distributed energy resources (DERs) are attractive because of their flexibility and demand response capabilities;however, there are numerous challenges concerning their integration into the electric grid - including l...
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ISBN:
(纸本)9781728161273
distributed energy resources (DERs) are attractive because of their flexibility and demand response capabilities;however, there are numerous challenges concerning their integration into the electric grid - including lack of visibility and control, as well as misalignments in the interests of privately-owned DERs with respect to the collective interests of the grid. In order to coordinate the behavior of these DERs, we treat the grid as a multiagent system and propose a service-oriented broker architecture (SOBA). SOBA enables the behavior of privately owned DERs to be influenced by system operators through service requests, and autonomous peer discovery. We illustrate SOBA's features and motivate service requests through a scenario with a network of small-scale solar photovoltaics, inverters, and batteries.
The representation of words by means of vectors, also called Word Embeddings (WE), has been receiving great attention from the Natural Language Processing (NLP) field. WE models are able to express syntactic and seman...
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ISBN:
(纸本)9781728165820
The representation of words by means of vectors, also called Word Embeddings (WE), has been receiving great attention from the Natural Language Processing (NLP) field. WE models are able to express syntactic and semantic similarities, as well as relationships and contexts of words within a given corpus. Although the most popular implementations of WE algorithms present low scalability, there are new approaches that apply High-Performance computing (HPC) techniques. This is an opportunity for an analysis of the main differences among the existing implementations, based on performance and scalability metrics. In this paper, we present a study which addresses resource utilization and performance aspects of known WE algorithms foetid in the literature. To improve scalability and usability we propose a wrapper library for local and remote execution environments that contains a set of optimizations such as the pWord2vec, pWord2vec_MPI, Wang2vec and the original Word2vec algorithm. Utilizing these optimizations it is possible to achieve an average performance gain of 15x for multicores and 105x for multinodes compared to the original version. There is also a big reduction in the memory footprint compared to the most popular python versions.
Knowledge Graph has become a dominant research field in graph theory, but its incompleteness and sparsity hinder its application in various fields. Knowledge Graph Reasoning aims to alleviate these problems by deducin...
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Knowledge Graph has become a dominant research field in graph theory, but its incompleteness and sparsity hinder its application in various fields. Knowledge Graph Reasoning aims to alleviate these problems by deducing new knowledge or identifying false knowledge from existing knowledge. Recently, the Graph Convolution Network (GCN) based method is one of the most advanced methods to realize knowledge graph reasoning. However, it still suffers from some problems such as incomplete neighbor information aggregation and slow training speed. This paper proposes a knowledge graph reasoning model named GK for the link prediction task, which obtains better performance than existing GCN-based methods by introducing Graphormer into knowledge reasoning. The GK first proposes that nodes and their surroundings can be regarded as a hierarchical architecture that enables the model to capture more practical reasoning information to improve prediction accuracy. In addition, to accelerate the training speed of the model on the large-scale Knowledge Graph, we present a faster shortest path-finding method F-SPF in the edge coding process. Extensive experimental results show that the GK model can obtain the state-of-the-art prediction results of current GCN-based methods and can improve the training speed.
The proceedings contain 11 papers. The topics discussed include: accelerate distributed stochastic descent for Nonconvex optimization with momentum;accelerating GPU-based machine learning in python using MPI library: ...
ISBN:
(纸本)9780738110783
The proceedings contain 11 papers. The topics discussed include: accelerate distributed stochastic descent for Nonconvex optimization with momentum;accelerating GPU-based machine learning in python using MPI library: a case study with MVAPICH2-GDR;deep learning-based low-dose tomography reconstruction with hybrid-dose measurements;EventGraD: event-triggered communication in parallel stochastic gradient descent;a benders decomposition approach to correlation clustering;high-bypass learning: automated detection of tumor cells that significantly impact drug response;deep generative models that solve PDEs: distributedcomputing for training large data-free models;automatic particle trajectory classification in plasma simulations;reinforcement learning-based solution to power grid planning and operation under uncertainties;and predictions of steady and unsteady flows using machine-learned surrogate models.
In the future heterogeneous integrated wireless network environment, delay limited network has the characteristics of distributed self-organization, multi hop transmission, delay tolerance and intermittent link connec...
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Authenticating users based on their typing patterns has always been a target for cybersecurity professionals and researchers. Although previous studies have worked on free text-based authentication on mobile devices, ...
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Authenticating users based on their typing patterns has always been a target for cybersecurity professionals and researchers. Although previous studies have worked on free text-based authentication on mobile devices, none of them addressed the necessity of continuous learning under these settings. In this work, we propose a variational autoencoder (VAE) model that deals with this issue. As a case study, we consider a scenario where the model is initially trained on data collected from the user in a particular language (English). Then, the model is supposed to recognize the user when typing in another language (Korean). One way to adapt to that change is to retrain the model on a subset of the Korean data when it becomes available. By then, two scenarios can arise: 1) The English data still exists and the model is trained on the combination of English and Korean data; 2) The English data does not exist for security reasons or limited storage issues, and thus, we use the decoder part of our VAE to generate data based on what has been learned and then retrain the model on the mix. The average Equal Error Rate achieved among 50 participants was 3.23% and 3.55% for scenarios 1 and 2, respectively (~14% less than the baseline case where the model is not retrained). These results prove the need for continuous retraining of authentication models and highlight the efficiency of the proposed model and its ability to continuously learn, even without having access to the previous training data.
Aiming at the high complexity of parameter optimization for portfolio models, this paper designs a distributed high-performance portfolio optimization platform(HPPO) based on parallelcomputing framework and event dri...
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ISBN:
(纸本)9780738111162
Aiming at the high complexity of parameter optimization for portfolio models, this paper designs a distributed high-performance portfolio optimization platform(HPPO) based on parallelcomputing framework and event driven architecture. The platform consists of the data layer, the model layer, and the excursion layer, which is built in a component, pluggable, and loosely coupled way. The platform adopts parallelization acceleration for backtesting and optimizing parameters of portfolio models in a certain historical interval. The platform is able to docking portfolio model with real-time market. Based on the HPPO platform, a parallel program is designed to optimize the parameters of the value at risk(VAR) model. The performance of the platform are summarized by analyzing the experimental results and comparing with the open source framework Zipline and Rqalpha.
Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. Software systems must satisfy rapidly increasing demands imposed by emerging...
Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. Software systems must satisfy rapidly increasing demands imposed by emerging applications. For example, new AI applications, such as autonomous driving, require quick responses to an environment that is changing continuously. At the same time, software systems must be fault-tolerant in order to ensure a high degree of availability. As it stands, however, developing these new distributed software systems is extremely challenging even for expert software engineers due to the interplay of concurrency, asynchronicity, and failure of components. The objective of our research is to develop reusable solutions to the above challenges by means of novel programming models and frameworks that can be used to build a wide range of applications. This talk reports on our work on the design, implementation, and foundations of programming models and languages that enable the robust construction of large-scale concurrent and distributed software systems.
With the rapid development of the 5G and Internet of Things (IoT), mobile edge computing has gained considerable popularity in academic and industrial field, which provides physical resources closer to end users. Serv...
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Detection and analysis framework of anomalous Internet crime data based on edge computing is designed in this paper. The edge server is both the edge data processing center and the data storage center. The edge server...
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ISBN:
(纸本)9781728146850
Detection and analysis framework of anomalous Internet crime data based on edge computing is designed in this paper. The edge server is both the edge data processing center and the data storage center. The edge server receives the edge device data for processing and returns the processing result to the edge device. It complements cloud computing and cloud services, is close to users and data sources, and provides a new computing model for intelligent computing. Therefore, edge computing is another new computing model after distributedcomputing, gridcomputing, and cloud computing. Inspired by the features of this technology, this paper proposes the novel data crime analytic framework. The numerical experiment has proven the satisfactory performance of the proposed method.
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