Sentiment analysis with deep neural networks: comparative study and performance assessment

The latest results in Deep Neural Networks (DNNs) have greatly improved the accuracy and performance of a variety of intelligent applications. However, running such computation-intensive DNN-based applications on resource-constrained mobile devices definitely leads to long latency and huge energy consumption. The traditional way is performing DNNs in the central cloud, but it requires significant amounts of data to be transferred to the cloud over the wireless network and also results in long latency. To solve this problem, offloading partial DNN computation to edge clouds has been proposed, to realize the collaborative execution between mobile devices and edge clouds. In addition, the mobility of mobile devices is easily to cause the computation offloading failure. In this paper, we develop a mobility-included DNN partition offloading algorithm (MDPO) to adapt to user’s mobility. The objective of MDPO is minimizing the total latency of completing a DNN job when the mobile user is moving. The MDPO algorithm is suitable for both DNNs with chain topology and graphic topology. We evaluate the performance of our proposed MDPO compared to local-only execution and edge-only execution, experiments show that MDPO significantly reduces the total latency and improves the performance of DNN, and MDPO can adjust well to different network conditions.

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Stock Trend Prediction using Deep Neural Networks in Time Series and Social Sentiment Analysis

Proceedings of the Fist International Conference on Advanced Scientific Innovation in Science, Engineering and Technology, ICASISET 2020, 16-17 May 2020, Chennai, India ◽

10.4108/eai.16-5-2020.2304210 ◽

2021 ◽

Author(s):

P. Ravichandran ◽

J. Rose ◽

K. Vijayakumar

Keyword(s):

Neural Networks ◽

Time Series ◽

Sentiment Analysis ◽

Deep Neural Networks ◽

Trend Prediction

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Iterative Algorithm for Parameterization of Two-Region Piecewise Uniform Quantizer for the Laplacian Source

Mathematics ◽

10.3390/math9233091 ◽

2021 ◽

Vol 9 (23) ◽

pp. 3091

Author(s):

Jelena Nikolić ◽

Danijela Aleksić ◽

Zoran Perić ◽

Milan Dinčić

Keyword(s):

Neural Networks ◽

Performance Assessment ◽

Probability Density ◽

Probability Density Functions ◽

Density Functions ◽

Uniform Quantization ◽

Memory Footprint ◽

High Bit Rates ◽

And Performance ◽

Laplacian Distribution

Motivated by the fact that uniform quantization is not suitable for signals having non-uniform probability density functions (pdfs), as the Laplacian pdf is, in this paper we have divided the support region of the quantizer into two disjunctive regions and utilized the simplest uniform quantization with equal bit-rates within both regions. In particular, we assumed a narrow central granular region (CGR) covering the peak of the Laplacian pdf and a wider peripheral granular region (PGR) where the pdf is predominantly tailed. We performed optimization of the widths of CGR and PGR via distortion optimization per border–clipping threshold scaling ratio which resulted in an iterative formula enabling the parametrization of our piecewise uniform quantizer (PWUQ). For medium and high bit-rates, we demonstrated the convenience of our PWUQ over the uniform quantizer, paying special attention to the case where 99.99% of the signal amplitudes belong to the support region or clipping region. We believe that the resulting formulas for PWUQ design and performance assessment are greatly beneficial in neural networks where weights and activations are typically modelled by the Laplacian distribution, and where uniform quantization is commonly used to decrease memory footprint.

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