Community detection is the problem of finding naturally forming clusters in networks. It is an important problem in mining and analyzing social and other complex networks. Community detection can be used to analyze co...
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ISBN:
(纸本)9783031785405;9783031785412
Community detection is the problem of finding naturally forming clusters in networks. It is an important problem in mining and analyzing social and other complex networks. Community detection can be used to analyze complex systems in the real world and has applications in many areas, including network science, data mining, and computational biology. Label propagation is a community detection method that is simpler and faster than other methods such as Louvain, InfoMap, and spectral-based approaches. Some real-world networks can be very large and have billions of nodes and edges. Sequential algorithms might not be suitable for dealing with such large networks. This paper presents distributed-memory and hybrid parallel community detection algorithms based on the label propagation method. We incorporated novel optimizations and communication schemes, leading to very efficient and scalable algorithms. We also discuss various load-balancing schemes and present their comparative performances. These algorithms have been implemented and evaluated using large high-performance computing systems. Our hybrid algorithm is scalable to thousands of processors and has the capability to process massive networks. This algorithm was able to detect communities in the Metaclust50 network, a massive network with 282 million nodes and 42 billion edges, in 654 s using 4096 processors.
Early Exit Neural networks (EENNs) achieve enhanced efficiency compared to traditional models, but creating them is challenging due to the many additional design choices required. To address this, we propose an automa...
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ISBN:
(纸本)9783031783791;9783031783807
Early Exit Neural networks (EENNs) achieve enhanced efficiency compared to traditional models, but creating them is challenging due to the many additional design choices required. To address this, we propose an automated augmentation flow that converts existing models into EENNs, making all necessary design decisions for deployment on heterogeneous or distributed embedded targets. Our framework is the first to perform all these steps, including EENN architecture construction, subgraph mapping, and decision mechanism configuration. We evaluated our approach on embedded Deep Learning scenarios, achieving significant performance improvements. Our solution reduced latency by 65.95% on a speech command detection problem and mean operations per inference by 78.3% on an ECG classification task. This showcases the potential for EENNs in embedded applications.
Certifying software-based systems is a time-consuming and expensive task that requires much manual human effort. We introduce Online Certification, a partly automated version of the certification process, where partic...
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ISBN:
(纸本)9783031744976;9783031744983
Certifying software-based systems is a time-consuming and expensive task that requires much manual human effort. We introduce Online Certification, a partly automated version of the certification process, where participants provide the necessary information dynamically. All information is cryptographically signed to ensure integrity and authorization, and a system of certificates allows for fine-grained delegation of competencies. The requirements for certification, as well as the information needed to fulfill them, are represented in a subset of first-order logic. Consequently, validation is performed using automated logic reasoning. Compared to existing approaches, Online Certification enhances flexibility and agility. In cases where automatic generation of certification data is not possible, human certification processes can be integrated.
We present a distributed railway interlocking (IXL) method based on trains communicating with switch boxes deployed along the railway network for switching points and monitoring the occupancy states of track elements....
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Satellite networks, integral to future communication, benefit from SDN’s advantages. Combining SDN with satellite networks, especially leveraging distributed controllers, shows promise. To facilitate research, we pro...
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This paper describes a novel virtual platform for university teaching, which in particular allows the creation and use of complex IT infrastructures even for non-experts. Until now, complex network infrastructures in ...
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Payment Channel networks (PCNs) can significantly enhance the scalability of blockchain transactions without requiring major modifications to the underlying distributed ledger protocol. However, to enable efficient an...
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The proceedings contain 9 papers. The special focus in this conference is on distributed Computing and Artificial Intelligence. The topics include: A Deep Learning-Based OCR System Implementation for Traceability...
ISBN:
(纸本)9783031809453
The proceedings contain 9 papers. The special focus in this conference is on distributed Computing and Artificial Intelligence. The topics include: A Deep Learning-Based OCR System Implementation for Traceability Ensurement in a Metal Manufacturing Workshop;dimensional Reduction Techniques for the Characterization of Behavioral Patterns in Dairy Cows;geothermal Heat Exchanger’s Temperature Input Sensor Prediction Based on Deep Learning Modelling Technique;a Hybrid Intelligence Model Forecasts the Temperature of a Battery Used in Electric Vehicles;a Comparative Analysis of Algorithms and Metrics to Perform Clustering;wind Speed Virtual Sensor for Small Wind Turbine;reconstructing Turbulence-Distorted Wavefronts Through Laser-Beam Profiles.
The proceedings contain 9 papers. The special focus in this conference is on distributed Computing and Artificial Intelligence. The topics include: A Deep Learning-Based OCR System Implementation for Traceability...
ISBN:
(纸本)9783031820724
The proceedings contain 9 papers. The special focus in this conference is on distributed Computing and Artificial Intelligence. The topics include: A Deep Learning-Based OCR System Implementation for Traceability Ensurement in a Metal Manufacturing Workshop;dimensional Reduction Techniques for the Characterization of Behavioral Patterns in Dairy Cows;geothermal Heat Exchanger’s Temperature Input Sensor Prediction Based on Deep Learning Modelling Technique;a Hybrid Intelligence Model Forecasts the Temperature of a Battery Used in Electric Vehicles;a Comparative Analysis of Algorithms and Metrics to Perform Clustering;wind Speed Virtual Sensor for Small Wind Turbine;reconstructing Turbulence-Distorted Wavefronts Through Laser-Beam Profiles.
In recent years, detecting sophisticated attacks in distributed microservice environments has become increasingly challenging, mainly due to containerization, which adds another dimension of complexity for collecting ...
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