The last decade has witnessed an increase in harmful content on social media. The great proliferation of hate speech and other forms of aggressive language serves as evidence of this trend. The significant growth of u...
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Sentiment analysis is a valuable method for gauging people's sentiments and emotions towards various subjects. Within this realm, emotion detection stands out by predicting specific emotions rather than categorizi...
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The proceedings contain 28 papers. The topics discussed include: performance and usability implications of multiplatform and WebAssembly containers;operations patterns for hybrid quantum applications;optimization of c...
ISBN:
(纸本)9789897587474
The proceedings contain 28 papers. The topics discussed include: performance and usability implications of multiplatform and WebAssembly containers;operations patterns for hybrid quantum applications;optimization of cloud-native application execution over the edge cloud continuum enabled by DVFS;energy-aware node selection for cloud-based parallel workloads with machinelearning and infrastructure as code;security-aware allocation of replicated data in distributed storage systems;performance analysis of mdx ii: a next-generation cloud platform for cross-disciplinary data science research;data orchestration platform for AI workflows execution across computing continuum;framework for decentralized data strategies in virtual banking: navigating scalability, innovation, and regulatory challenges in Thailand;and anomaly detection for partially observable container systems based on architecture profiling.
Feature selection is considered as a crucial step in machinelearning, particularly when dealing with dimensional dataset. Feature selection involves selecting a subset of the most relevant features from a dataset. Se...
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Enabling efficient and accurate deep neural network (DNN) inference on microcontrollers is non-trivial due to the constrained on-chip resources. Current methodologies primarily focus on compressing larger models yet a...
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ISBN:
(纸本)9798350326031;9798350326048
Enabling efficient and accurate deep neural network (DNN) inference on microcontrollers is non-trivial due to the constrained on-chip resources. Current methodologies primarily focus on compressing larger models yet at the expense of model accuracy. In this paper, we rethink the problem from the inverse perspective by constructing small/weak models directly and improving their accuracy. Thus, we introduce DiTMoS, a novel DNN training and inference framework with a selector-classifiers architecture, where the selector routes each input sample to the appropriate classifier for classification. DiTMoS is grounded on a key insight: a composition of weak models can exhibit high diversity and the union of them can significantly boost the accuracy upper bound. To approach the upper bound, DiTMoS introduces three strategies including diverse training data splitting to increase the classifiers' diversity, adversarial selector-classifiers training to ensure synergistic interactions thereby maximizing their complementarity, and heterogeneous feature aggregation to improve the capacity of classifiers. We further propose a network slicing technique to alleviate the extra memory overhead incurred by feature aggregation. We deploy DiTMoS on the Neucleo STM32F767ZI board and evaluate it based on three time-series datasets for human activity recognition, keywords spotting, and emotion recognition, respectively. The experiment results manifest that: (a) DiTMoS achieves up to 13.4% accuracy improvement compared to the best baseline;(b) network slicing almost completely eliminates the memory overhead incurred by feature aggregation with a marginal increase of latency. Code is released at https://***/TheMaXiao/DiTMoS
machinelearning's ability to analyze and predict complex data enables many scientific discoveries and industrial advances. However, state-of-the-art machinelearning requires extensive computational resources for...
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machinelearning has transitioned from an individualistic approach to a collaborative one, enabling the collective effort to address increasingly complex challenges as they arise. One challenge that emerges is the man...
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The Internet of things is a contemporary technology that acknowledges the amount of data amassed over time by multiple sensors, with a variety of uses. The IoT techniques drive results in high volume, real-time data s...
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Cardiovascular disease (CVD) is a challenging condition to manage, impacting a significant global population. Within the realm of cardiology, rapid and accurate identification of heart disease is imperative for effect...
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The proceedings contain 13 papers. The topics discussed include: a study on the performance of distributed storage systems in edge computing environments;RESCAPE: a resource estimation system for microservices with gr...
ISBN:
(纸本)9798350387339
The proceedings contain 13 papers. The topics discussed include: a study on the performance of distributed storage systems in edge computing environments;RESCAPE: a resource estimation system for microservices with graph neural network and profile engine;PrometheusMigrate: efficient live migration of confidential virtual machine with software abstraction;the cost perspective of adopting large language model-as-a-service;DCSA: the deployment mechanism of chained serverless applications in JointCloud environment;parallel computation in dynamic fog computing networks: a multi-armed bandit learning-based decentralized matching approach;and IBRI: an IoT solution for building collapse risk identification in smart cities.
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