Computerized Decision Support Systems (CDSSs) can be a vital component in a medical setting to foster the use of evidence based medicine and minimize malpractice. Surprisingly, the adoption rate of CDSSs has remained ...
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ISBN:
(数字)9783030110512
ISBN:
(纸本)9783030110512;9783030110505
Computerized Decision Support Systems (CDSSs) can be a vital component in a medical setting to foster the use of evidence based medicine and minimize malpractice. Surprisingly, the adoption rate of CDSSs has remained far below expectations and there has been little impact of CDSSs on measurable health outcomes. We outline the components of clinical work environments in order to elaborate on the driving forces for technology acceptance. The components address issues such as high involvement work systems and distributed intelligence. The reflection of these characteristics leads us to the conclusion that the perceived usefulness of a technology and its ease of use is a necessary but not a sufficient condition. Technological acceptance primarily depends on the perceived mindfulness of individual intelligence in workplace design.
In the last two decades deep learning has attracted a lot of attention internationally, solving problems in different application domains and achieving results beyond expectations. For example it has been applied in b...
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Knowledge Graph Embedding (KGE) is used to represent the entities and relations of a KG in a low dimensional vector space. KGE can then be used in a downstream task such as entity classification, link prediction and k...
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ISBN:
(纸本)9781728145358
Knowledge Graph Embedding (KGE) is used to represent the entities and relations of a KG in a low dimensional vector space. KGE can then be used in a downstream task such as entity classification, link prediction and knowledge base completion. Training on large KG datasets takes a considerable amount of time. This work proposes three strategies which lead to faster training in distributed setting. The first strategy is a reduced communication approach which decreases the All-Gather size by sparsifying the Sparse Gradient Matrix (SGM). The second strategy is a variable margin approach that takes advantage of reduced communication for lower margins but retains the accuracy as obtained by the best fixed margin. The third strategy is called DistAdam which is a distributed version of the popular Adam optimization algorithm. Combining the three strategies results in reduction of training time for the FB250K dataset from twenty-seven hours on one processing node to under one hour on thirty-two nodes with each node consisting of twenty-four cores.
P systems are distributed and parallelcomputing models. In this paper, we proposed an improved Quicksort algorithm, called ECTPP-Quicksort, which is based on evolution-communication tissuelike P systems with promoter...
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High utility sequential pattern mining (HUSPM) is an emerging research topic in pattern mining. Aiming at this problem, many efficient algorithms have been proposed in recent years. However, most of them are serial al...
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In this paper, we propose a distributed Graph Model (DGM) and data structure to enable communication-aware heuristics in distributed load balancers (LBs). DGM is motivated by the desire to maintain and use information...
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There are nearly one hundred parallel and distributed graph processing packages. Selecting the best package for a given problem is difficult;some packages require GPUs, some are optimized for distributed or shared mem...
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ISBN:
(纸本)9781450362863
There are nearly one hundred parallel and distributed graph processing packages. Selecting the best package for a given problem is difficult;some packages require GPUs, some are optimized for distributed or shared memory, and some require proprietary compilers or perform better on different hardware. Furthermore, performance may vary wildly depending on the graph itself. This complexity makes selecting the optimal implementation manually infeasible. We develop an approach to predict the performance of parallel graph processing using both regression models and binary classification by labeling configurations as either well-performing or not. We demonstrate our approach on six graph processing packages: GraphMat, the Graph500, the Graph Algorithm Platform Benchmark Suite, GraphBIG, Galois, and PowerGraph and on four algorithms: PageRank, single-source shortest paths, triangle counting, and breadth first search. Given a graph, our method can estimate execution time or suggest an implementation and thread count expected to perform well. Our method correctly identifies well-performing configurations in 97% of test cases.
The following document presents metrics and pointers for datacenter performance evaluation, whose production workflow will be improved by a parallelcomputing software, each cluster instance was virtualized providing ...
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The proceedings contain 75 papers. The special focus in this conference is on Artificial Intelligence Applications and Innovations. The topics include: Manifold learning for innovation funding: Identification of poten...
ISBN:
(纸本)9783030491604
The proceedings contain 75 papers. The special focus in this conference is on Artificial Intelligence Applications and Innovations. The topics include: Manifold learning for innovation funding: Identification of potential funding recipients;network aggregation to enhance results derived from multiple analytics;PolicyCLOUD: Analytics as a service facilitating efficient data-driven public policy management;demand forecasting of short life cycle products using data mining techniques;Arbitrary scale super-resolution for brain MRI images;knowledge-based fusion for image tampering localization;Transfer learning using convolutional neural network architectures for brain tumor classification from MRI images;a novel learning automata-based strategy to generate melodies from chordal inputs;graph neural networks to advance anticancer drug design;Boosted ensemble learning for anomaly detection in 5G RAN;optimizing self-organizing lists-on-lists using transitivity and pursuit-enhanced object partitioning;task-projected hyperdimensional computing for multi-task learning;cross-domain authorship attribution using pre-trained language models;indoor localization with multi-objective selection of Radiomap models;STDP plasticity in TRN within hierarchical spike timing model of visual information processing;Tensor-based CUDA optimization for ANN inferencing using parallel acceleration on embedded GPU;the random neural network in price predictions;joint multi-object detection and segmentation from an untrimmed video;robust 3D detection in traffic scenario with tracking-based coupling system;Automated MeSH indexing of biomedical literature using contextualized word representations;machine learning for cognitive load classification – A case study on contact-free approach;knowledge-based management and reasoning on cultural and natural touristic routes;ontological foundations of modelling security policies for logical analytics;RDF reasoning on large ontologies: A study on cultural heritage
The role of the Internet has significantly changed due to the development in communication technologies. Nowadays, billions of people and physical devices are connected via Internet. In near future, storage and comput...
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