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Dynamic Sensor Scheduling for data Size Reduction in a Sensor Cloud System Based on Minimum Reconstruction Error

WIRELESS PERSONAL COMMUNICATIONS 2024年第3期135卷 1423-1447页

作者： Shylashree, N. Kumar, Sachin VTU RV Coll Engn Dept Elect & Commun Bengaluru 560059 India South Ural State Univ Big Data & Machine Learning Lab Chelyabinsk Russia

Sensing and subsequent analysis of the environmental data of a given geographical area is an essential requisite for the planned development of that region. Nowadays, IoT Sensor-Cloud ecosystem has been adopted to collect data from IoT sensors and transmit it to the chosen Cloud Server for further processing and dissemination. In a large Wireless Sensor Network formed by the IoT sensors, there will be a significant amount of redundancy in the dataset when the nodes are placed closely, and the sensed data varies slowly and gradually with regard to time and space. Then, avoiding redundant data transmission can lead to lower energy consumption and communication overhead. Adaptive subset selection of sensor nodes for data size reduction in a Wireless Sensor Network is an approach to efficiently managing the amount of data transmitted within the network. Then, in the current time schedule, it is possible to optimally select a subset of the sensor nodes for data collection without very much affecting the overall data fidelity. An optimal sensor node subset selection scheme that reduces the communication load with minimum information loss is proposed to achieve this task. The unselected nodes are put in sleep mode, which consequently results in lower sensor energy expenditure. The subset selection algorithm is implemented based on the derivative-free pattern search optimizer that minimizes the reconstruction error during the associated extrapolation. This approach differs entirely from the Compressive data Gathering approach. The simulation results reveal that the performance of the proposed scheme is superior to other similar competitive methods in terms of the mean square error, which is found to be 1.95, with the percentage participation nodes equal to 50% and when the sensor data is uniformly distributed over 20 and 30 units.

关键词： Pattern search algorithm Grid data extrapolation Minimum reconstruction error Keyframes Intermediate frames Wakeup duty cycle

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A Novel Probabilistic-Based Deep Neural Network: Toward the Selection of Wart Treatment

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COGNITIVE COMPUTATION 2022年第5期14卷 1643-1659页

作者： Mishra, Abinash Uyyala, Srinivasulu Reddy Reddy, Venkataswamy A. Natl Inst Technol Machine Learning & Data Analyt Lab Tiruchirapalli India Natl Inst Technol Big Data Analyt Lab Tiruchirapalli India

In clinical research, adequate use of gathered information to provide an intelligent framework to assist the doctors is a great challenge for the current biomedical research community. This study proposed a probabilistic deep neural network (PDNN) to select wart treatment method, where the layered structure of artificial neurons plays a crucial role in generating the optimal feature space. However, the probabilistic and thresholding technique is used to minimize the false negative and false positive instances. In the existing approaches, prediction accuracy and biasedness are major concerns in identifying the best wart treatment method. The benchmark dataset consists of 180 patients toward the selection of immunotherapy and cryotherapy treatment methods. Based on the feature descriptors about the wart, the baseline classifiers such as Naive Bayes (NB), logistic regression and ensemble (LR), support vector machine (SVM), decision tree (DT), bagging, random forest (RF), and eXtreme Gradient Boosting (XGB) along with the developed PDNN was constructed by taking splitting ratio criteria into account. The standard statistical measures such as the measure of accuracy (MoA), error rate, sensitivity, specificity, and area under the curve (AUC) were considered to evaluate the predictive behavior. The proposed PDNN approach obtained promising results: moA, error rate, sensitivity, specificity, and measure of AUC as 0.9778, 0.0222, 0.9762, 0.9792, and 0.9818 while selecting immunotherapy and 0.9889, 0.0111, 1.0000, 0.9796, and 0.9970 in case of cryotherapy. The developed PDNN outperforms baseline classifiers and existing state-of-the-art wart treatment expert systems. The proposed model will improve the success rate and saves the diagnosing time. PDNN-based wart treatment identification system can be implemented in real time after consulting with a domain specialist.

关键词： Deep neural networks

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A customized cost penalized boosting approach for the selection of wart treatment methods

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第11期83卷 33393-33419页

作者： Mishra, Abinash Reddy, U. Srinivasulu Reddy, A. Venkataswamy Natl Forens Sci Univ Sch Cyber Secur & Digital Forens Cyber Secur Lab Delhi Campus Delhi 382007 India Natl Inst Technol Dept Comp Applicat Machine Learning & Data Analyt Lab Tiruchirappalli 620015 Tamil Nadu India Natl Inst Technol Dept Comp Applicat Big Data Analyt Lab Tiruchirappalli 620015 Tamil Nadu India

Warts are benign tumors infected by the Human Papillomavirus. Physicians and medical practitioners are endeavoring to identify the best wart treatment method. The present study finds the response of well-known wart treatment methods, namely immunotherapy and cryotherapy, towards the removal of predominant wart types such as plantar and common warts. The present study utilized the optimal feature space generated by the measure of Fisher score, information gain, and univariate statistical test. In addition, the proposed method finds the customized cost in terms of class weighted and non-class weighted to reduce the miss-classified instances for the positive class sample. The class-weighted and non-class-weighted approaches explicitly incorporated with the well-known classification algorithm extreme gradient boosting approach, which provides a maximum measure of true positive rate, true negative rate, positive predicted value, F-measure, and area under receiver operating characteristic curve of 100.00, 100.00, 100.00, 80.00, and 82.00% respectively on immunotherapy dataset, 100.00, 100.00, 100.00, 100.00, 92.00% respectively on cryotherapy dataset. While validating the performance on the benchmark dataset with the state-of-the-art approach, the proposed model gives an improvement of 6.40% to a maximum of 43.00% in terms of specificity on the immunotherapy dataset. However, the proposed model improves 3.33 - 30%, 5.70 -30%, and 0 - 31% in terms of accuracy, sensitivity, and specificity, respectively on cryotherapy dataset. Also, the proposed framework achieved a maximum sensitivity of 91.30%, which dominates the existing state-of-the-art approaches by a margin of 1.87% and 10.82%, respectively on the merged dataset.

关键词： Immunotherapy Cryotherapy Cost penalize Class distribution Extreme gradient boosting

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Diffusion-Based Method with TTS Guidance for Foreign Accent Conversion 14

Diffusion-Based Method with TTS Guidance for Foreign Accent ...

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14th International Symposium on Chinese Spoken Language Processing

作者： Bai, Qibing Wang, Shuai Liu, Zhijun Zhang, Mingyang Rao, Wei Wang, Yannan Li, Haizhou Chinese Univ Hong Kong Sch Data Sci Shenzhen Peoples R China Chinese Univ Hong Kong Shenzhen Res Inst Big Data Shenzhen Peoples R China Tencent Tencent Ethereal Audio Lab Shenzhen Peoples R China Univ Bremen Machine Learning Lab Bremen Germany

ISBN: (纸本)9798331516833;9798331516826

Accent conversion (AC) aims to alter the accent of spoken language while preserving the original content and speaker characteristics. While any accent can be selected as a target, foreign accent conversion (FAC) that focuses on L2 speakers is particularly noteworthy due to its wide-ranging applications. Compared to general voice conversion tasks, which focus on speaker conversion, research related to accent conversion is relatively scarce, and the audio quality is often limited. In this article, we introduce a diffusion decoder into the conventional TTS-guided accent conversion framework and propose a phonemelevel acoustic-linguistic alignment strategy. Subjective evaluations on the Chinese-accent source speech confirm that the proposed method outperforms the baseline in terms of speech naturalness, accentedness, and speaker similarity(1).

关键词： Accent conversion speech synthesis voice conversion

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Adversarial Attention Networks for Early Action Recognition

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年第2期9卷 1581-1594页

作者： Zhang, Hong-Bo Pan, Wei-Xiang Du, Ji-Xiang Lei, Qing Chen, Yan Liu, Jing-Hua Huaqiao Univ Dept Comp Sci & Technol Xiamen 361000 Peoples R China Huaqiao Univ Fujian Key Lab Big Data Intelligence & Secur Xiamen 361000 Peoples R China Huaqiao Univ Xiamen Key Lab Comp Vis & Pattern Recognit Xiamen 361000 Peoples R China Huaqiao Univ Fujian Prov Univ Key Lab Comp Vis & Machine Learning Xiamen 361000 Peoples R China

Early action recognition endeavors to deduce the ongoing action by observing partial video, presenting a formidable challenge due to limited information available in the initial stages. To tackle this challenge, we introduce an innovative adversarial attention network based on generative adversarial networks. This network leverages the characteristics of both the generator and discriminator to generate unobserved action information from partial video input. The proposed method comprises a cross attention generator, self Attention discriminator, and feature fusion module. The cross attention generator captures temporal relationships in input action sequences, generating discriminative unobserved action information. The self attention discriminator adds global attention to the input sequence, capturing global context information for accurate evaluation of consistency in generated unobserved feature from cross attention generator. Finally, the feature fusion module helps the model obtain richer and more comprehensive feature representations. The proposed method is evaluated through experiments on the HMDB51, UCF101 and Something-Something v2 datasets. Experimental results demonstrate that the proposed approach outperforms existing methods across different observation ratios. Detailed ablation studies confirm the effectiveness of each component in the proposed method.

关键词： Early action recognition adversarial attention network cross attention generator self attention discriminator feature fusion module Early action recognition adversarial attention network cross attention generator self attention discriminator feature fusion module

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Interaction Confidence Attention for Human-Object Interaction Detection

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INTERNATIONAL JOURNAL OF COMPUTER VISION 2025年 1-20页

作者： Zhang, Hong-Bo Lin, Wang-Kai Su, Hang Lei, Qing Liu, Jing-Hua Du, Ji-Xiang Huaqiao Univ Dept Comp Sci & Technol Xiamen 361021 Peoples R China Huaqiao Univ Fujian Key Lab Big Data Intelligence & Secur Xiamen 361021 Peoples R China Huaqiao Univ Xiamen Key Lab Comp Vis & Pattern Recognit Xiamen 361021 Peoples R China Fujian Prov Univ Huaqiao Univ Key Lab Comp Vis & Machine Learning Xiamen 361021 Peoples R China

In human-object interaction (HOI) detection task, ensuring that interactive pairs receive higher attention weights while reducing the weight of non-interaction pairs is imperative for enhancing HOI detection accuracy. Guiding attention learning is also a key aspect of existing transformer-based algorithms. To tackle this challenge, this study proposes a novel approach termed Interaction Confidence Score learning Attention (ICSLA), which introduces weakening and augmentation operations into the original attention weight calculation and feature extraction processes. In ICSLA, feature learning is coupled with confidence score learning, simultaneously. Leveraging ICSLA, a new and universal decoder is devised, establishing a transformer-based one-stage HOI detection architecture. Experimental results demonstrate the effectiveness of the proposed method in improving HOI detection accuracy, offering valuable insights for further optimization of attention mechanisms.

关键词： Human-object interaction detection Interaction confidence score learning attention Weakening operation Augmentation operation

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HDFA-Net: A high-dimensional decoupled frequency attention network for steel surface defect detection

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MEASUREMENT 2025年 242卷

作者： Liang, Fangfang Wang, Zhaoyang Ma, Wei Liu, Bo En, Qing Wang, Dong Duan, Lijuan Hebei Agr Univ Hebei Key Lab Agr Big Data Baoding Peoples R China Beijing Jiaotong Univ Fac Comp Sci & Technol Beijing Peoples R China Beijing Univ Technol Fac Informat Technol Beijing Peoples R China Carleton Univ Sch Comp Sci Artificial Intelligence & Machine Learning AIML La Ottawa ON Canada

Accurately detecting surface defects is crucial for maintaining the quality of steel products. The existing methods often struggle with identifying small defects in complex scenes. To overcome this limitation, we propose a high-dimensional decoupled frequency attention network (HDFA-Net), which features a novel HDFA module. This module considers frequency domain feature information at both the channel and spatial levels and uniquely decouples feature representations into low-frequency components and high-frequency components, conveying global contextual information and highlighting local details, respectively. This innovative approach enables the network to apply distinct attention mechanisms to each frequency domain, significantly enhancing the ability to detect small defects. The HDFA-Net architecture comprises three main subnetworks: a backbone for feature extraction, a neck for multiscale feature interaction, and a head for precise defect localization. The experimental results demonstrate that HDFA-Net outperforms the state-of-the-art defect detection methods, highlighting the effectiveness of the HDFA module in improving the detection accuracy.

关键词： Defect detection Steel surface High-dimensional decoupled frequency attention

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Progressive Multi-Resolution Loss for Crowd Counting

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年第5期34卷 3232-3244页

作者： Yan, Ziheng Qi, Yuankai Li, Guorong Liu, Xinyan Zhang, Weigang Yang, Ming-Hsuan Huang, Qingming Univ Chinese Acad Sci Sch Comp Sci & Technol Key Lab Big Data Min & Knowledge Management Beijing 101408 Peoples R China Univ Adelaide Australian Inst Machine Learning Adelaide SA 5005 Australia Harbin Inst Technol Weihai Weihai 264209 Peoples R China Univ Calif Merced EECS Merced CA 95343 USA

Crowd counting is usually handled in a density map regression fashion, which is supervised via an L2 loss between the predicted density map and ground truth. To effectively regulate models, various improved L2 loss functions have been developed to find a better correspondence between predicted density and annotation positions. In this paper, we propose to predict the density map at one resolution but measure its quality via a derived log-formed loss at multiple resolutions. Unlike existing methods that assume density maps at different resolutions are independent, our loss is obtained by modeling the likelihood function inspired by the relationship of density maps across multi-resolutions. We find that the traditional single-resolution L2 loss is a particular case of our derived log-likelihood. We mathematically prove it is superior to a single-resolution L2 loss. Without bells and whistles, the proposed loss substantially improves several baselines and performs favorably compared to state-of-the-art methods on five crowd counting datasets: NWPU-Crowd, ShanghaiTech A & B, UCF-QNRF, and JHU-Crowd++. The source code and trained models are released at https://***/streamer-AP/PML_***.

关键词： Annotations Kernel Loss measurement Bayes methods Costs Predictive models Bandwidth Crowd counting crowd analysis multi-resolution cascade loss

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A Comparative Study on Multi-modal Fusion for Automated Lung Disease Diagnostics 4

A Comparative Study on Multi-modal Fusion for Automated Lung...

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4th International Conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Kumar, Sachin Mallick, Pradeep Kumar Vorfolomeyeva, Olga South Ural State University Big Data and Machine Learning Lab Chelyabinsk Russia Kiit Deemed to Be University School of Computer Engineering Odisha Bhubaneswar India West Kazakhstan State University Faculty of Arts and Culture Uralsk Kazakhstan

ISBN: (纸本)9798350349856

High-quality X-rays are now available to diagnose lung diseases with the help of radiologists. However, the diagnostic process is time consuming and depends on specialist availability in medical institutions. Patient information may include chest X-rays of varying quality, medical test results, doctors' notes and prescriptions, and medication details, among others. In this study, we present a model for classifying pulmonary diseases using multimodal data from patient clinical studies and radiographic images. Various methods were used to generate artificial samples for both images and tabular data on the laboratory study results during data preparation. We also proposed a method for establishing a correspondence between the generated modals. The late fusion architecture of the proposed multimodal model was implemented. We conducted experiments on pulmonary data-set with two modalities. Results shows that an increase in accuracy and other parameters were observed for multimodal data fusion using our model in comparison with image only modality and clinical data only modality. It strengthen the fact that multimodality provides more insight for the fusion model to learn and provide a more precise diagnosis than single modality. © 2024 IEEE.

关键词： Biological organs

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Adversarial learning With Cost-Sensitive Classes

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IEEE TRANSACTIONS ON CYBERNETICS 2023年第8期53卷 4855-4866页

作者： Shen, Haojing Chen, Sihong Wang, Ran Wang, Xizhao Shenzhen Univ Big Data Inst Coll Comp Sci & Software Engn Guangdong Key Lab Intelligent Informat Proc Shenzhen 518060 Guangdong Peoples R China Shenzhen Univ Coll Math & Stat Shenzhen 518060 Peoples R China Shenzhen Univ Shenzhen Key Lab Adv Machine Learning & Applicat Shenzhen 518060 Peoples R China

It is necessary to improve the performance of some special classes or to particularly protect them from attacks in adversarial learning. This article proposes a framework combining cost-sensitive classification and adversarial learning together to train a model that can distinguish between the protected and unprotected classes, such that the protected classes are less vulnerable to adversarial examples. We find in this framework an interesting phenomenon during the training of deep neural networks, called the Min-Max property, that is, the absolute values of most parameters in the convolutional layer approach 0 while the absolute values of a few parameters are significantly larger, becoming bigger. Based on this Min-Max property which is formulated and analyzed in a view of random distribution, we further build a new defense model against adversarial examples for adversarial robustness improvement. An advantage of the built model is that it performs better than the standard one and can combine with adversarial training to achieve improved performance. It is experimentally confirmed that, regarding the average accuracy of all classes, our model is almost as same as the existing models when an attack does not occur and is better than the existing models when an attack occurs. Specifically, regarding the accuracy of protected classes, the proposed model is much better than the existing models when an attack occurs.

关键词： Training Robustness Costs Adversarial machine learning Perturbation methods Task analysis Neural networks Adversarial examples adversarial training attack and defense cost sensitive robustness

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