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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Al-Sharhanee, Kareem Ali Malalah Najim, Ali Hamza Alheeti, Khattab M. Ali Satar, Nurhizam Safie Mohd Hashim, Aisha Hassan Abdallah Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq College of Engineering Techniques Department of Communication Technical Engineering Al-Farahidi University Baghdad10011 Iraq Department of Computer Technical Al-Diwaniyah Iraq College of Computer and Information Technology University of Anbar Computer Networking Systems Department Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

WSNs are applied in many disciplines where certain conditions require the ability to adapt to network sink mobility and changes in the dynamics of the area coverage. To meet these needs, it is necessary to develop integrated systems where the ''terrestrial' systems and the Unmanned Aerial Vehicles (UAVs)' collect data from sensors efficiently. Planning of UAV trajectories is another challenging issue if many WSN nodes are randomly deployed. This challenge has a big impact on the overall effectiveness of the system. This paper presents a novel approach for reducing the traveled distance of unscrewed aerial vehicles (UAVs) to improve the data collection efficiency in WSNs with poor node density. Yet the method that has been developed is aimed at increasing 'the efficiency and efficacy of data collection... with the assistance of Unmanned Aerial Vehicles (UAVs)' which means this work reveals practical application solutions. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Performance comparison of different algorithms for land surface temperature (LST) retrieval from Landsat-8 and the use of deep neural network regression to enhance the LST result (Case Study: Dallas, Texas and surrounding areas) 8

Performance comparison of different algorithms for land surf...

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Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques, and Applications VIII 2024

作者： Ayudyanti, Amalia Gita Chen, Ying-Nong Jamaluddin, Ilham Center for Space and Remote Sensing Research National Central University Taiwan Department of Computer Science and Information Engineering National Central University Taiwan

ISBN: (数字)9781510682757

ISBN: (纸本)9781510682740

Land Surface Temperature (LST) is important data for various fields, especially for monitoring global warming. Landsat-8 satellite imagery provides thermal data with a spatial resolution of 30m (resampled from 100m) as the main data for LST retrieval. This research aims to compare several LST retrieval algorithms such as LST retrieval using band 10, Single Channel Model (SCM), Qin's Split-Window Algorithm (Q-SWA), Sobrino's Split-Window Algorithm (S-SWA), and LST from the analysis ready data of Landsat 8 Level-2. The study focuses on Dallas, Texas, and surrounding areas in March 2023. We collected air temperature data from 20 U.S. Environmental Protection Agency (EPA) stations for indirect validation. Based on the results, the Q-SWA method outperformed other retrieval algorithms with mean absolute error (MAE) and root mean squared error (RMSE) were 0.683°C and 0.842°C, respectively. Given that Landsat-8's thermal band data is resampled from 100m to 30m, this research also explores enhancing the retrieved LST using deep neural network regression (DNNR). The best retrieved LST from the Q-SWA method served as the target data while several bands and spectral indices from Landsat-8 were used as the input for DNNR model. Due to the large scale of data, we randomly selected 10 million pixels and divided into 80% of training and 20% of testing data. The DNNR model achieved MAE of 1.022°C on the testing dataset. The enhanced LST from the DNNR model was also validated with the same air temperature validation data and achieved the MAE score of 1.037°C. Based on the visual comparison result, the DNNR model successfully enhanced the retrieved LST by providing more detailed results at the same 30m resolution and showing promising performance based on error metrics. This finding suggests the potential for using deep learning regression in LST downscaling to achieve better spatial resolution. © 2024 SPIE.

关键词： Landsat

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Leveraging Edge Computing for Enhancing Next Generation Network Services 15

Leveraging Edge Computing for Enhancing Next Generation Netw...

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

作者： Naveena, S.Sathiya Goyal, Prakhar Narmadha Verma, Devika A. Balaji, N.V. Rampal, Sourav Prince Shri Venkateshwara Padmavathy Engineering College Department of Business Administration Chennai127 India Quantum University Research Center Quantum University India School of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research for Business and Technology Punjab India

ISBN: (纸本)9798350370249

Aspect computing is quickly turning into a possible answer for augmenting and streamlining the overall performance of next era community services. Edge computing gives a disbursed computing architecture that allows computation and records storage competencies to are living closer to the edge network, rather than in, valuable records middle. This enables applications to enjoy faster reaction times and reduced latency, growing the rate of facts shipping. Side computing also presents greater scalability, simplifying the combination of IoT gadgets, permitting statistics streaming from more than one asset. As networks pass to 5G, area computing will offer the important flexibility and stability to deal with big amounts of statistics visitors securely and successfully. By leveraging the power of edge computing, corporations can create an agile and resilient service structure, imparting more advantageous safety and a variety of customizable abilities that permit them to capitalize on the success of the 5 G community. © 2024 IEEE.

关键词： Abilities Advantageous Capitalize Customizable Permit Streaming Success Variety

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Vidu4D: Single Generated Video to High-Fidelity 4D Reconstruction with Dynamic Gaussian Surfels 38

Vidu4D: Single Generated Video to High-Fidelity 4D Reconstru...

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38th Conference on Neural information Processing Systems, NeurIPS 2024

作者： Wang, Yikai Wang, Xinzhou Chen, Zilong Wang, Zhengyi Sun, Fuchun Zhu, Jun Department of Computer Science and Technology BNRist Center Tsinghua University China ShengShu China College of Electronic and Information Engineering Tongji University China

Video generative models are receiving particular attention given their ability to generate realistic and imaginative frames. Besides, these models are also observed to exhibit strong 3D consistency, significantly enhancing their potential to act as world simulators. In this work, we present Vidu4D, a novel reconstruction model that excels in accurately reconstructing 4D (i.e., sequential 3D) representations from single generated videos, addressing challenges associated with non-rigidity and frame distortion. This capability is pivotal for creating high-fidelity virtual contents that maintain both spatial and temporal coherence. At the core of Vidu4D is our proposed Dynamic Gaussian Surfels (DGS) technique. DGS optimizes time-varying warping functions to transform Gaussian surfels (surface elements) from a static state to a dynamically warped state. This transformation enables a precise depiction of motion and deformation over time. To preserve the structural integrity of surface-aligned Gaussian surfels, we design the warped-state geometric regularization based on continuous warping fields for estimating normals. Additionally, we learn refinements on rotation and scaling parameters of Gaussian surfels, which greatly alleviates texture flickering during the warping process and enhances the capture of fine-grained appearance details. Vidu4D also contains a novel initialization state that provides a proper start for the warping fields in DGS. Equipping Vidu4D with an existing video generative model, the overall framework demonstrates high-fidelity text-to-4D generation in both appearance and geometry. Project page: https://***. © 2024 Neural information processing systems foundation. All rights reserved.

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Modeling and Clustering of Parabolic Granular Data

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3728-3742页

作者： Tang, Yiming Gao, Jianwei Pedrycz, Witold Hu, Xianghui Xi, Lei Ren, Fuji Hu, Min Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2V4 Canada Hefei University of Technology School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Polish Academy of Sciences Systems Research Institute Warsaw00-901 Poland Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey Southeast University School of Computer Science and Engineering Nanjing211189 China University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China

At present, there exist some problems in granular clustering methods, such as lack of nonlinear membership description and global optimization of granular data boundaries. To address these issues, in this study, revolving around the parabolic granular data, we propose an overall architecture for parabolic granular modeling and clustering. To begin with, novel coverage and specificity functions are established, and then a parabolic granular data structure is proposed. The fuzzy c-means (FCM) algorithm is used to obtain the numeric prototypes, and then particle swarm optimization (PSO) is introduced to construct the parabolic granular data from the global perspective under the guidance of principle of justifiable granularity (PJG). Combining the advantages of FCM and PSO, we propose the parabolic granular modeling and optimization (PGMO) method. Moreover, we put forward attribute weights and sample weights as well as a distance measure induced by the Gaussian kernel similarity, and then come up with the algorithm of weighted kernel fuzzy clustering for parabolic granularity (WKFC-PG). In addition, the assessment mechanism of parabolic granular clustering is discussed. In summary, we set up an overall architecture including parabolic granular modeling, clustering, and assessment. Finally, comparative experiments on artificial, UCI, and high-dimensional datasets validate that our overall architecture delivers a good improvement over previous strategies. The parameter analysis and time complexity are also given for WKFC-PG. In contrast with related granular clustering algorithms, it is observed that WKFC-PG performs better than other granular clustering algorithms and has superior stability in handling outliers, especially on high-dimensional datasets. © 2020 IEEE.

关键词： Fuzzy clustering

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Is the Volume of a Credal Set a Good Measure for Epistemic Uncertainty? 39

Is the Volume of a Credal Set a Good Measure for Epistemic U...

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39th Conference on Uncertainty in Artificial Intelligence, UAI 2023

作者： Caprio, Michele Hüllermeier, Eyke Sale, Yusuf Germany PRECISE Center Department of Computer and Information Science University of Pennsylvania United States Munich Center for Machine Learning Germany

Adequate uncertainty representation and quantification have become imperative in various scientific disciplines, especially in machine learning and artificial intelligence. As an alternative to representing uncertainty via one single probability measure, we consider credal sets (convex sets of probability measures). The geometric representation of credal sets as d-dimensional polytopes implies a geometric intuition about (epistemic) uncertainty. In this paper, we show that the volume of the geometric representation of a credal set is a meaningful measure of epistemic uncertainty in the case of binary classification, but less so for multi-class classification. Our theoretical findings highlight the crucial role of specifying and employing uncertainty measures in machine learning in an appropriate way, and for being aware of possible pitfalls. © UAI 2023. All rights reserved.

关键词： Machine learning

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QUALYST: Data Quality Assessment System for Thailand Open Government Data 20

QUALYST: Data Quality Assessment System for Thailand Open Go...

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20th International joint Conference on computer science and Software Engineering, JCSSE 2023

作者： Samakit, Tanapat Anutariya, Chutiporn Buranarach, Marut School of Engineering and Technology Asian Institute of Technology Computer Science & Information Management Ict Department Thailand National Electronics and Computer Technology Center Data Science and Analytics Research Group Thailand

ISBN: (纸本)9798350300505

Open Government Data (OGD) refers to the provision of data produced by the government to the general public, in a format that is readily readable and can be used by machines with ease. It can also promote transparency, improve decision-making, enhance accountability, create economic opportunities, and encourage civic engagement. The OGD can help citizens understand the government and its legitimacy and transparency. Thus, when the government shares its data with people, it helps to create trust by being transparent, accountable, and promoting innovative solutions that benefit everyone. However, each published dataset has no indication of its quality assessment at all;thus, making it difficult for citizens to assess the reliability of the data from the OGD. Therefore, a data quality assessment for OGD should be developed. This will help create effective datasets which in turn help users understand the data. This study proposes QUALYST, a system that assesses Thailand's OGD dataset and validates it for analytic and visualization purposes. The study focuses on designing the data storage and implementing the assessment system. Furthermore, the proposed data quality dimensions, the developed data pipeline, and the assessment process are elaborated. Finally, the prototype system is demonstrated using Thailand's OGD datasets with examples in a visualized format. © 2023 IEEE.

关键词： Transparency

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KARRIEREWEGE: A Large Scale Career Path Prediction Dataset 31

KARRIEREWEGE: A Large Scale Career Path Prediction Dataset

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31st International Conference on Computational Linguistics, COLING 2025

作者： Senger, Elena Campbell, Yuri van der Goot, Rob Plank, Barbara MaiNLP Center for Information and Language Processing LMU Munich Germany Fraunhofer Center for International Management and Knowledge Economy IMW Germany Department of Computer Science IT University of Copenhagen Denmark

ISBN: (纸本)9798891761971

Accurate career path prediction can support many stakeholders, like job seekers, recruiters, HR, and project managers. However, publicly available data and tools for career path prediction are scarce. In this work, we introduce KARRIEREWEGE, a comprehensive, publicly available dataset containing over 500k career paths, significantly surpassing the size of previously available datasets. We link the dataset to the ESCO taxonomy to offer a valuable resource for predicting career trajectories. To tackle the problem of free-text inputs typically found in resumes, we enhance it by synthesizing job titles and descriptions resulting in KARRIEREWEGE+. This allows for accurate predictions from unstructured data, closely aligning with real-world application challenges. We benchmark existing state-of-the-art (SOTA) models on our dataset and a prior benchmark and observe improved performance and robustness, particularly for free-text use cases, due to the synthesized data. ©2025 Association for Computational Linguistics.

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quantum-Inspired Optimization for Task Scheduling in Software Development Projects 4

Quantum-Inspired Optimization for Task Scheduling in Softwar...

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4th IEEE International Conference on quantum Computing and Engineering, QCE 2023

作者： Ou, Chia-Ho Li, Yu-Hong Chen, Chih-Yu Wu, Chi-Hsuan Tsai, Yu-Chen Yan, Zhi-You Chang, Ching-Ray National Pingtung University Department of Computer Science and Information Engineering Pingtung Taiwan Chung Yuan Christian University Quantum Information Center Taoyuan Taiwan Chung Yuan Christian University Department of Physics Taoyuan Taiwan

ISBN: (纸本)9798350343236

Software project development, characterized by numerous tasks and several engineers, necessitates effective project scheduling and personnel allocation for successful and timely completion. Tackling the inherent complexities of Software Project Scheduling (SPS) including personnel quality requirements and capability constraints is of paramount importance for software companies. This study aims to engineer robust project scheduling to enhance task completion efficiency, reduce resource waste, and ensure the punctual delivery of project milestones. Our proposed solution models the problem as a Software Project Scheduling Problem (SPSP), subsequently transformed into a Quadratic Unconstrained Binary Optimization (QUBO) model using quantum-inspired techniques. This model is then solved using a digital annealing device. We examine our proposed quantum-inspired method's effectiveness in solving SPSP through this experimental implementation, comparing its performance with the Simulated Annealing (SA) algorithm. The experimental findings reveal that the objective function, encapsulating the weighted sum of all job personnel costs and job end times, produced superior outcomes under the Digital Annealing (DA) algorithm compared to the SA algorithm. Moreover, the DA algorithm demonstrated greater stability and reduced runtime as the volume of input data expanded. © 2023 IEEE.

关键词： Simulated annealing

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Leveraging Deep Learning for Precise Chronic Bronchitis Identification in X-Ray Modalities

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computers, Materials & Continua 2025年第4期83卷 381-405页

作者： Fahad Ahmad Saad Awadh Alanazi Kashaf Junaid Maryam Shabbir Asim Ali School of Computing Faculty of TechnologyUniversity of PortsmouthWinston Churchill Ave SouthseaPortsmouthPO12UPUK Portsmouth Artificial Intelligence and Data Science Center University of PortsmouthWinston Churchill AveSouthseaPortsmouthPO12UPUK Department of Computer Science College of Computer and Information SciencesJouf UniversitySakakaAljouf72341Saudi Arabia School of Biological and Behavioural Sciences Queen Mary University of LondonLondonE14NSUK Department of Computer Sciences Bahria UniversityLahorePunjab54700Pakistan

Image processing plays a vital role in various fields such as autonomous systems,healthcare,and cataloging,especially when integrated with deep learning(DL).It is crucial in medical diagnostics,including the early detection of diseases like chronic obstructive pulmonary disease(COPD),which claimed 3.2 million lives in ***,a life-threatening condition often caused by prolonged exposure to lung irritants and smoking,progresses through *** diagnosis through image processing can significantly improve survival *** encompasses chronic bronchitis(CB)and emphysema;CB particularly increases in smokers and generally affects individuals between 50 and 70 years *** damages the lungs’air sacs,reducing oxygen transport and causing symptoms like coughing and shortness of *** such as beta-agonists and inhaled steroids are used to manage symptoms and prolong lung ***,COVID-19 poses an additional risk to individuals with CB due to its impact on the respiratory *** proposed system utilizes convolutional neural networks(CNN)to diagnose *** this system,CNN extracts essential and significant features from X-ray modalities,which are then fed into the neural *** network undergoes training to recognize patterns and make accurate predictions based on the learned *** leveraging DL techniques,the system aims to enhance the precision and reliability of CB *** research specifically focuses on a subset of 189 lung disease images,carefully selected for model *** further refine the training process,various data augmentation and noise removal techniques are *** techniques significantly enhance the quality of the training data,improving the model’s robustness and *** a result,the diagnostic accuracy has improved from 98.6%to 99.2%.This advancement not only validates the efficacy of our proposed model but also represents a significant improvement over existing *** h

关键词： Deep learning chronic obstructive pulmonary disease chronic bronchitis convolutional neural network X-ray images

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