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2025 IEEE International Conference on Big data and Smart computing, BigComp 2025

作者： Zabin, Mahe Ayon, Syed Tasnimul Karim Siraj, Farhan Md Shuvo, Mehedi Hasan Choi, Ho-Jin Uddin, Jia Korea Advanced Institute of Science and Technology School of Computing Daejeon Korea Republic of BRAC University Department of Computer Science and Engineering Dhaka Bangladesh Dhaka University of Engineering & Technology Department of Computer Science and Engineering Dhaka Gazipur Bangladesh Woosong University AI and Big Data Department Endicott College Daejeon Korea Republic of

The deep learning models are computationally burdensome, and their accuracy depends on the number of labeled datasets used in training. The scarcity of the labeled dataset in industrial machine diagnosis is a significant concern as it is pretty impossible to collect data from faulty machines in industrial environments. To address this issue, in this paper, we presented a few-shot learning-based machine fault diagnosis model on a lightweight SqueezeNet architecture using generated texture images from audio sensor data. Subsets of two public audio datasets, MIMII and ToyADMOS, containing limited data- 10 images per class were used to validate the model. The experimental results demonstrate that the model has exhibited 89.6% and 98.3% accuracy for MIMII and ToyADMOS, respectively, with only ten labeled images of each fault class. As the model has only 73 thousand trainable parameters and requires only 300 KB of memory, it is expected to be deployable in a portal device for machine fault diagnosis. © 2025 IEEE.

关键词： Deep learning

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Semantic Pre-Extraction for Energy-Efficient AoI Minimization in UAV-Assisted Wireless Networks

Semantic Pre-Extraction for Energy-Efficient AoI Minimizatio...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Long, Yusi Lee, Gary C.F. Li, Lanhua Gong, Shimin Sun, Sumei Niyato, Dusit School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen Campus China Guangdong Provincial Key Laboratory of Fire Science and Intelligent Emergency Technology China A∗star Singapore College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (纸本)9798350368369

This paper investigates an unmanned aerial vehicle (UAV)-assisted semantic communication network. The energy-limited ground users (GUs) provide semantic services to periodically generated raw data and a UAV relays the extracted semantic information to a base station (BS). Semantic extraction enhances data responsiveness and reduces the age-of-information (AoI) by transmitting only the most essential information. However, more complex semantic extraction increases energy consumption, making it easier for the GUs to deplete their energy. Therefore, we introduce a novel energy-efficient AoI (EAoI) metric to capture both information freshness and energy consumption of the GUs. We formulate a time-averaged EAoI minimization problem by jointly optimizing the GUs' scheduling, pre-extraction strategy, semantic control, computing resource allocation, and the UAV's trajectory. We further propose a semantic-aware joint pre-extraction and trajectory planning (Sem-JPT) algorithm to decompose the complex optimization problem into three subproblems, which are solved by a series of approximation methods. Simulation results demonstrate that semantic communication can reduce the overall EAoI by more than 18% compared with conventional bit-based communication. Moreover, the proposed Sem-JPT algorithm can maintain information freshness and prolong the GUs' lifetimes, outperforming existing baselines. © 2025 IEEE.

关键词： Drones

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NaFV-Net: An Adversarial Four-view Network for Mammogram Classification 39

NaFV-Net: An Adversarial Four-view Network for Mammogram Cla...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Lu, Feng Hou, Yuxiang Li, Wei Yang, Xiangying Zheng, Haibo Luo, Wenxi Chen, Leqing Cao, Yuyang Liao, Xiaofei Zhang, Yu Yang, Fan Zomaya, Albert Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Australia-China Joint Research Centre for Energy Informatics and Demand Response Technologies Centre for Distributed and High Performance Computing School of Computer Science University of Sydney Australia Tongji Hospital Tongji Medical College Huazhong University of Science and Technology China

ISBN: (纸本)157735897X

Breast cancer remains a leading cause of mortality among women, with millions of new cases diagnosed annually. Early detection through screening is crucial. Using neural networks to improve the accuracy of breast cancer screening has become increasingly important. In accordance with radiologists’ practices, we propose using images from the unaffected side to create adversarial samples with critical medical implications in our adversarial learning process. By introducing beneficial perturbations, this method aims to reduce overconfidence and improve the precision and robustness of breast cancer classification. Our proposed framework is an adversarial four-view classification network (NaFV-Net) incorporating images from both affected and unaffected perspectives. By comprehensively capturing local and global information and implementing adversarial learning from four mammography views, this framework allows for the fusion of features and the integration of medical principles and radiologist evaluation techniques, thus facilitating the accurate identification and characterization of breast tissues. Extensive experiments have shown the high effectiveness of our model in accurately distinguishing between benign and malignant findings, demonstrating state-of-the-art classification performance on both internal and public datasets. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Mammography

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ABXI: Invariant Interest Adaptation for Task-Guided Cross-Domain Sequential Recommendation

arXiv

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arXiv 2025年

作者： Bian, Qingtian Vinícius de Carvalho, Marcus Li, Tieying Xu, Jiaxing Fang, Hui Ke, Yiping College of Computing and Data Science Nanyang Technological University Singapore School of Computer Science and Engineering Northeastern University China Research Institute for Interdisciplinary Sciences Key Laboratory of Interdisciplinary Research of Computation and Economics Shanghai University of Finance and Economics Shanghai China

Cross-Domain Sequential Recommendation (CDSR) has recently gained attention for countering data sparsity by transferring knowledge across domains. A common approach merges domain-specific sequences into cross-domain sequences, serving as bridges to connect domains. One key challenge is to correctly extract the shared knowledge among these sequences and appropriately transfer it. Most existing works directly transfer unfiltered cross-domain knowledge rather than extracting domain-invariant components and adaptively integrating them into domain-specific modelings. Another challenge lies in aligning the domain-specific and cross-domain sequences. Existing methods align these sequences based on timestamps, but this approach can cause prediction mismatches when the current tokens and their targets belong to different domains. In such cases, the domain-specific knowledge carried by the current tokens may degrade performance. To address these challenges, we propose the A-B-Cross-to-Invariant Learning Recommender (ABXI). Specifically, leveraging LoRA’s effectiveness for efficient adaptation, ABXI incorporates two types of LoRAs to facilitate knowledge adaptation. First, all sequences are processed through a shared encoder that employs a domain LoRA for each sequence, thereby preserving unique domain characteristics. Next, we introduce an invariant projector that extracts domain-invariant interests from cross-domain representations, utilizing an invariant LoRA to adapt these interests into modeling each specific domain. Besides, to avoid prediction mismatches, all domain-specific sequences are aligned to match the domains of the cross-domain ground truths. Experimental results on three datasets demonstrate that our approach outperforms other CDSR counterparts by a large margin. The codes are available in https://***/DiMarzioBian/ABXI. © 2025, CC BY.

关键词： Knowledge transfer

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Time-Slotted On-Demand Predictive Routing for UAV Networks

Time-Slotted On-Demand Predictive Routing for UAV Networks

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IEEE Conference on Wireless Communications and Networking

作者： Houze Feng Jingjing Wang Jianrui Chen Xiangwang Hou Jiacheng Wang Geng Sun Dusit Niyato School of Cyber Science and Technology Beihang University Beijing China Department of Electronic Engineering Tsinghua University Beijing China College of Computing and Data Science Nanyang Technological University Singapore College of Computer Science and Technology Jilin University Changchun China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Flying ad hoc networks (FANETs) composed of small unmanned aerial vehicles (UAVs) are flexible, inexpensive, and fast to deploy, which have been used in an increasing number of mission scenarios. However, unstable link quality and frequently changing network topology pose significant challenges for adopting existing routing protocols in mobile ad hoc networks (MANETs). In this paper, we propose a time-slotted on-demand predictive (TSDP) routing protocol designed specifically for UAV networks. The TSDP protocol introduces a novel approach to route selection by incorporating multiple criteria, including delivery ratio, adjacent degree, and mobility prediction factor, to ensure reliable and efficient data transmission. By addressing high latency in route discovery and excessive broadcast overhead, TSDP employs a time-slotted communication mechanism that reduces packet drop rates and enhances route stability. Simulation results demonstrate that TSDP consistently outperforms ad hoc on-demand distance vector (AODV) and dynamic source routing (DSR) protocols in terms of throughput, packet delivery ratio, end-to-end delay, and overhead, particularly in highly dynamic network environments.

关键词： Simulation Stability criteria Autonomous aerial vehicles Throughput Routing Routing protocols Vectors Delays Vehicle dynamics AODV

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Performance and Security Evaluation of Chaotic Maps for Image Scrambling in Real-Time Applications

Performance and Security Evaluation of Chaotic Maps for Imag...

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3rd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2025

作者： Subashini, B. Panjatcharam, V.G. Saradha, S. Srm Institute of Science and Technology School of Computing College of Engineering and Technology Dept of Data Science and Business Systems Chennai India Vet Institute of Arts & Science(Co-Ed) College Dept of Mca Thindal Erode India Nandha Engineering College (Autonomous) Dept of Mca Erode India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Image scrambling is an essential technique in secure image transmission, designed to obscure visual data from unauthorized access while preserving the ability to recover the original content. This study investigates the application of five chaotic maps such as Logistic Map, Arnold Cat Map, Lorenz System, Henon Map and Tent Map for image scrambling. Performance is evaluated using key metrics: Normalized Cross-Correlation (NCC), entropy, Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM) and time complexity. Results indicate that the Arnold Cat Map provides the highest entropy (5.8680) and strong decorrelation (NCC = 0.8715), making it ideal for high-security applications. The Logistic Map, with the fastest execution time (0.0039 seconds), is suitable for real-time applications, offering moderate security and good image quality (PSNR = 11.5170, SSIM = 0.0229). The Lorenz System exhibited the best decorrelation (NCC = 0.8657), but its high computational cost limits its use in time-sensitive environments. By comparing the PSNR and SSIM results, this study highlights the balance between visual quality and security, suggesting that chaotic maps can be tailored for specific s ecurity n eeds. The novelty lies in the systematic comparison of these chaotic maps, providing a comprehensive evaluation for optimizing security and performance. © 2025 IEEE.

关键词： Arnold Cat Map Henon Map Image Scrambling Logistic Map Lorenz System Tent Map

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Time-Slotted On-Demand Predictive Routing for UAV Networks

Time-Slotted On-Demand Predictive Routing for UAV Networks

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Feng, Houze Wang, Jingjing Chen, Jianrui Hou, Xiangwang Wang, Jiacheng Sun, Geng Niyato, Dusit School of Cyber Science and Technology Beihang University Beijing China Tsinghua University Department of Electronic Engineering Beijing China College of Computing and Data Science Nanyang Technological University Singapore College of Computer Science and Technology Jilin University Changchun China

ISBN: (纸本)9798350368369

关键词： Drones

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data Insights as data: Quick Overview and Exploration of Automated data Insights

arXiv

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arXiv 2025年

作者： Wu, Shangxuan Luan, Wendi Wang, Yong Zeng, Dan Shen, Qiaomu Tang, Bo Department of Computer Science and Engineering Southern University of Science and Technology Guangdong Shenzhen China College of Computing and Data Science Nanyang Technological University Singapore Singapore Singapore School of Artificial Intelligence Sun Yat-sen University Guangdong Zhuhai China

Automated data insight mining and visualization have been widely used in various business intelligence applications (e.g., market analysis and product promotion). However, automated insight mining techniques often output the same mining results to different analysts without considering their personal preferences, while interactive insight discovery requires significant manual effort. This paper fills the gap by integrating automated insight mining with interactive data visualization and striking a proper balance between them to facilitate insight discovery and exploration. Specifically, we regard data insights as a special type of data and further present InsightMap, a novel visualization approach that uses the map metaphor to provide a quick overview and in-depth exploration of different data insights, where a metric is proposed to measure the similarity between different insights. The effectiveness and usability of InsightMap are demonstrated through extensive case studies and in-depth user interviews. Copyright © 2025, The Authors. All rights reserved.

关键词： data assimilation

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Few-Shot Learning-Based Machine Fault Diagnosis Using EMD-Gammatone Spectrogram with Limited Labeled Audio dataset

Few-Shot Learning-Based Machine Fault Diagnosis Using EMD-Ga...

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International Conference on Big data and Smart computing (BIGCOMP)

作者： Mahe Zabin Syed Tasnimul Karim Ayon Farhan Md Siraj Mehedi Hasan Shuvo Ho-Jin Choi Jia Uddin School of Computing Korea Advanced Institute of Science and Technology Daejeon Republic of Korea Department of Computer Science and Engineering BRAC University Dhaka Bangladesh Department of Computer Science and Engineering Dhaka University of Engineering & Technology Gazipur Dhaka Bangladesh AI and Big Data Department Endicott College Woosong University Daejeon Republic of Korea

ISBN: (数字)9798331529024

ISBN: (纸本)9798331529031

关键词： Fault diagnosis Training Accuracy Sensitivity Computational modeling Memory management data models Few shot learning Spectrogram Portals

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A Real-Time and Ubiquitous Network Attack Detection Based on Deep Belief Network and Support Vector Machine

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IEEE/CAA Journal of Automatica Sinica 2020年第3期7卷 790-799页

作者： Hao Zhang Yongdan Li Zhihan Lv Arun Kumar Sangaiah Tao Huang IEEE National Engineering Laboratory for Educational Big Data Central China Normal UniversityWuhan 430072China Lanzhou Central Sub-branch of The People’s Bank of China Lanzhou 730000China School of Data Science and Software Engineering Qingdao UniversityQingdao 266071China School of Computing Science and Engineering Vellore Institute of Technology UniversityTamil Nadu 632014India

In recent years, network traffic data have become larger and more complex, leading to higher possibilities of network intrusion. Traditional intrusion detection methods face difficulty in processing high-speed network data and cannot detect currently unknown attacks. Therefore, this paper proposes a network attack detection method combining a flow calculation and deep learning. The method consists of two parts: a real-time detection algorithm based on flow calculations and frequent patterns and a classification algorithm based on the deep belief network and support vector machine(DBN-SVM). Sliding window(SW) stream data processing enables real-time detection, and the DBN-SVM algorithm can improve classification accuracy. Finally, to verify the proposed method, a system is *** on the CICIDS2017 open source data set, a series of comparative experiments are conducted. The method's real-time detection efficiency is higher than that of traditional machine learning algorithms. The attack classification accuracy is 0.7 percentage points higher than that of a DBN, which is 2 percentage points higher than that of the integrated algorithm boosting and bagging methods. Hence, it is suitable for the real-time detection of high-speed network intrusions.

关键词： Deep belief network(DBN) flow calculation frequent pattern intrusion detection sliding window support vector machine(SVM)

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