This paper presents a non-trivial reconstruction of a previous joint topic-sentiment-preference review model TSPRA with stick-breaking representation under the framework of variational inference (VI) and stochastic variational inference (SVI). TSPRA is a Gibbs Sampling based model that solves topics, word sentiments and user preferences altogether and has been shown to achieve good performance, but for large data set it can only learn from a relatively small sample. We develop the variational models vTSPRA and svTSPRA to improve the time use, and our new approach is capable of processing millions of reviews. We rebuild the generative process, improve the rating regression, solve and present the coordinate-ascent updates of variational parameters, and show the time complexity of each iteration is theoretically linear to the corpus size, and the experiments on Amazon data sets show it converges faster than TSPRA and attains better results given the same amount of time. In addition, we tune svTSPRA into an online algorithm ovTSPRA that can monitor oscillations of sentiment and preference overtime. Some interesting fluctuations are captured and possible explanations are provided. The results give strong visual evidence that user preference is better treated as an independent factor from sentiment.

Abstract--Previous attempts at music artist classification use frame-level audio features which summarize frequency content within short intervals of time. Comparatively, more recent music information retrieval tasks take advantage of temporal structure in audio spectrograms using deep convolutional and recurrent models. This paper revisits artist classification with this new framework and empirically explores the impacts of incorporating temporal structure in the feature representation. To this end, an established classification architecture, a Convolutional Recurrent Neural Network (CRNN), is applied to the artist20 music artist identification dataset under a comprehensive set of conditions. These include audio clip length, which is a novel contribution in this work, and previously identified considerations such as dataset split and feature-level. Our results improve upon baseline works, verify the influence of the production details on classification performance and demonstrate the tradeoffs between sample length and training set size. The best performing model achieves an average F1-score of 0.937 across three independent trials which is a substantial improvement over the corresponding baseline under similar conditions. Finally, to showcase the effectiveness ofthe CRNN's feature extraction capabilities, we visualize audio samples at its bottleneck layer demonstrating that learned representations segment into clusters belonging to their respective artists. I. INTRODUCTION Music information retrieval (MIR) encompasses most audio analysis tasks such as genre classification, song identification, chord recognition, sound event detection, mood detection and feature extraction [1], [2].

A panel discussion on artificial intelligence will highlight a luncheon scheduled for noon to 2 p.m. on Thursday, Jan. 24 at Saratoga Springs City Center. "The AI Opportunity: Developing an AI Ecosystem in Upstate New York" will tell why artificial intelligence matters, and what opportunities exist locally and regionally. Panelists will share ideas, experiences, and viewpoints about AI technology, research and development, ethics, and policies. There will be time to network with local leaders, industry experts, and community stakeholders following a discussion and question-and-answer session. This event is the first in a series of economic development "Lunch and Learns."

The digital world has experienced spectacular growth in the last years with exponential technology advances like robotics, internet of things, Smart cars, robotics, Smart cities, artificial intelligence or quantum computing. The challenge for people, the society, governments and businesses is to face the implications of digital change.

Artificial intelligence tools have already demonstrated an ability to make private businesses more effective, and there's substantial reason to think that …

The device monitors the pool 24/7 and uses artificial intelligence to analyze real-time video captured by the camera.