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arXiv 2024年

作者： Wang, Zhiyuan Duan, Jinhao Yuan, Chenxi Chen, Qingyu Chen, Tianlong Zhang, Yue Wang, Ren Shi, Xiaoshuang Xu, Kaidi Center for Future Media School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China Department of Computer Science Drexel University PhiladelphiaPA19104 United States Perelman School of Medicine University of Pennsylvania PhiladelphiaPA19104 United States Section of Biomedical Informatics & Data Science Yale School of Medicine Yale University New HavenCT06510 United States Computer Science & Artificial Intelligence Laboratory Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical and Computer Engineering Illinois Institute of Technology ChicagoIL60616 United States

Uncertainty estimation is crucial for the reliability of safety-critical human and artificial intelligence (AI) interaction systems, particularly in the domain of healthcare engineering. However, a robust and general uncertainty measure for free-form answers has not been well-established in open-ended medical question-answering (QA) tasks, where generative inequality introduces a large number of irrelevant words and sequences within the generated set for uncertainty quantification (UQ), which can lead to biases. This paper introduces Word-Sequence Entropy (WSE), a method that calibrates uncertainty at both the word and sequence levels, considering semantic relevance. WSE quantifies uncertainty in a way that is more closely aligned with the reliability of LLMs during uncertainty quantification (UQ). We compare WSE with six baseline methods on five free-form medical QA datasets, utilizing seven popular large language models (LLMs). Experimental results demonstrate that WSE exhibits superior performance in UQ under two standard criteria for correctness evaluation. Additionally, in terms of real-world medical QA applications, the performance of LLMs is significantly enhanced (e.g., a 6.36% improvement in model accuracy on the COVID-QA dataset) by employing responses with lower uncertainty that are identified by WSE as final answers, without any additional task-specific fine-tuning or architectural modifications. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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A SURVEY ON COMPUTATIONAL SOLUTIONS FOR RECONSTRUCTING COMPLETE OBJECTS BY REASSEMBLING THEIR FRACTURED PARTS

arXiv

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arXiv 2024年

作者： Lu, Jiaxin Liang, Yongqing Han, Huijun Hua, Jiacheng Jiang, Junfeng Li, Xin Huang, Qixing Computer Science Department The University of Texas at Austin 2317 Speedway AustinTX United States Department of Computer Science and Engineering Texas A&M University 435 Nagle St College StationTX United States Department of Computer Science and Technology Tsinghua University 30 Shuangqing Road Beijing China College of Artificial Intelligence and Automation Hohai University 1915 Hohai Avenue Jiangsu Changzhou China Section of Visual Computing & Computational Media Department of Computer Science and Engineering Texas A&M University 789 Ross Street College StationTX United States

Reconstructing a complete object from its parts is a fundamental problem in many scientific domains. The purpose of this article is to provide a systematic survey on this topic. This reassembly problem requires understanding the attributes of individual pieces and establishing matches between different pieces. Many approaches also model priors of the underlying complete object. Existing approaches are tightly connected problems of shape segmentation, shape matching, and learning shape priors. We provide existing algorithms in this context and emphasize their similarities and differences to general-purpose approaches. We also survey the trends from early procedural approaches to more recent deep learning approaches. In addition to algorithms, this survey will also describe existing datasets, open-source software packages, and applications. To the best of our knowledge, this is the first comprehensive survey on this topic in computer graphics. Copyright © 2024, The Authors. All rights reserved.

关键词： Open source software

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Coyote Optimization Using Fuzzy System for Energy Efficiency in WSN

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computers, Materials & Continua 2022年第8期72卷 3269-3281页

作者： Ahmed S.Almasoud Taiseer Abdalla Elfadil Eisa Marwa Obayya Abdelzahir Abdelmaboud Mesfer Al Duhayyim Ishfaq Yaseen Manar Ahmed Hamza Abdelwahed Motwakel Department of Information Systems College of Computer and Information SciencesPrince Sultan UniversitySaudi Arabia Department of Information Systems-Girls Section King Khalid UniversityMahayil62529Saudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Systems College of Science and Artsat MahayilKing Khalid UniversitySaudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

In recent days,internet of things is widely implemented in Wireless Sensor Network(WSN).It comprises of sensor hubs associated together through the *** WSNis generally affected by the power in battery due to the linked sensor *** order to extend the lifespan of WSN,clustering techniques are used for the improvement of energy *** methods divide the nodes in WSN and form a ***,it consists of unique Cluster Head(CH)in each *** the existing system,Soft-K means clustering techniques are used in energy consumption in *** soft-k means algorithm does not work with the large-scale wireless sensor networks,therefore it causes reliability and energy consumption *** overcome this,the proposed Load-Balanced Clustering conjunction with Coyote Optimization with Fuzzy Logic(LBC-COFL)algorithm is *** main objective is to perform the lifespan by balancing the gateways with the load of less *** proposed algorithm is evaluated using the metrics such as energy consumption,throughput,central tendency,network lifespan,and total energy utilization.

关键词： Clustering wireless sensor networks fuzzy logic energy efficient optimization

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A fault-tolerant adaptive genetic algorithm for service scheduling in internet of vehicles

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Applied Soft Computing 2023年 143卷

作者： Abbasi, Shirin Rahmani, Amir Masoud Balador, Ali Sahafi, Amir Computer Engineering Department Science and Research Branch Islamic Azad University Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin64002 Taiwan Mälardalen University Västerås72123 Sweden Faculty of Computer Engineering Department South Tehran Branch Islamic Azad University Tehran Iran

Over the years, a range of Internet of Vehicles services has emerged, along with improved quality parameters. However, the field still faces several limitations, including resource constraints and the time response requirement. This paper extracts cost, energy, processing power, service management, and resource allocation parameters. Mathematical equations are then defined based on these parameters. To simplify the process complexity and ensure scalability, we propose an algorithm that uses the genetic algorithm for fault and cost management during resource allocation to services. The main concept is to pick resources for services using a genetic algorithm. We discuss the processing and energy costs associated with this function, which is the algorithm's objective function and is created to optimize cost. Our approach goes beyond the conventional genetic algorithm in two stages. In the first step, services are prioritized, and resources are allocated in accordance with those priorities;in the second step, load balancing in message transmission paths is ensured, and message failures are avoided. The algorithm's performance is evaluated using various parameters, and it was shown to outperform other metaheuristic algorithms like the classic genetic algorithm, particle swarm, and mathematical models. Different scenarios with various nodes and service variables are defined in various system states, including fault occurrences to various percentages of 10, 20, and 30. To compare methods, we consider different parameters, the most significant being performance success rate. Moreover, the cost optimization has a good convergence after iterations, and the rate of improvement in the big scenario has slowed down after 150 iterations. Besides, it provides acceptable performance in response time for services. © 2023 Elsevier B.V.

关键词： Genetic algorithms

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Fault tolerance in fog-based Social Internet of Things

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Knowledge-Based Systems 2023年第1期265卷

作者： Mohammadi, Venus Rahmani, Amir Masoud Darwesh, Aso Sahafi, Amir Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan Department of Information Technology University of Human Development Sulaimanyah Iraq Department of Computer Engineering South Tehran Branch Islamic Azad University Tehran Iran

The Social Internet of Things (SIoT) is a glimpse into the future for self-establishment of inter-things social relationships that guarantee object-to-object transactions. This scenario adopted an intelligent strategy by the trust characteristics and typology of leveraging objects to eradicate the interference of defective nodes. In addition, SIoT networks are error-prone due to heterogeneity and potential resource constraints on the devices, which necessitates the inclusion of a mechanism to enhance the lifetime and dependability of devices. Therefore, fault tolerance mechanisms are indispensable to ensure optimal performance through fault detection and recovery. Although fog architecture and SIoT are two stand-alone paradigms, we extend fog competence into SIoT to empower resource-poor objects for handling intensive tasks. Hence a fault-tolerant model based on a clustering algorithm for fault detection and retrieval is presented. In the proposed scheme, the Markov chains model all faults diagnosed by the fog node. In the recovery stage, the status of the nodes is determined based on the type of faults and the assigned threshold to make a substitution by hot standby nodes. The four performance criteria, namely the reliability, availability, probability density function, the average time to failure, and their mathematical equations were developed on the Markov model. Furthermore, the achievements were demonstrated through either theoretical analysis or simulation results. Then, the proposed scheme increases the number of live nodes, improves the detection accuracy in forwarded requests, lowers the number of invalid requests, and enhances availability and reliability compared to networks with analogous properties. © 2023 Elsevier B.V.

关键词： Internet of things

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Design of an adaptive fuzzy-neural inference system-based control approach for robotic manipulators

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Applied Soft Computing 2023年第PartA期149卷

作者： Barhaghtalab, Mojtaba Hadi Sepestanaki, Mohammadreza Askari Mobayen, Saleh Jalilvand, Abolfazl Fekih, Afef Meigoli, Vahid Department of Electrical Engineering Faculty of Engineering University of Zanjan Zanjan Iran Graduate School of Intelligent Data Science National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin640301 Taiwan Department of Electrical and Computer Engineering University of Louisiana at Lafayette LafayetteLA70504 United States Department of Electrical Engineering Faculty of Engineering Persian Gulf University Bushehr Iran

This paper proposes an adaptive fuzzy-neural inference system (ANFIS)-based control approach for a six degrees of freedom (6-DoF) robotic manipulator. Its main objective is to guarantee the error convergence of the controlled system in the presence of uncertainties and unknown disturbances. The suggested controller is a parallel combination of an ANFIS network with a proportional-integral-derivative (PID) controller. The ANFIS system is used as an estimator to approximate a part of the system and then applied as the feedback linearization in the suggested control structure. The convergence of system errors to zero was proven using Barbalat's lemma. The suggested control law combines the simplicity and ease of implementation of PID control with the estimation properties of ANFIS networks. The suggested approach was evaluated using a simulation study and further validated experimentally using the 6-DoF IRB-120 robotic manipulator (IRB-120-RM). The obtained results confirmed its superior performance and suitability for practical implementation to industrial actuators. © 2023 Elsevier B.V.

关键词： Feedback linearization

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DentAssignNet: Assignment Network for Dental Cast Labeling in the Presence of Dental Abnormalities

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Dascalu, Tudor Ramezanzade, Shaqayeq Bakhshandeh, Azam Bjorndal, Lars Iurcov, Raluca Vrtovec, Tomaz Ibragimov, Bulat University of Copenhagen Department of Computer Science Copenhagen Denmark University of Copenhagen Cariology and Endodontics Section of Clinical Oral Microbiology Department of Odontology Faculty of Health and Medical Sciences Copenhagen Denmark University of Oradea Department of Dentistry Oradea Romania University of Ljubljana Faculty of Electrical Engineering Ljubljana Slovenia

This study focuses on the challenging problem of labeling a collection of objects with inherent morphological and positional dependencies, where instances may be missing or duplicated. We integrate principles of assignment theory in the design of a convolutional neural network to find the optimal label set given pairwise geometrical features extracted from the candidate objects. The objective function aims to minimize the distance between the one-hot encoded labels of the objects and the scores produced by the model, with added emphasis on the scores corresponding to the optimal assignment plan. We tested our solution in the dental domain on the task of finding the teeth labels given a set of candidate instances. The study database included 1200 dental casts of upper and lower jaws from 600 patients. The model reached identification accuracies of 0.952 and 0.968 for the lower and upper jaws, respectively. Moreover, we presented a solution for generating teeth candidates using a multi-step pipeline consisting of coarse and fine segmentations. The algorithm was tested on a database consisting of 600 dental casts, reaching an F1 score of 0.968. © 2013 IEEE.

关键词： Convolutional neural networks

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Generalized Zero-Shot Learning Via Soft Visual-Semantic Alignment

SSRN

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SSRN 2022年

作者： Chen, Yan-He Yeh, Mei-Chen Department of Computer Science and Information Engineering National Taiwan Normal University No. 88 Section 4 Tingzhou Road Taipei Taiwan

We address the problem of generalized zero-shot learning where the task is to predict the label of a target image whether its label belongs to the seen or unseen category. We find a majority of methods learn a joint embedding space where image features and their corresponding class prototypes are aligned. Such a direct alignment can be difficult because of the inherent gap between the visual and the semantic space. We propose to relax the alignment requirement, accomplished by a learning framework that avoids performing pair-wise comparisons between the image and the class embeddings. The soft visual-semantic alignment is achieved by classifying a concatenated feature vector consisting of refined visual features-returned by a visual embedding model-and the class prototype of the target class. In particular, we employ a metric learning based loss function to optimize the visual embedding model that allows different penalty strength on different within-class and between-class similarities. Our extensive experiments show that the indirect alignment manner is more flexible to learn discriminative and generalized visual features. We demonstrate the superiority of the proposed method with performance on par with the state of the art on five benchmarks. © 2022, The Authors. All rights reserved.

关键词： Zero-shot learning

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Adaptive fast-reaching nonsingular terminal sliding mode tracking control for quadrotor UAVs subject to model uncertainties and external disturbances

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Ain Shams engineering Journal 2023年第8期14卷

作者： Mobayen, Saleh El-Sousy, Fayez F.M. Alattas, Khalid A Mofid, Omid Fekih, Afef Rojsiraphisal, Thaned Graduate School of Intelligent Data Science National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin640301 Taiwan Department of Electrical Engineering Prince Sattam Bin Abdulaziz University Al Kharj Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia Department of Electrical and Computer Engineering University of Louisiana at Lafayette Lafayette United States Advanced Research Center for Computational Simulation Department of Mathematics Faculty of Science Chiang Mai University Chiang Mai50200 Thailand Data Science Research Center Department of Mathematics Faculty of Science Chiang Mai University Chiang Mai50200 Thailand

This paper proposes an adaptive fast-reaching nonsingular terminal sliding mode control approach for the position and attitude tracking control of a quadrotor UAV subject to model uncertainty and external disturbances. To this end, a fast-reaching nonsingular terminal sliding surface is defined with the aim of fast convergence of the tracking error. The fast reachability of the nonsingular terminal sliding surface is proven using the Lyapunov stability theory. Since, in practice, the upper bounds of model uncertainties and external disturbances are unknown, an adaptive control scheme is considered to approximate them. The fast-reaching nonsingular terminal sliding mode control approach was augmented with the adaptive scheme to ensure both, the fast convergence of the switching surfaces and the rejection of model uncertainties and external disturbances. The effectiveness of the proposed adaptive fast-reaching nonsingular terminal sliding mode control approach is assessed using simulations of a quadrotor system implemented in MATLAB/Simulink software. The obtained results are further compared to those of an existing approach in terms of tracking performance, robustness, and integrator of absolute-value of error criteria. © 2022 THE AUTHORS

关键词： Unmanned aerial vehicles (UAV)

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Vehicle Map Mapping and Parking Occupancy Estimation System with No Ambiguity

Vehicle Map Mapping and Parking Occupancy Estimation System ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Cheonin Oh Sungwoong Shin Daesub Yoon Sunglok Choi Mobility UX research section Electronics and telecommunications research institute (ETRI) Daejeon Republic of Korea Mobility infra research section Electronics and telecommunications research institute (ETRI) Daejeon Republic of Korea Dept. of computer science and engineering Seoultech Seoul Republic of Korea

This paper presents a novel approach for vehicle map mapping and estimating parking occupancy in parking lots using a single camera without parking lines. The system is capable of detecting around 10 vehicles even with just one camera, and is able to track vehicles with the same ID as they enter and pass through the parking lot. Furthermore, it proposes a method to map the locations of vehicles on real-world maps, allowing for accurate estimation of parking occupancy. The system achieved high accuracy in vehicle detection and tracking, with a 98.4% recall rate and 100% tracking accuracy. The accuracy of parking occupancy estimation was calculated using root mean square error, with an average error of 0.24m and a maximum error of 0.36m. The results demonstrate the feasibility of detecting and tracking vehicles, as well as estimating parking occupancy, in parking lots without parking lines using only one camera. Further testing on multiple sites, including nighttime and adverse weather conditions, is needed to increase the reliability of the system.

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