by jsendak | Jan 9, 2024 | Computer Science
Metadata: A Crucial Component for Managing Omics Datasets
Metadata, often referred to as “data about data,” plays a vital role in the organization, comprehension, and management of vast omics datasets. With the explosion of biological data in fields such as genomics, proteomics, and metabolomics, the importance of metadata cannot be overstated. It serves as a guide that enables researchers to efficiently discover, integrate, and interpret data, leading to more effective data utilization.
One of the primary domains where metadata plays a critical role is scientific research. By providing detailed information about the datasets, metadata facilitates data reproducibility, reusability, and secondary analysis. Researchers can build upon existing studies and perform integrative meta-analyses, leading to more comprehensive insights. However, despite its significance, there exist numerous barriers that impede the sharing of metadata among researchers.
Identifying Key Barriers to Metadata Sharing
This study sheds light on several barriers that hinder the sharing of metadata in the scientific community. One such barrier is the lack of uniform standards for metadata. With varying guidelines and practices across different research domains, it becomes challenging to compare and integrate datasets effectively. The absence of a standardized framework limits the interoperability and collaboration between researchers.
Privacy and legal concerns also pose a significant barrier to metadata sharing. Researchers must navigate complex regulations related to data protection and confidentiality, making it difficult to share metadata without compromising sensitive information. Findings from this study suggest that efforts should be made to develop clear guidelines and protocols to address these concerns.
Another barrier highlighted in this study is the limitations in study design. Often, metadata collection is an afterthought in research projects, which leads to incomplete or insufficient metadata descriptions. Inadequate documentation hampers the understanding and usability of datasets, hindering their potential for reuse and integration. Research institutions should emphasize the importance of comprehensive study design and metadata collection protocols to mitigate this issue.
Limited incentives for metadata sharing also contribute to the current challenges. Researchers often prioritize primary data publication over sharing metadata, as it is perceived to have more impact on their careers. This emphasis on primary data disincentivizes metadata sharing, leading to a gap in the research infrastructure. Creating incentives and rewards for researchers who share high-quality metadata can help foster a culture of data sharing and collaboration.
Insufficient infrastructure is yet another barrier that hinders metadata sharing. Many research institutions lack the necessary resources, tools, and systems to effectively manage and share metadata. The development of robust infrastructure that supports metadata organization, storage, and retrieval is crucial for overcoming this hurdle.
Lastly, the dearth of well-trained personnel for metadata management and reuse poses another challenge. This study highlights the need for educational efforts to train researchers in metadata best practices. Providing researchers with the necessary skills and knowledge will enable them to effectively manage and utilize metadata, leading to improved research outcomes.
Proposed Solutions to Overcome Barriers
The study proposes several solutions to address the barriers identified. First and foremost, there should be a concerted effort to promote standardization in metadata practices. Developing common standards and guidelines will facilitate interoperability and collaboration across different research domains, enabling seamless data integration.
The role of journals and funding agencies also comes into play in promoting metadata sharing. These stakeholders can encourage researchers to provide comprehensive metadata descriptions as part of the publication process. Mandating metadata submission or providing additional recognition for thorough metadata practices can incentivize researchers to prioritize its sharing.
In addition to incentives, improving infrastructure is crucial for facilitating metadata sharing. Research institutions should invest in developing robust systems that enable efficient metadata organization, storage, and retrieval. Such infrastructure will provide researchers with the necessary tools and resources to manage metadata effectively.
Educational efforts also play a vital role in addressing the dearth of well-trained personnel for metadata management. Workshops, training programs, and online resources can help researchers acquire the skills needed to handle metadata effectively. By enhancing researchers’ understanding of metadata best practices, this barrier can be mitigated.
Conclusion
Metadata sharing is crucial for achieving more accurate, reliable, and impactful research outcomes. By addressing the barriers highlighted in this study, the scientific community can unlock the full potential of omics datasets. Adoption of standardized practices, development of infrastructure, provision of incentives, and educational initiatives are all integral components of building a robust metadata ecosystem. Collaboration between researchers, institutions, journals, and funding agencies is key to driving forward the sharing and utilization of metadata, ultimately advancing scientific progress.
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by jsendak | Jan 9, 2024 | Computer Science
Providing high-quality video with efficient bitrate is a main challenge in
video industry. The traditional one-size-fits-all scheme for bitrate ladders is
inefficient and reaching the best content-aware decision computationally
impractical due to extensive encodings required. To mitigate this, we propose a
bitrate and complexity efficient bitrate ladder prediction method using
transfer learning and spatio-temporal features. We propose: (1) using feature
maps from well-known pre-trained DNNs to predict rate-quality behavior with
limited training data; and (2) improving highest quality rung efficiency by
predicting minimum bitrate for top quality and using it for the top rung. The
method tested on 102 video scenes demonstrates 94.1% reduction in complexity
versus brute-force at 1.71% BD-Rate expense. Additionally, transfer learning
was thoroughly studied through four networks and ablation studies.
The article discusses the challenges faced in the video industry when it comes to providing high-quality video with efficient bitrate. The traditional approach of using a one-size-fits-all scheme for bitrate ladders is inefficient and computationally impractical. To address this issue, the authors propose a method that utilizes transfer learning and spatio-temporal features to predict the optimal bitrate ladder.
Transfer learning, a concept widely used in machine learning, is employed in this method by utilizing feature maps from pre-trained deep neural networks (DNNs) to predict the rate-quality behavior of videos. This approach allows for accurate predictions even with limited training data, reducing the computational complexity of encoding decisions.
In addition to transfer learning, the authors also propose improving the efficiency of the highest quality rung by predicting the minimum bitrate required for the top quality and using it as a reference for encoding. By doing so, the method achieves a significant reduction in complexity compared to traditional brute-force methods, while only incurring a minimal 1.71% BD-Rate expense.
This approach has several implications for the wider field of multimedia information systems. Firstly, it highlights the importance of considering the multi-disciplinary nature of video encoding, which combines concepts from computer vision, machine learning, and video compression. The use of pre-trained DNNs for feature extraction demonstrates how techniques from artificial intelligence can be leveraged to improve video quality.
Furthermore, this method is closely related to the fields of animations, augmented reality (AR), virtual reality (VR), and artificial reality (AR). These technologies heavily rely on high-quality video content to deliver immersive experiences. By optimizing the bitrate ladder, this method can improve the visual fidelity and streaming performance of multimedia content used in AR and VR applications.
In conclusion, the proposed method for efficient bitrate ladder prediction using transfer learning and spatio-temporal features is a significant advancement in the video industry. Its effectiveness in reducing complexity and its broader implications for multimedia information systems, animations, AR, VR, and artificial reality make it a valuable contribution to the field.
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by jsendak | Jan 9, 2024 | Computer Science
Accelerating and Refining Free-form Metasurface Designs through Deep Learning
In the world of fifth-generation (5G) microwave communication, metasurfaces have emerged as a cutting-edge technology with widespread applications. Among the various types of metasurfaces, free-form metasurfaces stand out for their ability to achieve intricate spectral responses that surpass those of regular-shaped counterparts.
However, traditional numerical methods for designing free-form metasurfaces are time-consuming and require specialized expertise. Recognizing this bottleneck, recent studies have explored the potential of deep learning to expedite and enhance the metasurface design process.
In this context, researchers have introduced XGAN, an extended generative adversarial network (GAN), with a surrogate that enables the generation of high-quality free-form metasurface designs. This surrogate imposes a physical constraint on XGAN, allowing it to accurately generate metasurfaces from input spectral responses in a monolithic manner.
To assess the performance of XGAN, comparative experiments were conducted involving 20,000 free-form metasurface designs. The results were impressive, with XGAN achieving an average accuracy of 0.9734 and demonstrating a speed improvement of 500 times compared to the conventional methodology.
By enabling the rapid generation of metasurfaces with specific spectral responses, this approach facilitates the building of a metasurface library tailored to various communication needs. Moreover, the applicability of XGAN extends beyond microwave communication into other domains, such as optical metamaterials, nanophotonic devices, and even drug discovery.
The integration of deep learning techniques like XGAN into metasurface design processes marks a significant step forward in accelerating research and development efforts. By reducing both the time and expertise required for metasurface design, scientists and engineers can focus on exploring new possibilities and pushing the boundaries of what metasurfaces can achieve.
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by jsendak | Jan 9, 2024 | Computer Science
Adversarial attacks can readily disrupt the image classification system,
revealing the vulnerability of DNN-based recognition tasks. While existing
adversarial perturbations are primarily applied to uncompressed images or
compressed images by the traditional image compression method, i.e., JPEG,
limited studies have investigated the robustness of models for image
classification in the context of DNN-based image compression. With the rapid
evolution of advanced image compression, DNN-based learned image compression
has emerged as the promising approach for transmitting images in many
security-critical applications, such as cloud-based face recognition and
autonomous driving, due to its superior performance over traditional
compression. Therefore, there is a pressing need to fully investigate the
robustness of a classification system post-processed by learned image
compression. To bridge this research gap, we explore the adversarial attack on
a new pipeline that targets image classification models that utilize learned
image compressors as pre-processing modules. Furthermore, to enhance the
transferability of perturbations across various quality levels and
architectures of learned image compression models, we introduce a saliency
score-based sampling method to enable the fast generation of transferable
perturbation. Extensive experiments with popular attack methods demonstrate the
enhanced transferability of our proposed method when attacking images that have
been post-processed with different learned image compression models.
Adversarial attacks have been a significant concern in the field of image classification systems, exposing the vulnerability of deep neural network (DNN) based recognition tasks. While previous studies have focused on attacking uncompressed or traditionally compressed images, there is a lack of understanding regarding the robustness of models for image classification in the context of DNN-based image compression.
In recent times, DNN-based learned image compression has gained traction as a powerful method for transmitting images in security-critical applications like cloud-based face recognition and autonomous driving. The performance of learned image compression surpasses that of traditional compression techniques. Therefore, it becomes crucial to investigate the robustness of classification systems that undergo learned image compression as a pre-processing step.
In order to fill this research gap, this study focuses on exploring adversarial attacks on image classification models that utilize learned image compressors. This new pipeline provides insights into how these models behave when faced with adversarial perturbations. Furthermore, to ensure the effectiveness of these attacks across different quality levels and architectures of learned image compression models, a saliency score-based sampling method is introduced. This method enables the rapid generation of transferable perturbations.
Extensive experiments were conducted using popular attack methods, and the results demonstrated the enhanced transferability of the proposed method when targeting images that have undergone various learned image compression models.
The Multidisciplinary Nature of the Concepts
This research article covers various multidisciplinary fields within multimedia information systems and related technologies such as animations, artificial reality, augmented reality, and virtual realities.
Firstly, it addresses the issue of vulnerability in image classification systems, which is a critical concern across multiple disciplines. Image classification models are used in various applications such as video games, virtual reality simulations, computer vision systems, and more. Understanding the vulnerabilities and developing countermeasures is crucial in fields where accurate and reliable image recognition is essential.
Secondly, the article delves into the realm of learned image compression and its impact on security-critical applications. This concept bridges the fields of multimedia information systems and artificial reality, as cloud-based face recognition and autonomous driving heavily rely on accurate and efficient image processing techniques. By exploring the robustness of classification systems post-processed by learned image compression, this research contributes to the advancement of these fields.
Lastly, the proposed saliency score-based sampling method for generating transferable perturbations adds value to the field of augmented reality. Augmented reality experiences often involve overlaying digital content onto real-world images or video streams, requiring reliable and efficient image classification. Understanding how adversarial attacks can affect augmented reality systems is crucial for maintaining the integrity and security of these experiences.
Conclusion
This research article highlights the need to investigate the robustness of image classification models that utilize learned image compression as a preprocessing step. By exploring the adversarial attacks on such models, the study provides valuable insights into their vulnerabilities and suggests a saliency score-based sampling method to enhance transferability across different compression models. The multidisciplinary nature of this research connects various fields within multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. This research serves as an important step towards enhancing the security and reliability of image classification systems in a rapidly evolving technological landscape.
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by jsendak | Jan 9, 2024 | Computer Science
Law enforcement officials rely heavily on Forensic Video Analytic (FVA) Software in their investigation of crimes. However, the current FVA software available in the market is complex, time-consuming, equipment-dependent, and expensive. This poses a problem for developing countries that struggle to gain access to this crucial technology.
To address these shortcomings, a team embarked on a Final Year Project to develop an efficient and effective FVA software. They conducted a thorough review of scholarly research papers, online databases, and legal documentation to identify the areas that needed improvement. The scope of their project covered multiple aspects of FVA, including object detection, object tracking, anomaly detection, activity recognition, tampering detection, image enhancement, and video synopsis.
To achieve their goals, the team employed various machine learning techniques, GPU acceleration, and efficient architecture development. They used CNN, GMM, multithreading, and OpenCV C++ coding to create their software. By implementing these methods, they aimed to speed up the FVA process, particularly through their innovative research on video synopsis.
The project yielded three significant research outcomes: Moving Object Based Collision-Free Video Synopsis, Forensic and Surveillance Analytic Tool Architecture, and Tampering Detection Inter-Frame Forgery. These outcomes were achieved through the integration of efficient algorithms and optimizations to overcome limitations in processing power and memory. The team had to strike a balance between real-time performance and accuracy to ensure the software’s practicality.
Additionally, the research outcomes included forensic and surveillance panel outcomes specifically tailored for the Sri Lankan context. This demonstrates the team’s focus on addressing the needs and challenges faced by law enforcement in their home country.
In conclusion, this Final Year Project successfully developed an efficient and effective FVA software by leveraging machine learning techniques, optimized algorithms, and innovative research on video synopsis. The implications of their work are far-reaching, potentially revolutionizing the way law enforcement agencies handle video evidence. This project serves as a stepping stone towards providing developing countries with better access to the tools and technology needed for effective crime investigation and prevention.
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by jsendak | Jan 9, 2024 | Computer Science
Multimodal Large Language Models (MLLMs) are experiencing rapid growth,
yielding a plethora of noteworthy contributions in recent months. The
prevailing trend involves adopting data-driven methodologies, wherein diverse
instruction-following datasets are collected. However, a prevailing challenge
persists in these approaches, specifically in relation to the limited visual
perception ability, as CLIP-like encoders employed for extracting visual
information from inputs. Though these encoders are pre-trained on billions of
image-text pairs, they still grapple with the information loss dilemma, given
that textual captions only partially capture the contents depicted in images.
To address this limitation, this paper proposes to improve the visual
perception ability of MLLMs through a mixture-of-experts knowledge enhancement
mechanism. Specifically, we introduce a novel method that incorporates
multi-task encoders and visual tools into the existing MLLMs training and
inference pipeline, aiming to provide a more comprehensive and accurate
summarization of visual inputs. Extensive experiments have evaluated its
effectiveness of advancing MLLMs, showcasing improved visual perception
achieved through the integration of visual experts.
Multimodal Large Language Models (MLLMs) have been gaining momentum in recent months, thanks to their ability to generate meaningful content by leveraging both text and visual inputs. However, a significant challenge that researchers face when working with MLLMs is the limited visual perception ability of these models.
The existing approach involves using CLIP-like encoders to extract visual information from inputs. These encoders are pre-trained on billions of image-text pairs but still struggle with information loss due to the partial capture of contents in textual captions.
To overcome this limitation, this paper proposes a novel method that enhances the visual perception ability of MLLMs by incorporating a mixture-of-experts knowledge enhancement mechanism. This approach integrates multi-task encoders and visual tools into the training and inference pipeline of MLLMs, enabling a more comprehensive and accurate summarization of visual inputs.
The significance of this research lies in its multi-disciplinary nature. It combines elements from various domains such as natural language processing, computer vision, and artificial intelligence. By leveraging the strengths of different disciplines, the proposed method aims to improve the overall performance of MLLMs when it comes to understanding and generating content based on visual inputs.
In the wider field of multimedia information systems, this research contributes to bridging the gap between textual and visual information processing. With the integration of visual experts into MLLMs, the models become more adept at understanding and leveraging visual cues, leading to enhanced performance in tasks such as image captioning, visual question answering, and content generation.
Additioally, this work has implications for the advancements in Animations, Artificial Reality, Augmented Reality, and Virtual Realities. With better visual perception ability, MLLMs can play a crucial role in generating realistic animations, improving the user experience in artificial and augmented reality applications, and enabling more immersive virtual reality environments. By training MLLMs to understand and interpret visual inputs effectively, these technologies can benefit from more accurate and context-aware content generation.
In conclusion, the proposed method for enhancing the visual perception ability of MLLMs through a mixture-of-experts knowledge enhancement mechanism presents a promising avenue for advancing these models. By incorporating multi-task encoders and visual tools, the proposed approach enables MLLMs to have a more comprehensive understanding of visual inputs, thereby improving their performance across various domains including multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.
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