by jsendak | Jan 10, 2024 | Computer Science
The workshop proceedings of the co-located workshops “Working with Troubles and Failures in Conversation with Humans and Robots” (WTF 2023) and “Is CUI Design Ready Yet?” shed light on important topics in the field of conversational user interfaces (CUIs).
Working with Troubles and Failures in Conversation with Humans and Robots (WTF 2023)
WTF 2023 focused on the challenges faced by researchers in human-robot interaction, dialogue systems, human-computer interaction, and conversation analysis. The workshop acknowledged that despite the progress made in robotic speech interfaces, they still suffer from brittleness and frequently encounter failures.
One noteworthy aspect highlighted by the workshop is the positive bias found in the technical literature towards the successful performance of robotic speech interfaces. This bias potentially limits our understanding of the true capabilities and limitations of these interfaces. To address this issue, WTF 2023 aimed to provide a platform for researchers to discuss communicative troubles and failures in human-robot interactions. By thoroughly investigating such failures and developing a taxonomy of them, the workshop sought to foster discussions on potential strategies for mitigating them.
This workshop brings attention to the need for more comprehensive research on the failures of robotic speech interfaces. By focusing on these failures, researchers can improve the overall reliability and performance of conversational interfaces, leading to more robust and effective human-robot interactions.
Is CUI Design Ready Yet?
As CUIs become increasingly prevalent in both academia and the commercial market, it is crucial to design usable and adoptable interfaces. The workshop “Is CUI Design Ready Yet?” aims to address the lack of discussion surrounding the overall community practice of developing design resources for practical CUI design.
While there has been significant growth in research on designing CUIs for commercial use, little attention has been given to the development of design resources that can aid in practical CUI design. This workshop seeks to bridge that gap by bringing the CUI community together to discuss current practices in developing tools and resources for practical CUI design.
The workshop also aims to explore the adoption, or non-adoption, of these tools and resources and how they are utilized in the training and education of new CUI designers entering the field. By examining the current practices and challenges in developing design resources, this workshop can contribute to the improvement of CUI design methodologies.
The outcomes of this workshop will be valuable for both researchers and practitioners in the field of CUI design. It will enable better collaboration and knowledge sharing, ultimately leading to more effective and user-friendly CUIs in the future.
For more information about the workshops and their proceedings, visit their respective websites: WTF 2023 and “Is CUI Design Ready Yet?”.
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by jsendak | Jan 9, 2024 | Computer Science
Machine learning tasks over image databases often generate masks that
annotate image content (e.g., saliency maps, segmentation maps, depth maps) and
enable a variety of applications (e.g., determine if a model is learning
spurious correlations or if an image was maliciously modified to mislead a
model). While queries that retrieve examples based on mask properties are
valuable to practitioners, existing systems do not support them efficiently. In
this paper, we formalize the problem and propose MaskSearch, a system that
focuses on accelerating queries over databases of image masks while
guaranteeing the correctness of query results. MaskSearch leverages a novel
indexing technique and an efficient filter-verification query execution
framework. Experiments with our prototype show that MaskSearch, using indexes
approximately 5% of the compressed data size, accelerates individual queries by
up to two orders of magnitude and consistently outperforms existing methods on
various multi-query workloads that simulate dataset exploration and analysis
processes.
Accelerating Queries over Image Masks: Introducing MaskSearch
In the field of multimedia information systems, image databases play a crucial role in various applications such as computer vision, machine learning, and augmented reality. Machine learning tasks often generate masks that annotate image content, enabling different applications like object recognition, image segmentation, and depth estimation. However, existing systems lack efficient support for queries based on mask properties.
In their paper, the authors introduce MaskSearch, a system that aims to accelerate queries over databases of image masks while ensuring the correctness of query results. The system leverages a novel indexing technique and an efficient filter-verification query execution framework, making it possible to retrieve examples based on mask properties more efficiently.
One of the key challenges in accelerating queries over image masks is the large amount of data involved. Image masks can be highly detailed and complicated, leading to significant storage requirements. To address this challenge, MaskSearch employs a compressed data size of approximately 5% of the original data. This reduction in storage size contributes to faster query execution times.
The authors conducted experiments with a prototype of MaskSearch to evaluate its performance. The results showed that MaskSearch outperformed existing methods in terms of query acceleration, achieving speeds up to two orders of magnitude faster. The system consistently performed well across various multi-query workloads simulating different dataset exploration and analysis processes.
MaskSearch’s indexing technique and efficient query execution framework have implications beyond image databases. The concept of accelerating queries based on specific properties can be extended to other areas such as video processing, virtual reality environments, and augmented reality applications. With the increasing demand for interactive multimedia experiences, the ability to efficiently retrieve and analyze data based on specific properties is becoming more crucial.
The multi-disciplinary nature of this research is evident as it touches upon multiple fields including computer vision, machine learning, database systems, and multimedia information retrieval. Researchers and practitioners in these domains can benefit from MaskSearch’s innovative approach to accelerating queries over image masks, opening up new possibilities for efficient data exploration and analysis.
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by jsendak | Jan 9, 2024 | Computer Science
Improving Named Entity Extraction from Hand-Written Text Recognition (HTR) Software
In the article titled “Improving Named Entity Extraction from Hand-Written Text Recognition (HTR) Software”, the authors present the project REE-HDSC, which focuses on enhancing the quality of named entities extracted automatically from texts generated by HTR software. This research is particularly relevant and timely, considering the increasing reliance on digital archives and the need for accurate information extraction.
The Six-Step Processing Pipeline
The authors outline a six-step processing pipeline that forms the basis of their work. This pipeline encompasses various stages, including preprocessing, recognition, validation, post-processing, and evaluation. This comprehensive approach ensures that the extracted named entities undergo rigorous processing to identify and improve their accuracy and precision.
Through the preprocessing step, the authors likely perform tasks such as noise removal, image enhancement, and text segmentation to prepare the handwritten documents for recognition. This stage is crucial in ensuring that the subsequent steps can extract high-quality named entities.
The recognition step involves utilizing Hand-Written Text Recognition (HTR) software to convert the handwritten text into machine-readable format. The authors do not delve into specific details about the HTR models used, but it can be inferred that they employ state-of-the-art techniques and models trained on large handwriting datasets.
Validation is a critical step in this pipeline as it aims to assess the accuracy of the extracted named entities. It is likely that the authors compare the recognized entities against ground truth data or annotated documents to identify potential errors or inconsistencies.
Post-processing involves further refining the extracted named entities to improve their quality. The article highlights that this stage plays a vital role in enhancing person name extraction precision. The researchers achieve this by retraining HTR models using names, applying advanced post-processing techniques, and identifying and removing incorrect or irrelevant names. This step showcases the authors’ innovative approach to address the challenge of low precision in person name extraction.
Evaluation is the final step, where the authors assess the performance of their six-step processing pipeline. By processing 19th and 20th-century death certificates from the civil registry of Curacao, they gain insights into the strengths and weaknesses of their approach.
Results and Expert Insights
The authors report high precision in extracting dates from the processed death certificates. This achievement suggests that the preprocessing, recognition, and validation steps are effective in accurately capturing temporal information from the handwritten texts.
However, they also find that the precision of person name extraction is low. This discovery underscores the challenges associated with extracting named entities from handwritten texts, particularly when it comes to personal names. The variability in handwriting styles, ambiguous characters, and potential errors in recognition contribute to this difficulty.
To address this issue, the authors propose several strategies. First, by retraining HTR models using names specifically, they can enhance the recognition of person names in the handwritten text. As names often follow certain patterns and exhibit distinct characteristics, this targeted retraining can lead to significant improvements.
Additionall, post-processing techniques are applied to further refine the extracted person names. These advanced techniques likely involve language models, statistical analyses, and rule-based approaches to identify and correct errors or inconsistencies in the recognized names.
An innovative approach mentioned in the article is the identification and removal of incorrect or irrelevant names. By leveraging external datasets or knowledge bases, the researchers can compare the recognized names against known entities and filter out any names that do not align with the context or domain of the documents being analyzed.
Future Directions and Conclusion
The research presented in this article showcases significant progress in improving named entity extraction from HTR software. It opens up possibilities for enhancing the usability and accuracy of digital archives, historical document analysis, and numerous other applications that rely on handwritten text recognition.
While the authors focus on death certificates from the civil registry of Curacao, their methodology can be adapted and applied to various other domains and historical records. Expanding the scope of their research to encompass a broader range of documents will allow for a more comprehensive evaluation of the proposed processing pipeline.
In the future, it would be interesting to explore additional techniques to further improve person name extraction precision. These could include using contextual information from surrounding words or leveraging machine learning algorithms to better handle variations and inconsistencies in handwriting styles.
Undoubtedly, the advancements presented in this article have significant implications for digitization efforts, historical research, and archival preservation. The ability to more accurately extract named entities from handwritten texts not only enhances access to historical information but also enables researchers to draw new insights and connections across different datasets. Overall, this work serves as a valuable contribution to the field of natural language processing and archival studies.
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by jsendak | Jan 9, 2024 | Computer Science
Recovering unknown, missing, damaged, distorted, or lost information in DCT
coefficients is a common task in multiple applications of digital image
processing, including image compression, selective image encryption, and image
communication. This paper investigates the recovery of sign bits in DCT
coefficients of digital images, by proposing two different approximation
methods to solve a mixed integer linear programming (MILP) problem, which is
NP-hard in general. One method is a relaxation of the MILP problem to a linear
programming (LP) problem, and the other splits the original MILP problem into
some smaller MILP problems and an LP problem. We considered how the proposed
methods can be applied to JPEG-encoded images and conducted extensive
experiments to validate their performances. The experimental results showed
that the proposed methods outperformed other existing methods by a substantial
margin, both according to objective quality metrics and our subjective
evaluation.
Recovering Unknown Information in DCT Coefficients: A Multi-disciplinary Approach
Introduction
In the field of digital image processing, recovering unknown, missing, damaged, distorted, or lost information in DCT (Discrete Cosine Transform) coefficients is a crucial task. This task is applicable in various multimedia information systems such as image compression, selective image encryption, and image communication. In this article, we will explore a research paper that investigates the recovery of sign bits in DCT coefficients of digital images. The paper proposes two different approximation methods to solve the associated problem and aims to improve the performance compared to existing methods.
The Multi-disciplinary Nature
The concepts discussed in this paper highlight the multi-disciplinary nature of digital image processing. It combines principles from mathematics, computer science, and multimedia technology to address the challenge of recovering unknown information. The methods proposed in the paper involve mathematical programming techniques and algorithms, but their practical application lies in the domain of multimedia systems. The success of these methods depends on understanding the underlying principles of image compression, encryption, and communication.
The Relation to Multimedia Information Systems
Recovering unknown information in DCT coefficients has direct implications for multimedia information systems. These systems deal with large volumes of digital media, including images and videos. The ability to recover missing or damaged information can significantly enhance the quality and usability of multimedia content. By improving the recovery of sign bits in DCT coefficients, the proposed methods can contribute to more efficient image compression algorithms, more secure selective image encryption techniques, and reliable image communication protocols.
Animations, Artificial Reality, Augmented Reality, and Virtual Realities
While the paper focuses on recovering sign bits in DCT coefficients specifically for digital images, the concepts discussed have wider implications for other forms of multimedia, such as animations, artificial reality, augmented reality, and virtual realities. These forms of multimedia often rely on image compression and communication techniques similar to those used in digital images. By improving the recovery of missing or distorted information, the proposed methods can enhance the quality and realism of animations, increase the fidelity of artificial reality simulations, improve the accuracy of augmented reality overlays, and enhance the immersive experience in virtual realities.
Conclusion
Recovering unknown information in DCT coefficients is a challenging task with broad applications in multimedia information systems. The research paper discussed in this article proposes two approximation methods to solve the associated problem. These methods demonstrate improved performance compared to existing approaches, both objectively and subjectively. The paper’s findings contribute to the wider field of multimedia information systems by enhancing image compression, selective image encryption, and image communication. Moreover, the concepts explored in the paper have implications for animations, artificial reality, augmented reality, and virtual realities, enabling the development of more immersive and realistic multimedia experiences.
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by jsendak | Jan 9, 2024 | Computer Science
Enhancing Knowledge Graph Embedding Learning with Contextual and Literal Information
Knowledge graphs play a crucial role in various domains, such as natural language processing, information retrieval, and recommender systems. The ability to effectively represent entities and relations in knowledge graphs is essential for tasks like link prediction, entity classification, and entity alignment. Recent studies have focused on knowledge graph embedding learning, which aims to encode these entities and relations into low-dimensional vector spaces.
However, existing models predominantly consider the structural aspects of knowledge graphs, overlooking the valuable contextual and literal information present within them. Incorporating such information can result in more powerful and accurate embeddings, thereby enhancing the performance of downstream tasks.
In this paper, the authors propose a novel model that addresses the limitation of structural-focused models by incorporating both contextual and literal information into entity and relation embeddings. This integration is made possible through the utilization of graph convolutional networks, a powerful framework for learning on graph-structured data.
For contextual information, the authors introduce confidence and relatedness metrics to quantify its significance. A rule-based method is developed to calculate the confidence metric, capturing the reliability of the contextual information associated with an entity or relation. On the other hand, the relatedness metric leverages the representations derived from the literal information present in the knowledge graph.
The significance of incorporating contextual information lies in its ability to capture dynamic properties related to entities and relations. A single snapshot or a static representation of knowledge graphs might fail to capture the evolving nature of these elements. By considering their context, we can uncover more fine-grained details and improve the quality of embeddings.
To evaluate the performance of their model, the authors conducted comprehensive experiments on two established benchmark datasets. The results demonstrate that their proposed approach outperforms existing models that rely solely on structural information. The incorporation of contextual and literal information leads to more accurate and informative knowledge graph embeddings.
Looking forward, this research opens up several avenues for future exploration. One potential direction is to explore more sophisticated methods for capturing the confidence of contextual information. Additionally, investigating different ways to utilize literal information within the graph convolutional network framework can further enhance the model’s performance. Furthermore, exploring the impact of different types of contextual and literal information on downstream tasks can shed light on the intricacies of knowledge graphs.
In conclusion, this paper introduces a novel model that incorporates contextual and literal information into entity and relation embeddings in knowledge graphs. By leveraging graph convolutional networks, the model outperforms existing approaches that overlook these aspects. This research significantly contributes to enhancing the effectiveness of knowledge graph embedding learning and paves the way for further advancements in the field.
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by jsendak | Jan 9, 2024 | Computer Science
The remarkable potential of multi-modal large language models (MLLMs) in
comprehending both vision and language information has been widely
acknowledged. However, the scarcity of 3D scenes-language pairs in comparison
to their 2D counterparts, coupled with the inadequacy of existing approaches in
understanding of 3D scenes by LLMs, poses a significant challenge. In response,
we collect and construct an extensive dataset comprising 75K
instruction-response pairs tailored for 3D scenes. This dataset addresses tasks
related to 3D VQA, 3D grounding, and 3D conversation. To further enhance the
integration of 3D spatial information into LLMs, we introduce a novel and
efficient prompt tuning paradigm, 3DMIT. This paradigm eliminates the alignment
stage between 3D scenes and language and extends the instruction prompt with
the 3D modality information including the entire scene and segmented objects.
We evaluate the effectiveness of our method across diverse tasks in the 3D
scene domain and find that our approach serves as a strategic means to enrich
LLMs’ comprehension of the 3D world. Our code is available at
https://github.com/staymylove/3DMIT.
The Potential of Multi-Modal Large Language Models in Understanding 3D Scenes
The integration of vision and language information has long been a goal in the field of multimedia information systems. The ability to comprehend and interpret both visual and textual content opens up a wide range of possibilities for applications such as animations, artificial reality, augmented reality, and virtual realities.
In this article, we explore the remarkable potential of multi-modal large language models (MLLMs) in comprehending 3D scenes. While MLLMs have shown great promise in understanding 2D images and text, the scarcity of 3D scenes-language pairs and the existing challenges in understanding 3D scenes have posed significant obstacles.
To address this challenge, the authors of the article have collected and constructed an extensive dataset comprising 75K instruction-response pairs specifically tailored for 3D scenes. This dataset covers tasks related to 3D visual question answering (3D VQA), 3D grounding, and 3D conversation.
In addition to the dataset, the authors propose a novel paradigm called 3DMIT (3D Modality Information Tuning) to enhance the integration of 3D spatial information into MLLMs. This paradigm eliminates the need for an alignment stage between 3D scenes and language by extending the instruction prompt with 3D modality information, including the entire scene and segmented objects.
The effectiveness of the proposed method is evaluated across diverse tasks in the 3D scene domain, and the results show that this approach significantly enhances MLLMs’ comprehension of the 3D world. By bridging the gap between vision and language, MLLMs can now better understand and interpret complex 3D scenes, leading to improved performance in various applications.
This work highlights the multi-disciplinary nature of the concepts discussed. The integration of vision, language, and spatial information requires expertise from various fields, including computer vision, natural language processing, and graphics.
In the wider field of multimedia information systems, this research contributes to the development of more advanced animations, artificial reality, augmented reality, and virtual realities. By improving the capabilities of MLLMs to understand 3D scenes, we can expect enhanced user experiences and more immersive virtual environments. This has implications for industries such as gaming, virtual simulations, and virtual tours.
In conclusion, the potential of multi-modal large language models in comprehending 3D scenes is a significant advancement in the field of multimedia information systems. The combination of vision and language information, coupled with novel techniques like 3DMIT, opens up new possibilities for a wide range of applications. By addressing the challenges in understanding 3D scenes, this research paves the way for more sophisticated and interactive multimedia experiences.
Code Availability: The code for the proposed method is available at https://github.com/staymylove/3DMIT.
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