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Towards the Integration of Metaverse and Multimedia Information Retrieval

Towards the Integration of Metaverse and Multimedia Informat...

Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), IEEE International Conference on

作者： Patrick Steinert Stefan Wagenpfeil Ingo Frommholz Matthias L Hemmje Faculty of Mathematics and Computer Science University of Hagen Hagen Germany Computing and Mathematical Sciences School of Engineering University of Wolverhampton Wolverhampton UK

Multimedia Information Retrieval is the field of indexing and retrieval of multimedia content objects. The meta-verse is a collection of virtual worlds, build from multimedia. We examined the intersection of both and identified relevant use cases. In this paper, we present two use case classes which reveal challenges in the integration of MMIR and the metaverse. The first use case class addresses the use of MMIR from the metaverse, the second describes metaverse recordings as a new type of multimedia, required to be supported by MMIR. For the integration, first, we propose a generic architecture to integrate multimedia in the metaverse. Second, we describe our integration approach of the new type of metaverse multimedia into Multimedia Information Retrieval systems.

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Assessing the adequacy of the gompertz regression model in the presence of right censored data 28

Assessing the adequacy of the gompertz regression model in t...

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28th Simposium Kebangsaan Sains Matematik, SKSM 2021

作者： Maarof, Nur Niswah Naslina Azid Arasan, Jayanthi Zulkafli, Hani Syahida Adam, Mohd Bakri Department of Mathematics Faculty of Science Universiti Putra Malaysia Malaysia Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA Cawangan Kelantan Malaysia

The Gompertz distribution is often used to model human mortality and establish actuarial tables. Cox-Snell residual is the most general practice to evaluate the model adequacy. However, standard statistical procedures are not amenable to handle the censored observations. Therefore, this research investigates the adequacy of the two parameter Gompertz parametric survival model that was extended to incorporate with fixed covariate in the presence of right censored data. Performance of the model is assessed and compared at various combinations of sample sizes and censoring proportions via simulation study. The newly proposed modifications to the Cox-Snell residuals based on the geometric mean and harmonic mean as well as the jackknife means were compared with Cox-Snell and Modified Cox-Snell residuals via simulation studies at different settings. The log-cumulative hazard plot of residuals is obtained by plotting the proposed residuals against the cumulative hazard function to assess the model's fit. Based on the model adequacy study, we found out that jackknife geometric mean and jackknife harmonic mean outperform CS, MCS, GMCS and HMCS residuals. © Published under licence by IOP Publishing Ltd.

关键词： Regression analysis

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Beyond Vanilla Fine-Tuning: Leveraging Multistage, Multilingual, and Domain-Specific Methods for Low-Resource Machine Translation

arXiv

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arXiv 2025年

作者： Thillainathan, Sarubi Yuan, Songchen Lee, En-Shiun Annie Jayasena, Sanath Ranathunga, Surangika Department of Computer Science and Engineering University of Moratuwa Sri Lanka Department of Language Science and Technology Saarland Informatics Campus Saarland University Germany Department of Computer Science University of Toronto Canada Faculty of Science University of Ontario of Technology Canada School of Mathematical and Computational Sciences Massey University New Zealand

Fine-tuning multilingual sequence-to-sequence large language models (msLLMs) has shown promise in developing neural machine translation (NMT) systems for low-resource languages (LRLs). However, conventional single-stage fine-tuning methods struggle in extremely low-resource NMT settings, where training data is very limited. This paper contributes to artificial intelligence by proposing two approaches for adapting msLLMs in these challenging scenarios: (1) continual pre-training (CPT), where the msLLM is further trained with domain-specific monolingual data to compensate for the under-representation of LRLs, and (2) intermediate task transfer learning (ITTL), a method that fine-tunes the msLLM with both in-domain and out-of-domain parallel data to enhance its translation capabilities across various domains and tasks. As an application in engineering, these methods are implemented in NMT systems for Sinhala, Tamil, and English (six language pairs) in domain-specific, extremely low-resource settings (datasets containing fewer than 100,000 samples). Our experiments reveal that these approaches enhance translation performance by an average of +1.47 bilingual evaluation understudy (BLEU) score compared to the standard single-stage fine-tuning baseline across all translation directions. Additionally, a multi-model ensemble further improves performance by an additional BLEU score. © 2025, CC BY.

关键词： Neural machine translation

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A Longitudinal Study on the Effects of Circadian Fatigue on Sound Source Identification and Localization using a Heads-Up Display 25

A Longitudinal Study on the Effects of Circadian Fatigue on ...

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2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

作者： Minton, Alexander G Zhu, Howe Yuan Chen, Hsiang-Ting Wang, Yu-Kai Zhuang, Zhuoli Notaro, Gina Galvan, Raquel Allen, James Ziegler, Matthias D Lin, Chin-Teng School of Computer Science Feit University of Technology Sydney SydneyNSW Australia Discipline of Design University of Sydney CamperdownNSW Australia School of Computer and Mathematical Sciences University of Adelaide AdelaideSA Australia University of Technology Sydney SydneyNSW Australia Advanced Technology Laboratories Lockheed Martin Cherry HillNJ United States Lockheed Martin ArlingtonVA United States Australian Ai Institute School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney Ultimo Australia

ISBN: (纸本)9798400713941

Circadian fatigue, largely caused by sleep deprivation, significantly diminishes alertness and situational awareness. This issue becomes critical in environments where auditory awareness - such as responding to verbal instructions or localizing alarms - is essential for performance and safety. While head-mounted displays have demonstrated potential in enhancing situational awareness through visual cues, their effectiveness in supporting sound localization under the influence of circadian fatigue remains under-explored. This study addresses this knowledge gap through a longitudinal study (N=19) conducted over 2-4 months, tracking participants' fatigue levels through daily assessments. Participants were called in to perform non-line-of-sight sound source identification and localization tasks in a virtual environment under high- and low-fatigue conditions, both with and without head-up display assistance. The results show task-dependent effects of circadian fatigue. Unexpectedly, reaction times were shorter across all tasks under high-fatigue conditions. Yet, in sound localization, where precision is key, the HUD offered the greatest performance enhancement by reducing pointing error. The results suggest the auditory channel is a robust means of enhancing situational awareness and providing support for incorporating spatial audio cues and HUD as standard features in augmented reality platforms for fatigue-prone scenarios. © 2025 Copyright held by the owner/author(s).

关键词： Head-up displays

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Systematic Literature Review on Organizational Cyber Security Deficiency in Mitigating Mobile Device Risk 2

Systematic Literature Review on Organizational Cyber Securit...

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2nd International Conference on computer science and Engineering, IC2SE 2021

作者： Abu Othman, Noor Ashitah Norman, Azah Anir Kiah, Laiha Mat Mutlaq Alnajrani, Hussain Universiti Malaya Faculty of Computer Science and Information Technology Kuala Lumpur Malaysia Universiti Teknologi MARA Faculty of Computer and Mathematical Sciences Melaka Malaysia Universiti Malaya Faculty of Computer Science and Information Technology Department of Information Systems Kuala Lumpur Malaysia Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur Malaysia Albaha University Faculty of Computer Science and Information Technology Albaha Saudi Arabia

ISBN: (纸本)9781665400459

The use of mobile devices by employees to access organizational Information Systems (IS) is common nowadays, and it can be seen from the trend to the formalization of Bring Your Own Device (BYOD) and remote working settings. However, allowing IS resources to be accessed by mobile devices that enable work flexibility across time, location, and network also increased the cybersecurity risk and vulnerabilities towards organizational resources, including network, system, and data. Therefore, this paper presents a systematic literature review on this context to explore the cybersecurity deficiency and mitigating techniques proposed for mobile device risk mitigation in organizational IS. Following the Systematic Literature Review protocol, 31 research articles are selected and reviewed using Thematic Analysis. Three themes were set to classify the output presented by the selected articles according to the context of Security Control, Cybersecurity Pillars, and Information System Management (ISM). This thematic analysis has successfully integrated various terms and narratives. Through analysis, nine security controls have been identified, with the most frequent concern being on Cybersecurity systems and Mobile Device profiling. The authors also discovered that deficiency in the Process factor is mostly discussed compared to the People and Technology factor, which also called for improvement in the ISM Planning phase. This study is beneficial in understanding the organizational cybersecurity deficiency in mitigating the risk of mobile devices and providing a reference for the significant security control in the context of the discussed environment. © 2021 IEEE.

关键词： Cybersecurity

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Metric-Based Resolvability of Quartz Structure

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computers, Materials & Continua 2022年第4期71卷 2053-2071页

作者： Muhammad Imran Ali Ahmad Muhammad Azeem Kashif Elahi Department of Mathematical Sciences United Arab Emirates UniversityAl AinUnited Arab Emirates College of Computer Science&Information Technology Jazan University JazanSaudi Arabia Department of Aerospace Engineering Faculty of EngineeringUniversiti Putra MalaysiaMalaysia Deanship of E-learning and Information Technology Jazan UniversityJazanSaudi Arabia

Silica has three major varieties of crystalline. Quartz is the main andabundant ingredient in the crust of our earth. While other varieties are formedby the heating of quartz. Silica quartz is a rich chemical structure containingenormous properties. Any chemical network or structure can be transformedinto a graph, where atoms become vertices and the bonds are converted toedges, between vertices. This makes a complex network easy to visualize towork on it. There are many concepts to work on chemical structures in termsof graph theory but the resolvability parameters of a graph are quite advanceand applicable topic. Resolvability parameters of a graph is a way to getting agraph into unique form, like each vertex or edge has a unique identification bymeans of some selected vertices, which depends on the distance of vertices andits pattern in a particular graph. We have dealt some resolvability parametersof SiO2 quartz. We computed the resolving set for quartz structure and itsvariants, wherein we proved that all the variants of resolvability parameters ofquartz structures are constant and do not depend on the order of the graph.

关键词： Quartz polycyclic aromatic hydrocarbon related structure metric dimension metric or distance-based resolvability parameters

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MetaCon: Revitalizing Internet Congestion Control with Meta-Reinforcement Learning

MetaCon: Revitalizing Internet Congestion Control with Meta-...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： He Bai Hui Li Jianming Que Minglong Zhang Peter Han Joo Chong Kalupahana Liyanage Kushan Sudheera Xinyuan Pei School of Electronic and Computer Engineering Peking University Shenzhen China School of Computer Science Fuyao University of Science and Technology Fuzhou China Department of Electrical and Computer Engineering Mississippi State University Starkville USA School of Engineering Computer and Mathematical Sciences Auckland University of Technology Auckland New Zealand Department of Electrical and Information Engineering Faculty of Engineering University of Ruhuna Sri Lanka

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Effective congestion control algorithms (CCAs) are crucial for the smooth operation of Internet communication infrastructure. CCAs adjust transmission rates based on congestion signals, optimizing resource utilization and user experience. However, existing studies, both rule-based and learning-based CCAs, often struggle with generalization and underperform when deployed in real-world environments. When applied to unseen network conditions, hand-crafted schemes or pre-trained models may experience significant performance degradation. To address this challenge, we propose MetaCon, a novel adaptive Internet congestion control approach based on meta-reinforcement learning. MetaCon leverages knowledge learned from prior scenarios to quickly adapt to new environments. Experimental results show that MetaCon outperforms existing algorithms by exhibiting superior generalization and achieving better transmission performance across a wide variety of network conditions.

关键词： Degradation Adaptation models Signal processing algorithms Signal processing User experience Acoustics Internet Resource management Speech processing

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Fair Distribution of Supplies Through a Disaster Information Sharing System Utilizing Blockchain

Fair Distribution of Supplies Through a Disaster Information...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Yuka Shiozaki Haruhisa Hasegawa Division of Mathematical and Physical Sciences Graduate School of Science Japan Women's University Tokyo Japan Department of Mathematics Physics and Computer Science Faculty of Science Japan Women's University Tokyo Japan

ISBN: (数字)9798350355079

ISBN: (纸本)9798350355086

In Japan, where disasters occur frequently, sharing information after a disaster is crucial. However, the distribution of information may become impossible due to communication disruptions. Additionally, victims who have not evacuated to designated shelters often struggle to obtain information about supplies. We address these issues with an information sharing system utilizing blockchain, which has high fault tolerance. Multiple blockchains are structured hierarchically by assigning nodes the role of intermediating information. This enables data transformation and extraction, facilitating flexible information sharing. During disruptions, isolated nodes continue processing locally. Once communication is restored, they synchronize their processing with the entire system to ensure fair distribution of supplies. We verify its fairness through simulation and show its efficiency.

关键词： Resistance Fault tolerance Databases Disasters Fault tolerant systems Information sharing Blockchains Recording Synchronization Data mining

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Enhancing Urban Road Safety: A 5G NR Model for Vulnerable Road Users Awareness

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IEEE Access 2025年 13卷 99170-99182页

作者： Yáñez, Alexis Salas, Felipe Azurdia-Meza, César Sandoval, Jorge Céspedes, Sandra Concordia University Gina Cody School of Engineering and Computer Science Department of Computer Science & Software Engineering Montreal H3G 1M8 QC Canada University of Chile Faculty of Physical and Mathematical Sciences Department of Electrical Engineering Santiago 8370451 Chile Bain & Company Inc. Boston MA United States

The widespread adoption of cellular networks has played an important role in the integration of road safety applications, capitalizing on the ubiquitous use of this technology by road users and its extensive market penetration. This paper presents the formulation of an analytical model to assess the awareness of vulnerable road users (VRU) defined as VRU Awareness Probability (VAP) achieved by vehicles communicating via 5G New Radio (NR) cellular vehicle-to-everything C-V2X networks at urban intersections. The main assumption in our model is that all VRU are connected to a 5G network. Our analytical model is designed to represent the dynamics of the 5G NR network operating in communication mode 2 in an urban intersection setting;we consider the influencing factors of signal propagation, such as obstacles created by buildings and realistic mobility patterns for vehicles and VRU based on SUMO traces. Our research shows that the proposed model exhibits high accuracy in intersection scenarios without buildings and maintains this efficiency in partially and highly obstructed scenarios, showing a Root Mean Square Error (RMSE) of 0.0265 and 0.0309, respectively;using the fitted model as a baseline in the non-obstructed scenario. The efficiency of this model can facilitate the study of the impact of the implementation and development of road safety applications on the safety of VRU. This VRU-centered approach allows for evaluating VRU awareness in different traffic density contexts, communication network conditions, and architectures where safety applications can be deployed. © 2013 IEEE.

关键词： Autonomous Resource Allocation Cellular Vehicle-to-everything (C-V2X) New Radio (NR) Semi-persistent Scheduling (SPS) Vulnerable Road Users (VRU)

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Brain Age Estimation Using Structural MRI: A Clustered Federated Learning Approach

Brain Age Estimation Using Structural MRI: A Clustered Feder...

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Omni-layer Intelligent Systems (COINS), International Conference on

作者： Seyyed Saeid Cheshmi Abtin Mahyar Anita Soroush Zahra Rezvani Bahar Farahani Department of Computer and Data Sciences Shahid Beheshti University Tehran Iran School of Computer Science Institute for Research in Fundamental Sciences (IPM) Tehran Iran Department of Computer Science Faculty of Mathematical Sciences Alzahra University Tehran Iran Cyberspace Research Institute Shahid Beheshti University Tehran Iran

Estimating brain age based on structural Magnetic Resonance Imaging (MRI) is one of the most challenging and prominent areas of research in recent medical imaging and neuroscience studies. The significance of brain age prediction in the early diagnosis of neurological disorders has fueled a resurgence of interest in this field. Various studies have addressed this issue using a spectrum of techniques, from traditional machine learning to deep neural networks. The majority of these techniques employ centralized paradigms, which do not adequately preserve privacy. To tackle this problem, a handful of studies have utilized a federated approach. In this study, we propose a novel hierarchical clustered federated learning approach that carefully captures and considers the similarities of the clients’ predictions on a certain benchmark dataset. This method enhances performance in Non-Independent and Identically Distributed (Non-IID) environments while preserving privacy. We use a multi-site dataset that provides a broad variety of MRI scans, characterizing a proper Non-IID environment. Our method achieves a Mean Absolute Error (MAE) of 3.86, while the non-clustered FedAvg federated approach attains a 4.14 MAE on the test set. 1 1 Code is available at https://***/Abtinmy/Clustered-FL-BrainAGE.

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