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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5828-5843页

作者： Guo, Zhongyuan Li, Jiawei Lei, Jia Liu, Jinyuan Zhou, Shihua Wang, Bin Kasabov, Nikola K. Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China University of the Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Dalian University of Technology School of Mechanical Engineering Dalian116024 China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland1061 New Zealand University of Ulster Intelligent Systems Research Center LondonderryBT48 7JL United Kingdom Bulgarian Academy of Sciences IICT Sofia1000 Bulgaria

High-resolution multispectral (HRMS) images combine spatial and spectral information originating from panchromatic (PAN) and reduced-resolution multispectral (LRMS) images. Pansharpening performs well and is widely used to obtain HRMS images. However, most pansharpening approaches determine the ratio of PAN and LRMS images through direct interpolation, which may introduce artifacts and distort the color of the fused results. To address this issue, an unsupervised progressive pansharpening framework, MSBANet, is proposed, which adopts a multistage fusion strategy. Each stage contains an attention interactive extraction module (AIEM) and a multiscale bilateral fusion module (MBFM). The AIEM extracts spatial and spectral features from input images and captures the correlations between features. The MBFM can efficiently integrate information from the AIEM and improve MSBANet context awareness. We design a hybrid loss function that enhances the ability of the fusion network to store spectral and texture details. In qualitative and quantitative experimental studies on four datasets, MSBANet outperformed state-of-the-art pansharpening techniques. The code will be released. © 2024 IEEE.

关键词： Data mining

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Talk to your data: Enhancing Business Intelligence and Inventory Management with LLM-Driven Semantic Parsing and Text-to-SQL for Database Querying 4

Talk to your data: Enhancing Business Intelligence and Inven...

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4th International Conference on Data Analytics for Business and Industry, ICDABI 2023

作者： Zhu, Jerry Bazaz, Saad Ahmed Dutta, Srimonti Anuraag, Bhavaraju Haider, Imran Bandopadhyay, Srijita University of Waterloo Faculty of Mathematics ON Canada National University of Computer and Emerging Sciences Dept. of Computer Science Islamabad Pakistan Indian Institute of Technology Guwahati School of Agro and Rural Technology Guwahati India Vellore Institute of Technology Dept. of Electronics and Communication Engineering India Indian Institute of Technology Kharagpur Dept. of Industrial and Systems Engineering Kharagpur India University of Engineering and Management Dept. of Electronics and Communication Engineering Kolkata India

ISBN: (纸本)9798350369786

This paper delves into the potential of Large Language Models (LLMs) in revolutionizing business intelligence and inventory management through semantic parsing and text-to-SQL methodologies. It assesses various LLM models, such as DIN-SQL, DSP, NSQL, GPT, CoPilot, and LLaMa, elucidating their capabilities and contributions. Two critical analyses are presented here. The first compares cutting-edge LLM models using cosine similarity and cost efficiency metrics. The second analysis enhances GPT's precision through prompt engineerings, like few-shot techniques, and explores frameworks like DIN-SQL, NSQL, and DSP. DIN-SQL substantially boosts accuracy, and NSQL demonstrates potential in specific scenarios. This research underscores the transformative potential of LLM-driven models in business intelligence and inventory management. DIN-SQL, in particular, emerges as a game-changer with the potential to reshape inventory management practices. GPT showcases its versatility through fine-tuning for tasks beyond conventional programming, while CoPilot offers a cost-effective alternative. This study emphasizes the importance of cost-effectiveness in real-world applications, with LLaMa and CoPilot being practical choices. NSQL, with its budget-friendly and semi-accurate solution, holds promise for semantic parsing in growing companies. These insights are a foundation for further innovation, promising unmatched efficiency and competitiveness across industries in the evolving Artificial intelligence landscape. © 2023 IEEE.

关键词： Structured Query Language

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MARViN: Mobile AR Dataset with Visual-Inertial Data

MARViN: Mobile AR Dataset with Visual-Inertial Data

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Changkun Liu Yukun Zhao Tristan Braud Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Division of Integrative Systems and Design The Hong Kong University of Science and Technology Hong Kong

ISBN: (数字)9798350374490

ISBN: (纸本)9798350374506

Accurate camera relocalisation is a fundamental technology for extended reality (XR), facilitating the seamless integration and persistence of digital content within the real world. Benchmark datasets that measure camera pose accuracy have driven progress in visual re-localisation research. Despite notable progress in this field, there is a limited availability of datasets incorporating Visual Inertial Odometry (VIO) data from typical mobile AR frameworks such as ARKit or ARCore. This paper presents a new dataset, MARViN, comprising diverse indoor and outdoor scenes captured using heterogeneous mobile consumer devices. The dataset includes camera images, ARCore or ARKit VIO data, and raw sensor data for several mobile devices, together with the corresponding ground-truth poses. MARViN allows us to demonstrate the capability of ARKit and ARCore to provide relative pose estimates that closely approximate ground truth within a short timeframe. Subsequently, we evaluate the performance of mobile VIO data in enhancing absolute pose estimations in both desktop simulation and user study. MARViN is available at https://***/XRIM-Lab/MarViN.

关键词： Performance evaluation Visualization Solid modeling Three-dimensional displays Pose estimation User interfaces Cameras

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Performance Analysis of Distributed Filtering under Mismatched Noise Covariances

arXiv

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arXiv 2024年

作者： Lyu, Xiaoxu Wen, Guanghui Shi, Ling Duan, Peihu Duan, Zhisheng The Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong The Laboratory of Security Operation and Control for Intelligent Autonomous Systems Department of Systems Science School of Mathematics Southeast University Nanjing211189 China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering Science College of Engineering Peking University Beijing100871 China

This paper systematically investigates the performance of consensus-based distributed filtering under mismatched noise covariances. First, we introduce three performance evaluation indices for such filtering problems, namely the standard performance evaluation index, the nominal performance evaluation index, and the estimation error covariance. We derive difference expressions among these indices and establish one-step relations among them under various mismatched noise covariance scenarios. We particularly reveal the effect of the consensus fusion on these relations. Furthermore, the recursive relations are introduced by extending the results of the one-step relations. Subsequently, we demonstrate the convergence of these indices under the collective observability condition, and show this convergence condition of the nominal performance evaluation index can guarantee the convergence of the estimation error covariance. Additionally, we prove that the estimation error covariance of the consensus-based distributed filter under mismatched noise covariances can be bounded by the Frobenius norms of the noise covariance deviations and the trace of the nominal performance evaluation index. Finally, the effectiveness of the theoretical results is verified by numerical simulations. Copyright © 2024, The Authors. All rights reserved.

关键词： Wiener filtering

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Lecture-Free Instruction: Does Gender Matter?

Lecture-Free Instruction: Does Gender Matter?

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IEEE Education engineering (EDUCON)

作者： Shamma Alwheibi Abdulhadi Shoufan Department of Computer Science Khalifa University Abu Dhabi United Arab Emirates Department of Computer and Communication Engineering Center for Cyber Physical Systems Khalifa University Abu Dhabi United Arab Emirates

ISBN: (数字)9798350394023

ISBN: (纸本)9798350394030

Lecture-free teaching and learning is an instructional method that omits the traditional lecture and replaces it with self-paced learning activities in or outside the classroom. While previous research showed evidence for the effectiveness of this method, the impact of gender on students' performance and perceptions remained unexplored. This study attempts to close this gap by analyzing learning and self-report data from two lecture-free courses in a computer engineering program. Our findings, drawn from learning analytics, reveal that the performance of female students in the learning activities is comparable to that of their male counterparts, even though males complete the activities more quickly, on average. Regarding perceptions, female students report slightly lower or higher levels of interest and challenge, respectively. Moreover, the gender effect can be moderated by the complexity of the learning activity. These results suggest that engineering programs employing lecture-free instruction should consider allocating more time for female learners. Additionally, the learning activities should be designed in a way that enhances females' interest and motivation and lowers their perceived level of challenge. Further research is needed to understand the sources of interest and challenges in lecture-free learning activities and to examine the correlations between interest, challenge, and performance.

关键词： Correlation Adaptive arrays Complexity theory engineering education Investment

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Lung Melanoma Recognition through Platelet Count Composition Using Supervised ML Technique

Lung Melanoma Recognition through Platelet Count Composition...

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2022 International Conference on Augmented Intelligence and Sustainable systems, ICAISS 2022

作者： Beaulah, H.Llilly Revathi, R. Deepika, J. Oliver, A.Sheryl Krishnamoorthy, R. Thiyagarajan, R. Mahendra College of Engineering Computer Science and Engineering Salem India Karpagam Academy of Higher Education Department of Computer Science India Bannari Amman Institute of Technology Department of IT Tamilnadu Sathyamangalam638401 India St.Joseph's College of Engineering India Centre for Nonlinear Systems Chennai Institute of Technology Chennai India Prathyusha Engineering College Department of IT Chennai India

ISBN: (纸本)9781665489621

Blood samples are used to diagnose the health illness in the human body. Complete blood routine test is used as whole-body screening to detect illness in human body. Highly sensitive diagnosis can be detected by blood count such as by using RBC, RNA [12], WBC, DNA, platelet count or even protein biomarkers. Biopsies of liquid blood sample are effectively used nowadays even to detect diagnosis of cancer. By determining the routine indices of blood samples, lung carcinoma can be detected. Rather than using PET scan which is high in cost, Lung carcinoma can be detected by blood indices samples. Machine Learning can be used to decipher the multiple ranges of statistical data by using svm (support vector machine) technique from supervised learning framework. This technique can be used for identification purpose both in clinical research and by patients respectively. From the detailed report, physicians can aid the treatment cycle for the patient. Each patient's report is sent to their registered phone number via SMS using Sim900 with Arduino Uno sensor. The generated detailed report is sent to the registered mobile number as short messaging service. In this approach, advanced way of using blood indices to identify the lung carcinoma is been developed. © 2022 IEEE.

关键词： Support vector machines

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TripletGO: Integrating Transcript Expression Profiles with Protein Homology Inferences for Gene Function Prediction

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Genomics, Proteomics & Bioinformatics 2022年第5期20卷 1013-1027页

作者： Yi-Heng Zhu Chengxin Zhang Yan Liu Gilbert S.Omenn Peter L.Freddolino Dong-Jun Yu Yang Zhang School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China Department of Computational Medicine and Bioinformatics University of MichiganAnn ArborMI 48109USA Departments of Internal Medicine and Human Genetics and School of Public HealthUniversity of MichiganAnn ArborMI 48109USA Department of Biological Chemistry University of MichiganAnn ArborMI 48109USA

Gene Ontology(GO)has been widely used to annotate functions of genes and gene ***,we proposed a new method,Triplet GO,to deduce GO terms of protein-coding and noncoding genes,through the integration of four complementary pipelines built on transcript expression profile,genetic sequence alignment,protein sequence alignment,and naīve *** GO was tested on a large set of 5754 genes from 8 species(human,mouse,Arabidopsis,rat,fly,budding yeast,fission yeast,and nematoda)and 2433 proteins with available expression data from the third Critical Assessment of Protein Function Annotation challenge(CAFA3).Experimental results show that Triplet GO achieves function annotation accuracy significantly beyond the current state-of-the-art *** analyses show that the major advantage of Triplet GO lies in the coupling of a new triplet network-based profiling method with the feature space mapping technique,which can accurately recognize function patterns from transcript expression ***,the combination of multiple complementary models,especially those from transcript expression and protein-level alignments,improves the coverage and accuracy of the final GO annotation *** standalone package and an online server of Triplet GO are freely available at https://***/Triplet GO/.

关键词： Gene function annotation Gene Ontology Transcript expression profile Triplet network Protein-level alignment

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Hotpatching on the Fly - Mitigating Drone Incidents Arising from Incorrect Configuration

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Han, Ruidong Li, Juanru Ma, Zhuo Lo, David Shaghaghi, Arash Ma, JianFeng Ma, Siqi Xidian University School of Cyber Engineering Xi'an China Singapore Management University School of Computing and Information Systems Singapore Feiyu Security Shanghai China Unsw Sydney School of Computer Science and Engineering Australia

Manufacturers offer adjustable control parameters for flight control systems to accommodate diverse environments and missions. To ensure flight safety, they also develop established boundaries, i.e., range specifications for parameter values. However, even when the configuration parameters fall within the prescribed manufacturer range, they could still lead to instability or even severe incidents like crashes, which are referred to as Range Specification Bugs. Prior research has suggested shrinking the range of parameter values to protect drones from the adverse effects of such bugs. However, narrowing the range of parameters may only reduce the probability of errors and could potentially limit the drone's adaptability. To overcome this limitation, we present an online approach that analyzes a sequence of flight states to detect any potential triggering of bugs and rectify the drone by dynamically adjusting its parameters. We implemented the rectification approach, ConFix, and applied it in current prevalent flight control systems, Ardupilot and PX4. The results demonstrated that ConFix achieved an average rectification success at 80% on average. © 2004-2012 IEEE.

关键词： Specifications

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Blockchain-based Patient Recommendation System for Smart Healthcare

Blockchain-based Patient Recommendation System for Smart Hea...

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International Conference on e-health Networking, Applications and Services (HealthCom)

作者： Raj Mehta Mahek Mehta Riya Kakkar Parita Oza Smita Agrawal Sudeep Tanwar Ashit Kumar Dutta Joel J.P.C. Rodrigues Department of Computer Science and Engineering Institute of Technology Nirma University Gujarat India Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Kingdom of Saudi Arabia Amazonas State University Manaus AM Brazil

ISBN: (数字)9798350350548

ISBN: (纸本)9798350350555

In recent times data breaches in various sectors of industry have become a common threat. It has become very crucial to secure patient data in the health industry. The upcoming Healthcare 4.0 techniques can play an important role in this. We have implemented these techniques in our proposed model to protect personalised health information of the individual health profiles of the patient using blockchain. The proposed model is also a recommending system with the aim to offer relevant advice to the patients to keep a check on the various health parameters like blood pressure, body temperature, blood sugar etc.

关键词： Industries Medical services Data breach Blood pressure Blockchains Recommender systems

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A Comprehensive Survey of Text Classification Techniques and Their Research Applications: Observational and Experimental Insights

arXiv

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arXiv 2024年

作者： Taha, Kamal Yoo, Paul D. Yeun, Chan Homouz, Dirar Taha, Aya Department of Computer Science Khalifa University United Arab Emirates Department of Computer Science & Information Systems University of London Birkbeck College United Kingdom Center for Cyber-Physical Systems Dept. of Electrical Engineering and Computer Science Khalifa University United Arab Emirates Department of Physics Khalifa University United Arab Emirates Brighton College Dubai United Arab Emirates

The exponential growth of textual data presents substantial challenges in management and analysis, notably due to high storage and processing costs. Text classification, a vital aspect of text mining, provides robust solutions by enabling efficient categorization and organization of text data. These techniques allow individuals, researchers, and businesses to derive meaningful patterns and insights from large volumes of text. This survey paper introduces a comprehensive taxonomy specifically designed for text classification based on research fields. The taxonomy is structured into hierarchical levels: research field-based category, research field-based sub-category, methodology-based technique, methodology sub-technique, and research field applications. We employ a dual evaluation approach: empirical and experimental. Empirically, we assess text classification techniques across four critical criteria. Experimentally, we compare and rank the methodology sub-techniques within the same methodology technique and within the same overall research field sub-category. This structured taxonomy, coupled with thorough evaluations, provides a detailed and nuanced understanding of text classification algorithms and their applications, empowering researchers to make informed decisions based on precise, field-specific insights. © 2024, CC BY-NC-SA.

关键词： Deep learning

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