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17th Asian Conference on Intelligent Information and database Systems, ACIIDS 2025

作者： Hong, Tzung-Pei Chen, Chi-Chien Chen, Chun-Hao Li, Katherine Shu-Min Department of Computer Science and Information Engineering National University of Kaohsiung Kaohsiung City Taiwan Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung City Taiwan Department of Computer Science and Information Engineering National Kaohsiung University of Science and Technology Kaohsiung City Taiwan

ISBN: (纸本)9789819660070

With the rapid growth of network technology and data storage, big data applications have expanded significantly, making data mining essential for extracting valuable insights. However, protecting sensitive information during data mining is critical. This paper proposes two privacy-preserving methods for federated data mining. The first method uses pseudonymized SHA encryption and clustering to mine frequent itemsets while ensuring privacy. The second one employs the dummy itemset injection to enhance privacy by adding fake itemsets, reducing the risk of reverse engineering. Experiments are made to validate the effectiveness of the proposed methods, revealing the impact of character modifications on mining performance and offering a solid foundation for privacy-preserving federated mining. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Reverse engineering

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Sensing Metal Coil Vibration of Headsets for Eavesdropping on Online Conversations With Out-of-Vocabulary Words Using RFID

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IEEE Transactions on Mobile Computing 2025年

作者： Chen, Yunzhong Yu, Jiadi Chen, Yingying Kong, Linghe Zhu, Yanmin Chen, Yichao Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China Rutgers University Department of Electrical and Computer Engineering United States

As one of the most essential accessories, headsets have been widely used in common online conversations. The metal coil vibration patterns of headset speakers/microphones have been proven to be highly correlated with the speaker-produced/microphone-received sound. This paper presents an online conversation eavesdropping system, RFSpy, which uses only one RFID tag attached on a headset to alternately sense metal coil vibrations of headset speaker and microphone for eavesdropping on speaker-produced and microphone-received sound. In some accessible scenarios, assuming attackers secretly attach a small, battery-free RFID tag under one ear cushion of an eavesdropped user's headset without being noticed. Meanwhile, RFID readers are camouflaged as decorations placed in/out of rooms to transmit and receive RF signals. When the eavesdropped user talks with other users online through the headset, RFSpy first activates the RFID tag to capture the metal coil vibration patterns of headset speaker and microphone upon RF signals. Then, RFSpy reconstructs sound spectrograms from the RF signal-based vibration patterns for not only trained words but also untrained (i.e., out-of-vocabulary) words utilizing designed SSR network. Finally, RFSpy converts the sound spectrograms to conversation content through sound recognition API. Extensive experiments demonstrate that RFSpy can eavesdrop on online conversations with out-of-vocabulary words effectively. © 2002-2012 IEEE.

关键词： Microphones

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Deep Reinforcement Learning-Based Framework for Enhancing Cybersecurity

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第3期19卷 170-190页

作者： Al-Nawashi, Malek M. Al-Hazaimeh, Obaida M. Tahat, Nedal M. Gharaibeh, Nasr Abu-Ain, Waleed A. Abu-Ain, Tarik Al-Huson University College Al-Balqa Applied University Irbid Jordan Faculty of Information Technology Department of Information Security and Cybersecurity Philadelphia University Amman Jordan Faculty of Science The Hashemite University Zarqa Jordan College of Science and Computer Engineering Taibah University Yanbu Saudi Arabia College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia

The detection of cyberattacks has been increasingly emphasized in recent years, focusing on both infrastructure and people. Conventional security measures such as intrusion detection, firewalls, and encryption are insufficient in protecting cyber systems against growing and changing threats. In order to address this problem, scholars have explored reinforcement learning (i.e., RL) as a potential solution for intricate cybersecurity decision-making difficulties. Nevertheless, the use of RL faces several obstacles, including dynamic attack scenarios, insufficient training data, and the challenge of replicating real-world complexities. This study presents a novel framework that uses deep reinforcement learning (i.e., DRL) to simulate harmful cyberattacks and improve cybersecurity. This study presents an agent-based framework that is capable of ongoing learning and adaptation in a dynamic network security environment. The agent determines the optimal course of action by considering the current state of the network and the rewards it receives for its decisions. The CIC-IDS-2018 database, constructed using Python 3.7 programming, was used. The conducted studies yielded outstanding results, with a detection accuracy of 98.82% achieved for the CIC-IDS-2018 database in cyber-attack classification. © 2025 by the authors of this article. Published under CC-BY.

关键词： Cybersecurity

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Automated Topology Synthesis of Analog Integrated Circuits With Frequency Compensation

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年第3期44卷 832-844页

作者： Zhao, Zhenxin Liu, Jun Zhao, Wen-Sheng Zhang, Lihong Hangzhou Dianzi University Innovation Center for Electronic Design Automation Technology Zhejiang Hangzhou310018 China Memorial University of Newfoundland Faculty of Engineering and Applied Science Department of Electrical and Computer Engineering St. John'sNLA1C 5S7 Canada

Analog circuit topology synthesis suffers from weak synthesis capability and low-synthesis efficiency, which result in a bottleneck toward its practical industrial applications. This article presents a proximal-policy-optimization-based circuit topology synthesis framework, which features a superior convergence rate. To further promote its synthesis efficiency, we have improved a deterministic optimization method by incorporating a bias-aware scheme and group concept, which is applied as a filter to eliminate the undesirable topologies in the early evaluation stage. Moreover, a graph-based refinement scheme is proposed to perform deterministically on the generated circuit topologies, which can efficiently add frequency compensation circuits. Compared with the state-of-the-art approaches, our proposed method not only boosts the synthesis efficiency by at least 3 times but also enhances the synthesis capability with a deterministic compensation scheme, showcasing significant advancement of performance efficacy. © 2024 IEEE.

关键词： Network topology

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Scalable Cosmic AI Inference using Cloud Serverless Computing with FMI

arXiv

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arXiv 2025年

作者： Staylor, Mills Fathkouhi, Amirreza Dolatpour Islam, Md Khairul O’Hara, Kaleigh Goudjil, Ryan Ghiles Fox, Geoffrey Fox, Judy Department of Computer Science University of Virginia United States School of Data Science University of Virginia CharlottesvilleVA United States

Large-scale astronomical image data processing and prediction is essential for astronomers, providing crucial insights into celestial objects, the universe’s history, and its evolution. While modern deep learning models offer high predictive accuracy, they often demand substantial computational resources, making them resource-intensive and limiting accessibility. We introduce the Cloud-based Astronomy Inference (CAI) framework to address these challenges. This scalable solution integrates pre-trained foundation models with serverless cloud infrastructure through a Function-as-a-Service (FaaS) Message Interface (FMI). CAI enables efficient and scalable inference on astronomical images without extensive hardware. Using a foundation model for redshift prediction as a case study, our extensive experiments cover user devices, HPC (High-Performance Computing) servers, and Cloud. CAI’s significant scalability improvement on large data sizes provides an accessible and effective tool for the astronomy community. The code is accessible at https://***/UVA-MLSys/AI-for-Astronomy. © 2025, CC BY-SA.

关键词： Cloud platforms

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Developing a Consumer Electronics Robotics With a Large Language Model Based on a Trustworthy AI Framework

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IEEE Transactions on Consumer Electronics 2025年

作者： Wu, Hsin-Te Wei, Wei Li, Syuan-Hao Chen, Mu-Yen National Taitung University Computer Science and Information Engineering Taitung950309 Taiwan Xi'an University of Technology School of Computer Science and Engineering Xi'an710048 China National Cheng Kung University Department of Engineering Science Tainan70142 Taiwan

As many countries face the challenges of an aging population and declining birth rates, the demand for labor, particularly for assisting the elderly, is increasing. Traditional robots, being standardized products, require engineers to adjust parameters to fit the unique lifestyle of each household, which is time-consuming and inconvenient. However, with the rapid development of artificial intelligence and consumer electronics, there is a growing need for home robots with smarter interfaces to achieve the goal of intelligent living. This paper proposes a home robot based on a trustworthy AI framework, integrated with large language models (LLM). These LLM can perform natural language processing and object recognition, allowing users to control the robot's operations through natural language commands. This innovation further advances consumer electronics. The robot's arm can remember these actions and operate according to instructions. Additionally, the robot arm is equipped with monitoring functions, capable of overseeing the operation of other robots and using cameras to detect errors. This development is significant in the field of consumer electronics. The robot also uses Long Short-Term Memory (LSTM) networks to predict the motion paths of the robotic arm, ensuring smooth and efficient operation. This integration of AI and robotics aims to enhance the adaptability and functionality of home robots, making them more suitable for the diverse needs of modern households, improving the quality of life for the elderly, and driving innovation in the consumer electronics field. © 1975-2011 IEEE.

关键词： Robotic arms

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Revolutionizing Classroom Engagement with Face Recognition and Attention-Based Attendance

Revolutionizing Classroom Engagement with Face Recognition a...

引用

Disruptive Technologies (ICDT), International Conference on

作者： Anushka Thakur Aayush Pande Naincy Pande Richa Khandelwal Prashant Dwibedy Department of Electronics and Computer Science Engineering Ramdeobaba University Nagpur India Department of Computer Science Engineering YCCE Nagpur India Department of Electronics Engineering Ramdeobaba University Nagpur India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Modern educational environments require effective and efficient systems to track attendance and participation to ensure better learning outcomes and increased productivity. Traditional systems often mark attendance automatically, regardless of the level of student engagement. This paper introduces a novel system, “Revolutionizing Classroom Engagement with Face Recognition and Attention-Based Attendance,” designed to detect multiple faces in real time and automate the process of attendance marking based on students' attention levels. In contrast to traditional methods, attendance is only recorded when students surpass a predefined attention threshold (e.g. 75%) based on their focus during the lecture. This approach fosters a more dynamic, interactive, and focused learning environment. The proposed system leverages advanced face detection and recognition techniques, integrating Haar Cascade Classifiers, Deep Learning-based Face Detection, and K-Nearest Neighbors (KNN) to offer robust and accurate identification even in large, diverse classrooms. Real-time video processing is handled by OpenCV, which captures and analyzes classroom footage, while NumPy processes complex numerical computations for image data. Pandas is utilized for efficient attendance logging, storing data in easily accessible CSV files. The system's attention-tracking feature is another key innovation, as it analyzes students' gaze and behavioral cues to assess their level of engagement. This ensures that attendance is only recorded when students are genuinely focused and attentive. Designed to be scalable and non-intrusive the system can be adapted to classrooms of varying sizes and is easily incorporated into existing educational frameworks. By providing accurate attendance tracking and engagement analysis, the system not only simplifies administrative tasks but also contributes to fostering a smarter, more engaging, and more productive classroom environment.

关键词： Deep learning Accuracy Head Face recognition Scalability Nearest neighbor methods Real-time systems Recording Face detection Usability

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A ML Framework for Genetic Sequence Identification Using 2D Electrical Conductance Probability Distributions from Mixed data Sets

IEEE Transactions on Computational Biology and Bioinformatic...

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IEEE Transactions on Computational Biology and Bioinformatics 2025年第2期22卷 790-799页

作者： Yiren Wang Hongning Wang Arindam K. Das M. P. Anantram Department of Electrical and Computer Engineering University of Washington Seattle WA USA Department of Computer Science and Electrical Engineering Eastern Washington University Cheney WA USA

Genetic sequence identification from electrical characterization of single molecules has emerged as a promising alternative to traditional approaches. Since electrical data on single molecules is extremely noisy due to the limitations of even state-of-the-art approaches, achieving high detection rates is challenging, particularly when the task involves being able to distinguish a sequence from its single base-pair mismatches. To address this issue, we propose an architecture based on combining a convolutional neural network with an ensemble learning method, XGBoost. In addition, four different input feature representations are considered, 1D conductance probability distributions and 2D conductance versus distance probability distributions which can be viewed as images, with or without averaging over the experimental parameters. The with averaging case corresponds to feature matrices derived from mixed datasets. We find that 2D probability distributions are helpful with respect to classifier accuracy, but averaged conductance probability distributions are much more impactful and significantly enhance prediction accuracy. Our quantitative analysis of multiple sequences shows an impressive performance increase of approximately 10% for all sequences. While the basis of our analysis is conductance data of DNA strands and their single base-pair mismatches, our method is generally applicable to other single-molecule identification based on their conductance.

关键词： Accuracy Histograms DNA Sensitivity Bioinformatics Probability distribution Fingerprint recognition Noise Electrodes Computational biology

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A Modified U-Net for Semantic Segmentation of Liver and Liver Tumors From CT Scans 1st

A Modified U-Net for Semantic Segmentation of Liver and Li...

引用

1st International Conference on Computation of Artificial Intelligence and Machine Learning, ICCAIML 2024

作者： Rashmi, R. Girisha, S. Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

ISBN: (纸本)9783031714832

Medical image analysis algorithms have been quite popular for automating the segmentation of the liver and liver tumours in recent years. A system like this would also lessen radiologists’ workload and subjective interpretations. Deep learning models are commonly used in this field due to their performance improvement over conventional methods of medical image processing. However, deep learning models’ capacity for class imbalance adaptation is rarely studied, particularly in the instance of segmenting liver tumours given their tiny size. Therefore, in the current study, a straightforward Convolutional Neural Network (CNN) model based on the well-known U-Net architecture is suggested in order to capture more contextual information and solve the class imbalance issue. Additionally, a combination of the loss function is intended to guarantee precise segmentation of liver tumours. A detailed qualitative and quantitative analysis is carried out on LiTs dataset to validate the proposed method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： computer Aided Diagnostic Systems CT scan Deep Learning Liver Tumor Semantic Segmentation

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Automatic speaker identification system based on MLP network and deep learning in the presence of severe interference

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Multimedia Tools and Applications 2025年 1-42页

作者： Shafik, Amira Sedik, Ahmed El-Shafai, Walid Khalaf, Ashraf A. M. El-Rabaie, El-Sayed M. El-Samie, Fathi E. Abd Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Electrical Engineering Faculty of Engineering Minia University Minia61519 Egypt Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafr Elsheikh University Kafr El‑Shaikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Automatic Speaker Identification (ASI) is so crucial for security. Current ASI systems perform well in quiet and clean surroundings. However, in noisy situations, the robustness of an ASI system against additive noise and interference is a crucial factor. An investigation of the impact of interference on ASI system performance is presented in this paper, which introduces algorithms for achieving high ASI system performance. The objective is to resist the interference of various forms. This paper presents two models for the ASI task in the presence of interference. The first one depends on Normalized Pitch Frequency (NPF) and Mel-Frequency Cepstral Coefficients (MFCCs) as extracted features and Multi-Layer Perceptron (MLP) as a classifier. In this model, we investigate the utilization of a Discrete Transform (DT), such as Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Discrete Sine Transform (DST), to increase the robustness of extracted features against different types of degradation through exploiting the sub-band decomposition characteristics of DWT and the energy compaction property of DCT and DST. This is achieved by extracting features directly from contaminated speech signals in addition to features extracted from discrete transformed signals to create hybrid feature vectors. The enhancement techniques, such as Spectral Subtraction (SS), Winer Filter, and adaptive Wiener filter, are used in a preprocessing stage to eliminate the effect of the interference on the ASI system. In the second model, we investigate the utilization of Deep Learning (DL) based on a Convolutional Neural Network (CNN) with speech signal spectrograms and their Radon transforms to increase the robustness of the ASI system against interference effects. One of this paper goals is to introduce a comparison between the two models and build a more robust ASI system against severe interference. The experimental results indicate that the two proposed models lead to satisfa

关键词： Additive noise

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