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  • Association for Computing Machinery (ACM)  (10)
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  • Association for Computing Machinery (ACM)  (10)
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  • 1
    Online Resource
    Online Resource
    Dynamic Planning of Bicycle Stations in Dockless Public Bicycle-sharing System Using Gated Graph Neural Network
    Association for Computing Machinery (ACM) ; 2021
    In:  ACM Transactions on Intelligent Systems and Technology Vol. 12, No. 2 ( 2021-04-30), p. 1-22
    Details
    In: ACM Transactions on Intelligent Systems and Technology, Association for Computing Machinery (ACM), Vol. 12, No. 2 ( 2021-04-30), p. 1-22
    Abstract: Benefiting from convenient cycling and flexible parking locations, the Dockless Public Bicycle-sharing (DL-PBS) network becomes increasingly popular in many countries. However, redundant and low-utility stations waste public urban space and maintenance costs of DL-PBS vendors. In this article, we propose a Bicycle Station Dynamic Planning (BSDP) system to dynamically provide the optimal bicycle station layout for the DL-PBS network. The BSDP system contains four modules: bicycle drop-off location clustering, bicycle-station graph modeling, bicycle-station location prediction, and bicycle-station layout recommendation. In the bicycle drop-off location clustering module, candidate bicycle stations are clustered from each spatio-temporal subset of the large-scale cycling trajectory records. In the bicycle-station graph modeling module, a weighted digraph model is built based on the clustering results and inferior stations with low station revenue and utility are filtered. Then, graph models across time periods are combined to create a graph sequence model. In the bicycle-station location prediction module, the GGNN model is used to train the graph sequence data and dynamically predict bicycle stations in the next period. In the bicycle-station layout recommendation module, the predicted bicycle stations are fine-tuned according to the government urban management plan, which ensures that the recommended station layout is conducive to city management, vendor revenue, and user convenience. Experiments on actual DL-PBS networks verify the effectiveness, accuracy, and feasibility of the proposed BSDP system.
    Type of Medium: Online Resource
    ISSN: 2157-6904 , 2157-6912
    URL: Article
    DOI: 10.1145/3446342
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2021
    detail.hit.zdb_id: 2584437-4
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  • 2
    Online Resource
    Online Resource
    Uncertainty Theory Based Partitioning for Cyber-Physical Systems with Uncertain Reliability Analysis
    Association for Computing Machinery (ACM) ; 2022
    In:  ACM Transactions on Design Automation of Electronic Systems Vol. 27, No. 3 ( 2022-05-31), p. 1-19
    Details
    In: ACM Transactions on Design Automation of Electronic Systems, Association for Computing Machinery (ACM), Vol. 27, No. 3 ( 2022-05-31), p. 1-19
    Abstract: Reasonable partitioning is a critical issue for cyber-physical system (CPS) design. Traditional CPS partitioning methods run in a determined context and depend on the parameter pre-estimations, but they ignore the uncertainty of parameters and hardly consider reliability. The state-of-the-art work proposed an uncertainty theory based CPS partitioning method, which includes parameter uncertainty and reliability analysis, but it only considers linear uncertainty distributions for variables and ignores the uncertainty of reliability. In this paper, we propose an uncertainty theory based CPS partitioning method with uncertain reliability analysis. We convert the uncertain objective and constraint into determined forms; such conversion methods can be applied to all forms of uncertain variables, not just for linear. By applying uncertain reliability analysis in the uncertainty model, we for the first time include the uncertainty of reliability into the CPS partitioning, where the reliability enhancement algorithm is proposed. We study the performance of the reliability obtained through uncertain reliability analysis, and experimental results show that the system reliability with uncertainty does not change significantly with the growth of task module numbers.
    Type of Medium: Online Resource
    ISSN: 1084-4309 , 1557-7309
    URL: Article
    DOI: 10.1145/3490177
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2022
    detail.hit.zdb_id: 1501152-5
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  • 3
    Online Resource
    Online Resource
    Dynamic Personalized POI Sequence Recommendation with Fine-Grained Contexts
    Association for Computing Machinery (ACM) ; 2023
    In:  ACM Transactions on Internet Technology Vol. 23, No. 2 ( 2023-05-31), p. 1-28
    Details
    In: ACM Transactions on Internet Technology, Association for Computing Machinery (ACM), Vol. 23, No. 2 ( 2023-05-31), p. 1-28
    Abstract: The Point Of Interest (POI) sequence recommendation is the key task in itinerary and travel route planning. Existing works usually consider the temporal and spatial factors in travel planning. However, the external environment, such as the weather, is usually overlooked. In fact, the weather is an important factor because it can affect a user’s check-in behaviors. Furthermore, most of the existing research is based on a static environment for POI sequence recommendation. While the external environment (e.g., the weather) may change during travel, it is difficult for existing works to adjust the POI sequence in time. What’s more, people usually prefer the attractive routes when traveling. To address these issues, we first conduct comprehensive data analysis on two real-world check-in datasets to study the effects of weather and time, as well as the features of the POI sequence. Based on this, we propose a model of Dynamic Personalized POI Sequence Recommendation with fine-grained contexts ( DPSR for short). It extracts user interest and POI popularity with fine-grained contexts and captures the attractiveness of the POI sequence. Next, we apply the Monte Carlo Tree Search model (MCTS for short) to simulate the process of recommending POI sequence in the dynamic environment, i.e., the weather and time change after visiting a POI. What’s more, we consider different speeds to reflect the fact that people may take different transportation to transfer between POIs. To validate the efficacy of DPSR , we conduct extensive experiments. The results show that our model can improve the accuracy of the recommendation significantly. Furthermore, it can better meet user preferences and enhance experiences.
    Type of Medium: Online Resource
    ISSN: 1533-5399 , 1557-6051
    URL: Article
    DOI: 10.1145/3583687
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2023
    detail.hit.zdb_id: 2060058-6
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  • 4
    Online Resource
    Online Resource
    Locality Sensitive Hash Aggregated Nonlinear Neighborhood Matrix Factorization for Online Sparse Big Data Analysis
    Association for Computing Machinery (ACM) ; 2021
    In:  ACM/IMS Transactions on Data Science Vol. 2, No. 4 ( 2021-11-30), p. 1-27
    Details
    In: ACM/IMS Transactions on Data Science, Association for Computing Machinery (ACM), Vol. 2, No. 4 ( 2021-11-30), p. 1-27
    Abstract: Matrix factorization (MF) can extract the low-rank features and integrate the information of the data manifold distribution from high-dimensional data, which can consider the nonlinear neighborhood information. Thus, MF has drawn wide attention for low-rank analysis of sparse big data, e.g., Collaborative Filtering (CF) Recommender Systems, Social Networks, and Quality of Service. However, the following two problems exist: (1) huge computational overhead for the construction of the Graph Similarity Matrix (GSM) and (2) huge memory overhead for the intermediate GSM. Therefore, GSM-based MF, e.g., kernel MF, graph regularized MF, and so on, cannot be directly applied to the low-rank analysis of sparse big data on cloud and edge platforms. To solve this intractable problem for sparse big data analysis, we propose Locality Sensitive Hashing (LSH) aggregated MF (LSH-MF), which can solve the following problems: (1) The proposed probabilistic projection strategy of LSH-MF can avoid the construction of the GSM. Furthermore, LSH-MF can satisfy the requirement for the accurate projection of sparse big data. (2) To run LSH-MF for fine-grained parallelization and online learning on GPUs, we also propose CULSH-MF, which works on CUDA parallelization. Experimental results show that CULSH-MF can not only reduce the computational time and memory overhead but also obtain higher accuracy. Compared with deep learning models, CULSH-MF can not only save training time but also achieve the same accuracy performance.
    Type of Medium: Online Resource
    ISSN: 2691-1922
    URL: Article
    DOI: 10.1145/3497749
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2021
    detail.hit.zdb_id: 3026898-9
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  • 5
    Online Resource
    Online Resource
    Dynamic Bicycle Dispatching of Dockless Public Bicycle-sharing Systems Using Multi-objective Reinforcement Learning
    Association for Computing Machinery (ACM) ; 2021
    In:  ACM Transactions on Cyber-Physical Systems Vol. 5, No. 4 ( 2021-10-31), p. 1-24
    Details
    In: ACM Transactions on Cyber-Physical Systems, Association for Computing Machinery (ACM), Vol. 5, No. 4 ( 2021-10-31), p. 1-24
    Abstract: As a new generation of Public Bicycle-sharing Systems (PBS), the Dockless PBS (DL-PBS) is an important application of cyber-physical systems and intelligent transportation. How to use artificial intelligence to provide efficient bicycle dispatching solutions based on dynamic bicycle rental demand is an essential issue for DL-PBS. In this article, we propose MORL-BD, a dynamic bicycle dispatching algorithm based on multi-objective reinforcement learning to provide the optimal bicycle dispatching solution for DL-PBS. We model the DL-PBS system from the perspective of cyber-physical systems and use deep learning to predict the layout of bicycle parking spots and the dynamic demand of bicycle dispatching. We define the multi-route bicycle dispatching problem as a multi-objective optimization problem by considering the optimization objectives of dispatching costs, dispatch truck's initial load, workload balance among the trucks, and the dynamic balance of bicycle supply and demand. On this basis, the collaborative multi-route bicycle dispatching problem among multiple dispatch trucks is modeled as a multi-agent and multi-objective reinforcement learning model. All dispatch paths between parking spots are defined as state spaces, and the reciprocal of dispatching costs is defined as a reward. Each dispatch truck is equipped with an agent to learn the optimal dispatch path in the dynamic DL-PBS network. We create an elite list to store the Pareto optimal solutions of bicycle dispatch paths found in each action, and finally get the Pareto frontier. Experimental results on the actual DL-PBS show that compared with existing methods, MORL-BD can find a higher quality Pareto frontier with less execution time.
    Type of Medium: Online Resource
    ISSN: 2378-962X , 2378-9638
    URL: Article
    DOI: 10.1145/3447623
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2021
    detail.hit.zdb_id: 2887359-2
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    Online Resource
  • 6
    Online Resource
    Online Resource
    Citywide Traffic Flow Prediction Based on Multiple Gated Spatio-temporal Convolutional Neural Networks
    Association for Computing Machinery (ACM) ; 2020
    In:  ACM Transactions on Knowledge Discovery from Data Vol. 14, No. 4 ( 2020-08-31), p. 1-23
    Details
    In: ACM Transactions on Knowledge Discovery from Data, Association for Computing Machinery (ACM), Vol. 14, No. 4 ( 2020-08-31), p. 1-23
    Abstract: Traffic flow prediction is crucial for public safety and traffic management, and remains a big challenge because of many complicated factors, e.g., multiple spatio-temporal dependencies, holidays, and weather. Some work leveraged 2D convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) to explore spatial relations and temporal relations, respectively, which outperformed the classical approaches. However, it is hard for these work to model spatio-temporal relations jointly. To tackle this, some studies utilized LSTMs to connect high-level layers of CNNs, but left the spatio-temporal correlations not fully exploited in low-level layers. In this work, we propose novel spatio-temporal CNNs to extract spatio-temporal features simultaneously from low-level to high-level layers, and propose a novel gated scheme to control the spatio-temporal features that should be propagated through the hierarchy of layers. Based on these, we propose an end-to-end framework, multiple gated spatio-temporal CNNs (MGSTC), for citywide traffic flow prediction. MGSTC can explore multiple spatio-temporal dependencies through multiple gated spatio-temporal CNN branches, and combine the spatio-temporal features with external factors dynamically. Extensive experiments on two real traffic datasets demonstrates that MGSTC outperforms other state-of-the-art baselines.
    Type of Medium: Online Resource
    ISSN: 1556-4681 , 1556-472X
    URL: Article
    DOI: 10.1145/3385414
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2020
    detail.hit.zdb_id: 2257358-6
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  • 7
    Online Resource
    Online Resource
    Modeling Temporal Patterns with Dilated Convolutions for Time-Series Forecasting
    Association for Computing Machinery (ACM) ; 2022
    In:  ACM Transactions on Knowledge Discovery from Data Vol. 16, No. 1 ( 2022-02-28), p. 1-22
    Details
    In: ACM Transactions on Knowledge Discovery from Data, Association for Computing Machinery (ACM), Vol. 16, No. 1 ( 2022-02-28), p. 1-22
    Abstract: Time-series forecasting is an important problem across a wide range of domains. Designing accurate and prompt forecasting algorithms is a non-trivial task, as temporal data that arise in real applications often involve both non-linear dynamics and linear dependencies, and always have some mixtures of sequential and periodic patterns, such as daily, weekly repetitions, and so on. At this point, however, most recent deep models often use Recurrent Neural Networks (RNNs) to capture these temporal patterns, which is hard to parallelize and not fast enough for real-world applications especially when a huge amount of user requests are coming. Recently, CNNs have demonstrated significant advantages for sequence modeling tasks over the de-facto RNNs, while providing high computational efficiency due to the inherent parallelism. In this work, we propose HyDCNN, a novel hybrid framework based on fully Dilated CNN for time-series forecasting tasks. The core component in HyDCNN is a proposed hybrid module, in which our proposed position-aware dilated CNNs are utilized to capture the sequential non-linear dynamics and an autoregressive model is leveraged to capture the sequential linear dependencies. To further capture the periodic temporal patterns, a novel hop scheme is introduced in the hybrid module. HyDCNN is then composed of multiple hybrid modules to capture the sequential and periodic patterns. Each of these hybrid modules targets on either the sequential pattern or one kind of periodic patterns. Extensive experiments on five real-world datasets have shown that the proposed HyDCNN is better compared with state-of-the-art baselines and is at least 200% better than RNN baselines. The datasets and source code will be published in Github to facilitate more future work.
    Type of Medium: Online Resource
    ISSN: 1556-4681 , 1556-472X
    URL: Article
    DOI: 10.1145/3453724
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2022
    detail.hit.zdb_id: 2257358-6
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  • 8
    Online Resource
    Online Resource
    A Survey on Applications of Artificial Intelligence in Fighting Against COVID-19
    Association for Computing Machinery (ACM) ; 2022
    In:  ACM Computing Surveys Vol. 54, No. 8 ( 2022-11-30), p. 1-32
    Details
    In: ACM Computing Surveys, Association for Computing Machinery (ACM), Vol. 54, No. 8 ( 2022-11-30), p. 1-32
    Abstract: The COVID-19 pandemic caused by the SARS-CoV-2 virus has spread rapidly worldwide, leading to a global outbreak. Most governments, enterprises, and scientific research institutions are participating in the COVID-19 struggle to curb the spread of the pandemic. As a powerful tool against COVID-19, artificial intelligence (AI) technologies are widely used in combating this pandemic. In this survey, we investigate the main scope and contributions of AI in combating COVID-19 from the aspects of disease detection and diagnosis, virology and pathogenesis, drug and vaccine development, and epidemic and transmission prediction. In addition, we summarize the available data and resources that can be used for AI-based COVID-19 research. Finally, the main challenges and potential directions of AI in fighting against COVID-19 are discussed. Currently, AI mainly focuses on medical image inspection, genomics, drug development, and transmission prediction, and thus AI still has great potential in this field. This survey presents medical and AI researchers with a comprehensive view of the existing and potential applications of AI technology in combating COVID-19 with the goal of inspiring researchers to continue to maximize the advantages of AI and big data to fight COVID-19.
    Type of Medium: Online Resource
    ISSN: 0360-0300 , 1557-7341
    URL: Article
    DOI: 10.1145/3465398
    RVK:
    SA 2943
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2022
    detail.hit.zdb_id: 215909-0
    detail.hit.zdb_id: 1495309-2
    detail.hit.zdb_id: 626472-4
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  • 9
    Online Resource
    Online Resource
    A Survey of AI-enabled Dynamic Manufacturing Scheduling: From Directed Heuristics to Autonomous Learning
    Association for Computing Machinery (ACM) ; 2023
    In:  ACM Computing Surveys Vol. 55, No. 14s ( 2023-12-31), p. 1-36
    Details
    In: ACM Computing Surveys, Association for Computing Machinery (ACM), Vol. 55, No. 14s ( 2023-12-31), p. 1-36
    Abstract: As one of the most complex parts in manufacturing systems, scheduling plays an important role in the efficient allocation of resources to meet individual customization requirements. However, due to the uncertain disruptions (e.g., task arrival time, service breakdown duration) of manufacturing processes, how to respond to various dynamics in manufacturing to keep the scheduling process moving forward smoothly and efficiently is becoming a major challenge in dynamic manufacturing scheduling. To solve such a problem, a wide spectrum of artificial intelligence techniques have been developed to (1) accurately construct dynamic scheduling models that can represent both personalized customer needs and uncertain provider capabilities and (2) efficiently obtain a qualified schedule within a limited time. From these two perspectives, this article systemically makes a state-of-the-art literature survey on the application of these artificial intelligence techniques in dynamic manufacturing modeling and scheduling. It first introduces two types of dynamic scheduling problems that consider service- and task-related disruptions in the manufacturing process, respectively, followed by a bibliometric analysis of artificial intelligence techniques for dynamic manufacturing scheduling. Next, various kinds of artificial-intelligence-enabled schedulers for solving dynamic scheduling problems including both directed heuristics and autonomous learning methods are reviewed, which strive not only to quickly obtain optimized solutions but also to effectively achieve the adaption to dynamics. Finally, this article further elaborates on the future opportunities and challenges of using artificial-intelligence-enabled schedulers to solve complex dynamic scheduling problems. In summary, this survey aims to present a thorough and organized overview of artificial-intelligence-enabled dynamic manufacturing scheduling and shed light on some related research directions that are worth studying in the future.
    Type of Medium: Online Resource
    ISSN: 0360-0300 , 1557-7341
    URL: Article
    DOI: 10.1145/3590163
    RVK:
    SA 2943
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2023
    detail.hit.zdb_id: 215909-0
    detail.hit.zdb_id: 1495309-2
    detail.hit.zdb_id: 626472-4
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  • 10
    Online Resource
    Online Resource
    Inferring Activity Patterns from Sparse Step Counts Data with Recurrent Neural Networks
    Association for Computing Machinery (ACM) ; 2023
    In:  ACM Transactions on Computing for Healthcare Vol. 4, No. 1 ( 2023-01-31), p. 1-20
    Details
    In: ACM Transactions on Computing for Healthcare, Association for Computing Machinery (ACM), Vol. 4, No. 1 ( 2023-01-31), p. 1-20
    Abstract: As an accurate measurement of physical activity, step counts data can be collected expediently by smartphones and wearable devices. Complete and high time-resolution step counts data record the time and intensity of individuals’ physical activity in a day, and can be used to mine activity habits or to recommend customized workout plans. However, sparse step counts data are common in practice due to hardware and software limitations. Understanding the value of sparse step counts data can contribute to its application in healthcare, and also can help us design cost-effective hardware and software. In this article, we aim to infer activity patterns from sparse step counts data. We design a deep learning model based on recurrent neural networks, namely MLP-GRU, which considers bidirectional short-term dependency and long-term regularity of sparse step counts data, and implements data-driven imputation and classification. We also develop an interpretable and elastic method to obtain sparse step counts data labeled with multi-granular activity patterns to train MLP-GRU. Evaluations on real-world datasets reveal that MLP-GRU outperforms other strong baseline methods. The results also show that activity patterns can be inferred from extremely sparse step counts data with high accuracy, provided that proper granularity is used for data of different sparsity.
    Type of Medium: Online Resource
    ISSN: 2691-1957 , 2637-8051
    URL: Article
    DOI: 10.1145/3560468
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2023
    detail.hit.zdb_id: 3026919-2
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