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Correction to: Automated explainable and interpretable framework for anomaly detection and human activity recognition in smart homes (Neural Computing and Applications, (2025), 10.1007/s00521-025-10991-3)

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Neural Computing and Applications 2025年第16期37卷 9309-9310页

作者： Kim, Tae Hoon Ojo, Stephen Krichen, Moez Alamro, Meznah A. Mihoub, Alaeddine Sampedro, Gabriel Avelino School of Information and Electronic Engineering Zhejiang University of Science and Technology No. 318 Zhejiang Hangzhou China Department of Electrical and Computer Engineering College of Engineering Anderson University AndersonSC29621 United States Al-Baha University Al-Baha65528 Saudi Arabia ReDCAD Laboratory University of Sfax Sfax3038 Tunisia Department of Information Technology College of Computer and Information Science Princess Nourah Bint Abdul Rahman University Riyadh11564 Saudi Arabia Department of Management Information Systems College of Business and Economics Qassim University P.O. Box: 6640 Buraidah51452 Saudi Arabia Department of Computer Science University of the Philippines Diliman Quezon City1101 Philippines

In this article, the author’s name Stephen Ojo was incorrectly written as Stepehn Ojo and the affiliation details for author Stephen Ojo was incorrectly given as ‘Department of electrical and computer engineering, College of engineering Anderson, Anderson University, Anderson, SC 29621, USA’ but should have been ‘Department of electrical and computer engineering, College of engineering, Anderson University, Anderson, SC 29621, USA’. In addition, the affiliation details for author Gabriel Avelino Sampedro were incorrectly given as ‘College of computer Studies, De La Salle University, 2401 Taft Ave., Malate, 1004 Manila, Philippines and Faculty of Information and Communication Studies, University of the Philippines Open University, 4031 Los Baños, Philippines’ but should have been ‘Department of computer Science, University of the Philippines Diliman, 1101 Quezon City, Philippines’. The original article has been corrected. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

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LF-CNN:Deep Learning-Guided Small Sample Target Detection for Remote Sensing Classification

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computer Modeling in engineering & Sciences 2022年第4期131卷 429-444页

作者： Chengfan Li Lan Liu Junjuan Zhao Xuefeng Liu School of Computer Engineering and Science Shanghai UniversityShanghai200444China Key Laboratory for Digital Land and Resources of Jiangxi Province East China University of TechnologyNanchang330013China School of Electronic and Electrical Engineering Shanghai University of Engineering ScienceShanghai201620China School of Communication&Information Engineering Shanghai UniversityShanghai200444China

Target detection of small samples with a complex background is always difficult in the classification of remote sensing *** propose a new small sample target detection method combining local features and a convolutional neural network(LF-CNN)with the aim of detecting small numbers of unevenly distributed ground object targets in remote sensing *** k-nearest neighbor method is used to construct the local neighborhood of each point and the local neighborhoods of the features are extracted one by one from the convolution *** the local features are aggregated by maximum pooling to obtain global feature *** classification probability of each category is then calculated and classified using the scaled expected linear units function and the full connection *** experimental results show that the proposed LF-CNN method has a high accuracy of target detection and classification for hyperspectral imager remote sensing data under the condition of small *** drawbacks in both time and complexity,the proposed LF-CNN method can more effectively integrate the local features of ground object samples and improve the accuracy of target identification and detection in small samples of remote sensing images than traditional target detection methods.

关键词： Small samples local features convolutional neural network(CNN) k-nearest neighbor(KNN) target detection

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Weighted AoI Minimization for RIS-Assisted Multi-User mmWave Communications 23

Weighted AoI Minimization for RIS-Assisted Multi-User mmWave...

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23rd IEEE International Conference on Communication Technology, ICCT 2023

作者： Guo, Ziqi Niu, Yong Wen, Shuxian Fang, Ying Han, Zhu Mao, Shiwen Guan, Shunji Wang, Ning Ai, Bo Beijing Jiaotong University State Key Laboratory of Advanced Rail Autonomous Operation Beijing100044 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Auburn University Department of Electrical and Computer Engineering AuburnAL36849-5201 United States School of Information Engineering Zhengzhou University Zhengzhou450001 China

ISBN: (纸本)9798350325959

In this paper, we focus on a reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) communications system, where a RIS is deployed to create reliable reflection links and alleviate multi-user interference. In the system, each user equipment (UE) is concerned with the freshness of the information received from the base station (BS) and has a certain transmission requirement. The age of information (AoI) is adopted as the metric for information freshness and on this basis, we define the weighted AoI to take into account the satisfaction of UE transmission requirements. Then, an average weighted AoI minimization problem is formulated by jointly optimizing the precoding, the discrete RIS phase shifts, and the scheduling strategy. The problem is decomposed into two subproblems. For the first precoding and RIS design subproblem, we aim to satisfy the transmission requirements of the scheduled UEs with minimum power consumption under a given scheduling strategy. We use the zero-force (ZF)-based precoder and adopt the local search method for the RIS design. For the second scheduling strategy design subproblem, we propose a heuristic scheduling algorithm to minimize the system's average weighted AoI. Simulation results verify the effectiveness of our proposed method over several baseline schemes. © 2023 IEEE.

关键词： millimeter-wave (mmWave) communication Reconfigurable intelligent surface (RIS) scheduling strategy weighted age of information (AoI)

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Learning Probabilistic Coordinate Fields for Robust Correspondences

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2023年第10期45卷 12004-12021页

作者： Zhao, Weiyue Lu, Hao Ye, Xinyi Cao, Zhiguo Li, Xin Huazhong University of Science and Technology The Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Wuhan430074 China West Virginia University The Lane Department of Computer Science and Electrical Engineering MorgantownWV26506-6109 United States

We introduce Probabilistic Coordinate Fields (PCFs), a novel geometric-invariant coordinate representation for image correspondence problems. In contrast to standard Cartesian coordinates, PCFs encode coordinates in correspondence-specific barycentric coordinate systems (BCS) with affine invariance. To know when and where to trust the encoded coordinates, we implement PCFs in a probabilistic network termed PCF-Net, which parameterizes the distribution of coordinate fields as Gaussian mixture models. By jointly optimizing coordinate fields and their confidence conditioned on dense flows, PCF-Net can work with various feature descriptors when quantifying the reliability of PCFs by confidence maps. An interesting observation of this work is that the learned confidence map converges to geometrically coherent and semantically consistent regions, which facilitates robust coordinate representation. By delivering the confident coordinates to keypoint/feature descriptors, we show that PCF-Net can be used as a plug-in to existing correspondence-dependent approaches. Extensive experiments on both indoor and outdoor datasets suggest that accurate geometric invariant coordinates help to achieve the state of the art in several correspondence problems, such as sparse feature matching, dense image registration, camera pose estimation, and consistency filtering. Further, the interpretable confidence map predicted by PCF-Net can also be leveraged to other novel applications from texture transfer to multi-homography classification. © 1979-2012 IEEE.

关键词： Image registration

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Activated Parameter Locating via Causal Intervention for Model Merging

arXiv

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arXiv 2024年

作者： Kong, Fanshuang Zhang, Richong Wang, Ziqiao SKLSDE School of Computer Science and Engineering Beihang University Beijing China Zhongguancun Laboratory Beijing China School of Electrical Engineering and Computer Science University of Ottawa Ottawa Canada

Model merging combines multiple homologous models into one model, achieving convincing generalization without the necessity of additional training. A key challenge in this problem is resolving parameter redundancies and conflicts across multiple models. Existing models have demonstrated that dropping a portion of delta parameters can alleviate conflicts while maintaining performance. However, these methods often drop parameters either randomly or based on magnitude, overlooking task-specific information embedded in fine-tuned models. In this paper, we propose an Activated Parameter Locating (APL) method that utilizes causal intervention to estimate parameter importance, enabling more precise parameter drops and better conflict mitigation. Moreover, to reduce the computational complexity associated with a large number of parameter partitions, we also introduce a theoretically supported gradient approximation strategy for APL. Experiments on model merging within both in-domain and out-of-domain settings, along with associated analyses, showcase the effectiveness of APL. Copyright © 2024, The Authors. All rights reserved.

关键词： APL (programming language)

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Deep Neural Networks for the Qualitative Analysis of Myocardial Perfusion Emission Computed Tomography Images 15

Deep Neural Networks for the Qualitative Analysis of Myocard...

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15th International Conference on Information Technology and electrical engineering, ICITEE 2023

作者： Pruthipanyasakul, Nareekarn Kanungsukkasem, Nont Urruty, Thierry Leelanupab, Teerapong Bangkok10520 Thailand University of Poitiers Xlim Laboratory Umr Cnrs 7252 Poitiers France School of Electrical Engineering and Computer Science The University of Queensland BrisbaneQLD4072 Australia

ISBN: (纸本)9798350304466

Integrating AI into medical diagnosis can provide a more accurate diagnosis when medical staff make treatment decisions. This paper studied on several deep neural networks, re-used with further training for a specific task in classifying the stenosis of a patient's coronary artery. From a 4DM-SPECT application, we collected polar map images that report, for example, myocardial perfusion, function and defect severity from cardiac emission computed tomography examination. We conducted a comparative study to identify the optimal combination of various state-of-the-art pre-trained models (i.e., VGG19, ResNet50, DenseNet121, and EfficientNetB0-B3) and eight different modalities of the myocardial perfusion images for classifying the stenosis of the coronary artery. © 2023 IEEE.

关键词： Deep neural networks

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On the Accuracy-Energy Tradeoff for Hierarchical Federated Learning via Satisfaction Equilibrium

On the Accuracy-Energy Tradeoff for Hierarchical Federated L...

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International Conference on Distributed Computing in Sensor systems (DCOSS)

作者： Panagiotis Charatsaris Maria Diamanti Symeon Papavassiliou Institute of Communication and Computer Systems School of Electrical and Computer Engineering National Technical University of Athens Athens Greece

Hierarchical Federated Learning (HFL) has recently emerged as a promising method to overcome the limitations of conventional Federated Learning (FL) in terms of communication inability of the end users with the cloud and increased backhaul network traffic when considering implementations over wireless networks. Nevertheless, to reap the benefits of HFL, proper user association with the different edge servers and wireless resource allocation is required. Unlike existing works - that in their majority treat unilaterally a single objective using centralized optimization techniques - in this paper, we particularly aim to explore the tradeoff induced between the users' local model training accuracy and personal energy consumption in a distributed manner. The joint problem of user-to-edge-server association and uplink power allocation for transmitting the users' local model parameters to the edge is formulated and solved as a game in satisfaction form. Each user autonomously seeks to achieve a target accuracy-energy ratio by selecting their edge association and uplink transmission power level, while their cumulative decisions result in a desired Satisfaction Equilibrium (SE) point. Specifically, to determine the respective SE of the formulated game, a Reinforcement Learning (RL) algorithm is utilized. Numerical results obtained via modeling and simulation demonstrate the operational and performance characteristics of the proposed framework in terms of achieving the users' personally desired tradeoff value.

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Wireless Semantic Communication Based on Probability Distribution: An Initial Work

Wireless Semantic Communication Based on Probability Distrib...

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IEEE Conference on Wireless Communications and Networking

作者： Qingxiang Luo Yashuang Guo Aoran Zheng Zhitong Ni F. Richard Yu Victor C.M. Leung School of Computer Science and Technology Beijing Jiaotong University Big Data Technologies Centre University of Technology Sydney Department of Systems and Computer Engineering Carleton University Department of Electrical and Computer Engineering The University of British Columbia

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

In the paper, we consider the general semantic transmission in wireless networks based on probability distribution. Firstly, we extract a multidimensional semantic probability distribution function, independent of any a specific wireless channel model, by using the variational inference technique. Secondly, we propose a new semantic similarity metric for measuring the difference between the received semantics and the expected semantics based on Kullback-Leibler divergence. Then, we formulate the semantic transmission problem as an optimization problem of transmission symbol adjustment with the aim to maximize the semantic similarity. Finally, we develop an optimal semantic transformation and transmission (STT) algorithm to obtain the optimal transmission symbol adjustment decision. This decision makes the closed-form expression of semantic transmission symbol available, which can realize lossless semantic transmission with energy constraint. Simulation results verify the effectiveness and robustness of the proposed STT algorithm.

关键词： Measurement Wireless networks Simulation Semantics Symbols Semantic communication Propagation losses Probability distribution Robustness Optimization

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Pneumonia Classification using deep learning: A comparative study 8

Pneumonia Classification using deep learning: A comparative ...

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8th IEEE International Conference on Image and Signal Processing and their Applications, ISPA 2024

作者： Tiar, Mohamed Lakhdar Terki, Nadjiba Dominguez-Jimenez, Juan Jose University of Mohamed Khider Biskra IL3CUB Laboratory Algeria University of Mohamed Khider Biskra Electrical Engineering Department Algeria University of Cadiz Escuela Superior de Ingenieria Cadiz Dept. Computer Languages and Systems Spain

ISBN: (纸本)9798350309249

Pneumonia is an infectious disease of the lungs, caused by viruses, bacteria or fungi. Pneumonia is distinguished by acute inflammation of the lung tissue, causing the consolidation of the terminal bronchioles and alveoli. According to the WHO (the World Health Organization), this disease causes about 4 million deaths. Among the methods of diagnosing pneumonia uses a chest X-ray. This is widely used to visualize pulmonary abnormalities. This work aims at the detection and characterization of pneumonia by the development of computer-assisted diagnosis systems (DAOC). We use deep learning algorithms and chest X-ray images from a pneumonia database to examine the classification of pneumonia. To improve the accuracy, we compare several deep learning models. This research contributes to meeting the growing demand for medical personnel by addressing the global incidence of chest disorders. © 2024 IEEE.

关键词： Classification (of information)

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NPRportrait 1.0:A three-level benchmark for non-photorealistic rendering of portraits

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Computational Visual Media 2022年第3期8卷 445-465页

作者： Paul L.Rosin Yu-Kun Lai David Mould Ran Yi Itamar Berger Lars Doyle Seungyong Lee Chuan Li Yong-Jin Liu Amir Semmo Ariel Shamir Minjung Son Holger Winnemöller School of Computer Science and Informatics Cardiff UniversityCardiffUK School of Computer Science Carleton UniversityOttawaCanada Department of Computer Science and Technology Tsinghua UniversityBeijingChina Reichman University(the Interdisciplinary Center) HerzliyaIsrael Department of Computer Science and Engineering Pohang University of Science and TechnologyPohangRepublic of Korea Lambda Labs Inc.San FranciscoUSA Hasso Plattner Institute University of PotsdamPotsdamGermany Multimedia Processing Laboratory Samsung Advanced Institute of TechnologySuwonRepublic of Korea Adobe Systems Inc.San JoseUSA

Recently,there has been an upsurge of activity in image-based non-photorealistic rendering(NPR),and in particular portrait image stylisation,due to the advent of neural style transfer(NST).However,the state of performance evaluation in this field is poor,especially compared to the norms in the computer vision and machine learning ***,the task of evaluating image stylisation is thus far not well defined,since it involves subjective,perceptual,and aesthetic *** make progress towards a solution,this paper proposes a new structured,threelevel,benchmark dataset for the evaluation of stylised portrait *** criteria were used for its construction,and its consistency was validated by user ***,a new methodology has been developed for evaluating portrait stylisation algorithms,which makes use of the different benchmark levels as well as annotations provided by user studies regarding the characteristics of the *** perform evaluation for a wide variety of image stylisation methods(both portrait-specific and general purpose,and also both traditional NPR approaches and NST)using the new benchmark dataset.

关键词： non-photorealistic rendering(NPR) image stylization style transfer portrait evaluation benchmark

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