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International Conference on Computer and communications (ICCC)

作者： Yifan Xie Wenan Zhou Huamiao Hu Zhicheng Lu Mengyuan Wu School of Computer Science Beijing University of Posts and Telecommunications Beijing China Science and Technology on Communication Information Security Control Laboratory Beijing China

ISBN: (数字)9781728186351

ISBN: (纸本)9781728186368

Code similarity detection is one of the important means to maintain the healthy development of the software eco-environment. Early code similarity detection method is mainly based on the code attribute measurement technology, this method can quickly detect more obvious similar parts on small-scale code datasets, but when faced with large-scale datasets or code segments with similar syntax and semantics, the detection performance is not effective. The new code similarity detection technology based on tree-structure representation and machine learning can adapt to different data changes, thus providing new solutions. This paper proposes a new detection method that combines the abstract syntax tree (AST) structure and unsupervised clustering, this new method can improve the detection efficiency of code clustering analysis, because it not only considers the syntax and semantic information of the code, but also reduces the workload of code data preprocessing through unsupervised clustering.

关键词： Clustering methods Semantics Clustering algorithms Machine learning Syntactics Tools Benchmark testing

Prediction of exosomal piRNAs based on deep learning for sequence embedding with attention mechanism

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Prediction of exosomal piRNAs based on deep learning for seq...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yajun Liu Yulian Ding Aimin Li Rong Fei Xie Guo Fangxiang Wu Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an University of Technology Xi'an China Division of Biomedical Engineering University of Saskatchewan Saskatoon Canada Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China Department of Computer Science Department of Mechanical Engineering Division of Biomedical Engineering University of Saskatchewan Saskatoon Canada

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

PIWI-interacting RNAs (piRNAs) are a type of small non-coding RNAs which bind with the PIWI proteins to exert biological effects in various regulatory mechanisms. A growing amount of evidence reveals that exosomal piRNAs are potential biomarkers for diagnosis and treatment of complex diseases. Effective methods for the prediction of exosomal piRNAs are the foundation of piRNA functional research. In this study, we propose an end-to-end deep network for identifying exosomal piRNAs based on features learned from natural language processing (NLP) models for sequence embedding with attention mechanism. First, a benchmark dataset is constructed by processing piRNA subcellular localization annotated data and sequence data. Moreover, bagging positive unlabeled learning is applied to get the reliable negative set. Finally, we treat a piRNA sequence as a sentence and its k-mer subsequence as a token. Sequence embedding models with self-attention mechanism is designed to extract features from exosome piRNA sequences, which are used for the prediction task. Compared with three competing methods, our model achieves the best performance and reveals the key factors of exosomal piRNA sequences by the attention mechanism. Our model characterizes exosomal piRNAs and could be beneficial for researchers to investigate exosomal piRNAs’ functions.

关键词： Proteins Location awareness RNA Biological system modeling Predictive models Feature extraction Natural language processing

A Deep Exploration of BitLocker Encryption and security Analysis

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A Deep Exploration of BitLocker Encryption and Security Anal...

International Conference on communication technology (ICCT)

作者： Cheng Tan Lijun Zhang Liang Bao Science and Technology on Communication Security Laboratory Chengdu Key Lab of Information Network Security Ministry of Public Security Shanghai China

ISBN: (数字)9781728181417

ISBN: (纸本)9781728181424

Due to the popularity of Windows system, BitLocker is widely used as a built-in disk encryption tool. As a commercial application, the design of BitLocker has to consider a capability of disaster recovery, which helps a user to recover data stored on encrypted disk when a regular access is not available. In this case, it will inevitably lead to some security risks when using BitLocker. We have a deep exploration of BitLocker encryption mechanism in this paper. We present the decryption method of encrypted VMK in case of system partition encryption and non-system partition encryption, respectively. VMK is the core key in BitLocker, with which the encrypted partition or the entire disk can be further decrypted. As for security analysis on BitLocker, we firstly make a difficulty analysis of brute force cracking on BitLocker keys, and then we analyze a possible threat caused by key theft. Based on this, we propose a few countermeasures about BitLocker usage. Additionally, we give some suggestions about security enhancement of BitLocker encryption.

关键词： Encryption Password Force Partitioning algorithms Universal Serial Bus Hard disks Dictionaries

Parallel in-memory wireless computing

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arXiv 2023年

作者： Wang, Cong Ruan, Gong-Jie Yang, Zai-Zheng Yangdong, Xing-Jian Li, Yixiang Wu, Liang Ge, Yingmeng Zhao, Yichen Pan, Chen Wei, Wei Wang, Li-Bo Cheng, Bin Zhang, Zaichen Zhang, Chuan Liang, Shi-Jun Miao, Feng Institute of Brain-inspired Intelligence National Laboratory of Solid State Microstructures School of Physics Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing China National Mobile Communications Research Laboratory Southeast University Frontiers Science Center for Mobile Information Communication and Security Southeast University Purple Mountain Laboratories Nanjing China Institute of Interdisciplinary Physical Sciences School of Science Nanjing University of Science and Technology Nanjing China Nanjing Drum Tower Hospital The Affiliated Hospital of Nanjing University Medical School Nanjing China

Parallel wireless digital communication with ultralow power consumption is critical for emerging edge technologies such as 5G and Internet of Things. However, the physical separation between digital computing units and analogue transmission units in traditional wireless technology leads to high power consumption. Here we report a parallel in-memory wireless computing scheme. The approach combines in-memory computing with wireless communication using memristive crossbar arrays. We show that the system can be used for the radio transmission of a binary stream of 480 bits with a bit error rate of 0/480. The in-memory wireless computing uses two orders of magnitude less power than conventional technology (based on digital-to-analogue and analogue-to-digital converters). We also show that the approach can be applied to acoustic and optical wireless communications. Copyright © 2023, The Authors. All rights reserved.

关键词： Radio transmission

Deterministic generation of nonclassical mechanical states in cavity optomechanics via reinforcement learning

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Physical Review A 2025年第5期111卷 053517-053517页

作者： Yu-Hong Liu Qing-Shou Tan Le-Man Kuang Jie-Qiao Liao Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Key Laboratory for Matter Microstructure and Function of Hunan Province Department of Physics and Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha Hunan 410081 China Hunan Research Center of the Basic Discipline for Quantum Effects and Quantum Technologies Hunan Normal University Changsha 410081 China Key Laboratory of Hunan Province on Information Photonics and Freespace Optical Communication College of Physics and Electronics Hunan Institute of Science and Technology Yueyang Hunan 414000 China Institute of Interdisciplinary Studies Hunan Normal University Changsha Hunan 410081 China

Nonclassical mechanical states, as vital quantum resources for exploring macroscopic quantum behavior, have wide applications in the study of the fundamental quantum mechanics and modern quantum technology. In this work, we propose a scheme for deterministically generating nonclassical mechanical states in cavity optomechanical systems. By working in the eigenrepresentation of the nonlinear optomechanical systems, we identify the carrier-wave resonance conditions and seek optimal driving pulses for state preparations. Concretely, we employ the reinforcement learning method to optimize the pulsed driving fields, effectively suppressing the undesired transitions induced by both the pulsed driving fields and dissipations. This approach enables the high-fidelity preparation of phononic Fock states and superposed Fock states in the single-resonator optomechanical systems, as well as two-mode entangled states in the two-resonator optomechanical systems. The statistical properties of the generated states are also examined. Our results create an opportunity for quantum state engineering in quantum optics and quantum information science via reinforcement learning.

关键词： Quantum optics

A Classification–Detection Approach of COVID-19 Based on Chest X-ray and CT by Using Keras Pre-Trained Deep Learning Models

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Computer Modeling in Engineering & sciences 2020年第11期125卷 579-596页

作者： Xing Deng Haijian Shao Liang Shi Xia Wang Tongling Xie School of Computer Science Jiangsu University of Science and TechnologyZhenjiang212003China School of Automation Key Laboratory of Measurement and Control for CSEMinistry of EducationSoutheast UniversityNanjing210096China School of Computer Science and Communication Engineering Jiangsu UniversityZhenjiang212003China School of Information Science and Technology Nantong UniversityNantong226019China Department of Electrical and Computer Engineering National University of Singapore117583Singapore The People’s Hospital of RUGAO Nantong226500China

The Coronavirus Disease 2019(COVID-19)is wreaking havoc around the world,bring out that the enormous pressure on national health and medical staff *** of the most effective and critical steps in the fight against COVID-19,is to examine the patient’s lungs based on the Chest X-ray and CT generated by radiation *** this paper,five keras-related deep learning models:ResNet50,InceptionResNetV2,Xception,transfer learning and pre-trained VGGNet16 is applied to formulate an classification-detection approaches of *** benchmark methods SVM(Support Vector Machine),CNN(Conventional Neural Networks)are provided to compare with the classification-detection approaches based on the performance indicators,i.e.,precision,recall,F1 scores,confusion matrix,classification accuracy and three types of AUC(Area Under Curve).The highest classification accuracy derived by classification-detection based on 5857 Chest X-rays and 767 Chest CTs are respectively 84%and 75%,which shows that the keras-related deep learning approaches facilitate accurate and effective COVID-19-assisted detection.

关键词： COVID-19 detection deep learning transfer learning pre-trained models

Sensing With Random communication Signals

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arXiv 2025年

作者： Lu, Shihang Liu, Fan Xiong, Yifeng Du, Zhen Cui, Yuanhao Li, Shuangyang Yuan, Weijie Yang, Jie Jin, Shi National Mobile Communications Research Laboratory Southeast University Nanjing210096 China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China School of Electronic and Information Engineering Nanjing University of Information Science and Technology Nanjing210044 China Faculty of Electrical Engineering and Computer Science Technical University of Berlin Berlin10587 Germany Southern University of Science and Technology Shenzhen518055 China Frontiers Science Center for Mobile Information Communication and Security and also The Key Laboratory of Measurement and Control of Complex Systems of Engineering Southeast University Nanjing211189 China

communication-centric Integrated Sensing and communication (ISAC) has been recognized as a promising methodology to implement wireless sensing functionality over existing network architectures, due to its cost-effectiveness and backward compatibility to legacy cellular systems. However, the inherent randomness of the communication signal may incur huge fluctuations in sensing capabilities, leading to unfavorable detection and estimation performance. To address this issue, we elaborate on random ISAC signal processing methods in this article, aiming at improving the sensing performance without unduly deteriorating the communication functionality. Specifically, we commence by discussing the fundamentals of sensing with random communication signals, including the performance metrics and optimal ranging waveforms. Building on these concepts, we then present a general framework for random ISAC signal transmission, followed by an in-depth exploration of time-domain pulse shaping, frequency-domain constellation shaping, and spatial-domain precoding methods. We provide a comprehensive overview of each of these topics, including models, results, and design guidelines. Finally, we conclude this article by identifying several promising research directions for random ISAC signal transmission. © 2025, CC BY-SA.

关键词： Signaling

STC-IDS: Spatial-Temporal Correlation Feature Analyzing based Intrusion Detection System for Intelligent Connected Vehicles

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arXiv 2022年

作者： Cheng, Pengzhou Han, Mu Li, Aoxue Zhang, Fengwei The Department of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai201100 China The School of Computer Science and Communication Engineering Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace Jiangsu University Zhenjiang212013 China The School of Automotive and Traffic Engineering Jiangsu University Zhenjiang212013 China The Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518000 China

Intrusion detection is an important defensive measure for automotive communications security. Accurate frame detection models assist vehicles to avoid malicious attacks. Uncertainty and diversity regarding attack methods make this task challenging. However, the existing works have the limitation of only considering local features or the weak feature mapping of multi-features. To address these limitations, we present a novel model for automotive intrusion detection by spatial-temporal correlation features of in-vehicle communication traffic (STC-IDS). Specifically, the proposed model exploits an encoding-detection architecture. In the encoder part, spatial and temporal relations are encoded simultaneously. To strengthen the relationship between features, the attention-based convolutional network still captures spatial and channel features to increase the receptive field, while attention-LSTM builds meaningful relationships from previous time series or crucial bytes. The encoded information is then passed to detector for generating forceful spatial-temporal attention features and enabling anomaly classification. In particular, single-frame and multi-frame models are constructed to present different advantages respectively. Under automatic hyper-parameter selection based on Bayesian optimization, the model is trained to attain the best performance. Extensive empirical studies based on a real-world vehicle attack dataset demonstrate that STC-IDS has outperformed baseline methods and obtains fewer false-alarm rates while maintaining efficiency. © 2022, CC BY.

关键词： Intrusion detection

Compacting deep neural networks for internet of things: Methods and applications

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arXiv 2021年

作者： Zhang, Ke Ying, Hanbo Dai, Hong-Ning Li, Lin Peng, Yuangyuang Guo, Keyi Yu, Hongfang School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Science and Technology on Electronic Information Control Laboratory Chengdu China School of Communication and Information Engineering University of Electronic Science and Technology of China Chengdu China Faculty of Information Technology Macau University of Science and Technology China Courant Institute of Mathematical Science New York University New York United States

Deep Neural Networks (DNNs) have shown great success in completing complex tasks. However, DNNs inevitably bring high computational cost and storage consumption due to the complexity of hierarchical structures, thereby hindering their wide deployment in Internet-of-Things (IoT) devices, which have limited computational capability and storage capacity. Therefore, it is a necessity to investigate the technologies to compact DNNs. Despite tremendous advances in compacting DNNs, few surveys summarize compacting-DNNs technologies, especially for IoT applications. Hence, this paper presents a comprehensive study on compacting-DNNs technologies. We categorize compacting-DNNs technologies into three major types: 1) network model compression, 2) Knowledge Distillation (KD), 3) modification of network structures. We also elaborate on the diversity of these approaches and make side-by-side comparisons. Moreover, we discuss the applications of compacted DNNs in various IoT applications and outline future *** Codes 68T07 © 2021, CC BY-NC-SA.

关键词： Internet of things