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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by jsendak | Apr 8, 2024 | Computer Science
arXiv:2404.04037v1 Announce Type: cross
Abstract: We present InstructHumans, a novel framework for instruction-driven 3D human texture editing. Existing text-based editing methods use Score Distillation Sampling (SDS) to distill guidance from generative models. This work shows that naively using such scores is harmful to editing as they destroy consistency with the source avatar. Instead, we propose an alternate SDS for Editing (SDS-E) that selectively incorporates subterms of SDS across diffusion timesteps. We further enhance SDS-E with spatial smoothness regularization and gradient-based viewpoint sampling to achieve high-quality edits with sharp and high-fidelity detailing. InstructHumans significantly outperforms existing 3D editing methods, consistent with the initial avatar while faithful to the textual instructions. Project page: https://jyzhu.top/instruct-humans .
InstructHumans: Enhancing Instruction-driven 3D Human Texture Editing
In the field of multimedia information systems, the concept of instruction-driven 3D human texture editing plays a crucial role in enhancing the visual quality and realism of virtual characters. This emerging area combines elements from multiple disciplines, including animations, artificial reality, augmented reality, and virtual realities.
The article introduces a novel framework called InstructHumans, which aims to improve the process of instruction-driven 3D human texture editing. It addresses the limitations of existing text-based editing methods that use Score Distillation Sampling (SDS) to distill guidance from generative models. The authors argue that relying solely on these scores can harm the editing process by compromising the consistency with the source avatar.
To overcome this challenge, the researchers propose an alternative approach called Score Distillation Sampling for Editing (SDS-E). This method selectively incorporates subterms of SDS across diffusion timesteps, ensuring that edits maintain consistency with the original avatar. Furthermore, SDS-E is enhanced with spatial smoothness regularization and gradient-based viewpoint sampling to achieve high-quality edits with sharp and high-fidelity detailing.
The results of the study demonstrate that InstructHumans outperforms existing 3D editing methods in terms of preserving consistency with the source avatar while faithfully following the given textual instructions. This advancement in the field of instruction-driven 3D human texture editing paves the way for more immersive and realistic virtual experiences.
The significance of this work extends beyond the specific application of 3D human texture editing. By combining insights from animations, artificial reality, augmented reality, and virtual realities, the researchers contribute to the broader field of multimedia information systems. These interdisciplinary collaborations enable the development of more advanced and sophisticated techniques for creating and manipulating virtual content.
In conclusion, the InstructHumans framework represents a valuable contribution to the field of instruction-driven 3D human texture editing. Its novel approach addresses the limitations of existing methods and demonstrates improved consistency and fidelity in edits. This work demonstrates the importance of interdisciplinary collaboration in advancing the field of multimedia information systems and highlights its relevance to the wider domains of animations, artificial reality, augmented reality, and virtual realities.
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by jsendak | Apr 8, 2024 | Computer Science
The rapid evolution of Internet of Things (IoT) technology has led to the widespread adoption of Human Activity Recognition (HAR) in various daily life domains. Federated Learning (FL) has emerged as a popular approach for building global HAR models by aggregating user contributions without transmitting raw individual data. While FL offers improved user privacy protection compared to traditional methods, challenges still exist.
One particular challenge arises from regulations like the General Data Protection Regulation (GDPR), which grants users the right to request data removal. This poses a new question for FL: How can a HAR client request data removal without compromising the privacy of other clients?
In response to this query, we propose a lightweight machine unlearning method for refining the FL HAR model by selectively removing a portion of a client’s training data. Our method leverages a third-party dataset that is unrelated to model training. By employing KL divergence as a loss function for fine-tuning, we aim to align the predicted probability distribution on forgotten data with the third-party dataset.
Additionally, we introduce a membership inference evaluation method to assess the effectiveness of the unlearning process. This evaluation method allows us to measure the accuracy of unlearning and compare it to traditional retraining methods.
To validate the efficacy of our approach, we conducted experiments using diverse datasets. The results demonstrate that our method achieves unlearning accuracy that is comparable to retraining methods. Moreover, our method offers significant speedups, ranging from hundreds to thousands.
Expert Analysis
This research addresses a critical challenge in federated learning, which is the ability for clients to request data removal while still maintaining the privacy of other clients. With the increasing focus on data privacy and regulations like GDPR, it is crucial to develop techniques that allow individuals to have control over their personal data.
The proposed lightweight machine unlearning method offers a practical solution to this challenge. By selectively removing a portion of a client’s training data, the model can be refined without compromising the privacy of other clients. This approach leverages a third-party dataset, which not only enhances privacy but also provides a benchmark for aligning the predicted probability distribution on forgotten data.
The use of KL divergence as a loss function for fine-tuning is a sound choice. KL divergence measures the difference between two probability distributions, allowing for effective alignment between the forgotten data and the third-party dataset. This ensures that the unlearning process is efficient and accurate.
The introduction of a membership inference evaluation method further strengthens the research. Evaluating the effectiveness of the unlearning process is crucial for ensuring that the model achieves the desired level of privacy while maintaining performance. This evaluation method provides a valuable metric for assessing the accuracy of unlearning and comparing it to retraining methods.
The experimental results presented in the research showcase the success of the proposed method. Achieving unlearning accuracy comparable to retraining methods is a significant accomplishment, as retraining typically requires significant computational resources and time. The speedups offered by the lightweight machine unlearning method have the potential to greatly enhance the efficiency of FL models.
Future Implications
The research presented in this article lays the groundwork for further advancements in federated learning and user privacy protection. The lightweight machine unlearning method opens up possibilities for other domains beyond HAR where clients may need to request data removal while preserving the privacy of others.
Additionally, the use of a third-party dataset for aligning probability distributions could be extended to other privacy-preserving techniques in federated learning. This approach provides a novel way to refine models without compromising sensitive user data.
Future research could explore the application of the proposed method in more complex scenarios and evaluate its performance in real-world settings. This would provide valuable insights into the scalability and robustness of the lightweight machine unlearning method.
In conclusion, the lightweight machine unlearning method proposed in this research offers a promising solution to the challenge of data removal in federated learning. By selectively removing a client’s training data and leveraging a third-party dataset, privacy can be preserved without compromising the overall performance of the model. This research paves the way for further advancements in privacy-preserving techniques and opens up possibilities for the application of federated learning in various domains.
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by jsendak | Apr 5, 2024 | Computer Science
arXiv:2404.03161v1 Announce Type: cross
Abstract: This paper introduces a biochemical vision-and-language dataset, which consists of 24 egocentric experiment videos, corresponding protocols, and video-and-language alignments. The key challenge in the wet-lab domain is detecting equipment, reagents, and containers is difficult because the lab environment is scattered by filling objects on the table and some objects are indistinguishable. Therefore, previous studies assume that objects are manually annotated and given for downstream tasks, but this is costly and time-consuming. To address this issue, this study focuses on Micro QR Codes to detect objects automatically. From our preliminary study, we found that detecting objects only using Micro QR Codes is still difficult because the researchers manipulate objects, causing blur and occlusion frequently. To address this, we also propose a novel object labeling method by combining a Micro QR Code detector and an off-the-shelf hand object detector. As one of the applications of our dataset, we conduct the task of generating protocols from experiment videos and find that our approach can generate accurate protocols.
A Multidisciplinary Approach to Biochemical Vision-and-Language Dataset
In this groundbreaking study, the authors introduce a biochemical vision-and-language dataset that offers valuable insights into the field of wet-lab experiments. This dataset consists of 24 egocentric experiment videos, corresponding protocols, and video-and-language alignments, providing a comprehensive resource for researchers in the field.
One of the key challenges in the wet-lab domain is the difficulty in detecting equipment, reagents, and containers, as the lab environment is often cluttered and objects can be indistinguishable. Previous studies have relied on manual annotation of objects, which is both time-consuming and costly. This paper addresses this issue by proposing the use of Micro QR Codes for automatic object detection.
Micro QR Codes are small, high-density QR Codes that can be easily placed on objects in the lab. By using computer vision techniques, the researchers can detect these codes and identify corresponding objects. However, the authors acknowledge that detecting objects solely based on Micro QR Codes can be challenging due to the frequent blur and occlusion caused by researchers manipulating the objects. Hence, they propose a novel object labeling method that combines a Micro QR Code detector with an off-the-shelf hand object detector.
The Multidisciplinary Nature of the Concepts
This study highlights the multidisciplinary nature of the concepts involved in biochemical experiments. By combining computer vision techniques with biochemical protocols, the authors bridge the gap between visual analysis and language understanding. The dataset and proposed methods serve as a foundation for further research in the field of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.
Researchers in the field of multimedia information systems can leverage this dataset to develop more advanced algorithms for object detection and recognition in complex environments. The use of animations can enhance the understanding of biochemical processes and assist in generating accurate protocols.
For artificial reality, augmented reality, and virtual realities, this dataset can provide a valuable resource for creating immersive laboratory simulations. By accurately detecting and labeling objects, researchers can create virtual environments that closely resemble real-world laboratory settings, allowing for more effective training and experimentation.
Potential Future Directions
This study opens up several exciting possibilities for future research. One potential direction is the development of more robust and accurate object detection techniques specifically tailored to the challenges of wet-lab environments. By incorporating deep learning algorithms and advanced image processing techniques, researchers can improve the performance of object detection and tracking, even in the presence of blurring and occlusion.
Furthermore, the authors’ approach of generating protocols from experiment videos can be extended to other domains beyond biochemistry. Researchers in various fields can benefit from automated generation of protocols, saving time and effort in experimental setup and documentation.
Additionally, the proposed dataset and methods can be used for collaborative research and education purposes. By sharing the dataset with a wider community, researchers can collectively improve the accuracy and applicability of object detection algorithms in different laboratory settings.
In conclusion, this paper presents a significant contribution to the field of biochemical vision-and-language understanding. By introducing a multidisciplinary approach and dataset, the authors pave the way for advancements in multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. The proposed methods and future research directions have the potential to revolutionize the way we perform and document laboratory experiments, ultimately enhancing scientific research and discovery.
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by jsendak | Apr 5, 2024 | Computer Science
Article Commentary: Artificial Cardiac Conduction System (ACCS) Metaheuristic
The Artificial Cardiac Conduction System (ACCS) is a novel bio-inspired metaheuristic algorithm that takes inspiration from the human cardiac conduction system to optimize problem-solving. This algorithm utilizes the functional behavior of the human heart, where signals are generated and sent to the heart muscle to initiate contractions.
The ACCS algorithm models four nodes found in the myocardium layer: the sinoatrial node, atrioventricular node, bundle of His, and Purkinje fibers. These nodes play a crucial role in generating and controlling the heart rate. The algorithm implements the mechanisms of controlling the heart rate through these nodes, simulating their behavior in the optimization process.
One of the strengths of the ACCS algorithm lies in its ability to determine the balance between exploitation and exploration during the optimization process. To evaluate its performance, the algorithm was benchmarked on 19 well-known mathematical test functions. This analysis allows for assessing the algorithm’s capability to uncover optimal solutions while exploring different areas of the search space.
In the study, the ACCS algorithm was compared against several established metaheuristic algorithms such as Whale Optimization Algorithm (WOA), Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Differential Evolution (DE), and Fast Evolutionary Programming (FEP). These algorithms are known for their effectiveness in solving optimization problems.
The results of the comparative study showcase that the ACCS algorithm is capable of producing competitive results compared to the aforementioned well-known metaheuristics and other conventional methods. This demonstrates the potential of the bio-inspired ACCS algorithm as an effective alternative for optimization tasks across various domains.
Overall, the development of the Artificial Cardiac Conduction System (ACCS) algorithm presents a promising contribution to the field of metaheuristics. By mimicking the human cardiac conduction system, this algorithm incorporates biological principles into optimization, potentially improving the search for optimal solutions in a wide range of applications.
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