by jsendak | Dec 29, 2023 | Computer Science
Reducing Complexity and Enhancing Robustness in Speech Emotion Recognition
Representations derived from models like BERT and HuBERT have revolutionized speech emotion recognition, achieving remarkable performance. However, these representations come with a high memory and computational cost, as they were not specifically designed for emotion recognition tasks. In this article, we uncover lower-dimensional subspaces within these pre-trained representations that can significantly reduce model complexity without compromising emotion estimation accuracy. Furthermore, we introduce a novel approach to incorporate label uncertainty, in the form of grader opinion variance, into the models, resulting in improved generalization capacity and robustness. Additionally, we conduct experiments to evaluate the robustness of these emotion models against acoustic degradations and find that the reduced-dimensional representations maintain similar performance to their full-dimensional counterparts, making them highly promising for real-world applications.
Abstract:Representations derived from models such as BERT (Bidirectional Encoder Representations from Transformers) and HuBERT (Hidden units BERT), have helped to achieve state-of-the-art performance in dimensional speech emotion recognition. Despite their large dimensionality, and even though these representations are not tailored for emotion recognition tasks, they are frequently used to train large speech emotion models with high memory and computational costs. In this work, we show that there exist lower-dimensional subspaces within the these pre-trained representational spaces that offer a reduction in downstream model complexity without sacrificing performance on emotion estimation. In addition, we model label uncertainty in the form of grader opinion variance, and demonstrate that such information can improve the models generalization capacity and robustness. Finally, we compare the robustness of the emotion models against acoustic degradations and observed that the reduced dimensional representations were able to retain the performance similar to the full-dimensional representations without significant regression in dimensional emotion performance.
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by jsendak | Dec 29, 2023 | Computer Science
A Method for Estimating Users’ Preferences for Websites
A site’s recommendation system relies on understanding its users’ preferences in order to offer relevant recommendations. These preferences are based on the attributes that make up the items and content shown on the site, and they are estimated from the data of users’ interactions with the site. However, there is another important aspect of users’ preferences that is often overlooked – their preferences for the site itself over other sites. This shows the users’ base level propensities to engage with the site.
Estimating these preferences for the site faces significant obstacles. Firstly, the focal site usually has no data on its users’ interactions with other sites, making these interactions their unobserved behaviors for the focal site. Secondly, the Machine Learning literature in recommendation does not provide a model for this particular situation. Even if a model is developed, the problem of lacking ground truth evaluation data still remains.
In this article, we present a method to estimate individual users’ preferences for a focal site using only the data from that site. By computing the focal site’s share of a user’s online engagements, we can personalize recommendations to individual users. We introduce a Hierarchical Bayes Method and demonstrate two different ways of estimation – Markov Chain Monte Carlo and Stochastic Gradient with Langevin Dynamics.
We also propose an evaluation framework for the model using only the focal site’s data. This allows the site to test the model and assess its effectiveness. Our results show strong support for this approach to computing personalized share of engagement and its evaluation.
Abstract:A site’s recommendation system relies on knowledge of its users’ preferences to offer relevant recommendations to them. These preferences are for attributes that comprise items and content shown on the site, and are estimated from the data of users’ interactions with the site. Another form of users’ preferences is material too, namely, users’ preferences for the site over other sites, since that shows users’ base level propensities to engage with the site. Estimating users’ preferences for the site, however, faces major obstacles because (a) the focal site usually has no data of its users’ interactions with other sites; these interactions are users’ unobserved behaviors for the focal site; and (b) the Machine Learning literature in recommendation does not offer a model of this situation. Even if (b) is resolved, the problem in (a) persists since without access to data of its users’ interactions with other sites, there is no ground truth for evaluation. Moreover, it is most useful when (c) users’ preferences for the site can be estimated at the individual level, since the site can then personalize recommendations to individual users. We offer a method to estimate individual user’s preference for a focal site, under this premise. In particular, we compute the focal site’s share of a user’s online engagements without any data from other sites. We show an evaluation framework for the model using only the focal site’s data, allowing the site to test the model. We rely upon a Hierarchical Bayes Method and perform estimation in two different ways – Markov Chain Monte Carlo and Stochastic Gradient with Langevin Dynamics. Our results find good support for the approach to computing personalized share of engagement and for its evaluation.
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by jsendak | Dec 29, 2023 | Computer Science
Industrial cascade recommendation systems (RS) play a crucial role in delivering relevant items to users in today’s digital landscape. However, the growing size and complexity of these systems have led to significant energy consumption and carbon emissions. To address this concern, a new paper introduces GreenFlow, a practical computation allocation framework for RS that takes into account both accuracy and carbon emission during inference.
The framework focuses on optimizing computation in each stage of a cascade RS, such as recall, pre-ranking, and ranking. When a user triggers a request, the framework considers two key actions: the trained instances of models with different computational complexity and the number of items to be inferred in each stage. These actions form chains, and a reward score is estimated for each chain. The framework then uses dynamic primal-dual optimization to balance both the reward and computation budget.
The effectiveness of GreenFlow is demonstrated through extensive experiments. In an industrial mobile application, the framework reduces computation consumption by 41% without compromising commercial revenue. Additionally, it leads to significant energy savings, saving approximately 5000kWh of electricity and reducing 3 tons of carbon emissions per day.
Abstract:Given the enormous number of users and items, industrial cascade recommendation systems (RS) are continuously expanded in size and complexity to deliver relevant items, such as news, services, and commodities, to the appropriate users. In a real-world scenario with hundreds of thousands requests per second, significant computation is required to infer personalized results for each request, resulting in a massive energy consumption and carbon emission that raises concern.
This paper proposes GreenFlow, a practical computation allocation framework for RS, that considers both accuracy and carbon emission during inference. For each stage (e.g., recall, pre-ranking, ranking, etc.) of a cascade RS, when a user triggers a request, we define two actions that determine the computation: (1) the trained instances of models with different computational complexity; and (2) the number of items to be inferred in the stage. We refer to the combinations of actions in all stages as action chains. A reward score is estimated for each action chain, followed by dynamic primal-dual optimization considering both the reward and computation budget. Extensive experiments verify the effectiveness of the framework, reducing computation consumption by 41% in an industrial mobile application while maintaining commercial revenue. Moreover, the proposed framework saves approximately 5000kWh of electricity and reduces 3 tons of carbon emissions per day.
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by jsendak | Dec 29, 2023 | Computer Science
Abstract:The fiercely competitive business environment and increasingly personalized customization needs are driving the digital transformation and upgrading of the manufacturing industry. IIoT intelligence, which can provide innovative and efficient solutions for various aspects of the manufacturing value chain, illuminates the path of transformation for the manufacturing industry. It is time to provide a systematic vision of IIoT intelligence. However, existing surveys often focus on specific areas of IIoT intelligence, leading researchers and readers to have biases in their understanding of IIoT intelligence, that is, believing that research in one direction is the most important for the development of IIoT intelligence, while ignoring contributions from other directions. Therefore, this paper provides a comprehensive overview of IIoT intelligence. We first conduct an in-depth analysis of the inevitability of manufacturing transformation and study the successful experiences from the practices of Chinese enterprises. Then we give our definition of IIoT intelligence and demonstrate the value of IIoT intelligence for industries in fucntions, operations, deployments, and application. Afterwards, we propose a hierarchical development architecture for IIoT intelligence, which consists of five layers. The practical values of technical upgrades at each layer are illustrated by a close look on lighthouse factories. Following that, we identify seven kinds of technologies that accelerate the transformation of manufacturing, and clarify their contributions. Finally, we explore the open challenges and development trends from four aspects to inspire future researches.
