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3DGCformer: 3-Dimensional Graph Convolutional transformer for multi-step origin–destination matrix forecasting

Applied intelligence 2025年第6期55卷 1-18页

作者： Huang, Yiou Deng, Hao Zhao, Shengjie School of Computer Science and Technology Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Shanghai China Engineering Research Center of Key Software Technologies for Smart City Perception and Planning Ministry of Education Shanghai China

Forecasting Human mobility is of great significance in the simulation and control of infectious diseases like COVID-19. To get a clear picture of potential future outbreaks, it is necessary to forecast multi-step Origin–Destination (OD) matrices for a relatively long period in the future. However, multi-step Origin–Destination Matrix Forecasting (ODMF) is a non-trivial problem. First, previous ODMF models only forecast the OD matrix for the next time-step, and they cannot perform well on long-term multi-step forecasts due to error accumulation. Second, many ODMF methods capture spatial and temporal dependencies with separate modules, which is insufficient to model spatio-temporal correlations in the time-varying OD matrix sequence. To address the challenges in multi-step ODMF, we propose 3-Dimensional Graph Convolutional Transformer (3DGCformer). As an enhancement of the original 3DGCN, we propose a novel Origin–Destination Feature Propagation (ODFP) rule between 3DGCN layers and integrate 2 3DGCNs with different spatio-temporal graphs and corresponding feature propagation rules to model the formation of OD flows in a more comprehensive way. For multi-step forecasts, 3DGCformer uses Transformer to capture long-term global temporal dependency, and adapt its decoder using labeled tokens to avoid error accumulation and improve time efficiency. To avoid information loss as the number of regions increases, we propose a patch embedding approach to convert data from 3DGCNs to the Transformer module. We perform extensive experiments on 4 real-world human mobility datasets, and the results show that our proposed model outperforms the state-of-the-art methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolution

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IoT monitoring and control system of Distribution Transformers in Rwanda 23

IoT monitoring and control system of Distribution Transforme...

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12th International Conference on Software and Computer Applications, ICSCA 2023

作者： Bimenyimana, Emmanuel Nsengiyumva, Philibert Ngoga, Said R. University of Rwanda College of Science and Technology African center of Excellency in Internet of Things Embedded Computing Systems Rwanda University of Rwanda College of Science and Technology Rwanda

ISBN: (纸本)9781450398589

In developing countries, many customers do not get good quality of electricity power supply due to frequent and prolonged power fluctuations/cuts. Distribution transformer (DT) is a service transformer that provides the final voltage transformation in the electric power distribution system, stepping down the voltage to the level used by the customers. Monitoring and control of DT (as a crucial and expensive asset in the network) is a key enabler of power stability to consumers. A power utility is said to be a business oriented with good image representation if it delivers a reliable and affordable electricity. Modern technologies such as the Internet of Things (IoT) offer a wide range of applications in the energy sector to smoothly monitor, control and optimize processes. Currently, many energy companies in developing countries are not yet implementing the remote system to control and monitor the secondary side of DTs and timely get the notifications of fluctuations/abnormalities occurred on those DTs. That is why it is still challenging and time consuming to intervene urgently and do the necessary actions to prevent severe and prolonged power cuts/fluctuations and safeguard the damage of DTs themselves with customer’s appliances connected on those DTs. The secondary side of DT is the one connected directly and supply power to the customers. For this reason, we developed an affordable IoT system that automatically detects the abnormalities/fluctuations of three core technical parameters of DT (which are voltage, current and temperature) using current sensors, voltage sensors, and temperature sensor with ATmega 328P Microcontroller to collect and process data from sensors connected to DT system. Once one or all of those technical parameters become abnormal, the system cut off automatically the secondary side of DT in 2 seconds to isolate and protect the customers’ load with safeguarding DT itself using a power relay. At the same time, GSM/GPRS module uploads the sen

关键词： Internet of things

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An Adaptive Heterogeneous Credit Card Fraud Detection Model Based on Deep Reinforcement Training Subset Selection

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial intelligence 2024年第8期5卷 4026-4041页

作者： Zhu, Kun Zhang, Nana Ding, Weiping Jiang, Changjun the Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai201804 China Collaborative Innovation Center for Financial Network Security Tongji University Shanghai201804 China the School of Computer Science and Technology Donghua University Shanghai201620 China the School of Information Science and Technology Nantong University Nantong226019 China

Heterogeneous fraud detection is an important means of credit card security assurance, which can utilize historical transaction records in a source and target domain to build an effective fraud detection model. Nevertheless, large feature distribution differences between source and target transaction instances and the complex intrinsic structure hidden behind transaction data make it difficult for existing credit card fraud detection (CCFD) models to capture and transfer the most informative feature representations and seriously hinder detection performance. In this work, we propose a novel adaptive heterogeneous CCFD model named adaptive heterogeneous credit card fraud detection model based on deep reinforcement training subset selection (RTAHC) based on deep reinforcement training subset selection, which mainly contains two components: selection distribution generator (SDG) and transaction fraud detector (TFD, including feature extractor with an attention mechanism and classifier). The SDG can generate the selection probability distribution vector via the reinforcement reward mechanism, and then transaction instances in the source domain relevant to the target domain are selected. The feature extractor with an attention mechanism can learn the abstract deep semantic feature representations of selected source transaction instances and the target domain. The joint training of SDG and TFD can provide more real-time and accurate transaction feature representations to reduce the distribution discrepancy between the two domains. We verify the detection performance of RTAHC across a large real-world credit card transaction dataset and four public datasets. Experimental results demonstrate that the RTAHC model can exhibit competitive CCFD performance. Impact Statement—With the rise of artificial intelligence (AI)generated models, credit card fraud has become increasingly rampant, which also causes tens of billions of U.S. dollars in credit card losses worldwide every year

关键词： Hidden Markov models

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POPA: Expressing High and Portable Performance across Spatial and Vector Architectures for Tensor Computations 24

POPA: Expressing High and Portable Performance across Spatia...

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32nd ACM International Symposium on Field-Programmable Gate Arrays, FPGA 2024

作者： Hao, Xiaochen Rong, Hongbo Zhang, Mingzhe Sun, Ce Jiang, Hong Liang, Yun Peking University China Parallel Computing Lab Intel United States Tsinghua University China University of Science and Technology of China China Intel United States Peking University & Beijing Advanced Innovation Center for Integrated Circuits China

ISBN: (纸本)9798400704185

This paper aims at high and portable performance for tensor computations across spatial (e.g., FPGAs) and vector architectures (e.g., GPUs). The state-of-the-art usually address performance portability across vector architectures (CPUs and GPUs). However, they either miss FPGAs or do not achieve high performance. Without a common architectural abstraction, they program and optimize spatial and vector devices separately, causing low portability. We propose a unified programming framework, POPA, which achieves portability via architectural abstraction and performance via specialization. A parallel dataflow machine is proposed as a unified, abstract hardware target that hides differences of concrete architectures. The machine consists of software-defined systolic arrays and a tensor-specific cache hierarchy, which captures pipeline parallelism and customizable memories on FPGAs, as well as multithreading parallelism on GPUs. The machine is specified in a unified programming model as two dataflow graphs for scheduling compute and data movement, respectively. A compiler then specializes the abstract machine to exploit the properties of FPGAs and GPUs, bridging the gap between the abstract machine and a concrete architecture. We evaluate POPA on several intel FPGAs and GPUs with high-profile tensor kernels, and this is the first system that achieves >=80% performance of expert-written code or machine peak across architectures, to the best of our knowledge. © 2024 ACM.

关键词： Graphics processing unit

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Information Security Evaluation by Information Flow Analysis Based on Stochastic Petri Nets

Information Security Evaluation by Information Flow Analysis...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Tu, Hanqian Xiang, Dongming Lin, Wang Liu, Guanjun Zhejiang Sci-Tech University Department of Computer Science and Technology Hangzhou310018 China Shanghai Electronic Transactions and Information Service Collaborative Innovation Center Tongji University Key Laboratory of Embedded System and Service Computing Ministry of Education Department of Computer Science Shanghai201804 China

ISBN: (纸本)9781665410205

The Petri-net-based information flow analysis offers an effective approach for detecting information leakage by the concept of non-interference. Although the related studies propose efficient solutions, they lack quantitative evaluation on information leakage. In this paper, we propose a novel method for quantitative evaluation of information security based on stochastic labeled Petri nets (SLPNs) and information flow analysis. Specifically, we introduce four different levels of security metrics, and provide a methodology for evaluating the information security. Furthermore, a case study is presented to show the feasibility of our method. © 2024 IEEE.

关键词： Information leakage

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Heuristic Elastic Scaling for Kubernetes Heterogeneous Microservices 21

Heuristic Elastic Scaling for Kubernetes Heterogeneous Micro...

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21st International Conference on Networking, Sensing and Control, ICNSC 2024

作者： Wang, Song He, Sheng Ning, Yishuang Gao, Xiang Ding, Zhijun The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai201804 China The Department of Computer Science and Technology Tongji University Shanghai201804 China Kingdee Research Kingdee International Software Group Co. Ltd Shenzhen518057 China Intelligent Computing Facility Innovation Center ZhejiangLab Hangzhou30012 China

ISBN: (纸本)9798350365221

Kubernetes has become the basic platform for building cloud native applications. However, existing horizontal scaling methods based on Kubernetes have problems with resource redundancy. Furthermore, the combined horizontal and vertical scaling based on Kubernetes takes a long time. Regarding the issue above, first, we describe the execution processes of three scaling techniques and compare them in different dimensions;second, we construct a formal elastic scaling model for expansion scenarios, with the goal of minimizing the total cost;and third, we introduce a heuristic expansion algorithm after balancing resource redundancy and execution time as the heuristic information. The experiment results show that the elastic scaling algorithm proposed in this paper achieves better performance than other algorithms. © 2024 IEEE.

关键词： Cloud platforms

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Makespan and Security-Aware Workflow Scheduling for Cloud Service Cost Minimization Using Firefly Optimizer 22nd

Makespan and Security-Aware Workflow Scheduling for Cloud ...

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22nd International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2022

作者： Zhou, Chengliang Wang, Tian Li, Liying Sun, Jin Zhou, Junlong School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China National Trusted Embedded Software Engineering Technology Research Center East China Normal University Shanghai200062 China

ISBN: (纸本)9783031226762

As efficient commercial information technology, cloud computing has attracted more and more users to submit their requests to the cloud platform and pay for them based on the amount and quality of services. One of the most important concerns of cloud service providers is to minimize the monetary cost for completing users’ requests while meeting constraints on the quality of services such as makespan and security. To solve this concern, this paper proposes a makespan and security-aware workflow scheduling method based on an improved firefly optimizer that consists of a new solution initialization scheme, a firefly position updating scheme, a firefly-to-solution mapping operator, and two task-to-virtual machine assignment schemes. We carry out extensive simulation experiments on real-world workflows to validate our proposed workflow scheduling method. The results reveal that the monetary cost reduction achieved by our method can be up to 54.0% compared to a baseline and two state-of-the-art approaches. © 2023, Springer Nature Switzerland AG.

关键词： Cloud computing

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Portability and Scalability of OpenMP Offloading on State-of-the-Art Accelerators 38th

Portability and Scalability of OpenMP Offloading on State...

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38th International Conference on High Performance computing, ISC High Performance 2023

作者： Fridman, Yehonatan Tamir, Guy Oren, Gal Department of Computer Science Ben-Gurion University of the Negev Be’er Sheva Israel Department of Physics Nuclear Research Center - Negev Be’er Sheva Israel Scientific Computing Center Nuclear Research Center - Negev Be’er Sheva Israel Intel Corporation Santa Clara United States Department of Computer Science Technion - Israel Institute of Technology Haifa Israel

ISBN: (纸本)9783031408427

Over the last decade, most of the increase in computing power has been gained by advances in accelerated many-core architectures, mainly in the form of GPGPUs. While accelerators achieve phenomenal performances in various computing tasks, their utilization requires code adaptations and transformations. Thus, OpenMP, the most common standard for multi-threading in scientific computing applications, introduced offloading capabilities between host (CPUs) and accelerators since v4.0, with increasing support in the successive v4.5, v5.0, v5.1, and the latest v5.2 versions. Recently, two state-of-the-art GPUs – the intel Ponte Vecchio Max 1100 and the NVIDIA A100 GPUs – were released to the market, with the oneAPI and NVHPC compilers for offloading, correspondingly. In this work, we present early performance results of OpenMP offloading capabilities to these devices while specifically analyzing the portability of advanced directives (using SOLLVE’s OMPVV test suite) and the scalability of the hardware in representative scientific mini-app (the LULESH benchmark). Our results show that the coverage for version 4.5 is nearly complete in both latest NVHPC and oneAPI tools. However, we observed a lack of support in versions 5.0, 5.1, and 5.2, which is particularly noticeable when using NVHPC. From the performance perspective, we found that the PVC1100 and A100 are relatively comparable on the LULESH benchmark. While the A100 is slightly better due to faster memory bandwidth, the PVC1100 reaches the next problem size (4003 ) scalably due to the larger memory size. The results are available at: https://***/Scientific-computing-Lab-NRCN/Accel-OpenMP-Portability-Scalability. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Scalability

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Pixel Image Segmentation of Cerebral Hematoma Based on U-Net and Clustering

Pixel Image Segmentation of Cerebral Hematoma Based on U-Net...

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Computer, Vision and intelligent technology (ICCVIT), International Conference on

作者： Hai-Feng Zhu Yao-Hui Liu Kun Guo Si-Jie Zhang Ge Chen Department of Computer Science and Technology XiangNan University ChenZhou China Hunan Engineering Research Center of Advanced Embedded Computing and Intelligent Medical Systems Xiangnan University ChenZhou China

ISBN: (数字)9798331540043

ISBN: (纸本)9798331540050

Intracerebral hemorrhage is a disease with high fatality rate, and the expansion of hematoma caused by hemorrhage is one of the factors leading to poor prognosis. Fast and accurate segmentation of the contours of cerebral hematoma will help doctors to make diagnosis to a large extent. In order to solve the problem of insufficient precision of U-net segmentation, pixel image segmentation based on U-net and clustering is proposed. The CT images are divided into multiple channels by preprocessing, and on this basis, the cluster is processed twice, and then they enter the improved U-net network respectively. Finally, multiple results are fused to obtain the final segmentation result.

关键词： Image segmentation Accuracy Computed tomography Clustering algorithms Feature extraction Classification algorithms Kernel Hemorrhaging Unsupervised learning Diseases

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Virtual-Source and Virtual-Swarm-Based Particle Swarm Optimizer for Large-Scale Multi-Source Location via Robot Swarm

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Zhang, JunQi Lin, YuXuan Zhou, MengChu Department of Computer Science and Technology the Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai Electronic Transactions and Information Service Collaborative Innovation Center Tongji University Shanghai China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

Multi-source location is a significant application in the field of robot swarm and is required to find all sources whose number and distribution are unknown in advance. With few parameters and fast search, Particle Swarm Optimizer (PSO) variants that have certain grouping capability have been applied to address multi-source location problems by dividing a swarm such that every source has robots to locate. However, they are difficult to predetermine the exact number of groups, require a big number of robots, and are easily trapped in the no-signal areas when the proportion of no-signal areas is high. This work proposes a Virtual-source and Virtual-swarm-based PSO (VVPSO) to divide a search area into multiple cells equally, each of which has a virtual source in its center. Then, instead of robots grouping, only one group of robots is employed to traverse all virtual sources, and search their corresponding cells to locate real sources by a new PSO called Real-virtual mapping PSO (RMPSO). RMPSO asymmetrically maps a robot into a particle swarm with multiple virtual particles to perform PSO, which greatly reduces the requirements for the number of robots. Experimental results show that VVPSO has great search scalability and can solve large-scale multi-source location problems than two state-of-the-art grouping methods and three representative multimodal PSO variants, even with only one robot. Hence, this work greatly advances the field of multi-source location by using mobile robot swarm. IEEE

关键词： Robots

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