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IEEE International Conference on Data science in Cyberspace (DSC)

作者： Xueying Zhai Yunfeng Peng Xiuping Guo Liang Zhang Wei Zhang School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China School of Economics and Management Beijing University of Posts and Telecommunications Beijing China Guizhou Key Laboratory of Electric Power Big Data Guizhou Institute of Technology Guizhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

The rapid development of Internet of Things (IoT) technology has driven the intelligent transformation of various industries, including manufacturing, agriculture, and healthcare, etc., significantly enhancing their management capabilities and decision-making efficiency. Data analysis is crucial, serving as both the core technology of this transformation and a major driving force for advancing industry intelligence. However, the increasing volume of data has led to high energy consumption during data analysis, posing significant challenges to environmental and economic sustainability. Integrating renewable energy sources (RESs) is an effective way to reduce greenhouse gas emissions due to their low-carbon and renewable nature. Additionally, the spatiotemporal variability and complementarity of RES generation and grid electricity prices provide favorable conditions for distributed data processing. Therefore, a two-stage demand response scheme is proposed to reduce carbon emissions and electricity costs by prioritizing data processing in data centers (DCs) where RES is abundant or electricity prices are low, based on RES generation and electricity price signals. Simulation results show that the proposed scheme reduces 36.37% carbon emissions and 20.49% electricity costs compared to the benchmark algorithm, indicating its potential for both environmental and economic optimization.

关键词： Renewable energy sources Energy consumption Costs Electricity Simulation Carbon dioxide Spatiotemporal phenomena Internet of Things Sustainable development Optimization

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Fused Fuzzy Deep Learning and information Steganography for Privacy Preservation in Medical Consumer Electronics

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IEEE Transactions on Consumer Electronics 2025年

作者： Chen, Jingxue Zhao, Pengbiao Deng, Erqiang Khan, Fazlullah Alturki, Ryan Alshawi, Bandar Xiong, Hu School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China the School of Computer Science Faculty of Science and Engineering University of Nottingham Ningbo China Zhejiang Ningbo315104 China the KKR and KSR institute of technology and sciences Andhra Pradesh Guntur India the Division of Research and Development Lovely Professional University Phagwara India the University Centre of Research and Development Chandigarh University Mohali India the Department of Computer and Network Engineering College of Computing Umm Al-Qura University Makkah Saudi Arabia the Department of Software Engineering College of Computing Umm Al-Qura University Makkah Saudi Arabia

—Medical Consumer Electronics (MCE) have greatly facilitated people’s lives, allowing patients to obtain personal medical information including medical images. These medical images obtained from MCE need to be processed using segmentation, diagnosis, recognition to obtain useful information. Deep learning plays an important role in medical image segmentation and recognition. However, other personal information about the patient, such as previous cases, is often required to determine the final diagnosis. However, sending personal information and medical images generated through MCE to the clinic directly exposes patient privacy. Therefore, in this paper, we propose a digital medical image privacy protection framework based on information steganography with fuzzy deep learning. The proposed framework transforms patient privacy information into invisible noise and hides it in the deep covert space of the image. This method plays a crucial role in ensuring patient privacy and data security. Furthermore, this method can ensure a high level of image segmentation accuracy that supports the development of personalized medical services. The experimental results show that the proposed framework can embed patient information in medical images without affecting the segmentation task, and effectively protect privacy. Finally, the proposed framework provides new possibilities and impetus for the popularization of MCE. © 2025 IEEE.

关键词： Medical imaging

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A novel automatic wind power prediction framework based on multi-time scale and temporal attention mechanisms

arXiv

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arXiv 2023年

作者： Jiang, Meiyu Shen, Jun Jiang, Xuetao Luo, Lihui Zhou, Rui Zhou, Qingguo School of Information Science and Engineering Lanzhou University Gansu Lanzhou730000 China School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia Gansu Provincial Industry Technology Center of Intelligent Equipment & Big Data for Disaster Prevention Northwest Institute of Eco-Environment and Resources Chinese Academy of Sciences Gansu Lanzhou730000 China

Wind energy is a widely distributed, renewable, and environmentally friendly energy source that plays a crucial role in mitigating global warming and addressing energy shortages. Nevertheless, wind power generation is characterized by volatility, intermittence, and randomness, which hinder its ability to serve as a reliable power source for the grid. Accurate wind power forecasting is crucial for developing a new power system that heavily relies on renewable energy sources. However, traditional wind power forecasting systems primarily focus on ultra-short-term or short-term forecasts, limiting their ability to address the diverse adjustment requirements of the power system simultaneously. To overcome these challenges, We propose an automatic framework capable of forecasting wind power across multi-time scale. The framework based on the tree-structured Parzen estimator (TPE) and temporal fusion transformer (TFT) that can provide ultra-short-term, short-term and medium-term wind power forecasting power. Our approach employs the TFT for wind power forecasting and categorizes features based on their properties. Additionally, we introduce a generic algorithm to simultaneously fine-tune the hyperparameters of the decomposition method and model. We evaluate the performance of our framework by conducting ablation experiments using three commonly used decomposition algorithms and six state-of-the-art models for forecasting multi-time scale. The experimental results demonstrate that our proposed method considerably improves prediction accuracy on the public dataset Engie https://***. Compared to the second-best state-of-the-art model, our approach exhibits a reduction of 31.75% and 28.74% in normalized mean absolute error (nMAE) for 24-hour forecasting, and 20.79% and 16.93% in nMAE for 48-hour forecasting, respectively. © 2023, CC BY.

关键词： Wind power

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Mathematical Model with Social Distancing Parameter for Early Estimation of COVID-19 Spread 1

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International Conference on Machine Vision and Augmented Intelligence, MAI 2021

作者： Chandra, Saroj Kumar Singh, Avaneesh Bajpai, Manish Kumar School of Computing Science and Engineering Galgotias University Uttar Pradesh Gautam Budhha Nagar Greater Noida India Department of Computer Science and Engineering PDPM Indian Institute of Information Technology Design and Manufacturing Jabalpur India

ISBN: (数字)9789811650789

ISBN: (纸本)9789811650772

COVID-19 is well known to everyone in the world. It has spread around the world. No vaccine or antiviral treatment is available till now. COVID-19 patients are increasing day by day. All countries have adopted social distancing as a preventive measure to reduce spread. It becomes necessary to estimate the number of people going to be affected by COVID-19 in advance so that necessary arrangements can be done. Mathematical models are used to provide early disease estimation based on limited parameters. In the present manuscript, a novel mathematical model with a social distancing parameter has been proposed to provide early COVID-19 spread estimation. The model has been validated with real data set. It has been observed that the proposed model is more accurate in spread estimation. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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Fair rank aggregation 22

Fair rank aggregation

引用

Proceedings of the 36th International Conference on Neural information Processing Systems

作者： Diptarka Chakraborty Syamantak Das Arindam Khan Aditya Subramanian School of Computing National University of Singapore Department of Computer Science and Engineering Indraprastha Institute of Information Technology Delhi Department of Computer Science and Automation Indian Institute of Science Bengaluru

ISBN: (纸本)9781713871088

Ranking algorithms find extensive usage in diverse areas such as web search, employment, college admission, voting, etc. The related rank aggregation problem deals with combining multiple rankings into a single aggregate ranking. However, algorithms for both these problems might be biased against some individuals or groups due to implicit prejudice or marginalization in the historical data. We study ranking and rank aggregation problems from a fairness or diversity perspective, where the candidates (to be ranked) may belong to different groups and each group should have a fair representation in the final ranking. We allow the designer to set the parameters that define fair representation. These parameters specify the allowed range of the number of candidates from a particular group in the top-k positions of the ranking. Given any ranking, we provide a fast and exact algorithm for finding the closest fair ranking for the Kendall tau metric under strong fairness, i.e., when the final ranking is fair for all values of k. We also provide an exact algorithm for finding the closest fair ranking for the Ulam metric under strong fairness when there are only O(1) number of groups. Our algorithms are simple, fast, and might be extendable to other relevant metrics. We also give a novel meta-algorithm for the general rank aggregation problem under the fairness framework. Surprisingly, this meta-algorithm works for any generalized mean objective (including center and median problems) and any fairness criteria. As a byproduct, we obtain 3- approximation algorithms for both center and median problems, under both Kendall tau and Ulam metrics. Furthermore, using sophisticated techniques we obtain a (3 - ε)-approximation algorithm, for a constant ε > 0, for the Ulam metric under strong fairness.

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Cluster-guided Contrastive Class-imbalanced Graph Classification 39

Cluster-guided Contrastive Class-imbalanced Graph Classifica...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Ju, Wei Mao, Zhengyang Yi, Siyu Qin, Yifang Gu, Yiyang Xiao, Zhiping Shen, Jianhao Qiao, Ziyue Zhang, Ming College of Computer Science Sichuan University Chengdu China School of Computer Science State Key Laboratory for Multimedia Information Processing PKU-Anker LLM Lab Peking University Beijing China College of Mathematics Sichuan University Chengdu China Paul G. Allen School of Computer Science and Engineering University of Washington SeattleWA United States Huawei Hisilicon Shanghai China School of Computing and Information Technology Great Bay University Dongguan China

ISBN: (纸本)157735897X

This paper studies the problem of class-imbalanced graph classification, which aims at effectively classifying the graph categories in scenarios with imbalanced class distributions. While graph neural networks (GNNs) have achieved remarkable success, their modeling ability on imbalanced graph-structured data remains suboptimal, which typically leads to predictions biased towards the majority classes. On the other hand, existing class-imbalanced learning methods in vision may overlook the rich graph semantic substructures of the majority classes and excessively emphasize learning from the minority classes. To address these challenges, we propose a simple yet powerful approach called C3GNN that integrates the idea of clustering into contrastive learning to enhance class-imbalanced graph classification. Technically, C3GNN clusters graphs from each majority class into multiple subclasses, with sizes comparable to the minority class, mitigating class imbalance. It also employs the Mixup technique to generate synthetic samples, enriching the semantic diversity of each subclass. Furthermore, supervised contrastive learning is used to hierarchically learn effective graph representations, enabling the model to thoroughly explore semantic substructures in majority classes while avoiding excessive focus on minority classes. Extensive experiments on real-world graph benchmark datasets verify the superior performance of our proposed method against competitive baselines. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Contrastive Learning

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MAA: Modulation-adaptive Acoustic Gesture Recognition

MAA: Modulation-adaptive Acoustic Gesture Recognition

引用

IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Yingxin Shan Peng Liao Xuyu Wang Lingling An Shiwen Mao Guangzhou Institute of Technology Xidian University Xi’an China Knight Foundation School of Computing & Information Sciences Florida International University Miami FL USA School of Computer Science and Technology Xidian University Xi’an China Department of Electrical and Computer Engineering Auburn University Auburn AL USA

In this paper, we propose a modulation-adaptive acoustic gesture recognition system with smartphones (termed, MAA), which can achieve a high recognition accuracy under various modulation schemes and quickly adapt to a new modulation at low cost. Specifically, MAA creates an acoustic channel model to capture temporal and spatial features, and leverages a domain adversarial network to eliminate the difference among modulation schemes when performing the same gesture. The proposed framework includes a data collection module, a signal preprocessing module, a channel construction module, and a domain adaptation module. For data collection, we determine the appropriate signal length and bandwidth for transmitted acoustic signals. In the signal preprocessing module, channel estimation and background noise removal are incorporated. Then, we develop a tensor reconstruction network and a feature mapping network in the channel construction module to directly map features to specific gestures. For domain adaptation, we train the above two networks in the source domain, and use an adversarial network to adapt to the target domain. Experimental results show that the proposed MAA achieves a good performance on gesture recognition with different modulation schemes, with better adaptation to new modulation schemes than several state-of-the-art baselines.

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Focal gradient harmonized loss function for object detection 3

Focal gradient harmonized loss function for object detection

引用

2020 3rd International Conference on Computer information science and Artificial Intelligence, CISAI 2020

作者： Sun, Muye Huang, Hongbo Hu, Zhiying Ran, Qinglin Liu, Yahui School of Computer Science Beijing Information Science and Technology University Beijing100101 China Institute of Computing Intelligence Beijing Information Science and Technology University Beijing100192 China School of Electromechanical Engineering Beijing Information Science and Technology University Beijing100192 China School of Information Management Beijing Information Science and Technology University Beijing100192 China

Mainstream detectors in the field of object detection usually can be divided into onestage and two-stage detectors. Compared to the two-stage detectors, one-stage detectors are more efficient and elegant, but are more easily disturbed by imbalanced data during the training. Inspired by the gradient harmonized single-stage detector (GHM detector)[1], we propose a focal gradient harmonizing loss function for classification (FGHM-C). FGHM-C loss function optimizes the method of generating example weights during training, thereby alleviating the impact of long-tailed distribution. Our proposed model achieves 42.0 mAP on COCO test-dev set, which is 0.4 points higher than the original GHM detector. © 2020 Institute of Physics Publishing. All rights reserved.

关键词： Object detection

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A Comprehensive Survey on Intelligent Reflecting Surfaces and Unmanned Aerial Vehicles Integration for Enhanced Communication in Massive Networks 11

A Comprehensive Survey on Intelligent Reflecting Surfaces an...

引用

11th International Conference on Mechatronics and Robotics Engineering, ICMRE 2025

作者： Attar, Hani Ababneh, Jafar Hafez, Mohamed Rachini, Ali Solyman, Ahmed Bahy, Ramy M. Zarqa University Faculty of Engineering Zarqa Jordan College of Engineering University of Business and Technology Jeddah21448 Saudi Arabia Zarqa University Faculty of Information Technology Cyber Security Department Zarqa Jordan INTI-IU-University Nilai Faculty of Engineering Feqs Malaysia Department of Computer Science and Information Technology Jounieh Lebanon School of Computing Engineering and Built Environment Glasgow Caledonian University Glasgow United Kingdom Technical Research Center Cairo Egypt

ISBN: (纸本)9798331509293

Intelligent Reflecting Surfaces (IRSs) offer a revolutionary approach to wireless communication by dynamically controlling the properties of electromagnetic waves. By manipulating wavefronts without requiring radio frequency links, IRSs can enhance spectrum and energy efficiency, reduce costs, and improve overall system performance. Unmanned Aerial Vehicles (UAVs) have garnered significant attention due to their mobility and adaptability. However, real-time data transmission between UAVs can be hindered by obstacles and security risks. By combining IRSs and UAVs, we can create unparalleled opportunities in demanding environments. Through coordinated movements and intelligent wave reflection, both systems can optimize wireless signal propagation. This paper provides a comprehensive review of IRS-assisted UAV communications. We explore existing research, introduce novel technologies and applications, and highlight potential research directions for developing low-cost and energy-efficient wireless systems. Additionally, we address current challenges and outline promising areas for future study. © 2025 IEEE.

关键词： Drones

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MagLive: Robust Voice Liveness Detection on Smartphones Using Magnetic Pattern Changes

arXiv

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arXiv 2024年

作者： Sun, Xiping Chen, Jing Wu, Cong He, Kun Xu, Haozhe Feng, Yebo Du, Ruiying Chen, Xianhao Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan430072 China Rizhao Institute of Information Technology Wuhan University Rizhao276800 China School of Computer Science and Engineering Nanyang Technological University Singapore Collaborative Innovation Center of Geospatial Technology Wuhan430079 China The Department of Electrical and Electronic Engineering HKU Musketeers Foundation Institute of Data Science University of Hong Kong Pok Fu Lam Hong Kong

Voice authentication has been widely used on smartphones. However, it remains vulnerable to spoofing attacks, where the attacker replays recorded voice samples from authentic humans using loudspeakers to bypass the voice authentication system. In this paper, we present MagLive, a robust voice liveness detection scheme designed for smartphones to mitigate such spoofing attacks. MagLive leverages the differences in magnetic pattern changes generated by different speakers (i.e., humans or loudspeakers) when speaking for liveness detection, which are captured by the built-in magnetometer on smartphones. To extract effective and robust magnetic features, MagLive utilizes a TF-CNN-SAF model as the feature extractor, which includes a time-frequency convolutional neural network (TF-CNN) combined with a self-attention-based fusion (SAF) model. Supervised contrastive learning is then employed to achieve user-irrelevance, device-irrelevance, and content-irrelevance. MagLive imposes no additional burden on users and does not rely on active sensing or specialized hardware. We conducted comprehensive experiments with various settings to evaluate the security and robustness of MagLive. Our results demonstrate that MagLive effectively distinguishes between humans and attackers (i.e., loudspeakers), achieving an average balanced accuracy (BAC) of 99.01% and an equal error rate (EER) of 0.77%. Copyright © 2024, The Authors. All rights reserved.

关键词： Smartphones

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