ACM Transactions on Spatial Algorithms and Systems最新文献

Cross- and Context-Aware Attention Based Spatial-Temporal Graph Convolutional Networks for Human Mobility Prediction 基于注意力的跨时空图卷积网络用于人类移动性预测

IF 1.9

ACM Transactions on Spatial Algorithms and Systems Pub Date : 2024-06-14 DOI: 10.1145/3673227

Zhaobin Mo, Haotian Xiang, Xuan Di

{"title":"Cross- and Context-Aware Attention Based Spatial-Temporal Graph Convolutional Networks for Human Mobility Prediction","authors":"Zhaobin Mo, Haotian Xiang, Xuan Di","doi":"10.1145/3673227","DOIUrl":"https://doi.org/10.1145/3673227","url":null,"abstract":"The COVID-19 pandemic has dramatically transformed human mobility patterns. Therefore, human mobility prediction for the “new normal” is crucial to infrastructure redesign, emergency management, and urban planning post the pandemic. This paper aims to predict people’s number of visits to various locations in New York City using COVID and mobility data in the past two years. To quantitatively model the impact of COVID cases on human mobility patterns and predict mobility patterns across the pandemic period, this paper develops a model CCAAT-GCN (Cross- and Context-Attention based Spatial-Temporal Graph Convolutional Networks). The proposed model is validated using SafeGraph data in New York City from August 2020 to April 2022. A rich set of baselines are performed to demonstrate the performance of our proposed model. Results demonstrate the superior performance of our proposed method. Also, the attention matrix learned by our model exhibits a strong alignment with the COVID-19 situation and the points of interest within the geographic region. This alignment suggests that the model effectively captures the intricate relationships between COVID-19 case rates and human mobility patterns. The developed model and findings can offer insights into the mobility pattern prediction for future disruptive events and pandemics, so as to assist with emergency preparedness for planners, decision-makers and policymakers.","PeriodicalId":43641,"journal":{"name":"ACM Transactions on Spatial Algorithms and Systems","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141340649","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

(Vision Paper) A Vision for Spatio-Causal Situation Awareness, Forecasting, and Planning (愿景文件）空间因果态势感知、预测和规划愿景

IF 1.9

ACM Transactions on Spatial Algorithms and Systems Pub Date : 2024-06-12 DOI: 10.1145/3672556

Fahim Tasneema Azad, K. Candan, Ahmet Kapkic, Mao-Lin Li, Huan Liu, Pratanu Mandal, Paras Sheth, Bilgehan Arslan, Gerardo Chowell-Puente, John Sabo, R. Muenich, Javier Redondo Anton, M. Sapino

{"title":"(Vision Paper) A Vision for Spatio-Causal Situation Awareness, Forecasting, and Planning","authors":"Fahim Tasneema Azad, K. Candan, Ahmet Kapkic, Mao-Lin Li, Huan Liu, Pratanu Mandal, Paras Sheth, Bilgehan Arslan, Gerardo Chowell-Puente, John Sabo, R. Muenich, Javier Redondo Anton, M. Sapino","doi":"10.1145/3672556","DOIUrl":"https://doi.org/10.1145/3672556","url":null,"abstract":"Successfully tackling many urgent challenges in socio-economically critical domains, such as public health and sustainability, requires a deeper understanding of causal relationships and interactions among a diverse spectrum of spatio-temporally distributed entities. In these applications, the ability to leverage spatio-temporal data to obtain causally-based situational awareness and to develop informed forecasts to provide resilience at different scales is critical. While the promise of a causally-grounded approach to these challenges is apparent, the core data technologies needed to achieve these are in the early stages and lack a framework to help realize their potential. In this paper, we argue that there is an urgent need for a novel paradigm of spatio-causal research built on computational advances in, spatio-temporal data and model integration, causal learning and discovery, large scale data- and model-driven simulations, emulations, and forecasting, spatio-temporal data-driven and model centric operational recommendations, and effective causally-driven visualization and explanation. We, thus, provide a vision, and a road-map, for spatio-causal situation awareness, forecasting, and planning.","PeriodicalId":43641,"journal":{"name":"ACM Transactions on Spatial Algorithms and Systems","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141353812","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

Mobility Data Science: Perspectives and Challenges 移动数据科学：视角与挑战

IF 1.9

ACM Transactions on Spatial Algorithms and Systems Pub Date : 2024-05-07 DOI: 10.1145/3652158

M. Mokbel, Mahmoud Sakr, Li Xiong, Andreas Züfle, Jussara Almeida, Taylor Anderson, W. Aref, G. Andrienko, N. Andrienko, Yang Cao, Sanjay Chawla, R. Cheng, P. Chrysanthis, Xiqi Fei, Gabriel Ghinita, Anita Graser, D. Gunopulos, C. S. Jensen, Joon-Seok Kim, Peer Kröger Kyoung-Sook Kim, John Krumm, Johannes Lauer, A. Magdy, Mario A. Nascimento, S. Ravada, Matthias Renz, Dimitris Sacharidis, Flora Salim, Mohamed Sarwat, M. Schoemans, Cyrus Shahabi, Bettina Speckmann, E. Tanin, Xu Teng, Y. Theodoridis, Kristian Torp, Goce Trajcevski, Mar van Kreveld, C. Wenk, Martin Werner, Raymond E. Wong, Song Wu, Jianqiu Xu, Moustafa Youssef, Demetris Zeinalipour, Mengxuan Zhang

{"title":"Mobility Data Science: Perspectives and Challenges","authors":"M. Mokbel, Mahmoud Sakr, Li Xiong, Andreas Züfle, Jussara Almeida, Taylor Anderson, W. Aref, G. Andrienko, N. Andrienko, Yang Cao, Sanjay Chawla, R. Cheng, P. Chrysanthis, Xiqi Fei, Gabriel Ghinita, Anita Graser, D. Gunopulos, C. S. Jensen, Joon-Seok Kim, Peer Kröger Kyoung-Sook Kim, John Krumm, Johannes Lauer, A. Magdy, Mario A. Nascimento, S. Ravada, Matthias Renz, Dimitris Sacharidis, Flora Salim, Mohamed Sarwat, M. Schoemans, Cyrus Shahabi, Bettina Speckmann, E. Tanin, Xu Teng, Y. Theodoridis, Kristian Torp, Goce Trajcevski, Mar van Kreveld, C. Wenk, Martin Werner, Raymond E. Wong, Song Wu, Jianqiu Xu, Moustafa Youssef, Demetris Zeinalipour, Mengxuan Zhang","doi":"10.1145/3652158","DOIUrl":"https://doi.org/10.1145/3652158","url":null,"abstract":"Mobility data captures the locations of moving objects such as humans, animals, and cars. With the availability of GPS-equipped mobile devices and other inexpensive location-tracking technologies, mobility data is collected ubiquitously. In recent years, the use of mobility data has demonstrated significant impact in various domains including traffic management, urban planning, and health sciences. In this paper, we present the domain of mobility data science. Towards a unified approach to mobility data science, we present a pipeline having the following components: mobility data collection, cleaning, analysis, management, and privacy. For each of these components, we explain how mobility data science differs from general data science, we survey the current state of the art, and describe open challenges for the research community in the coming years.","PeriodicalId":43641,"journal":{"name":"ACM Transactions on Spatial Algorithms and Systems","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141002628","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

Graph Sampling for Map Comparison 用于地图比较的图形取样

IF 1.9

ACM Transactions on Spatial Algorithms and Systems Pub Date : 2024-05-03 DOI: 10.1145/3662733

J. Aguilar, K. Buchin, M. Buchin, Erfan Hosseini Sereshgi, Rodrigo I. Silveira, C. Wenk

{"title":"Graph Sampling for Map Comparison","authors":"J. Aguilar, K. Buchin, M. Buchin, Erfan Hosseini Sereshgi, Rodrigo I. Silveira, C. Wenk","doi":"10.1145/3662733","DOIUrl":"https://doi.org/10.1145/3662733","url":null,"abstract":"\u0000 Comparing two road maps is a basic operation that arises in a variety of situations. A map comparison method that is commonly used, mainly in the context of comparing reconstructed maps to ground truth maps, is based on\u0000 graph sampling\u0000 . The essential idea is to first compute a set of point samples on each map, and then to match pairs of samples—one from each map—in a one-to-one fashion. For deciding whether two samples can be matched, different criteria, e.g., based on distance or orientation, can be used. The total number of matched pairs gives a measure of how similar the maps are.\u0000 \u0000 Since the work of Biagioni and Eriksson [11, 12], graph sampling methods have become widely used. However, there are different ways to implement each of the steps, which can lead to significant differences in the results. This means that conclusions drawn from different studies that seemingly use the same comparison method, cannot necessarily be compared.\u0000 In this work we present a unified approach to graph sampling for map comparison. We present the method in full generality, discussing the main decisions involved in its implementation. In particular, we point out the importance of the sampling method (GEO vs. TOPO) and that of the matching definition, discussing the main options used, and proposing alternatives for both key steps. We experimentally evaluate the different sampling and matching options considered on map datasets and reconstructed maps. Furthermore, we provide a code base and an interactive visualization tool to set a standard for future evaluations in the field of map construction and map comparison.","PeriodicalId":43641,"journal":{"name":"ACM Transactions on Spatial Algorithms and Systems","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141016446","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

Latent Representation Learning for Geospatial Entities 地理空间实体的潜在表征学习

IF 1.9

ACM Transactions on Spatial Algorithms and Systems Pub Date : 2024-05-02 DOI: 10.1145/3663474

Ween Jiann Lee, Hady W. Lauw

引用次数: 0

Leveraging Simulation Data to Understand Bias in Predictive Models of Infectious Disease Spread 利用模拟数据了解传染病传播预测模型的偏差

IF 1.9

ACM Transactions on Spatial Algorithms and Systems Pub Date : 2024-04-25 DOI: 10.1145/3660631

Andreas Züfle, Flora Salim, Taylor Anderson, M. Scotch, Li Xiong, Kacper Sokol, Hao Xue, Ruochen Kong, David Heslop, Hye-Young Paik, C. R. MacIntyre

{"title":"Leveraging Simulation Data to Understand Bias in Predictive Models of Infectious Disease Spread","authors":"Andreas Züfle, Flora Salim, Taylor Anderson, M. Scotch, Li Xiong, Kacper Sokol, Hao Xue, Ruochen Kong, David Heslop, Hye-Young Paik, C. R. MacIntyre","doi":"10.1145/3660631","DOIUrl":"https://doi.org/10.1145/3660631","url":null,"abstract":"The spread of infectious diseases is a highly complex spatiotemporal process, difficult to understand, predict, and effectively respond to. Machine learning and artificial intelligence (AI) have achieved impressive results in other learning and prediction tasks; however, while many AI solutions are developed for disease prediction, only a few of them are adopted by decision-makers to support policy interventions. Among several issues preventing their uptake, AI methods are known to amplify the bias in the data they are trained on. This is especially problematic for infectious disease models that typically leverage large, open, and inherently biased spatiotemporal data. These biases may propagate through the modeling pipeline to decision-making, resulting in inequitable policy interventions. Therefore, there is a need to gain an understanding of how the AI disease modeling pipeline can mitigate biased input data, in-processing models, and biased outputs. Specifically, our vision is to develop a large-scale micro-simulation of individuals from which human mobility, population, and disease ground truth data can be obtained. From this complete dataset – which may not reflect the real world – we can sample and inject different types of bias. By using the sampled data in which bias is known (as it is given as the simulation parameter), we can explore how existing solutions for fairness in AI can mitigate and correct these biases and investigate novel AI fairness solutions. Achieving this vision would result in improved trust in such models for informing fair and equitable policy interventions.","PeriodicalId":43641,"journal":{"name":"ACM Transactions on Spatial Algorithms and Systems","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140657355","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

Backbone Index and GNN Models for Skyline Path Query Evaluation over Multi-cost Road Networks 用于多成本路网天际线路径查询评估的骨干索引和 GNN 模型

IF 1.9

ACM Transactions on Spatial Algorithms and Systems Pub Date : 2024-04-23 DOI: 10.1145/3660632

Qixu Gong, Huiying Chen, Huiping Cao, Jiefei Liu

{"title":"Backbone Index and GNN Models for Skyline Path Query Evaluation over Multi-cost Road Networks","authors":"Qixu Gong, Huiying Chen, Huiping Cao, Jiefei Liu","doi":"10.1145/3660632","DOIUrl":"https://doi.org/10.1145/3660632","url":null,"abstract":"\u0000 Skyline path queries (SPQs) extend skyline queries to multi-dimensional networks, such as multi-cost road networks (MCRNs). Such queries return a set of non-dominated paths between two given network nodes. Despite the existence of extensive works on evaluating different SPQ variants, SPQ evaluation is still very inefficient due to the nonexistence of efficient index structures to support such queries. Existing index building approaches for supporting shortest-path query execution, when directly extended to support SPQs, use an unreasonable amount of space and time to build, making them impractical for processing large graphs. In this paper, we propose a novel index structure,\u0000 backbone index\u0000 , and a corresponding index construction method that condenses an initial MCRN to multiple smaller summarized graphs with different granularity. We present efficient approaches to find approximate solutions to SPQs by utilizing the backbone index structure. Furthermore, considering making good use of historical query and query results, we propose two models,\u0000 S\u0000 kyline\u0000 P\u0000 ath\u0000 G\u0000 raph\u0000 N\u0000 eural\u0000 N\u0000 etwork (SP-GNN) and\u0000 T\u0000 ransfer SP-GNN (TSP-GNN), to support effective SPQ processing. Our extensive experiments on real-world large road networks show that the backbone index can support finding meaningful approximate SPQ solutions efficiently. The backbone index can be constructed in a reasonable time, which dramatically outperforms the construction of other types of indexes for road networks. As far as we know, this is the first compact index structure that can support efficient approximate SPQ evaluation on large MCRNs. The results on the SP-GNN and TSP-GNN models also show that both models can help get approximate SPQ answers efficiently.\u0000","PeriodicalId":43641,"journal":{"name":"ACM Transactions on Spatial Algorithms and Systems","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140670799","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

Overcoming Catastrophic Forgetting in Continual Fine-Grained Urban Flow Inference 克服连续细粒度城市流推断中的灾难性遗忘

IF 1.9

ACM Transactions on Spatial Algorithms and Systems Pub Date : 2024-04-20 DOI: 10.1145/3660523

Xovee Xu, Ting Zhong, Haoyang Yu, Fan Zhou, Goce Trajcevski

{"title":"Overcoming Catastrophic Forgetting in Continual Fine-Grained Urban Flow Inference","authors":"Xovee Xu, Ting Zhong, Haoyang Yu, Fan Zhou, Goce Trajcevski","doi":"10.1145/3660523","DOIUrl":"https://doi.org/10.1145/3660523","url":null,"abstract":"Citywide fine-grained urban flow inference (FUFI) problem aims to infer the high-resolution flow maps from the coarse-grained ones, which plays an important role in sustainable and economic urban computing and intelligent traffic management. Previous models tackle this problem from spatial constraint, external factors and memory cost. However, utilizing the new urban flow maps to calibrate the learned model is very challenging due to the “catastrophic forgetting” problem and is still under-explored. In this paper, we make the first step in FUFI and present CUFAR – Continual Urban Flow inference with augmented Adaptive knowledge Replay – a novel framework for inferring the fine-grained citywide traffic flows. Specifically, (1) we design a spatial-temporal inference network that can extract better flow map features from both local and global levels; (2) then we present an augmented adaptive knowledge replay (AKR) training algorithm to selectively replay the learned knowledge to facilitate the learning process of the model on new knowledge without forgetting. We apply several data augmentation techniques to improve the generalization capability of the learning model, gaining additional performance improvements. We also propose a knowledge discriminator to avoid the “negative replaying” issue introduced by noisy urban flow maps. Extensive experiments on two large-scale real-world FUFI datasets demonstrate that our proposed model consistently outperforms strong baselines and effectively mitigates the forgetting problem.","PeriodicalId":43641,"journal":{"name":"ACM Transactions on Spatial Algorithms and Systems","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140680537","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

A Generic Machine Learning Model for Spatial Query Optimization based on Spatial Embeddings 基于空间嵌入的空间查询优化通用机器学习模型

IF 1.9

ACM Transactions on Spatial Algorithms and Systems Pub Date : 2024-04-13 DOI: 10.1145/3657633

A. Belussi, S. Migliorini, Ahmed Eldawy

{"title":"A Generic Machine Learning Model for Spatial Query Optimization based on Spatial Embeddings","authors":"A. Belussi, S. Migliorini, Ahmed Eldawy","doi":"10.1145/3657633","DOIUrl":"https://doi.org/10.1145/3657633","url":null,"abstract":"Machine learning (ML) and deep learning (DL) techniques are increasingly applied to produce efficient query optimizers, in particular in regards to big data systems. The optimization of spatial operations is even more challenging due to the inherent complexity of such kind of operations, like spatial join or range query, and the peculiarities of spatial data. Although a few ML-based spatial query optimizers have been proposed in literature, their design limits their use, since each one is tailored for a specific collection of datasets, a specific operation, or a specific hardware setting. Changes to any of these will require building and training a completely new model which entails collecting a new very large training dataset to obtain a good model.\u0000 \u0000 This paper proposes a different approach which exploits the use of the novel notion of\u0000 spatial embedding\u0000 to overcome these limitations. In particular, a preliminary model is defined which captures the relevant features of spatial datasets, independently from the operation to be optimized and in an unsupervised manner. This model is trained with a large amount of both synthetic and real-world data, with the aim to produce meaningful spatial embeddings. The construction of an embedding model could be intended as a preliminary step for the optimization of many different spatial operations, so the cost of its building can be compensated during the subsequent construction of specific models. Indeed, for each considered spatial operation, a specific tailored model will be trained but by using spatial embeddings as input, so a very little amount of training data points is required for them. Three peculiar operations are considered as proof of concept in this paper: range query, self-join, and binary spatial join. Finally, a comparison with an alternative technique, known as transfer learning, is provided and the advantages of the proposed technique over it are highlighted.\u0000","PeriodicalId":43641,"journal":{"name":"ACM Transactions on Spatial Algorithms and Systems","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140707657","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

Predictability in Human Mobility: From Individual to Collective (Vision Paper) 人类流动的可预测性：从个人到集体（展望论文）

IF 1.9

ACM Transactions on Spatial Algorithms and Systems Pub Date : 2024-04-09 DOI: 10.1145/3656640

Ying Zhang, Zhiwen Yu, Minling Dang, En Xu, Bin Guo, Yuxuan Liang, Yifang Yin, Roger Zimmermann

{"title":"Predictability in Human Mobility: From Individual to Collective (Vision Paper)","authors":"Ying Zhang, Zhiwen Yu, Minling Dang, En Xu, Bin Guo, Yuxuan Liang, Yifang Yin, Roger Zimmermann","doi":"10.1145/3656640","DOIUrl":"https://doi.org/10.1145/3656640","url":null,"abstract":"Human mobility is the foundation of urban dynamics and its prediction significantly benefits various downstream location-based services. Nowadays, while deep learning approaches are dominating the mobility prediction field where various model architectures/designs are continuously updating to push up the prediction accuracy, there naturally arises a question: whether these models are sufficiently good to reach the best possible prediction accuracy? To answer this question, predictability study is a method that quantifies the inherent regularities of the human mobility data and links the result to that limit. Mainstream predictability studies achieve this by analyzing the individual trajectories and merging all individual results to obtain an upper bound. However, the multiple individuals composing the city are not totally independent and the individual behavior is heavily influenced by its implicit or explicit surroundings. Therefore, the collective factor should be considered in the mobility predictability measurement, which has not been addressed before. This vision paper points out this concern and envisions a few potential research problems along such an individual-to-collective transition from both data and methodology aspects. We hope the discussion in this paper sheds some light on the human mobility predictability community.","PeriodicalId":43641,"journal":{"name":"ACM Transactions on Spatial Algorithms and Systems","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140725984","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