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CNsum:Automatic Summarization for Chinese News Text

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arXiv 2025年

作者： Zhao, Yu Huang, Songping Zhou, Dongsheng Ding, Zhaoyun Wang, Fei Nian, Aixin Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology Changsha410000 China National and Local Joint Engineering Laboratory of Computer Aided Design School of Software Engineering Dalian University Dalian116000 China

Obtaining valuable information from massive data efficiently has become our research goal in the era of Big Data. Text summarization technology has been continuously developed to meet this demand. Recent work has also shown that transformer-based pre-trained language models have achieved great success on various tasks in Natural Language Processing (NLP). Aiming at the problem of Chinese news text summary generation and the application of Transformer structure on Chinese, this paper proposes a Chinese news text summarization model (CNsum) based on Transformer structure, and tests it on Chinese datasets such as THUCNews. The results of the conducted experiments show that CNsum achieves better ROUGE score than the baseline models, which verifies the outperformance of the model. © 2025, CC BY.

关键词： Big data

An Online Integrated Development Environment for Automated Programming Assessment Systems

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arXiv 2025年

作者： Frankford, Eduard Crazzolara, Daniel Vierhauser, Michael Meißner, Niklas Krusche, Stephan Breu, Ruth University of Innsbruck Department of Computer Science Austria University of Stuttgart Institute of Software Engineering Germany Technical University of Munich School of Computation Information and Technology Germany

The increasing demand for programmers has led to a surge in participants in programming courses, making it increasingly challenging for instructors to assess student code manually. As a result, automated programming assessment systems (APASs) have been developed to streamline this process. These APASs support lecturers by managing and evaluating student programming exercises at scale. However, these tools often do not provide feature-rich online editors compared to their traditional integrated development environments (IDEs) counterparts. This absence of key features, such as syntax highlighting and autocompletion, can negatively impact the learning experience, as these tools are crucial for effective coding practice. To address this gap, this research contributes to the field of programming education by extracting and defining requirements for an online IDE in an educational context and presenting a prototypical implementation of an open-source solution for a scalable and secure online IDE. The usability of the new online IDE was assessed using the technology Acceptance Model (TAM), gathering feedback from 27 first-year students through a structured survey. In addition to these qualitative insights, quantitative measures such as memory (RAM) usage were evaluated to determine the efficiency and scalability of the tool under varying usage conditions. © 2025, CC BY-NC-ND.

关键词： Students

PTSC: an efficient random boolean consensus algorithm for distributed systems with the probabilistic propagation model

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Peer-to-Peer Networking and Applications 2025年第4期18卷 1-12页

作者： Cao, Yuke She, Kun Xiao, Yuxuan Wu, Yongyue Ren, Yuntao Chen, Ruidong School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Frontier Research Department Anheng Vision (Chengdu) Information Technology Co. LTD Chengdu China Frontier Research Department Chengdu Anheng Information Technology Co. LTD Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

As the foundation of distributed systems, consensus mechanisms play a crucial role in ensuring the proper operation of the system. In a distributed network composed of trusted peer nodes, nodes may need to perform operations on random Boolean values (0 or 1) during their execution. To address this, this paper, inspired by Paxos, Raft, and their derivative algorithms, proposes a random Boolean consensus algorithm based on the principle of the Probabilistic Propagation Model. In each consensus round, a Boolean value is selected with a smaller probability in the first round of communication, and the final decision is made with a larger probability in the second round, ensuring that the probabilities of the system obtaining 0 or 1 are approximately equal. The running time is further reduced by minimizing the participation of nodes. Experimental results show that this method enables nodes in the system to quickly reach a consensus on a random Boolean value, and as the number of nodes increases, the savings in system overhead become more apparent.

关键词： Parallel processing systems

Enhanced adaptive data rate strategies for energy-efficient Internet of Things communication in LoRaWAN

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International Journal of Communication Systems 2025年第2期38卷

作者： Ali Lodhi, Muhammad Wang, Lei Mahmood, Khalid Farhad, Arshad Chen, Jenhui Kumari, Saru School of Software Technology Dalian University of Technology Dalian China Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Douliu Taiwan Department of Computer Science FAST-National University of Computer and Emerging Sciences Islamabad Pakistan Department of Computer Science and Information Engineering Chang Gung University Taoyuan Taiwan Department of Mathematics Chaudhary Charan Singh University Meerut India School of Chemical Engineering and Physical Sciences Lovely Professional University Jalandhar India

The long-range wide area network (LoRaWAN) is a standard for the Internet of Things (IoT) because it has low cost, long range, not energy-intensive, and capable of supporting massive end devices (EDs). The adaptive data rate (ADR) adjusts parameters at both EDs and the network server (NS). This includes modifying the transmission spreading factor (SF) and transmit power (TP) to minimize packet errors and optimize transmission performance at the NS. The ADR managed by NS aims to provide reliable and energy-efficient resources (e.g., SF and TP) to EDs by monitoring the packets received from the EDs. However, since the channel condition changes rapidly in LoRaWAN due to mobility, the existing ADR algorithm is unsuitable and results in a significant amount of packet loss and retransmissions causing an increase in energy consumption. In this paper, we enhance the ADR by introducing Kalman filter-based ADR (KF-ADR) and moving median-based ADR (Median-ADR), which estimate the optimal SNR by considering the mobility later used to assign the SF and TP to EDs. The simulation results showed that the proposed techniques outperform the legacy ADRs in terms of convergence period, energy consumption, and packet success ratio. © 2024 John Wiley & Sons Ltd.

关键词： adaptive data rate Internet of Things Kalman filter LoRa LoRaWAN mobility resource allocation

CardiacMamba: A Multimodal RGB-RF Fusion Framework with State Space Models for Remote Physiological Measurement

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arXiv 2025年

作者： Wu, Zheng Xie, Yiping Zhao, Bo He, Jiguang Luo, Fei Deng, Ning Yu, Zitong School of Computing and Information Technology Great Bay University Dongguan523000 China Dongguan Key Laboratory for Intelligence and Information Technology Dongguan523000 China Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University Shenzhen518060 China

Heart rate (HR) estimation via remote photoplethysmography (rPPG) offers a non-invasive solution for health monitoring. However, traditional single-modality approaches (RGB or Radio Frequency (RF)) face challenges in balancing robustness and accuracy due to lighting variations, motion artifacts, and skin tone bias. In this paper, we propose CardiacMamba, a multimodal RGB-RF fusion framework that leverages the complementary strengths of both modalities. It introduces the Temporal Difference Mamba Module (TDMM) to capture dynamic changes in RF signals using timing differences between frames, enhancing the extraction of local and global features. Additionally, CardiacMamba employs a Bidirectional SSM for cross-modal alignment and a Channel-wise Fast Fourier Transform (CFFT) to effectively capture and refine the frequency domain characteristics of RGB and RF signals, ultimately improving heart rate estimation accuracy and periodicity detection. Extensive experiments on the EquiPleth dataset demonstrate state-of-the-art performance, achieving marked improvements in accuracy and robustness. CardiacMamba significantly mitigates skin tone bias, reducing performance disparities across demographic groups, and maintains resilience under missing-modality scenarios. By addressing critical challenges in fairness, adaptability, and precision, the framework advances rPPG technology toward reliable real-world deployment in healthcare. The codes are available at: https://***/WuZheng42/CardiacMamba. Copyright © 2025, The Authors. All rights reserved.

关键词： Fast Fourier transforms

Breaking the SSL-AL Barrier: A Synergistic Semi-Supervised Active Learning Framework for 3D Object Detection

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arXiv 2025年

作者： Wang, Zengran Zhang, Yanan Chen, Jiaxin Huang, Di State Key Laboratory of Complex and Critical Software Environment Beihang University Beijing100191 China School of Computer Science and Engineering Beihang University Beijing100191 China School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China

To address the annotation burden in LiDAR-based 3D object detection, active learning (AL) methods offer a promising solution. However, traditional active learning approaches solely rely on a small amount of labeled data to train an initial model for data selection, overlooking the potential of leveraging the abundance of unlabeled data. Recently, attempts to integrate semi-supervised learning (SSL) into AL with the goal of leveraging unlabeled data have faced challenges in effectively resolving the conflict between the two paradigms, resulting in less satisfactory performance. To tackle this conflict, we propose a Synergistic Semi-Supervised Active Learning framework, dubbed as S-SSAL. Specifically, from the perspective of SSL, we propose a Collaborative PseudoScene Pre-training (CPSP) method that effectively learns from unlabeled data without introducing adverse effects. From the perspective of AL, we design a Collaborative Active Learning (CAL) method, which complements the uncertainty and diversity methods by model cascading. This allows us to fully exploit the potential of the CPSP pre-trained model. Extensive experiments conducted on KITTI and Waymo demonstrate the effectiveness of our S-SSAL framework. Notably, on the KITTI dataset, utilizing only 2% labeled data, S-SSAL can achieve performance comparable to models trained on the full dataset. The code has been released at https://***/LandDreamer/S SSAL. Copyright © 2025, The Authors. All rights reserved.

关键词： Active learning

Optical digital dual image encryption scheme based on fast Fourier symmetry fusion and bit-level compression

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Physica Scripta 2025年第5期100卷

作者： Liu, Ping Ouyang, Junlin Shao, Zhuhong School of Computer Science and Engineering Hunan University of Science and Technology Hunan411201 China Hunan Software Vocational and Technical University Hunan411100 China College of Information Engineering Capital Normal University Beijing100048 China

Digital images serve as vital components of contemporary communication systems, yet their prevalence intensifies privacy risks. To ensure secure transmission, image encryption technology has become a crucial solution. This paper thoroughly examines the dual-image encryption problem and introduces an innovative hybrid scheme that integrates optical and digital image encryption. The scheme consists of two main parts. In the first part, the Fast Fourier Symmetry Fusion Encryption Method(FFSFEM) is proposed, which involves processing two plaintext images using the Fast Fourier Transform(FFT) and then fusing them. The resulting fused images are then subjected to an inverse Fast Fourier Transform(iFFT) followed by bit-level compression, yielding two images, the real part and the imaginary part. This part can be operated in a 4f optical system. In the second part, the pixels of both the real and imaginary images are diffused and confused, leading to the generation of the encrypted images. The results are: correlation coefficient is almost 0, average information entropy = 7.9993 bits, average NPCR = 99.6127%, average UACI = 33.5510% and it is able to resist selective plaintext attack. The proposed algorithm is verified to have strong security and encryption performance in terms of simulation experiments and security analysis. © 2025 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.

关键词： Inverse transforms

Hybrid Makes Better: Hybrid Differential Privacy Medical Image Classification Based on Federated Learning

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Jiang, Shunrong Liu, Yingjie Wang, Zhi Li, Yonggang Chi, Haotian Du, Xiaojiang China University of Mining and Technology School of Computer Science Xuzhou221116 China Shanxi University School of Automation and Software Engineering Taiyuan030006 China Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ United States

Machine learning has the potential to revolutionize medical image classification. However, machine learning requires large medical datasets to improve accuracy, which will compromise patient privacy. Federated learning is a promising technique that protects patient privacy and improves the accuracy of medical image classification. Unfortunately, current research shows that federated learning faces the risk of privacy leakage. In this paper, we propose a privacy-preserving federal learning scheme via hybrid differential privacy for medical image classification (FHDM). Specifically, we construct a local hybrid differential privacy algorithm (LHDP) against patients' privacy leakage. This hybrid algorithm combines Gaussian and Laplace differential privacy without enlarging the privacy budget. We prove that the algorithm applies to the model parameter. Moreover, we design loss optimization and global optimization strategies on the algorithm to achieve higher accuracy in medical image classification. Finally, we validate FHDM in terms of privacy-preserving and model accuracy on real datasets. Experiments show that FHDM effectively protects privacy and improves the average accuracy by 9.60% compared to previous differential privacy schemes with the same medical image dataset and privacy budget. © 2004-2012 IEEE.

关键词： Federated learning

Mixed CNN and Transformer for Specific Emitter Identification

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Zhou, Mingsheng Kong, Mingming Tian, Yuan Cui, Chuanan Luo, Xiao Xihua University School of Computer and Software Engineering Chengdu610097 China Xi'an Jiaotong University School of Electronic Science and Engineering Shaanxi Xi'an710049 China Technology Company Ltd. Sichuan Chengdu610017 China Chengyu Academy of Information and Communications Technology Sichuan Chengdu610200 China

Specific emitter identification (SEI) is a vital technique in radio sensing for identifying forged communication nodes and illegal base stations. In this study, we propose a mixed convolutional neural network (CNN)-transformer model that achieves state-of-the-art performance on datasets collected from real-world environments. First, we use large kernel expansion convolution for direct modeling of long time-series IQ data to avoid destroying the feature distribution of tiny radio frequency (RF) damage by traditional hand-designed algorithms. Second, we design the transformer module inspired by the vision transformer (ViT) architecture, which combines class labeling and absolute position encoding to facilitate global feature extraction. In a Bluetooth (BT) dataset collected from 33 mobile phones, our model achieves a Top-1 recognition rate of over 98.6% with four sampling rates. In experiments with a dataset of 16 WiFi devices, our data enhancement scheme is able to achieve excellent recognition rates in a small number of training sets, demonstrating its superior performance in SEI tasks in natural environments. All code is public: https://***/DuBianJun-007/Specific-Emitter-Identification. © 1963-2012 IEEE.

关键词： Deep learning