The proceedings contain 94 papers. The special focus in this conference is on Collaborative networks, Globalization and Production Management. The topics include: Relationships and centrality in a cluster of the milk ...
ISBN:
(纸本)9783319227559
The proceedings contain 94 papers. The special focus in this conference is on Collaborative networks, Globalization and Production Management. The topics include: Relationships and centrality in a cluster of the milk production network in the state of Parana/Brazil;public-private management;intelligent and accessible data flow architectures for manufacturing system optimization;social network analysis on grain production in the brazilian scenario;innovation and differentiation strategies integrating the business strategies and production in companies networks;towards platform-based co-development and co-evolution of product and production system;developing a collaborative framework for mapping and managing key drivers of future value creation based on intangible assets;key performance indicators for integrating maintenance management and manufacturing planning and control;ERP evaluation in cloud computing environment;dynamic documents in manufacturing;collaborative supplying networks;collaborative knowledge for analysis material flow of a complex long stud using multiple stoke cold heading;leagility in a triad with multiple decoupling points;information system as a tool to decrease the economic distortion in trade metrology;consumer attitudes toward cross-cultural products in convenience stores;logistics issues in the brazilian pig industry;design of an integrated model for the real-time disturbance management in transportation supply networks;the responsiveness of food retail supply chains;application of mass customization in the construction industry;a cybernetic reference model for production systems using the viable system model and manufacturing digitalization and its effects on production planning and control practices.
Modern computing has primarily shifted towards the distributed environment using commodity resources which results in increase in data and its security concern. This paper deals with design consideration of network In...
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ISBN:
(纸本)9781479962723
Modern computing has primarily shifted towards the distributed environment using commodity resources which results in increase in data and its security concern. This paper deals with design consideration of network Intrusion Detection System (NIDS) based on the Hadoop framework and acceleration of its performance by using General Purpose Graphical Processing Unit (GPGPU). The large volume of data from an entire infrastructure is assigned to Hadoop framework and intrusion detections are carried out on GPGPU. This approach improves NIDS performance and it enables to provide quick response to various attacks on the network. In order to perform the general purposed computation on the GPU, NVidia provides the Compute Unified Device Architecture (CUDA) which is a parallel programming model which performs high-end complex operations using GPU. In order to process large volumes of data in distributed networks, Hadoop framework has to configure with various supporting ecosystems like Flume, Pig, Hive and HBase. These ecosystems enable the Hadoop framework to handle streaming data on the network and large log files on servers. The proposed system is capable of performing analytics over intrusion pattern and their behavior on the network, which helps a network administrator to configure network security policy and settings. Analytics over intrusion is done by using a Score-Weight approach called as Pattern Frequency Inverse Cluster Frequency (PF-ICF). The design consideration of accelerated NIDS is a solution towards the performance issues of various NIDS that faces due to the large volumes of the network traffic.
Graph neural networks (GNNs) are one of the rapidly growing fields within deep learning. While many distributed GNN training frameworks have been proposed to increase the training throughput, they face three limitatio...
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ISBN:
(数字)9798400706318
ISBN:
(纸本)9798400706318
Graph neural networks (GNNs) are one of the rapidly growing fields within deep learning. While many distributed GNN training frameworks have been proposed to increase the training throughput, they face three limitations when applied to multi-server clusters. 1) They suffer from an inter-server communication bottleneck because they do not consider the inter-/intra-server bandwidth gap, a representative characteristic of multi-server clusters. 2) Redundant memory usage and computation hinder the scalability of the distributed frameworks. 3) Sampling methods, de facto standard in mini-batch training, incur unnecessary errors in multi-server clusters. We found that these limitations can be addressed by exploiting the characteristics of multi-server clusters. Here, we propose GraNNDis, a fast distributed GNN training framework for multi-server clusters. Firstly, we present Flexible Preloading, which preloads the essential vertex dependencies server-wise to reduce the low-bandwidth inter-server communications. Secondly, we introduce Cooperative Batching, which enables memory-efficient, less redundant mini-batch training by utilizing high-bandwidth intra-server communications. Thirdly, we propose Expansion-aware Sampling, a cluster-aware sampling method, which samples the edges that affect the system speedup. As sampling the intra-server dependencies does not contribute much to the speedup as they are communicated through fast intra-server links, it only targets a server boundary to be sampled. Lastly, we introduce One-Hop Graph Masking, a computation and communication structure to realize the above methods in multi-server environments. We evaluated GraNNDis on multi-server clusters, and it provided significant speedup over the state-of-the-art distributed GNN training frameworks. GraNNDis is open-sourced at https://***/AISSNU/GraNNDis_Artifact to facilitate its use.
This paper represented a way to build mathematical model on genetic multilevel forward neural network. Building the relationship between chemistry measurement values and near infrared spectrum datum. The near infrared...
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ISBN:
(纸本)9780387772523
This paper represented a way to build mathematical model on genetic multilevel forward neural network. Building the relationship between chemistry measurement values and near infrared spectrum datum. The near infrared spectrum data was input in this network, five kinds of content of fatty acids, which measured by chemistry method, were output. Training the weight of multilevel forward neural network by genetic algorithms, building the soybean fatty acids neural network detection model, and exploring the network model which can realize near infrared spectrum detection exactly and efficiently. The authors designed a multilevel forward neural network trained by genetic algorithms. Test showed that relative coefficient in five fatty acids of soybean can be round about 0.9, and can satisfy init detection of soybean breeding.
The bit permutation unit consists of bit permutation network and configuration information. In the article, a configuration information parallel extraction algorithm with time complexity O (log2N) based on Waksman net...
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In view of the complexity of information dissemination in agriculture and the uncertainty of farmers' demand for scientific and technological information, this paper makes a comprehensive and accurate analysis for...
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ISBN:
(纸本)9783030061791;9783030061784
In view of the complexity of information dissemination in agriculture and the uncertainty of farmers' demand for scientific and technological information, this paper makes a comprehensive and accurate analysis for the dissemination network of scientific and technological information of agriculture based on complex network technology. Firstly, this paper researches the characteristics of the peak degree distribution, average path length and clustering coefficient of a complex network established by the agricultural information dissemination network. Through the research on the three basic characteristics, this paper confirms that the agriculture information dissemination network has the characteristics of scale-free network and small-world network. The method of node research is analyzed by using a complex network. That is that this paper makes a comprehensive analysis for the information propagation speed, scope, analysis and application of dissemination network of agricultural scientific and technological information from degree centrality, betweenness centrality and relationship strength theory. The research results show that the degree centrality can accelerate the information dissemination and be conducive to the accuracy of the information;the betweenness centrality can quickly expand the information dissemination and accurately grasp the degree of control for information resources;the relationship strength theory can reduce the cost of information dissemination and improve the degree of information adoption. This conclusion proves that the application of the analysis method of complex network can effectively improve the speed and quality of agricultural information dissemination network and better serve the agricultural production and farmers' life.
Collaborative autonomous vehicles will appear in the near future and will transform deeply road transportation systems, addressing in part many issues such as safety, traffic efficiency, etc. Validation and testing of...
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ISBN:
(纸本)9781479930807
Collaborative autonomous vehicles will appear in the near future and will transform deeply road transportation systems, addressing in part many issues such as safety, traffic efficiency, etc. Validation and testing of complex scenarios involving sets of autonomous collaborative vehicles are becoming an important challenge. Each vehicle in the set is autonomous and acts asynchronously, receiving and processing huge amount of data in real time, coming from the environment and other vehicles. Simulation of such scenarios in real time require huge computing resources. This poster presents a simulation platform combining the real-time OPAL-RT Technologies for processing and parallelcomputing, and the Pro-SiVIC vehicular simulator from Civitec for realistic simulation of vehicles dynamic, road/environment, and sensors behaviors. The two platforms are complementary and their combining allow us to propose a real time simulator of collaborative autonomous systems.
Acoustic speech characteristics have previously been identified as possible indicators of respiratory disease when recorded in controlled lab settings. However, the ability to measure and leverage these indicators dur...
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ISBN:
(纸本)9781665416474
Acoustic speech characteristics have previously been identified as possible indicators of respiratory disease when recorded in controlled lab settings. However, the ability to measure and leverage these indicators during people's everyday lives has been largely under-explored. In this study, we use continuous audio data from smartwatches worn by individuals suffering from COPD, as well as symptom information through daily self-reports. By applying pre-trained models for voice activity detection and speaker verification models, we are able to isolate moments of the user's own speech and extract important speech features. We then use those features in an isolation forest outlier detector to discriminate between days with normal and worsening symptoms, achieving an AUC of nearly 0.60 on this challenging problem.
Register files (RF) are known to consume about 20% of the power inside a processor. Embedded systems, due to area and timing constraints, generally have small register files, which can cause significant register press...
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Nowadays, the role of anytime and situational models and algorithms has become important because they offer a way to handle atypical situations and to overcome problems of resource, time, and data insuffiency in chang...
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