by jsendak | Aug 30, 2024 | Computer Science
The book “Artificial Neural Network and Deep Learning: Fundamentals and Theory” provides a comprehensive overview of the key principles and methodologies in neural networks and deep learning. It starts by laying a strong foundation in descriptive statistics and probability theory, which are fundamental for understanding data and probability distributions.
One of the important topics covered in the book is matrix calculus and gradient optimization. These concepts are crucial for training and fine-tuning neural networks, as they allow model parameters to be updated in an efficient manner. The reader is introduced to the backpropagation algorithm, which is widely used in neural network training.
The book also addresses the key challenges in neural network optimization. Activation function saturation, vanishing and exploding gradients, and weight initialization are thoroughly discussed. These challenges can have a significant impact on the performance of neural networks, and understanding how to overcome them is essential for building effective models.
In addition to optimization techniques, the book covers various learning rate schedules and adaptive algorithms. These strategies help to fine-tune the training process and improve model performance over time. The book also explores techniques for generalization and hyperparameter tuning, such as Bayesian optimization and Gaussian processes, which are important for preventing overfitting and improving model robustness.
An interesting aspect of the book is the in-depth exploration of advanced activation functions. The different types of activation functions, such as sigmoid-based, ReLU-based, ELU-based, miscellaneous, non-standard, and combined types, are thoroughly examined for their properties and applications. Understanding the impact of these activation functions on neural network behavior is essential for designing efficient and effective models.
The final chapter of the book introduces complex-valued neural networks, which add another dimension to the study of neural networks. Complex numbers, functions, and visualizations are discussed, along with complex calculus and backpropagation algorithms. This chapter provides a unique perspective on neural networks and expands the reader’s understanding of the field.
Overall, “Artificial Neural Network and Deep Learning: Fundamentals and Theory” equips readers with the knowledge and skills necessary to design and optimize advanced neural network models. This is a valuable resource for anyone interested in furthering their understanding of artificial intelligence and contributing to its ongoing advancements.
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by jsendak | Aug 29, 2024 | Computer Science
arXiv:2408.15461v1 Announce Type: cross
Abstract: Text-to-image generation models have achieved remarkable advancements in recent years, aiming to produce realistic images from textual descriptions. However, these models often struggle with generating anatomically accurate representations of human hands. The resulting images frequently exhibit issues such as incorrect numbers of fingers, unnatural twisting or interlacing of fingers, or blurred and indistinct hands. These issues stem from the inherent complexity of hand structures and the difficulty in aligning textual descriptions with precise visual depictions of hands. To address these challenges, we propose a novel approach named Hand1000 that enables the generation of realistic hand images with target gesture using only 1,000 training samples. The training of Hand1000 is divided into three stages with the first stage aiming to enhance the model’s understanding of hand anatomy by using a pre-trained hand gesture recognition model to extract gesture representation. The second stage further optimizes text embedding by incorporating the extracted hand gesture representation, to improve alignment between the textual descriptions and the generated hand images. The third stage utilizes the optimized embedding to fine-tune the Stable Diffusion model to generate realistic hand images. In addition, we construct the first publicly available dataset specifically designed for text-to-hand image generation. Based on the existing hand gesture recognition dataset, we adopt advanced image captioning models and LLaMA3 to generate high-quality textual descriptions enriched with detailed gesture information. Extensive experiments demonstrate that Hand1000 significantly outperforms existing models in producing anatomically correct hand images while faithfully representing other details in the text, such as faces, clothing, and colors.
Analysis: Addressing Challenges in Text-to-Image Generation for Human Hands
Text-to-image generation models have shown remarkable advancements in recent years in generating realistic images from textual descriptions. However, these models often struggle when it comes to generating anatomically accurate representations of human hands. This article introduces a novel approach called Hand1000 that aims to address these challenges and enable the generation of realistic hand images with target gestures using only 1,000 training samples.
The complexity of hand structures and the difficulty in aligning textual descriptions with precise visual depictions of hands contribute to the issues faced by existing models. The proposed Hand1000 approach takes a multi-stage training process to tackle these challenges effectively.
Stage 1: Enhancing Understanding of Hand Anatomy
In the first stage, a pre-trained hand gesture recognition model is used to extract gesture representations. This step helps the model enhance its understanding of hand anatomy, which is crucial for generating accurate hand images. By leveraging the existing knowledge of gesture recognition, the model becomes more aware of the intricate details of hand movements and positioning.
Stage 2: Optimizing Text Embedding with Hand Gesture Representation
Building upon the extracted hand gesture representation, the second stage aims to optimize text embedding, improving alignment between textual descriptions and generated hand images. This stage ensures that the model incorporates the gesture information effectively, enabling it to generate hand images that align with the intended gestures described in the text. By considering detailed gesture information, the resulting hand images become more accurate and visually coherent.
Stage 3: Fine-tuning with Stable Diffusion Model
In the third stage, the optimized embedding produced in the previous stage is utilized to fine-tune the Stable Diffusion model. This model is responsible for generating realistic hand images. With the improved text embedding, the model can better translate textual descriptions into visually appealing hand images, considering factors such as hand morphology, shading, and texture. Fine-tuning allows the model to refine its understanding and generate high-quality images that faithfully represent the textual details.
In addition to the proposed approach, the article highlights the construction of the first publicly available dataset specifically designed for text-to-hand image generation. This dataset leverages advanced image captioning models and LLaMA3 to generate high-quality textual descriptions enriched with detailed gesture information. This dataset serves as a valuable resource for further research and development in the field.
Hand1000 demonstrates superior performance compared to existing models when it comes to producing anatomically correct hand images while faithfully representing other details in the text, such as faces, clothing, and colors. By addressing the challenges of anatomically accurate hand representation, this approach contributes to the wider field of multimedia information systems and its various sub-domains, including animations, artificial reality, augmented reality, and virtual realities.
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by jsendak | Aug 29, 2024 | Computer Science
Expert Commentary: The Perils of AI Hype in Technology Development and Society
In the age of rapid technological advancement, the risks associated with hype surrounding AI cannot be understated. This article highlights the interconnected nature of technological development, media representation, public perception, and governmental regulation. When these factors work in tandem, they can inadvertently lead to the spread of unfounded claims and a distorted understanding of the capabilities and risks associated with AI.
One key insight from this article is the role of the research community in the propagation of AI hype. As researchers push the boundaries of what AI can achieve, there is an increasing tendency to make sensationalized claims. While this can help attract funding and attention, it also creates unrealistic expectations among the public and policymakers. Consequently, when AI fails to live up to these expectations, it can lead to public disillusionment and setbacks in funding and support for further research.
Furthermore, the article highlights the detrimental impact of AI hype on shaping research and development directions. When a particular aspect of AI gains excessive attention, resources and efforts may be diverted towards that area, often at the expense of exploring other potentially important avenues. This can limit the overall progress of AI as a field and hinder the discovery and development of truly groundbreaking advancements.
In order to mitigate the risks of AI hype, the article suggests a set of measures that researchers, regulators, and the public can take. These include promoting transparency and open dialogue within the research community, encouraging responsible media coverage of AI advancements, fostering public understanding through education, and establishing regulatory frameworks that balance innovation and public safety.
An interesting aspect not explicitly discussed in the article is the potential role of industry stakeholders in perpetuating AI hype. As commercial interests drive the adoption and deployment of AI technologies, there is a temptation to oversell their capabilities to gain a competitive edge in the market. This can further contribute to the spread of unrealistic expectations and hinder the responsible development and deployment of AI systems.
In the future, it will be crucial for the research community, industry leaders, and policymakers to closely collaborate in order to temper the hype surrounding AI and cultivate a more realistic understanding of its capabilities and limitations. By doing so, we can ensure that AI development proceeds responsibly and ethically, maximizing its potential benefits while minimizing the associated risks.
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by jsendak | Aug 28, 2024 | Computer Science
arXiv:2408.14735v1 Announce Type: new
Abstract: Online video streaming has evolved into an integral component of the contemporary Internet landscape. Yet, the disclosure of user requests presents formidable privacy challenges. As users stream their preferred online videos, their requests are automatically seized by video content providers, potentially leaking users’ privacy.
Unfortunately, current protection methods are not well-suited to preserving user request privacy from content providers while maintaining high-quality online video services. To tackle this challenge, we introduce a novel Privacy-Preserving Video Fetching (PPVF) framework, which utilizes trusted edge devices to pre-fetch and cache videos, ensuring the privacy of users’ requests while optimizing the efficiency of edge caching. More specifically, we design PPVF with three core components: (1) textit{Online privacy budget scheduler}, which employs a theoretically guaranteed online algorithm to select non-requested videos as candidates with assigned privacy budgets. Alternative videos are chosen by an online algorithm that is theoretically guaranteed to consider both video utilities and available privacy budgets. (2) textit{Noisy video request generator}, which generates redundant video requests (in addition to original ones) utilizing correlated differential privacy to obfuscate request privacy. (3) textit{Online video utility predictor}, which leverages federated learning to collaboratively evaluate video utility in an online fashion, aiding in video selection in (1) and noise generation in (2). Finally, we conduct extensive experiments using real-world video request traces from Tencent Video. The results demonstrate that PPVF effectively safeguards user request privacy while upholding high video caching performance.
In this article, the authors discuss the privacy challenges associated with online video streaming and propose a novel framework called Privacy-Preserving Video Fetching (PPVF) to address these challenges. They highlight the importance of preserving user request privacy while ensuring the efficiency of edge caching in video content delivery.
The multi-disciplinary nature of this concept becomes evident as the authors discuss the three core components of the PPVF framework. Firstly, they introduce the “Online privacy budget scheduler” which utilizes an online algorithm to select non-requested videos as candidates based on assigned privacy budgets. This involves considering both video utilities and available privacy budgets, demonstrating the incorporation of online algorithms and optimization techniques.
Secondly, the “Noisy video request generator” is introduced, which generates redundant video requests utilizing correlated differential privacy. This technique aims to obfuscate the original video requests and enhance user request privacy. Differential privacy is a concept from the field of privacy-preserving data mining and by incorporating it into the video streaming context, the authors showcase the interdisciplinary nature of the PPVF framework.
The third core component is the “Online video utility predictor” which leverages federated learning to evaluate video utility in an online fashion. Federated learning is a technique from the field of machine learning where the model is trained on decentralized data, preserving privacy. By using federated learning in the context of video utility prediction, the authors demonstrate the integration of machine learning techniques into the PPVF framework.
Overall, this article is related to the wider field of multimedia information systems as it delves into the challenges of online video streaming and proposes a framework to address privacy concerns while optimizing video caching performance. The concepts of artificial reality, augmented reality, and virtual realities are not directly discussed in this specific article, but they are all areas where online video streaming plays a significant role. Privacy-preserving frameworks like PPVF can contribute to maintaining privacy and security in these immersive multimedia environments.
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by jsendak | Aug 28, 2024 | Computer Science
An Expert Commentary on Mobile Sensing for On-Street Parking Detection
This article discusses the use of mobile sensing as a cost-effective solution for on-street parking detection in the context of smart city development. It acknowledges the inherent accuracy limitations of mobile sensing due to detection intervals and introduces a novel Dynamic Gap Reduction Algorithm (DGRA) to address this challenge. The efficacy of the algorithm is evaluated through real drive tests and simulations, and a Driver-Side and Traffic-Based Model (DSTBM) is also presented to assess its performance.
Mobile sensing, in contrast to fixed sensing, holds great potential as a practical and cost-effective solution for on-street parking detection. By utilizing sensors on moving vehicles, it allows for wide coverage and real-time data collection. However, the accuracy limitations arising from detection intervals have been a major concern in the deployment of mobile sensing for parking detection.
The Dynamic Gap Reduction Algorithm (DGRA) proposed in this paper is a crowdsensing-based approach that aims to mitigate the accuracy limitations of mobile sensing. By leveraging the parking detection data collected by sensors on moving vehicles, the algorithm dynamically reduces the gap between parked vehicles, thereby improving accuracy. This approach is a significant step forward in addressing the accuracy challenges of mobile sensing for parking detection.
The efficacy of the DGRA is validated through both real drive tests and simulations. Real drive tests involve the deployment of sensors on vehicles driving through urban areas, collecting parking detection data. Simulations, on the other hand, allow for comprehensive evaluation and analysis of the algorithm’s performance under various scenarios. The combination of real drive tests and simulations provides strong evidence of the algorithm’s effectiveness.
In addition to the DGRA, the paper introduces the Driver-Side and Traffic-Based Model (DSTBM) to incorporate drivers’ parking decisions and traffic conditions. By considering these factors, the performance of the DGRA can be further evaluated and optimized. This model provides a holistic approach to assess the impact of traffic conditions and driver behavior on the accuracy of mobile sensing for parking detection.
The results of the study highlight the significant potential of the DGRA in reducing the accuracy gap of mobile sensing for on-street parking detection. By dynamically reducing the gap between parked vehicles, the algorithm improves the accuracy of mobile sensing and contributes to efficient urban parking management. This advancement is crucial in the development of smart cities, where effective parking management plays a pivotal role in reducing congestion and enhancing urban mobility.
In conclusion, the introduction of the Dynamic Gap Reduction Algorithm (DGRA) and the Driver-Side and Traffic-Based Model (DSTBM) marks a significant step forward in addressing the accuracy limitations of mobile sensing for on-street parking detection. The validation of the DGRA through real drive tests and simulations provides strong evidence of its efficacy. As smart cities continue to evolve, efficient urban parking management becomes increasingly vital, and the DGRA offers a promising solution to improve the accuracy and effectiveness of mobile sensing in this domain.
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by jsendak | Aug 27, 2024 | Computer Science
arXiv:2408.13608v1 Announce Type: new
Abstract: Speech-language multi-modal learning presents a significant challenge due to the fine nuanced information inherent in speech styles. Therefore, a large-scale dataset providing elaborate comprehension of speech style is urgently needed to facilitate insightful interplay between speech audio and natural language. However, constructing such datasets presents a major trade-off between large-scale data collection and high-quality annotation. To tackle this challenge, we propose an automatic speech annotation system for expressiveness interpretation that annotates in-the-wild speech clips with expressive and vivid human language descriptions. Initially, speech audios are processed by a series of expert classifiers and captioning models to capture diverse speech characteristics, followed by a fine-tuned LLaMA for customized annotation generation. Unlike previous tag/templet-based annotation frameworks with limited information and diversity, our system provides in-depth understandings of speech style through tailored natural language descriptions, thereby enabling accurate and voluminous data generation for large model training. With this system, we create SpeechCraft, a fine-grained bilingual expressive speech dataset. It is distinguished by highly descriptive natural language style prompts, containing approximately 2,000 hours of audio data and encompassing over two million speech clips. Extensive experiments demonstrate that the proposed dataset significantly boosts speech-language task performance in stylist speech synthesis and speech style understanding.
Analyzing the Multi-disciplinary Nature of Speech-Language Multi-modal Learning
This article discusses the challenges in speech-language multi-modal learning and the need for a large-scale dataset that provides a comprehensive understanding of speech style. The author highlights the trade-off between data collection and high-quality annotation and proposes an automatic speech annotation system for expressiveness interpretation.
The multi-disciplinary nature of this topic is evident in the various techniques and technologies used in the proposed system. The speech audios are processed using expert classifiers and captioning models, which require expertise in speech recognition, natural language processing, and machine learning. The fine-tuned LLaMA (Language Learning and Modeling of Annotation) algorithm further enhances the system’s ability to generate customized annotations.
From the perspective of multimedia information systems, the article emphasizes the importance of combining audio and natural language data to gain insights into speech style. This integration of multiple modalities (speech and text) is crucial for developing sophisticated speech synthesis and speech style understanding systems.
The concept of animations is related to this topic as it involves the creation of expressive and vivid movements and gestures to convey meaning. In speech-language multi-modal learning, the annotations generated by the system aim to capture the expressive nuances of speech, similar to the way animations convey emotions and gestures.
Artificial reality (AR), augmented reality (AR), and virtual realities (VR) can also benefit from the advancements in speech-language multi-modal learning. These immersive technologies often incorporate speech interactions, and understanding speech style can enhance the realism and effectiveness of these experiences. For example, in AR and VR applications, realistic and expressive speech can contribute to more engaging and lifelike virtual experiences.
What’s Next?
The development of the automatic speech annotation system described in this article opens up new possibilities for future research and applications. Here are a few directions that could be explored:
- Improving Annotation Quality: While the proposed system provides tailored natural language descriptions, further research could focus on enhancing the accuracy and richness of the annotations. Advanced machine learning models and linguistic analysis techniques could be employed to generate even more nuanced descriptions of speech styles.
- Expanding the Dataset: Although the SpeechCraft dataset mentioned in the article is extensive, future work could involve expanding the dataset to include more languages, dialects, and speech styles. This would provide a broader understanding of speech variation and enable the development of more inclusive and diverse speech-synthesis and style-understanding models.
- Real-Time Annotation: Currently, the annotation system processes pre-recorded speech clips. An interesting direction for further research would be to develop real-time annotation systems that can interpret and annotate expressive speech in live conversations or presentations. This would have applications in communication technologies, public speaking training, and speech therapy.
- Integration with Virtual Reality: As mentioned earlier, integrating speech-style understanding into virtual reality experiences can enhance immersion and realism. Future work could focus on developing techniques to seamlessly integrate the proposed annotation system and the generated datasets with virtual reality environments, creating more interactive and immersive speech-driven virtual experiences.
Overall, the advancements in speech-language multi-modal learning discussed in this article have significant implications in various fields, including multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. The proposed automatic speech annotation system and the SpeechCraft dataset pave the way for further research and applications in speech synthesis, style understanding, and immersive technologies.
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