pub Date : 2023-12-15 DOI: 10.3233/aic-230073

Hugo Carrillo, Taco de Wolff, Luis Martí, Nayat Sanchez-Pi

引用次数: 0

Some thoughts about artificial stupidity and artificial dumbness 关于人工愚蠢和人工笨拙的一些想法

IF 0.8 4区计算机科学

AI Communications Pub Date : 2023-12-14 DOI: 10.3233/aic-220322

Jean Lieber, Jean-Guy Mailly, Pierre Marquis, Henri Prade, François Rollin

引用次数: 0