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Intelligent Metaverse Technologies & Applications (iMETA), International Conference on

作者： Viviana Pentangelo Dario Di Dario Vincenzo De Martino Marco Dello Buono Stefano Lambiase Department of Computer Science Software Engineering (SeSa) Lab University of Salerno Salerno Italy

ISBN: (数字)9798350351514

ISBN: (纸本)9798350351521

The metaverse, a 3D immersive digital environment, is gaining significant interest due to its ability to connect people globally engaging and immersively, thanks to recent technological advancements. Developing high-quality 3D models is crucial for achieving realism and immersivity in the metaverse. However, this process is complex and resource-intensive, demanding specialized skills and substantial time. The emergence of novel automation tools and technologies, such as photogrammetry, which uses computer vision algorithms to reconstruct 3D models from 2D images, is beginning to address such challenges. Our research focused on analyzing the current state of such technologies in automating 3D scenes for the metaverse, comparing them to traditional manual modeling techniques. We conducted an experiment in which we built the same 3D scene using two techniques: a manual approach with Blender and a photogrammetry approach exploiting the Polycam tool on a mobile device. Our results have provided insights into the main strengths and limitations of using 3D automation techniques. The photogrammetry approach has significantly sped up the entire process, producing textures and models that accurately replicate real objects. However, it cannot wholly replace manual modeling approaches, without which it is impossible to obtain complete and efficient models. Lessons learned will serve as a foundation to guide developers in developing 3D scenes for the metaverse.

关键词： Solid modeling computer vision Three-dimensional displays Automation Metaverse Computational modeling Manuals Mobile handsets Photogrammetry Image reconstruction

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Explainable Artificial Intelligence (XAI) from a user perspective: A synthesis of prior literature and problematizing avenues for future research

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Technological Forecasting and Social Change 2023年第PartA期186卷

作者： Haque, AKM Bahalul Islam, A.K.M. Najmul Mikalef, Patrick Department of Software Engineering LUT University Finland Department of Computer Science Norwegian University of Science and Technology Norway

The rapid growth and use of artificial intelligence (AI)-based systems have raised concerns regarding explainability. Recent studies have discussed the emerging demand for explainable AI (XAI);however, a systematic review of explainable artificial intelligence from an end user's perspective can provide a comprehensive understanding of the current situation and help close the research gap. The purpose of this study was to perform a systematic literature review of explainable AI from the end user's perspective and to synthesize the findings. To be precise, the objectives were to 1) identify the dimensions of end users' explanation needs;2) investigate the effect of explanation on end user's perceptions, and 3) identify the research gaps and propose future research agendas for XAI, particularly from end users' perspectives based on current knowledge. The final search query for the Systematic Literature Review (SLR) was conducted on July 2022. Initially, we extracted 1707 journal and conference articles from the Scopus and Web of science databases. Inclusion and exclusion criteria were then applied, and 58 articles were selected for the SLR. The findings show four dimensions that shape the AI explanation, which are format (explanation representation format), completeness (explanation should contain all required information, including the supplementary information), accuracy (information regarding the accuracy of the explanation), and currency (explanation should contain recent information). Moreover, along with the automatic representation of the explanation, the users can request additional information if needed. We have also described five dimensions of XAI effects: trust, transparency, understandability, usability, and fairness. We investigated current knowledge from selected articles to problematize future research agendas as research questions along with possible research paths. Consequently, a comprehensive framework of XAI and its possible effects on user behavio

关键词： Transparency

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Stress detection system for social media users

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Procedia computer science 2023年 216卷 672-681页

作者： Mochamad Rizky Febriansyah Nicholas Rezki Yunanda Derwin Suhartono Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Software Engineering Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Stress detection is a growing topic in the field of natural language processing. The study of stress detection for mental health prediction has been proven to benefit the development of recommender systems and automated mental health assessments in previous studies. Additionally, the widespread usage of social media has served as a potential data source for developing such models. Our research tried to detect whether the users of social media were under stress or not. We used a dataset from Dreaddit consisting of posts from one of the popular social media platforms, Reddit. We propose a machine learning model consisting of Support Vector Machine (SVM), Naïve Bayes, Decision Tree, Random Forest, Bag of Words, and Term Frequency – inverse document frequency (TF-IDF) for stress detection. The final evaluation of the model achieved an 80.00% F-1 Score and 75.00% accuracy, and both were scored by SVM.

关键词： Machine Learning Stress Detection Social Media Natural Language Processing

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PrefixFPM: a parallel framework for general-purpose mining of frequent and closed patterns

PrefixFPM: a parallel framework for general-purpose mining o...

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作者： Yan, Da Qu, Wenwen Guo, Guimu Wang, Xiaoling Zhou, Yang Department of Computer Science The University of Alabama at Birmingham BirminghamAL United States Shanghai China Department of Computer Science and Software Engineering Auburn University AuburnAL United States

A frequent pattern is a substructure that appears in a database with frequency (aka. support) no less than a user-specified threshold, while a closed pattern is one that has no super-pattern that has the same support. Here, a substructure can refer to different structural forms, such as itemsets, subsequences, subtrees, and subgraphs, and mining such substructures is important in many real applications such as product recommendation and feature extraction. Currently, there lacks a general programming framework that can be easily customized to mine different types of patterns, and existing parallel and distributed solutions are IO-bound rendering CPU cores underutilized. Since mining frequent and/or closed patterns are NP-hard, it is important to fully utilize the available CPU cores. This paper presents such a general-purpose framework called PrefixFPM. The framework is based on the idea of prefix projection which allows a divide-and-conquer mining paradigm. PrefixFPM exposes a unified programming interface to users who can readily customize it to mine their desired patterns. We have adapted the state-of-the-art serial algorithms for mining patterns including subsequences, subtrees, and subgraphs on top of PrefixFPM, and extensive experiments demonstrate an excellent speedup ratio of PrefixFPM with the number of CPU cores. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Artificial intelligence

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A ResNet-Based DVFS Regulator for Heterogeneous Multi-Core Mobile Processors

A ResNet-Based DVFS Regulator for Heterogeneous Multi-Core M...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Hu, Shibo Xu, Xinzi Chen, Yuze Qin, Muyun Lian, Yong Zhao, Yang School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Beijing Xiaomi Mobile Software Co. Ltd. Beijing China York University Department of Electrical Engineering and Computer Science Toronto Canada

ISBN: (纸本)9798350350821

Dynamic voltage and frequency scaling (DVFS) is commonly used for the balance of performance and power of mobile processors. Conventional DVFS regulators take the processor cores with the same power and performance weight showing their inability in the regulation of heterogeneous processors. This paper proposes a core-aware frequency-power evaluation scheme that utilizes a multilayer perceptron (MLP) to assess the efficacy of DVFS regulation actions. Additionally, a ResNet-based DVFS regulator, trained with the assistance of the MLP, is developed for heterogeneous multi-core processors. Evaluated on Xiaomi 13 Pro powered by a Qualcomm Snapdragon 8 Gen 2, the proposed DVFS approach achieves an up to ×1.47 improvement in FPS Performance Per Watt (FPPW) compared with the Qualcomm's default DVFS governor in video recording scenario. © 2024 IEEE.

关键词： Dynamic frequency scaling

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CDGNet: A Cross-Time Dynamic Graph-Based Deep Learning Model for Vehicle-Based Traffic Speed Forecasting

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-14页

作者： Fang, Yuchen Luo, Haiyong Zhao, Fang Sun, Poly Z.H. Qin, Yanjun Zeng, Liang Hui, Bo Wang, Chenxing School of Computer Science National Pilot Software Engineering School Beijing University of Posts and Telecommunications Beijing China Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of Sciences Beijing China Department of Industrial Engineering School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China Tsinghua University Beijing China University of Tulsa Tulsa USA

Vehicle-based traffic speed forecasting aims to predict the average speed of vehicles on the road in the future, which is an essential side information in intelligent vehicles and beneficial to safe autonomous driving, yet is very challenging due to the complex and dynamic spatio-temporal dependencies in real-world vehicle-based traffic. To extract intricate correlations among multiple vehicle-based speed time series, previous methods have used graph convolution networks. However, the conventional static and dynamic graphs fail to reflect the traffic evolution and the hysteresis spatial influence caused by vehicle movement. To address this issue, we propose a novel cross-time dynamic graph-based deep learning model, named CDGNet, for vehicle-based traffic speed forecasting. The model is able to effectively capture hysteresis spatial dependencies between each time slice and its historical time slices through the cross-time dynamic graph-based GCN. Meanwhile, a gating mechanism is integrated into our cross-time dynamic graph, which conforms to the sparse correlation in the real world. Besides, GCNs are incorporated into a novel encoder-decoder architecture to forecast multi-step speed. Experimental results on three real-world vehicle-based traffic speed datasets demonstrate the superiority of our CDGNet over various state-of-the-art spatio-temporal forecasting methods and the effectiveness of each component. We additionally provide a visualization of our cross-time dynamic graph to show the capability of assisting intelligent vehicles to avoid congestion. IEEE

关键词： Forecasting

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Adaptive Deep Reinforcement Learning-based Resource Management for Complex Decision Making in Industry Internet of Things Applications 4

Adaptive Deep Reinforcement Learning-based Resource Manageme...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Karne, Niharika Ramagundam, Chandra Shekar Patnala, Ranga Rao Ramagundam, Shashishekhar Tadiboina, Sai Nitisha Sr. Principal Systems Engineer in C&RS Platform Services University of South Alabama Department of Electrical Engineering Masters in Electrical Engineering Amtrak DT United States Software Engineer in Cloud Platform Services City of Philadelphia Pennsylvania United States Vice President Neuberger Berman United States Software Development & Engineering in Content Discovery & AI Troy University Department of Computer Science Masters in Computer Science AL Comcast Montgomery Campus United States Department of Information Technology GEICO - Chevy Chase Chevy ChaseMD20815 United States

An independent industrial system may transform into a connected network through the assistance of Industrial Internet of Things (IIoT). The deployed sensors in the IIoT maintain surveillance of the industrial machinery and equipment. As a result, safety and reliability emerge as the primary concerns in IIoT. This presents a variety of well-known and increasing issues related to the industrial system. The IIoT devices are exposed to a wide range of malware, threats, and assaults. To prevent the IIoT devices from malware effects, effective protection plans must be implemented. But adequate security mechanisms are not be incorporated in IIoT devices with limited resources. It is essential to ensure the accuracy and dependability of information gathered by IIoT devices. Decisions taken with incomplete or inaccurate data might be devastating. To overcome these difficulties deep learning with reinforcement learning for complex decision-making in industry applications is developed in this research work. In this developed model, an Adaptive Deep Reinforcement learning (ADRL)-based resource management is performed to reduce the operation cost associated with IIoT deployments. Energy efficiency is essential in IIoT ecosystem, particularly for the devices that run on batteries. Through dynamic resource allocation based on workload needs and energy limits, ADRL-based resource management optimizes the usage of energy. The reliability of the designed model is enhanced by fine-tuning the parameters from DRL using the Ship Rescue Optimization (SRO) algorithm. Thus, ADRL-based resource management systems make real-time decisions based on current environmental conditions and system requirements. This helps the IIoT systems to react quickly to change demands and optimize resource allocation. Finally, the experimental analysis is performed to find the success rate of the developed resource management system via various metrics. Throughout the validation, the statistical analysis of the

关键词： Deep reinforcement learning

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Comparative Analysis of Random Forest and Multilayer Perceptron Algorithms for Predicting Gross Domestic Product 4

Comparative Analysis of Random Forest and Multilayer Percept...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Azeta, Ambrose A. Adewale, Muyideen D. Omote, Patrick O. Onaolapo, Funmi O. Bakare, Idowo A. Tjiraso, Steven Namibia University of Science and Technology Department of Software Engineering Namibia Africa Centre of Excellence on Technology Enhanced Learning National Open University of Nigeria Nigeria Crawford University Department of Computer and Mathematical Sciences Ogun StateNeria Nigeria

ISBN: (纸本)9798350353266

This study examines the efficacy of machine learning techniques, particularly Multilayer Perceptron (MLP), and Random Forest and in forecasting Nigeria's Gross Domestic Product (GDP). Traditional statistical methods frequently fail to capture intricate, nonlinear relationships, thus necessitating advanced techniques. The dataset employed in this research encompasses 22 instances from 2000 to 2021. It includes variables such as GDP, healthcare expenditure, population, and the index of economic freedom obtained from the World Bank as well as the Nigerian Perceptions Index for corruption. The data was prepared to undergo training and testing following the CRISP-DM methodology. The models were trained and evaluated using a five-member cross-validation method. Performance metrics indicated that MLP outperformed Random Forest, with MAE values of 16.8228 and 25.3074 and RMSE values of 23.2111 and 33.2027, respectively. These findings suggest that MLP offers more accurate GDP predictions for Nigeria, effectively managing complex data relationships. This study provides valuable insights for enhancing predictive accuracy and supporting more effective economic planning and decision-making. © 2024 IEEE.

关键词： Decision trees

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A Joint Secure Mechanism of Multi-task Learning For A UAV Team Under FDI Attacks

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IEEE Transactions on Mobile Computing 2025年

作者： Zeng, Rongfei Jiang, Chenyang Wang, Xingwei Li, Baochun Northeastern University Software College Shenyang China AliCloud Hangzhou China Northeastern University Department of Computer Science and Engineering Shenyang China University of Toronto Department of Electrical and Computer Engineering Canada

A UAV team shows tremendous potential for various mobile scenarios. However, some evidences reveal their vulnerability to False Data Injection (FDI) attacks, which can significantly jeopardize the flight security or even lead to catastrophic incidents. Existing studies primarily focus on detecting or defending against FDI attacks at the trajectory control of individual UAVs, leaving a gap in a comprehensive secure mechanism that can simultaneously detect, localize, and compensate for such attacks across an entire UAV team. The complexity of developing such a solution is magnified by the multiple design goals, the inherent sophistication of UAV team, and practical attack assumptions. In this paper, we propose a joint secure framework based on multi-task deep learning to simultaneously detect FDI attacks, localize the compromised components, and compensate control signals to mitigate the impact of FDI attacks on promising UAV teams. Specifically, we design an all-in-one deep learning model framework with a temporal-spatial information extraction module and a hierarchical multi-task module to perform three tasks simultaneously. Moreover, we introduce an iterative learning method with experience replay to counteract knowledge decay during model training. Extensive experiments and real flight demonstrations are presented to validate the improved performance and the benefits of our proposed secure method. A demonstration video and our source code can be accessed via https://***/WingFeiTsang/JS/. © 2025 IEEE. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Audio Based Machine Fault Diagnosis using Hybrid Feature Extraction and Ensemble Learning 15

Audio Based Machine Fault Diagnosis using Hybrid Feature Ext...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Singhal, Shashvat Goel, Bhavya Agrawal, Kshitij Sethi, Rithwick Sah, Shashi Jain, Rachit Vishwakarma, Dinesh K. Delhi Technological University Department of Computer Science Delhi India Delhi Technological University Department of Mechanical Engineering Delhi India Delhi Technological University Department of Ece Delhi India Delhi Technological University Department of Software Engineering Delhi India Delhi Technological University Department of Information Technology Delhi India

ISBN: (纸本)9798350370249

Over the last decade, proliferation of mechanical machines has surged exponentially, amplifying the challenge of monitoring their operational health due to the inevitability of wear and tear. Consequently, the conventional human centric approach to tracking machine conditions has become impractical. This underscores the demand for an innovative and efficient strategy. This research involves machine generated audio signals to detect and analyze faults or irregularities. Analyzing the acoustic patterns generated during machine operation makes detecting abnormal sounds associated with specific faults possible. This paper examines various techniques and algorithms for achieving this goal and compares them to recommend the most effective method. Initially, Short Time Fourier transform was performed on the preprocessed audio data. Then, various techniques to perform feature extraction on the transformed audio, such as spectral kurtosis, spectral centroid, zero crossing rate, and Mel spectrogram. Subsequently, the aggregate feature vector was input into various algorithms, including XGBoost, Support Vector Classifier, Random Forest Classifier and Ensemble Learning. The MIMII dataset was trained on these models. The evaluation showed that XGBoost Classifier performed the best as compared to the other models, achieving a total accuracy rate of 98 percent upon incorporating features extraction Melspectrogram, Spectral Kurtosis and Spectral Centroid. © 2024 IEEE.

关键词： Audio Classification Ensemble Learning Mel spectogram Random Forest Classifier Short time Fourier transform Spectral Centroid Spectral Kurtosis Support Vector Classifier XGBoost Zero Crossing Rate

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