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Leveraging Hybrid Classical-Quantum Methods for Efficient Load Rebalancing in HPC

Leveraging Hybrid Classical-Quantum Methods for Efficient Lo...

2024 Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Zawalska, Justyna Chung, Minh Rycerz, Katarzyna Schulz, Laura Schulz, Martin Kranzlmuller, Dieter AGH University of Krakow Institute of Computer Science Krakow Poland CYFRONET AGH Academic Computer Center Krakow Poland Garching bei München Germany Chair for Computer Architecture and Parallel Systems Germany MNM-Team Germany

ISBN: (纸本)9798350355543

Load imbalance is a challenge for parallel applications in High Performance Computing (HPC). It is caused by processes having different execution times or load values, leading to idle or wait times at synchronization points, where faster processes must wait for the slowest process to catch up. To mitigate this issue, applications can employ load balancing (LB) strategies, which migrate load between processes to even out load. This is often referred to as the Load Rebalancing Problem (LRP). While many approaches solving the LRP exist, they can only be heuristics and hence further optimization potential exists. In our work, we turn to a novel approach by using hybrid classical-quantum approaches and present two versions of the constrained quadratic model for solving the LRP;the two differ in how they balance the number of qubits required with the types of applied constraints. We compare the quantum-based methods with classical methods using heuristic algorithms Greedy, Karmarkar-Karp, and ProactLB. We evaluate our approaches using imbalance ratio and speedup as metrics, as well as the number of migrated tasks to indicate overhead caused by migrations. Our results show that the quantum-based methods outperform the classic methods. For example, we need only 1/4 of the number of migrated tasks in a realistic use case compared with classical methods, particularly Greedy and KK, to balance the load. © 2024 IEEE.

关键词： CQM HPC HPCQC integration Load Rebalancing Quantum Computing task migration

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A Template for Digital Human Representation in Digital Twin Simulations of Social Human-Robot Interaction 4

A Template for Digital Human Representation in Digital Twin ...

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4th International Conference on Electrical, computer, Communications and Mechatronics Engineering, ICECCME 2024

作者： Zebisch, Raoul Schilp, Johannes Institute of Applied Computer Science University of Augsburg Chair of Digital Manufacturing Augsburg Germany

ISBN: (纸本)9798350391183

The virtual validation of a robot's social capability using digital twins requires assessing the perceived quality of human-machine interaction, reflected through various observ-Able human parameters that influence the comfort experienced during the interaction. Due to this reason, the detailed digital representation of humans in such simulations is as essential as it is difficult to achieve. This paper introduces a template for digital human representation in a digital twin intended for simulation scenarios of social human-robot interaction. Parameters and properties are grouped into categories for physical behavior, physiological condition (including health), social-cognitive behav-ior, visualization, and sensory capabilities describing a simplified digital representation of a real-world person. The proposed model parameters are chosen based on a literature review of published work regarding digital twin solutions for applications in social robotics with a focus on human digital representation. © 2024 IEEE.

关键词： Digital elevation model

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Predictive-Reactive Scheduling to Increase Robustness of Production Systems 4

Predictive-Reactive Scheduling to Increase Robustness of Pro...

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4th International Conference on Electrical, computer, Communications and Mechatronics Engineering, ICECCME 2024

作者： Pleier, Florian Schilp, Johannes Institute of Applied Computer Science University of Augsburg Chair of Digital Manufacturing Augsburg Germany

ISBN: (纸本)9798350391183

In order to enhance the robustness and flexibility of production systems and improve their responsiveness to unforeseen events, it is essential to devise appropriate strategies. This paper aims to develop and evaluate a predictive-reactive scheduling method to increase the robustness of production planning and control. As disruptions can often be anticipated, but not always prevented, a scheduling method that combines pre-dictive planning and reactive control reduces the impact of such occurrences on the production system. In order to achieve this, a real-world use case is introduced, and the relevant disruptions are identified. To address the predictive aspect of the methodology, an allocation problem and subsequently a sequencing problem are first solved using heuristics and mathematical optimization in order to schedule the jobs. Reactive scheduling strategies are then developed and implemented into a discrete event simulation model for the purpose of evaluating the improvement of the results. The results demonstrate a significant enhancement in production planning, as evidenced by more balanced processing times and a sufficient buffer for rescheduling. Therefore, this method enables the generation of a robust and cost-minimized production plan. By implementing the rescheduling strategy for production control, the impact of disruptions are minimized, as evidenced by the obtained key figures which serve as a robustness indicator. © 2024 IEEE.

关键词： Advanced Manufacturing Automated Production Planning and Control Production Disruption Robustness Simulation

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Block-gram:Mining knowledgeable features for efficiently smart contract vulnerability detection

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Digital Communications and Networks 2025年第1期11卷 1-12页

作者： Xueshuo Xie Haolong Wang Zhaolong Jian Yaozheng Fang Zichun Wang Tao Li Tianjin Key Laboratory of Network and Data Security Technology TianjinChina College of Computer Science Nankai UniversityTianjinChina Key Laboratory of Blockchain and Cyberspace Governance of Zhejiang Province China State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesChina

Smart contracts are widely used on the blockchain to implement complex transactions,such as decentralized applications on *** vulnerability detection of large-scale smart contracts is critical,as attacks on smart contracts often cause huge economic *** it is difficult to repair and update smart contracts,it is necessary to find the vulnerabilities before they are ***,code analysis,which requires traversal paths,and learning methods,which require many features to be trained,are too time-consuming to detect large-scale on-chain ***-based methods will obtain detection models from a feature space compared to code analysis methods such as symbol *** the existing features lack the interpretability of the detection results and training model,even worse,the large-scale feature space also affects the efficiency of *** paper focuses on improving the detection efficiency by reducing the dimension of the features,combined with expert *** this paper,a feature extraction model Block-gram is proposed to form low-dimensional knowledge-based features from ***,the metadata is separated and the runtime code is converted into a sequence of opcodes,which are divided into segments based on some instructions(jumps,etc.).Then,scalable Block-gram features,including 4-dimensional block features and 8-dimensional attribute features,are mined for the learning-based model ***,feature contributions are calculated from SHAP values to measure the relationship between our features and the results of the detection *** addition,six types of vulnerability labels are made on a dataset containing 33,885 contracts,and these knowledge-based features are evaluated using seven state-of-the-art learning algorithms,which show that the average detection latency speeds up 25×to 650×,compared with the features extracted by N-gram,and also can enhance the interpretability of the detection model.

关键词： Smart contract Bytecode&opcode Knowledgeable features Vulnerability detection Feature contribution

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Modelling of aircraft trajectories for emergency landing using kinematoid chains

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CEAS Aeronautical Journal 2023年第3期14卷 679-692页

作者： Flämig, Steffen Graefenhan, Matthias Schiffmann, Wolfram FernUniversität in Hagen Chair of Computer Architecture Universitätsstraße 47 Hagen58097 Germany Department of Mathematics and Computer Science Philipps-Universität Marburg Hans-Meerwein-Straße 6 Marburg35032 Germany

Most methods that compute trajectories for un- or low-powered flight operate under simplifying assumptions such as constant curve radii and wind conditions. Likewise, changes of air density with altitude that lead to significant differences between equivalent airspeed (EAS) und true airspeed (TAS) are often not considered. Some approaches are based on Dubins paths, which are introduced in Dubins LE (Am J Math 79: 497, 1957). They combine three sections to form a trajectory, which is the shortest from a given start to an end position (In the original work, the position extended by the heading, is referred to as configuration. Since configuration has a different connotation in our context, we use the term state, which can contain other parameters in addition to the position and the heading, e.g. orientation, configuration of landing flaps, landing gear, etc.). A maximum distant landing spot can be reached this way. Often, the targeted landing spot is closer to the aircraft. If it is approached using a Dubins Path, the excess height must be dealt with. Here, we present a method addressing the problem directly, namely finding a trajectory which reduces the excess height over its entire length. Furthermore, it takes spatial and temporal changes of wind and air density into account. Several conditions influence the final shape of the trajectory. For example, avoidance of obstacles and predefined areas is easily achieved. Our method is motivated by kinematic chains, which are used in robotics and computer animation. We extend and modify this principle by incrementally transferring start and end states of the trajectory, modelled as state vectors, into each other. The resulting intermediate states form ends of chain links. To connect initial and final states through the resulting chain, we solve the inverse kinematic problem known from robotics. We extend it by several conditions, which are derived from the flight mechanical characteristics of the modeled aircraft on the on

关键词： Inverse problems

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Multivariate time series imputation for energy data using neural networks

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Energy and AI 2023年第3期13卷 25-35页

作者： Christopher Bulte Max Kleinebrahm Hasan Umitcan Yilmaz Juan Gomez-Romero Institute for Industrial Production(IIP) Chair of Energy EconomicsKarlsruhe Institute of Technology(KIT)Germany Department of Computer Science and Artificial Intelligence University of GranadaSpain

Multivariate time series with missing values are common in a wide range of applications,including energy *** imputation methods often fail to focus on the temporal dynamics and the cross-dimensional correlation *** this paper we propose a two-step method based on an attention model to impute missing values in multivariate energy time ***,the underlying distribution of the missing values in the data is *** information is then further used to train an attention based imputation *** learning the distribution prior to the imputation process,the model can respond flexibly to the specific characteristics of the underlying *** developed model is applied to European energy data,obtained from the European Network of Transmission System Operators for *** different evaluation metrics and benchmarks,the conducted experiments show that the proposed model is preferable to the benchmarks and is able to accurately impute missing values.

关键词： Missing value estimation Multivariate time series Neural networks Attention model Energy data

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An attention-based teacher-student model for multivariate short-term landslide displacement prediction incorporating weather forecast data

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Journal of Mountain science 2024年第8期21卷 2739-2753页

作者： CHEN Jun HU Wang ZHANG Yu QIU Hongzhi WANG Renchao Sichuan Artificial Intelligence Research Institute Yibin 644000China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China School of Architecture and Civil Engineering Chengdu UniversityChengdu 610106China

Predicting the displacement of landslide is of utmost practical importance as the landslide can pose serious threats to both human life and ***,traditional methods have the limitation of random selection in sliding window selection and seldom incorporate weather forecast data for displacement prediction,while a single structural model cannot handle input sequences of different lengths at the same *** order to solve these limitations,in this study,a new approach is proposed that utilizes weather forecast data and incorporates the maximum information coefficient(MIC),long short-term memory network(LSTM),and attention mechanism to establish a teacher-student coupling model with parallel structure for short-term landslide displacement *** MIC,a suitable input sequence length is selected for the LSTM *** investigate the influence of rainfall on landslides during different seasons,a parallel teacher-student coupling model is developed that is able to learn sequential information from various time series of different *** teacher model learns sequence information from rainfall intensity time series while incorporating reliable short-term weather forecast data from platforms such as China Meteorological Administration(CMA)and Reliable Prognosis(https://***)to improve the model’s expression capability,and the student model learns sequence information from other time *** attention module is then designed to integrate different sequence information to derive a context vector,representing seasonal temporal attention ***,the predicted displacement is obtained through a linear *** proposed method demonstrates superior prediction accuracies,surpassing those of the support vector machine(SVM),LSTM,recurrent neural network(RNN),temporal convolutional network(TCN),and LSTM-Attention *** achieves a mean absolute error(MAE)of 0.072 mm,root mean square error(RMSE)of 0.096 mm,and pearson correlation coefficients(PC

关键词： Landslide prediction MIC LSTM Attention mechanism Teacher Student model Prediction stability and interpretability

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Adaptive Marine Predator Optimization Algorithm(AOMA)-Deep Supervised Learning Classification(DSLC)based IDS framework for MANET security

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Intelligent and Converged Networks 2024年第1期5卷 1-18页

作者： M.Sahaya Sheela A.Gnana Soundari Aditya Mudigonda C.Kalpana K.Suresh K.Somasundaram Yousef Farhaoui Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600069India Department of Computer Science and Engineering Saveetha School of EngineeringSIMATSChennai 602105India JNIAS School of Planning and Architecture Hyderabad 500034India Department of Computer Science and Engineering NPR College of Engineering and TechnologyNatham Dindigul 624401India Department of Computer Science and Engineering PSNA College of Engineering and TechnologyPoolangulathupatti 620009India Department of Computer Science and Engineering Sri Muthukumaran Institute of TechnologyChennai 600069India Department of Computer Science Moulay Ismail UniversityMeknes 5003Morocco

Due to the dynamic nature and node mobility,assuring the security of Mobile Ad-hoc Networks(MANET)is one of the difficult and challenging tasks *** MANET,the Intrusion Detection System(IDS)is crucial because it aids in the identification and detection of malicious attacks that impair the network’s regular *** machine learning and deep learning methodologies are used for this purpose in the conventional works to ensure increased security of ***,it still has significant flaws,including increased algorithmic complexity,lower system performance,and a higher rate of ***,the goal of this paper is to create an intelligent IDS framework for significantly enhancing MANET security through the use of deep learning ***,the min-max normalization model is applied to preprocess the given cyber-attack datasets for normalizing the attributes or fields,which increases the overall intrusion detection performance of ***,a novel Adaptive Marine Predator Optimization Algorithm(AOMA)is implemented to choose the optimal features for improving the speed and intrusion detection performance of ***,the Deep Supervise Learning Classification(DSLC)mechanism is utilized to predict and categorize the type of intrusion based on proper learning and training *** evaluation,the performance and results of the proposed AOMA-DSLC based IDS methodology is validated and compared using various performance measures and benchmarking datasets.

关键词： Intrusion Detection System(IDS) Security Mobile Ad-hoc Network(MANET) min-max normalization Adaptive Marine Predator Optimization Algorithm(AOMA) Deep Supervise Learning Classification(DSLC)

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Selecting Resource-Efficient ML Models for Transport Mode Detection on Mobile Devices

Selecting Resource-Efficient ML Models for Transport Mode De...

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2022 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2022

作者： Matthes, Philipp Springer, Thomas Institute of Systems Architecture Chair of Computer Networks Tu Dresden Germany

ISBN: (数字)9798350396454

ISBN: (纸本)9798350396454

Processing data closer to the source to minimize latency and the amount of data to be transmitted is a major driver for research on the Internet of Things (IoT). Since data processing in many IoT scenarios heavily depends on machine learning (ML), designing ML models for resource constraint devices at the edge of IoT infrastructures is one of the big challenges. Which ML model performs best highly depends on the problem domain but also on the availability of resources. Thus, to find an appropriate ML model in the broad search space of options, the trade-off between accuracy and resource consumption in terms of memory, CPU, and energy needs to be considered. However, there are ML problems where most current research focuses on accuracy, and the resource consumption of applicable models is not well investigated yet. We show that transport mode detection (TMD) is such a problem and present a case study for designing an ML model running on smartphones. To transform the search for the needle in the haystack into a structured design process, we propose an engineering workflow to systematically evolve ML model candidates, considering portability and resource consumption in addition to model accuracy. At the example of the Sussex-Huawei-Locomotion (SHL) dataset, we apply this process to multiple ML architectures and find a suitable model that convinces with high accuracy and low measured resource consumption for smartphone deployment. We discuss lessons learned, enabling engineers and researchers to use our workflow as a blueprint to identify solutions for their ML problems systematically. © 2022 IEEE.

关键词： Internet of things

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Pedestrian wind flow prediction using spatial-frequency generative adversarial network

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Building Simulation 2024年第2期17卷 319-334页

作者： Pengyue Wang Maozu Guo Yingeng Cao Shimeng Hao Xiaoping Zhou Lingling Zhao School of Architecture and Urban Planning Beijing University of Civil Engineering and ArchitectureBeijing100044China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing100044China School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing100044China School of Computer Science and Technology Harbin Institute of TechnologyHarbin150001China

Pedestrian wind flow is a critical factor in designing livable residential environments under growing complex urban *** pedestrian wind flow during the early design stages is essential but currently suffers from inefficiencies in numerical *** learning,particularly generative adversarial networks(GAN),has been increasingly adopted as an alternative method to provide efficient prediction of pedestrian wind ***,existing GAN-based wind flow prediction schemes have limitations due to the lack of considering the spatial and frequency characteristics of wind flow *** study proposes a novel approach termed SFGAN,which embeds spatial and frequency characteristics to enhance pedestrian wind flow *** the spatial domain,Gaussian blur is employed to decompose wind flow into components containing wind speed and distinguished flow edges,which are used as the embedded spatial *** information of wind flow is obtained through discrete wavelet transformation and used as the embedded frequency *** spatial and frequency characteristics of wind flow are jointly utilized to enforce consistency between the predicted wind flow and ground truth during the training phase,thereby leading to enhanced *** results demonstrate that SFGAN clearly improves wind flow prediction,reducing Wind_MAE,Wind_RMSE and the Fréchet Inception Distance(FID)score by 5.35%,6.52%and 12.30%,compared to the previous best method,*** also analyze the effectiveness of incorporating the spatial and frequency characteristics of wind flow in predicting pedestrian wind *** reduces errors in predicting wind flow at large error intervals and performs well in wake regions and regions surrounding *** enhanced predictions provide a better understanding of performance variability,bringing insights at the early design stage to improve pedestrian wind *** proposed spatial-frequen

关键词： pedestrian wind flow prediction generative adversarial network Gaussian kernel wavelet transform objective function

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