by jsendak | Apr 11, 2024 | Computer Science
arXiv:2404.06563v1 Announce Type: cross
Abstract: We demonstrate MaskSearch, a system designed to accelerate queries over databases of image masks generated by machine learning models. MaskSearch formalizes and accelerates a new category of queries for retrieving images and their corresponding masks based on mask properties, which support various applications, from identifying spurious correlations learned by models to exploring discrepancies between model saliency and human attention. This demonstration makes the following contributions:(1) the introduction of MaskSearch’s graphical user interface (GUI), which enables interactive exploration of image databases through mask properties, (2) hands-on opportunities for users to explore MaskSearch’s capabilities and constraints within machine learning workflows, and (3) an opportunity for conference attendees to understand how MaskSearch accelerates queries over image masks.
MaskSearch: Accelerating Queries over Databases of Image Masks
In the field of machine learning, image masks play a crucial role in various applications, from object detection to semantic segmentation. However, querying databases of image masks has been a time-consuming and complex task. That’s where MaskSearch comes in. It is a system designed to accelerate queries over databases of image masks generated by machine learning models, providing a graphical user interface for interactive exploration of image databases through mask properties.
MaskSearch allows users to retrieve images and their corresponding masks based on mask properties, making it easier to identify spurious correlations learned by models and exploring discrepancies between model saliency and human attention. This demonstration showcases the capabilities of MaskSearch and provides a hands-on experience for users to understand its constraints within machine learning workflows.
The multi-disciplinary nature of MaskSearch is worth noting. It combines concepts from various fields, including multimedia information systems, animations, artificial reality, augmented reality, and virtual reality.
In the context of multimedia information systems, MaskSearch enables efficient querying and exploration of large databases of image masks. This is particularly valuable in applications where masks are used as annotations or ground truth for training machine learning models. Through its graphical user interface, users can easily navigate and analyze the properties of image masks, accelerating the discovery and analysis of patterns and correlations.
Animations play a significant role in MaskSearch, as the system provides visual representations of image masks and their properties. These animations help users gain a more intuitive understanding of the data and facilitate the identification of interesting patterns or discrepancies. By leveraging animations, MaskSearch enhances the interactive exploration of image databases, providing users with a more immersive and engaging experience.
Artificial reality, augmented reality, and virtual realities also come into play in the context of MaskSearch. These technologies can be utilized to enhance the visualization and interaction with image masks, allowing users to perceive and manipulate the data in novel ways. By integrating these technologies, MaskSearch opens up new possibilities for analyzing and understanding complex datasets, ultimately leading to more informed decision-making in machine learning workflows.
In conclusion, MaskSearch is a powerful system that accelerates queries over databases of image masks. Its graphical user interface and multidisciplinary nature provide users with an interactive and immersive experience, enabling them to explore image databases and analyze mask properties more efficiently. As machine learning continues to advance, tools like MaskSearch will play a crucial role in facilitating the discovery and understanding of patterns within complex datasets.
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by jsendak | Apr 11, 2024 | Computer Science
This work presents an interesting study on potential games and Markov potential games under stochastic cost and bandit feedback. The authors propose a variant of the Frank-Wolfe algorithm that incorporates sufficient exploration and recursive gradient estimation. The algorithm is proven to converge to the Nash equilibrium while achieving sublinear regret for each individual player.
One notable aspect of this algorithm is that it does not require additional projection steps, which sets it apart from existing methods. The algorithm achieves a Nash regret and a regret bound of O(T^(4/5)) for potential games, matching the best available result. This is a significant improvement and demonstrates the effectiveness of the proposed approach.
Furthermore, the authors extend their results to Markov potential games, where they improve the best available Nash regret from O(T^(5/6)) to O(T^(4/5)). This improvement is achieved by carefully balancing the reuse of past samples and exploration of new samples.
What is particularly interesting about this algorithm is that it does not require any prior knowledge of the game, such as the distribution mismatch coefficient. This provides more flexibility in practical implementation and makes the algorithm applicable to a wide range of scenarios.
The experimental results presented in this work confirm the theoretical findings and highlight the practical effectiveness of the proposed method. This lends further credibility to the algorithm and suggests its potential for real-world applications.
In conclusion, this work presents a novel algorithm for potential games and Markov potential games under stochastic cost and bandit feedback. The algorithm achieves impressive regret bounds and does not require prior knowledge of the game. It represents a valuable contribution to the field and opens up possibilities for further research and practical implementations.
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by jsendak | Apr 10, 2024 | Computer Science
arXiv:2404.05802v1 Announce Type: cross
Abstract: Battery recycling is a critical process for minimizing environmental harm and resource waste for used batteries. However, it is challenging, largely because sorting batteries is costly and hardly automated to group batteries based on battery types. In this paper, we introduce a machine learning-based approach for battery-type classification and address the daunting problem of data scarcity for the application. We propose BatSort which applies transfer learning to utilize the existing knowledge optimized with large-scale datasets and customizes ResNet to be specialized for classifying battery types. We collected our in-house battery-type dataset of small-scale to guide the knowledge transfer as a case study and evaluate the system performance. We conducted an experimental study and the results show that BatSort can achieve outstanding accuracy of 92.1% on average and up to 96.2% and the performance is stable for battery-type classification. Our solution helps realize fast and automated battery sorting with minimized cost and can be transferred to related industry applications with insufficient data.
Battery-Type Classification: A Machine Learning Approach
Battery recycling is a critical process that aims to minimize environmental harm and resource waste. However, the sorting of batteries based on their types has proven to be a challenging and costly task. In this paper, a machine learning-based approach called BatSort is introduced to address this issue.
BatSort utilizes transfer learning, which leverages the existing knowledge optimized with large-scale datasets, to classify battery types. The system customizes ResNet, a deep learning model, for the battery-type classification task. This approach is particularly useful as it tackles the problem of data scarcity, which is common in the battery sorting domain.
The authors collected an in-house battery-type dataset of small-scale to guide the knowledge transfer and conducted an experimental study to evaluate the performance of BatSort. The results show that the system achieves outstanding accuracy, with an average of 92.1%, and up to 96.2% accuracy for battery-type classification. Importantly, the performance of BatSort is stable, ensuring reliable and consistent results.
The multi-disciplinary nature of this research is worth highlighting. It combines concepts from machine learning, image classification, and battery recycling. By incorporating transfer learning, the authors bridge the gap between the wider field of multimedia information systems and the specific domain of battery sorting. This cross-disciplinary approach enhances the efficiency and effectiveness of the battery recycling process.
Furthermore, the application of BatSort extends beyond battery recycling. The concept of automated classification using machine learning can be adopted in other industries with insufficient data. The success of BatSort demonstrates the potential for similar approaches in optimizing resource utilization and minimizing cost in various sectors. Moreover, it opens doors for future research in related fields such as artificial reality, augmented reality, and virtual realities, where machine learning techniques can be further integrated.
In conclusion, the introduction of BatSort, a machine learning-based approach for battery-type classification, paves the way for fast and automated battery sorting with minimized cost. This innovation contributes to the wider field of multimedia information systems while addressing a critical challenge in battery recycling. The outstanding performance of BatSort emphasizes the potential of machine learning techniques in solving data scarcity issues in various industries. As technology continues to advance, it is expected that similar approaches will play a significant role in optimizing resource utilization and streamlining processes in multiple domains.
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by jsendak | Apr 10, 2024 | Computer Science
Introduction
This paper introduces an open source framework developed in Python that offers a comprehensive solution for manipulating symbolic representations of neutrosophic sets over various types of universes. The framework consists of three distinct classes that provide a simple and intuitive way to handle neutrosophic sets and mappings between them. It builds upon previous software solutions proposed by Salama et al., Saranya et al., El-Ghareeb, Topal et al., and Sleem, extending and generalizing their capabilities. The authors provide a detailed description of the code and present numerous examples and use cases to demonstrate the framework’s functionality.
Neutrosophic Sets and Their Manipulation
Neutrosophic sets are a mathematical concept introduced by Florentin Smarandache in the 1990s. They extend the traditional notion of sets by accommodating indeterminate, imprecise, and inconsistent elements. A neutrosophic set is represented by three components: the membership function, the indeterminacy function, and the non-membership function. These components capture the degrees of membership, indeterminacy, and non-membership of elements in a given universe.
The manipulation of neutrosophic sets has attracted significant research interest due to their potential applications in various domains, including decision making, pattern recognition, image processing, and uncertainty modeling. Several software solutions have been proposed in the past to facilitate the handling of neutrosophic sets, but the framework introduced in this paper aims to provide an improved and more versatile approach.
The Proposed Framework
The open source framework presented in this paper is implemented in Python and consists of three classes: SymbolicNeutrosophicSet, SymbolicNeutrosophicMapping, and UniversalSymbolicNeutrosophicSet. These classes are designed to enable efficient manipulation of neutrosophic sets and mappings between them.
The SymbolicNeutrosophicSet class allows the creation of neutrosophic sets with symbolic elements, providing a flexible representation for handling linguistic variables in neutrosophic set operations. The SymbolicNeutrosophicMapping class enables the definition of mappings between two neutrosophic sets, facilitating transformation and comparisons. Lastly, the UniversalSymbolicNeutrosophicSet class generalizes the framework to handle different types of universes, including discrete, continuous, fuzzy, and intuitionistic fuzzy sets.
Advantages and Implications
The framework described in this paper offers several advantages over previous approaches to neutrosophic set manipulation. By providing symbolic representations for neutrosophic elements, it enhances the expressiveness and applicability of the framework, particularly in domains where linguistic variables play a crucial role. The capability to define mappings between neutrosophic sets also expands the toolkit for analyzing and transforming these sets, opening up possibilities for more advanced data processing techniques.
The open source nature of the framework promotes collaboration and sharing of knowledge among researchers and practitioners working with neutrosophic sets. It encourages community-driven development and improvement of the code, fostering innovation and the establishment of best practices. Furthermore, the provision of detailed descriptions, examples, and use cases in the paper assists users in understanding and implementing the framework effectively.
Future Directions
The presented framework lays a solid foundation for further advancements in the field of neutrosophic set manipulation. Future research can focus on expanding the framework’s capabilities to handle larger and more complex datasets. Moreover, there is potential for integrating machine learning techniques with the framework to enhance the predictive power and decision-making abilities of neutrosophic set-based models.
Additionally, efforts can be directed towards developing user-friendly interfaces and visualization tools that simplify the interaction with the framework. Such interfaces would enable users to explore the properties of neutrosophic sets and understand the implications of their decisions more intuitively.
In conclusion, the framework presented in this paper addresses the need for an open source solution to efficiently manipulate neutrosophic sets. Its ability to handle symbolic representations, define mappings, and accommodate various types of universes makes it a valuable tool for researchers and practitioners working with neutrosophic sets. With the support of a collaborative community, the framework holds promise for further advancements in this field and the application of neutrosophic sets in real-world scenarios.
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by jsendak | Apr 9, 2024 | Computer Science
arXiv:2404.04545v1 Announce Type: new
Abstract: Multimodal Sentiment Analysis (MSA) endeavors to understand human sentiment by leveraging language, visual, and acoustic modalities. Despite the remarkable performance exhibited by previous MSA approaches, the presence of inherent multimodal heterogeneities poses a challenge, with the contribution of different modalities varying considerably. Past research predominantly focused on improving representation learning techniques and feature fusion strategies. However, many of these efforts overlooked the variation in semantic richness among different modalities, treating each modality uniformly. This approach may lead to underestimating the significance of strong modalities while overemphasizing the importance of weak ones. Motivated by these insights, we introduce a Text-oriented Cross-Attention Network (TCAN), emphasizing the predominant role of the text modality in MSA. Specifically, for each multimodal sample, by taking unaligned sequences of the three modalities as inputs, we initially allocate the extracted unimodal features into a visual-text and an acoustic-text pair. Subsequently, we implement self-attention on the text modality and apply text-queried cross-attention to the visual and acoustic modalities. To mitigate the influence of noise signals and redundant features, we incorporate a gated control mechanism into the framework. Additionally, we introduce unimodal joint learning to gain a deeper understanding of homogeneous emotional tendencies across diverse modalities through backpropagation. Experimental results demonstrate that TCAN consistently outperforms state-of-the-art MSA methods on two datasets (CMU-MOSI and CMU-MOSEI).
Multimodal Sentiment Analysis: Understanding Human Sentiment Across Modalities
As technology continues to advance, multimedia information systems, animations, artificial reality, augmented reality, and virtual realities are becoming increasingly prevalent in our everyday lives. One area where these technologies play a crucial role is in the field of multimodal sentiment analysis (MSA).
MSA aims to understand human sentiment by leveraging multiple modalities such as language, visual cues, and acoustic signals. However, the presence of inherent multimodal heterogeneities poses a challenge, with the contribution of different modalities varying considerably. This has led researchers to focus on improving representation learning techniques and feature fusion strategies.
Nevertheless, many previous efforts have overlooked the variation in semantic richness among different modalities, treating each modality uniformly. This approach can lead to underestimating the significance of strong modalities while overemphasizing the importance of weak ones. In light of these insights, the authors of this article propose a Text-oriented Cross-Attention Network (TCAN) to address these limitations.
The TCAN model takes unaligned sequences of the three modalities as inputs and allocates the extracted unimodal features into a visual-text and an acoustic-text pair. It then implements self-attention on the text modality and applies text-queried cross-attention to the visual and acoustic modalities. Through a gated control mechanism, the model mitigates the influence of noise signals and redundant features.
Furthermore, the authors introduce the concept of unimodal joint learning, which aims to gain a deeper understanding of homogeneous emotional tendencies across diverse modalities through backpropagation. By considering the unique properties and strengths of each modality, TCAN outperforms state-of-the-art MSA methods on two datasets (CMU-MOSI and CMU-MOSEI).
The importance of this research extends beyond the field of MSA. The multi-disciplinary nature of the concepts explored in this article highlights the interconnectedness of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. The insights gained from this research can have implications in developing more efficient and accurate sentiment analysis models across various domains.
In conclusion, the Text-oriented Cross-Attention Network (TCAN) presented in this article showcases the significance of considering the variation in semantic richness among different modalities in multimodal sentiment analysis. By emphasizing the role of the text modality and incorporating innovative techniques, TCAN outperforms existing methods and contributes to the broader field of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.
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by jsendak | Apr 9, 2024 | Computer Science
Expert Commentary
In this article, the authors address one of the major challenges faced by CMOS devices at nanometer scale – increasing parameter variation due to manufacturing imperfections. Variability in process parameters can significantly affect the performance and reliability of circuits, as the nominal operating conditions may not be sufficient to overcome timing violations across the entire variability spectrum.
Traditionally, timing guardbands have been used to account for process variations, but this approach often leads to pessimistic estimates and performance degradation. To overcome this limitation, the authors propose a novel circuit-agnostic framework for generating variability-aware approximate circuits.
The key idea behind their approach is to accurately portray variability effects by creating variation-aware standard cell libraries. These libraries are fully compatible with standard Electronic Design Automation (EDA) tools, ensuring that the generated circuits can be seamlessly integrated into existing design flows.
The authors take a comprehensive approach by calibrating the underlying transistors against industrial measurements from Intel’s 14nm FinFET technology. This allows them to accurately capture the electrical characteristics of the transistors and incorporate the variability effects into their framework.
In their experiments, the authors explore the design space of approximate variability-aware designs to automatically generate circuits with reduced variability and increased performance, all without the need for timing guardbands. The results show that by introducing a negligible functional error of merely .3times 10^{-3}$, their variability-aware approximate circuits can reliably operate under process variations without sacrificing application performance.
This work is significant as it addresses a critical challenge in nanometer-scale CMOS design. As process technology continues to advance, process variations become more pronounced, and traditional design techniques may not be sufficient to mitigate their impact. The proposed framework provides a promising solution for incorporating variability-aware approximate computing principles into circuit design, enabling improved performance and reliability.
Future research in this area could focus on exploring different trade-offs between functional error and performance improvement. The authors have shown that a small functional error can lead to significant gains in performance, but it would be interesting to investigate the limits of this trade-off and identify the optimal balance for different applications.
Furthermore, extending this approach to more advanced process nodes and different technologies would be valuable. The authors have validated their framework using Intel’s 14nm FinFET technology, but assessing its effectiveness in other manufacturing processes, such as those based on nanosheet or nanowire transistors, would provide valuable insights into its scalability and applicability.
In conclusion, this work presents a novel framework for generating variability-aware approximate circuits that eliminate the need for timing guardbands. By accurately capturing process variations and incorporating them into the design process, the proposed approach offers improved performance and reliability in nanometer-scale CMOS designs.
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