by jsendak | Jan 5, 2024 | Computer Science
Particle identification (PID) is a critical task in the field of high-energy physics, particularly in experiments like the ALICE experiment at CERN. The ability to accurately identify particles produced in ultrarelativistic collisions is essential for understanding the fundamental properties of matter and the universe.
Traditionally, PID methods have relied on hand-crafted selections that compare experimental data to theoretical simulations. While these methods have been effective to a certain extent, they have limitations in terms of accuracy and efficiency. This has motivated the exploration of novel approaches, such as machine learning models, to improve PID performance.
One of the challenges in PID is dealing with missing data. Due to the different detection techniques used by various subdetectors in ALICE, as well as limitations in detector efficiency and acceptance, some particles may not yield signals in all components. This leads to incomplete data, which cannot be trained with traditional machine learning techniques.
In this work, the authors propose a groundbreaking method for PID that can be trained using all available data examples, including those with missing values. This is a significant advancement in the field, as it enables the utilization of a larger dataset and improves the accuracy and efficiency of PID.
The exact details of the proposed method are not provided in this abstract, but it is likely that the authors have developed a technique to handle missing values in the training process. This could involve techniques such as imputation, where missing values are estimated based on the available data, or modifications to the machine learning algorithm itself to accommodate missing data.
The results of this work are promising, as it is stated that the proposed method improves the PID purity and efficiency for all investigated particle species. This suggests that the new approach is successful in accurately identifying particles even in cases with missing data.
Overall, this research represents an important step forward in the field of PID in high-energy physics experiments. By addressing the challenge of missing data, the proposed method opens up new possibilities for improving the accuracy and efficiency of particle identification and advancing our understanding of the fundamental building blocks of the universe.
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by jsendak | Jan 5, 2024 | Computer Science
Mining structured knowledge from tweets using named entity recognition (NER)
can be beneficial for many down stream applications such as recommendation and
intention understanding. With tweet posts tending to be multimodal, multimodal
named entity recognition (MNER) has attracted more attention. In this paper, we
propose a novel approach, which can dynamically align the image and text
sequence and achieve the multi-level cross-modal learning to augment textual
word representation for MNER improvement. To be specific, our framework can be
split into three main stages: the first stage focuses on intra-modality
representation learning to derive the implicit global and local knowledge of
each modality, the second evaluates the relevance between the text and its
accompanying image and integrates different grained visual information based on
the relevance, the third enforces semantic refinement via iterative cross-modal
interactions and co-attention. We conduct experiments on two open datasets, and
the results and detailed analysis demonstrate the advantage of our model.
Mining structured knowledge from tweets using named entity recognition (NER)
In the field of multimedia information systems, mining structured knowledge from tweets is an area of great interest. Tweets are a unique form of media that combines text, images, and sometimes even videos. This multimodal nature of tweets presents both challenges and opportunities for extracting valuable information from them.
One essential task in mining structured knowledge from tweets is named entity recognition (NER). NER involves identifying and classifying named entities, such as people, organizations, locations, and products, within a given text. Traditionally, NER techniques have focused on text-based data. However, with the rise of multimodal tweets, multimodal named entity recognition (MNER) has gained attention.
In this paper, the authors propose a novel approach that tackles the challenge of MNER. Their approach dynamically aligns the image and text sequence in a tweet and leverages cross-modal learning to improve textual word representation for MNER.
The authors divide their framework into three main stages:
- Intra-modality representation learning: In this stage, the framework learns the implicit global and local knowledge within each modality (text and image). This enables the model to understand the context and characteristics of the individual modalities.
- Relevance evaluation: The second stage focuses on evaluating the relevance between the text and its accompanying image. By assessing the semantic similarity and information overlap between the two modalities, the framework determines how much weight to assign to different grained visual information.
- Semantic refinement: The final stage enforces semantic refinement through iterative cross-modal interactions and co-attention. This iterative process allows the model to refine its understanding of the named entities by leveraging both textual and visual clues.
The proposed approach is evaluated on two open datasets, and the results demonstrate the advantages of their model in MNER. The authors provide a detailed analysis of their findings, further supporting the effectiveness of their approach.
From a broader perspective, this paper highlights the multi-disciplinary nature of multimedia information systems. It combines concepts from natural language processing, computer vision, and machine learning to tackle the challenge of MNER in multimodal tweets. This integration of different disciplines is crucial in advancing the field and developing innovative solutions for mining structured knowledge from multimedia data.
In relation to other concepts in the field, this work is closely related to animations, artificial reality, augmented reality, and virtual realities. Animations, particularly in the context of visual information, play a role in aligning and integrating different grained visual information. Artificial reality, augmented reality, and virtual realities are all immersive experiences that involve the integration of multiple modalities. Understanding and recognizing named entities accurately within these immersive environments can enhance user experiences and enable more sophisticated applications.
References:
- Author1, Author2, and Author3. (Year). Title of the Paper. Journal Name, Volume(Issue), Page Numbers.
- Author4, Author5, and Author6. (Year). Title of the Paper. Journal Name, Volume(Issue), Page Numbers.
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by jsendak | Jan 5, 2024 | Computer Science
Reputation-Based Threat Mitigation Framework for EEG Signal Classification
This paper introduces a reputation-based threat mitigation framework designed to enhance the security of electroencephalogram (EEG) signal classification during the model aggregation phase of Federated Learning. The use of EEG signal analysis has gained significant interest due to the emergence of brain-computer interface (BCI) technology. However, creating efficient learning models for EEG analysis is challenging due to the distributed nature of EEG data and concerns about privacy and security.
The proposed defending framework takes advantage of the Federated Learning paradigm, which enables collaborative model training using localized data from various sources while preserving privacy. Additionally, the framework incorporates a reputation-based mechanism to mitigate the influence of data poisoning attacks and identify compromised participants.
An essential aspect of the defending framework is the integration of Explainable Artificial Intelligence (XAI) techniques to assess the risk level of training data. By conducting data poisoning attacks based on this risk level, the framework can evaluate its effectiveness in defending against security threats on both publicly available EEG signal datasets and a self-established EEG signal dataset.
The experimental results demonstrate that the proposed reputation-based federated learning defense mechanism performs well in EEG signal classification while effectively reducing the risks associated with security threats. By leveraging the reputation-based approach, compromised participants can be identified, enabling their exclusion from model aggregation to ensure the integrity of the final model.
Expert Analysis
This research addresses a significant challenge in EEG signal analysis by leveraging Federated Learning to create more efficient learning models. The distributed nature of EEG data often limits the possibilities for centralizing data and conducting traditional machine learning approaches. By utilizing collaborative model training with localized data, Federated Learning offers a privacy-preserving solution that maintains data security.
However, security concerns arising from potential data poisoning attacks pose a considerable threat to the effectiveness and integrity of the model aggregation process. The proposed reputation-based mechanism in this framework provides a solution to this challenge. By analyzing the risk level of training data using Explainable Artificial Intelligence techniques, the framework is better equipped to detect compromised participants and mitigate their influence on the overall model.
The integration of XAI techniques adds transparency and interpretability to the reputation-based defense mechanism. This is crucial in understanding and validating the risk assessment process. Researchers can use this information to further refine and improve the reputation-based mechanism, enhancing its reliability and effectiveness.
The experimental results showcased the robustness of the proposed framework in dealing with security threats. By successfully defending against data poisoning attacks on both publicly available EEG signal datasets and a self-established EEG signal dataset, the framework demonstrated its ability to handle different scenarios and data distributions.
With the increasing adoption of EEG signal analysis in various applications, including healthcare, gaming, and neurofeedback systems, ensuring the security of these systems becomes paramount. This reputation-based threat mitigation framework provides a strong foundation for protecting EEG signal classification models from potential attacks, contributing to the overall reliability and trustworthiness of EEG-based technologies.
Future Outlook
While the proposed framework shows promising results, there are several avenues for further improvement and exploration. One aspect that could be enhanced is the reputation update mechanism. By continuously updating participant reputations based on their behavior during model aggregation, the framework could adapt to evolving security threats and improve its ability to identify compromised participants.
Additionally, future research could focus on investigating advanced Explainable Artificial Intelligence techniques to further enhance the risk assessment process. By utilizing techniques such as model interpretability and feature importance analysis, researchers can gain deeper insights into potential data poisoning attacks and improve the robustness of the defense strategy.
Furthermore, validating the proposed framework with larger and more diverse EEG signal datasets would strengthen its generalizability and applicability. The inclusion of real-world datasets from different sources and populations would provide a more comprehensive understanding of the framework’s performance and effectiveness.
In conclusion, this reputation-based threat mitigation framework presents a significant advancement in defending against security threats in EEG signal classification during Federated Learning. By combining the power of collaborative model training with localized data and a reputation-based mechanism, the framework offers a comprehensive solution to ensure the integrity and security of EEG-based technologies. Continued research and improvement in this area will contribute to the widespread adoption of EEG signal analysis and its applications in various domains.
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by jsendak | Jan 5, 2024 | Computer Science
Video moment retrieval (MR) and highlight detection (HD) based on natural
language queries are two highly related tasks, which aim to obtain relevant
moments within videos and highlight scores of each video clip. Recently,
several methods have been devoted to building DETR-based networks to solve both
MR and HD jointly. These methods simply add two separate task heads after
multi-modal feature extraction and feature interaction, achieving good
performance. Nevertheless, these approaches underutilize the reciprocal
relationship between two tasks. In this paper, we propose a task-reciprocal
transformer based on DETR (TR-DETR) that focuses on exploring the inherent
reciprocity between MR and HD. Specifically, a local-global multi-modal
alignment module is first built to align features from diverse modalities into
a shared latent space. Subsequently, a visual feature refinement is designed to
eliminate query-irrelevant information from visual features for modal
interaction. Finally, a task cooperation module is constructed to refine the
retrieval pipeline and the highlight score prediction process by utilizing the
reciprocity between MR and HD. Comprehensive experiments on QVHighlights,
Charades-STA and TVSum datasets demonstrate that TR-DETR outperforms existing
state-of-the-art methods. Codes are available at
url{https://github.com/mingyao1120/TR-DETR}.
Video moment retrieval (MR) and highlight detection (HD) are two important tasks in the field of multimedia information systems. MR aims to find relevant moments within videos based on natural language queries, while HD focuses on determining the highlight scores of different video clips. Both tasks require the understanding and analysis of video content.
In recent years, researchers have been working on developing DETR-based networks to solve MR and HD jointly. However, these methods often treat the tasks as separate entities and fail to fully exploit the reciprocal relationship between them.
In this paper, the authors propose a task-reciprocal transformer based on DETR (TR-DETR) to leverage the inherent reciprocity between MR and HD. The TR-DETR model consists of several key components:
- Local-global multi-modal alignment module: This module aligns features from various modalities, such as text and video, into a shared latent space. By doing so, the model ensures that the features are well-integrated and can be effectively utilized for both MR and HD.
- Visual feature refinement: This module aims to eliminate query-irrelevant information from visual features, ensuring that the modal interaction is more focused and accurate. By refining the visual features, the model can better capture the relevant information for both tasks.
- Task cooperation module: This module is designed to refine the retrieval pipeline and the highlight score prediction process by utilizing the reciprocity between MR and HD. It allows the two tasks to mutually benefit from each other’s insights and improve overall performance.
The experiments conducted on QVHighlights, Charades-STA, and TVSum datasets showcase the superior performance of TR-DETR compared to existing state-of-the-art methods. The proposed model effectively leverages the reciprocal relationship between MR and HD, leading to more accurate and informative results.
The concepts discussed in this article have a multidisciplinary nature, combining elements from computer science, artificial intelligence, natural language processing, and multimedia systems. The development of advanced algorithms and models for MR and HD has implications for various applications, including content recommendation systems, video summarization, and interactive multimedia experiences.
Furthermore, the ideas presented here are closely related to the wider field of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. The ability to accurately retrieve video moments and detect highlights is crucial in creating immersive multimedia experiences and virtual environments. These technologies rely on the analysis and understanding of video content, which can benefit greatly from the advancements in MR and HD.
In conclusion, the task-reciprocal transformer based on DETR (TR-DETR) introduced in this paper demonstrates a novel approach to jointly solve video moment retrieval and highlight detection. By leveraging the reciprocal relationship between the two tasks, TR-DETR achieves superior performance compared to existing methods. The concepts discussed here have implications for multimedia information systems, animations, artificial reality, augmented reality, and virtual realities, further advancing the field and enhancing user experiences.
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by jsendak | Jan 5, 2024 | Computer Science
Metaverse, the concept of creating a virtual world that mirrors the real world, is gaining momentum. The key to achieving a realistic and engaging metaverse lies in the ability to support large-scale real-time interactions. Artificial Intelligence (AI) models, particularly pre-trained ones, are playing a crucial role in achieving this goal. These AI models, through collaborative deep learning (CDL), are being trained collectively by multiple participants.
However, this collaborative approach brings with it certain security vulnerabilities that could pose a threat to both the trained models and the sensitive data sets owned by individuals. Malicious participants can exploit these weaknesses to compromise the integrity of the models or to illegally access private information.
In order to address these vulnerabilities, a new method called adversary detection-deactivation is proposed in this paper. This method aims to restrict and isolate the access of potential malicious participants, as well as prevent attacks such as Generative Adversarial Networks (GAN) and harmful backpropagation. By analyzing the behavior of participants and swiftly checking received gradients using a low-cost branch with an embedded firewall, the proposed protocol effectively protects the existing model.
Although the paper focuses on a Multiview CDL case for its protection analysis, the principles and techniques described can be applied more broadly. By implementing this adversary detection-deactivation method, the metaverse can ensure a more secure and trustworthy environment for collaborative deep learning.
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by jsendak | Jan 5, 2024 | Computer Science
AI-driven models are increasingly deployed in operational analytics
solutions, for instance, in investigative journalism or the intelligence
community. Current approaches face two primary challenges: ethical and privacy
concerns, as well as difficulties in efficiently combining heterogeneous data
sources for multimodal analytics. To tackle the challenge of multimodal
analytics, we present MULTI-CASE, a holistic visual analytics framework
tailored towards ethics-aware and multimodal intelligence exploration, designed
in collaboration with domain experts. It leverages an equal joint agency
between human and AI to explore and assess heterogeneous information spaces,
checking and balancing automation through Visual Analytics. MULTI-CASE operates
on a fully-integrated data model and features type-specific analysis with
multiple linked components, including a combined search, annotated text view,
and graph-based analysis. Parts of the underlying entity detection are based on
a RoBERTa-based language model, which we tailored towards user requirements
through fine-tuning. An overarching knowledge exploration graph combines all
information streams, provides in-situ explanations, transparent source
attribution, and facilitates effective exploration. To assess our approach, we
conducted a comprehensive set of evaluations: We benchmarked the underlying
language model on relevant NER tasks, achieving state-of-the-art performance.
The demonstrator was assessed according to intelligence capability assessments,
while the methodology was evaluated according to ethics design guidelines. As a
case study, we present our framework in an investigative journalism setting,
supporting war crime investigations. Finally, we conduct a formative user
evaluation with domain experts in law enforcement. Our evaluations confirm that
our framework facilitates human agency and steering in security-sensitive
applications.
Exploring the Challenges of Ethical and Multimodal Analytics in Operational Intelligence
In the evolving landscape of operational analytics, AI-driven models are playing an increasingly crucial role. Their applications range from investigative journalism to intelligence community operations. However, the deployment of these models faces two primary challenges: ethical concerns and difficulties in effectively combining heterogeneous data sources for multimodal analytics.
Ethical and privacy concerns have become paramount in recent years, particularly when it comes to the use of AI in sensitive domains. The potential for bias, discrimination, and violation of privacy rights has raised significant questions about the responsible deployment of these technologies.
The second challenge revolves around the complex task of integrating multiple data sources to enable comprehensive multimodal analytics. In operational intelligence exploration, it is essential to extract actionable insights from various types of data, such as text, images, and network connections. However, efficiently combining and analyzing these diverse sources can be a daunting task.
The Holistic Approach of MULTI-CASE
To address the challenges of ethical and multimodal analytics, researchers have developed the MULTI-CASE framework. This visual analytics framework aims to facilitate ethics-aware and multimodal intelligence exploration while ensuring an equal partnership between human analysts and AI systems.
MULTI-CASE leverages the power of Visual Analytics to enable human analysts to explore and assess heterogeneous information spaces. It provides a set of linked components, including a combined search function, annotated text view, and graph-based analysis. These components allow the exploration of different types of data in a cohesive and interconnected manner.
One key aspect of MULTI-CASE is its fully-integrated data model. By leveraging a unified approach to data representation, it enables seamless integration and analysis of diverse data sources. This integrative approach ensures that analysts can explore and compare information from various modalities, leading to a more comprehensive understanding of the analyzed domain.
The Role of AI and Language Models
MULTI-CASE incorporates AI capabilities, particularly through the use of a RoBERTa-based language model. This language model is fine-tuned to meet the specific requirements of the users, ensuring optimal performance in entity detection and analysis of textual information.
The underlying AI components complement the human analysts’ expertise, assisting in the identification and extraction of relevant entities and information. This collaborative approach allows analysts to leverage the power of AI while maintaining full control over the decision-making process and ensuring transparency in the analysis.
Evaluation and Case Study
To validate the effectiveness of MULTI-CASE, comprehensive evaluations were conducted. The benchmarking of the language model on relevant Named Entity Recognition (NER) tasks demonstrated state-of-the-art performance, attesting to its efficacy in entity detection.
The demonstrator’s intelligence capability was assessed using standardized evaluation methods, while the methodology was evaluated based on established ethics design guidelines. These evaluations provided insights into the framework’s strengths and opportunities for further improvement.
A case study was also presented, focusing on the framework’s application in an investigative journalism setting for supporting war crime investigations. This case study showcased MULTI-CASE’s ability to empower human analysts in complex and security-sensitive domains.
The final formative user evaluation involved domain experts from law enforcement. Their feedback provided valuable insights into the usability, effectiveness, and practical implications of the framework in real-world operational scenarios.
MULTI-CASE and the Wider Field of Multimedia Information Systems
The MULTI-CASE framework exemplifies the multi-disciplinary nature of multimedia information systems. It combines elements from visual analytics, artificial intelligence, and information retrieval to tackle the challenges of ethical and multimodal analytics in operational intelligence.
Furthermore, it is closely related to the domains of animations, artificial reality, augmented reality, and virtual realities. The incorporation of AI components, including language models, allows for enhanced virtual experiences and augmented decision-making capabilities.
The developments in the MULTI-CASE framework contribute to the ongoing evolution of multimedia information systems by providing a comprehensive and human-centered approach to ethics-aware and multimodal intelligence exploration. Its potential impact on operational analytics, investigative journalism, and intelligence community operations is significant and highlights the importance of responsible and collaborative deployments of AI-driven models.
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