In this paper, a directed network in which there is a specific percentage of loss for each arc is considered. These losses are due to evaporation, leaks, energy dissipation, theft, etc. We deal with the problem of fin...
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Resource loss systems (ReLS) with signals, in which arrival of a signal triggers resource reallocation of a customer, are often used in the performance analysis of the contemporary mobile networks, especially those th...
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Modern distribution networks are in a stage of transformation. The first type of transformation is in terms of their transition to grids with available distributed sources of electricity, the other one consists in cha...
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In recent years, there has been a significant proliferation of industrial Internet of Things (IoT) applications, with a wide variety of use cases being developed and put into operation. As the industrial IoT landscape...
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ISBN:
(纸本)9798350303490
In recent years, there has been a significant proliferation of industrial Internet of Things (IoT) applications, with a wide variety of use cases being developed and put into operation. As the industrial IoT landscape expands, the establishment of secure and reliable infrastructure becomes crucial to instil trust among users and stakeholders, particularly in addressing fundamental concerns such as traceability, integrity protection, and privacy that some industries still encounter today. This paper introduces a privacy-preserving method in the industry's IoT systems using blockchain-based data access control for remote industry safety monitoring and maintaining event information confidentiality, integrity and
Dance style recognition through video analysis during university training can significantly benefit both instructors and novice dancers. Employing video analysis in training offers substantial advantages, including th...
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Dance style recognition through video analysis during university training can significantly benefit both instructors and novice dancers. Employing video analysis in training offers substantial advantages, including the potential to train future dancers using innovative technologies. Over time, intricate dance gestures can be honed, reducing the burden on instructors who would, otherwise, need to provide repetitive demonstrations. Recognizing dancers' movements, evaluating and adjusting their gestures, and extracting cognitive functions for efficient evaluation and classification are pivotal aspects of our model. Deep learning currently stands as one of the most effective approaches for achieving these objectives, particularly with short video clips. However, limited research has focused on automated analysis of dance videos for training purposes and assisting instructors. In addition, assessing the quality and accuracy of performance video recordings presents a complex challenge, especially when judges cannot fully focus on the on-stage performance. This paper proposes an alternative to manual evaluation through a video-based approach for dance assessment. By utilizing short video clips, we conduct dance analysis employing techniques such as fine-grained dance style classification in video frames, convolutional neural networks (CNNs) with channel attention mechanisms (CAMs), and autoencoders (AEs). These methods enable accurate evaluation and data gathering, leading to precise conclusions. Furthermore, utilizing cloud space for real-time processing of video frames is essential for timely analysis of dance styles, enhancing the efficiency of information processing. Experimental results demonstrate the effectiveness of our evaluation method in terms of accuracy and F1-score calculation, with accuracy exceeding 97.24% and the F1-score reaching 97.30%. These findings corroborate the efficacy and precision of our approach in dance evaluation analysis.
The increasing pervasiveness of digital infrastructures, also extending into marine domains, makes Underwater Wireless Sensor networks (UWSNs) an essential tool for the development of novel marine sustainability and m...
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Social media, with its immediacy and convenience, has become an important channel for people to exchange information. However, this freedom of information dissemination also provides a breeding ground for the spread o...
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作者：
Grum, MarcusUniv Potsdam
Jr Chair Business Informat Syst Esp AI Based Appl Sys D-14482 Potsdam Germany
With the further development of more and more production machines into cyber-physical systems, and their greater integration with artificial intelligence (AI) techniques, the coordination of intelligent systems is a h...
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ISBN:
(纸本)9783031640728;9783031640735
With the further development of more and more production machines into cyber-physical systems, and their greater integration with artificial intelligence (AI) techniques, the coordination of intelligent systems is a highly relevant target factor for the operation and improvement of networked processes, such as they can be found in cross-organizational production contexts spanning multiple distributed locations. This work aims to extend prior research on managing their artificial knowledge transfers as coordination instrument by examining effects of different activation types (respective activation rates and cycles) on by Artificial Neural Network (ANN)-instructed production machines. For this, it provides a new integration type of ANN-based cyber-physical production system as a tool to research artificial knowledge transfers: In a design-science-oriented way, a prototype of a simulation system is constructed as Open Source information system which will be used in on-building research to (I) enable research on ANN activation types in production networks, (II) illustrate ANN-based production networks disrupted by activation types and clarify the need for harmonizing them, and (III) demonstrate conceptual management interventions. This simulator shall establish the importance of site-specific coordination mechanisms and novel forms of management interventions as drivers of efficient artificial knowledge transfer.
As deep learning models and input data continue to scale at an unprecedented rate, it has become inevitable to move towards distributed training platforms to fit the models and increase training throughput. State-of-t...
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ISBN:
(纸本)9798350397390
As deep learning models and input data continue to scale at an unprecedented rate, it has become inevitable to move towards distributed training platforms to fit the models and increase training throughput. State-of-the-art distributed training systems are adopting emerging approaches and techniques such as wafer-scale nodes, multi-dimensional network topologies, disaggregated memory systems, and optimized parallelization strategies. This results in a complex software/hardware co-design stack, necessitating a modeling/simulation infrastructure for design-space exploration. This paper introduces ASTRA-sim2.0, which extends the open-source ASTRA-sim infrastructure with capabilities to model state-of-the-art and emerging distributed training models and platforms. Specifically, we enable ASTRA-sim to (i) support arbitrary model parallelization strategies via a graph-based training-loop implementation, (ii) implement a parameterizable multi-dimensional heterogeneous topology generation infrastructure with the capability to simulate target systems at scale through analytical performance estimation, and (iii) enhance memory system modeling to support accurate modeling of in-network collective communication and disaggregated memory systems. With these capabilities, we conduct comprehensive case studies targeting emerging distributed models and platforms. ASTRA-sim2.0 enables system designers to swiftly traverse the complex co-design stack and gain meaningful insights when designing and deploying distributed training platforms at scale.
Identifying encrypted traffic from emerging applications is important and challenging as traditional traffic classification approaches fail to achieve the desired level of accuracy. This necessitates the elaborate str...
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