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Intermodal interference based refractive index sensor employing elliptical core photonic crystal fiber

Optoelectronics Letters 2021年第5期17卷 271-275页

作者： WU Xu-jie SONG Bin-bin WU Ji-xuan HUANG Wei The Key Laboratory of Computer Vision and System of Ministry of Education Tianjin Key Laboratory of Intelligence Computing and Novel Software TechnologyTianjin University of TechnologyTianjin 300384China Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems College of Electronic and Information EngineeringTiangong UniversityTianjin 300387China

A refractive index(RI)sensor based on elliptical core photonic crystal fiber(EC-PCF)has been *** asymmetric elliptical core introduces the polarization-dependent characteristics of the fiber core *** performances of intermodal interference between the intrinsic polarization fiber core modes are investigated by contrast in two interferometers based on the Mach-Zehnder(M-Z)and Sagnac interference *** addition,the RI sensing characteristics of the two interferometers are studied by successively filling the three layers air holes closest to the elliptical core in the *** results show that the M-Z interference between LP_(01)and LP_(11)mode in the same polarized direction is featured with the incremental RI sensing sensitivity as the decreasing interference length,and the infilled scope around the elliptical core has a weak correlation with the RI sensing *** to the high birefringence of LP11 mode,the Sagnac interferometer has better RI sensing performance,the maximum RI sensing sensitivity of 12000 nm/RIU is achieved under the innermost one layer air holes infilled with RI matching liquid of RI=1.39 at the pre-setting EC-PCF length of 12 cm,which is two orders of magnitude higher than the M-Z interferometer with the same fiber *** series of theoretical optimized analysis would provide guidance for the applications in the field of biochemical sensing.

关键词： Sagnac PCF Intermodal interference based refractive index sensor employing elliptical core photonic crystal fiber

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Transcriptomic analysis of Andrias davidianus meat and experimental validation for exploring its bioactive components as functional foods

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Food Science and Human Wellness 2024年第1期13卷 166-172页

作者： Changge Guan Zhenglin Tan Shucheng Li Yi Wang Naoyuki Yamamoto Chong Zhang Songjun Wang Junjie Chen Xinhui Xing Key Laboratory for Industrial Biocatalysis Ministry of Education of ChinaDepartment of Chemical EngineeringTsinghua UniversityBeijing 100084China School of Life Science and Technology Tokyo Institute of TechnologyYokohama 226-0026Japan Sensing System Research Center National Institute of Advanced Industrial Science and TechnologyTsukuba 305-8565Japan Center for Synthetic and Systems Biology Tsinghua UniversityBeijing 100084China Henan Giant Salamander Protection and Development Association Luoyang 471000China Intelligent Computing Research Center Harbin Institute of TechnologyShenzhen 518071China Institute of Biopharmaceutical and Health Engineering Tsinghua Shenzhen International Graduate SchoolShenzhen 518055China Institute of Biomedical Health Technology and Engineering Shenzhen Bay LaboratoryShenzhen 518067China

Andrias davidianus(Chinese giant salamander,CGS)is the largest and oldest extant amphibian species in the world and is a source of prospective functional food in ***,the progress of functional peptides mining was slow due to lack of reference genome and protein sequence *** this study,we illustrated full-length transcriptome sequencing to interpret the proteome of CGS meat and obtain 10703 coding DNA *** functional annotation and amino acid composition analysis,we have discovered various genes related to signal transduction,and 16 genes related to *** have also found vast variety of functional peptides through protein coding sequence(CDS)analysis by comparing the data obtained with the functional peptide ***-Pro-Ile predicted by the CDS analysis was released from the CGS meat through enzymatic hydrolysis,suggesting that our approach is *** study suggested that transcriptomic analysis can be used as a reference to guide polypeptide mining in CGS meat,thereby providing a powerful mining strategy for the bioresources with unknown genomic and proteomic sequences.

关键词： Chinese giant salamander Transcriptomic analysis Bioactive components Functional peptides mining

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Membership Data Privacy Protection and Poisoning Detection Scheme for Federated Learning 1st

Membership Data Privacy Protection and Poisoning Detection ...

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1st Conference on Blockchain and Web3.0 technology Innovation and Application, BWTAC 2024

作者： Li, Yafeng Sun, Zhijun Ma, Lichuan China CETC Key Laboratory of Technology on Data Link Xi’an710068 China State Key Laboratory of Integrated Services Networks Xidian University Xi’an710071 China Shaanxi Key Laboratory of Blockchain and Secure Computing Xi’an710071 China

ISBN: (纸本)9789819794119

In the era of big data, data privacy and security are highly valued, leading to the phenomenon of "data islands", which hinders the effective utilization of data. Federated learning, as a privacy-preserving distributed machine learning method, allows model training without sharing raw data, addressing the privacy concerns in cross-organizational data cooperation. Although federated learning protects data privacy, participants’ gradient parameters can still potentially leak sensitive information. As a result, extensive research has focused on conducting federated learning while protecting the privacy of gradient parameters and reducing the communication and computational overhead for participants. These efforts assume that each participant adheres honestly to the protocol;however, malicious submissions of incorrect gradient parameters can significantly bias the federated learning model. This paper proposes a federated learning scheme for privacy-protected gradient aggregation and poison detection that defends against malicious poisoning attacks while safeguarding data privacy, and optimizes computational and communication overheads. By employing inner product triples, the computation process is further optimized, enhancing system performance. The security of the scheme is demonstrated, and its efficiency is validated through practical tests, showing that the optimized solution significantly outperforms the basic scheme in terms of performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Deep User and Item Inter-matching Network for CTR Prediction 28th

Deep User and Item Inter-matching Network for CTR Predicti...

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28th International Conference on Database systems for Advanced Applications, DASFAA 2023

作者： Yuan, Zhiyang Xiao, Yingyuan Yang, Peilin Hao, Qingbo Wang, Hongya Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin University of Technology Tianjin300384 China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin300384 China College of Computer Science and Engineering Donghua University Shanghai China

ISBN: (纸本)9783031306716

CTR prediction plays an important role in increasing company revenue and user experience, and many efforts start with historical behavior to uncover user interest. There are two main problems with previous works: (1) When most previous works mined interests from users’ historical behaviors, they only focus on implicit or explicit interests. (2) When most previous works mined user interests through the relationship between target users and similar users, the representation of target users was not rich and accurate enough, resulting in less accurate item recommendations. Therefore the Deep User and Item Inter-Matching Network (DUIIN) is proposed in this paper to solve the above problems. First, we design Item-to-Item Network (IIN), using two different sub-networks Evolving Interest Network (EIN) and Feature Interaction Network (FIN) to mine the hidden interests and explicit interests shown by users’ historical behaviors, respectively. Then the User-to-User Network (UUN) is designed to mine user interests through the relationship between target users and similar users after representing the target users more accurately and richly. The experimental results show that the DUIIN model proposed in this paper performs better than other state-of-the-art models. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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Dual-demodulation large-scope high-sensitivity refractive index sensor based on twin-core PCF

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Optoelectronics Letters 2021年第4期17卷 193-198页

作者： LI Chen-yuan SONG Bin-bin WU Ji-xuan HUANG Wei WU Xu-jie JIN Chang The Key Laboratory of Computer Vision and System of Ministry of Education Tianjin Key Laboratory of Intelligence Computing and Novel Software TechnologyTianjin University of TechnologyTianjin 300384China Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems College of Electronic and Information EngineeringTiangong UniversityTianjin 3003S7China

In this paper, a refractive index(RI) sensor based on the twin-core photonic crystal fiber(TC-PCF) is presented. Introducing the rectangular array in the core area makes the PCF possible to obtain high birefringence and low confinement loss over the wavelength range from 0.6 μm to 1.7 μm. Therefore, the core region can enhance the interaction between the core mode and the filling material. We studied theoretically the evolution characteristics of the birefringence and operating wavelength corresponding to the strongest polarization point under the condition of filling the rectangular array with RI matching fluid range from 1.33 to 1.41. Simulation results reveal that the proposed TC-PCF has opposite evolutions of change rates between the B and wavelength, and the maximum RI sensing sensitivities of 1.809× 10-2 B/RIU and 8 700 nm/RIU at low and high RI infill are obtained respectively, which means that the TC-PCF features of dual-parameter demodulation for the RI sensing can maintain a high refractive index sensing sensitivity within a large scope of RI ranging from 1.33 to 1.41. Compared with the results of single-parameter demodulation, it is an optimized method to improve the sensitivity of low refractive index sensors, which has great application potency in the field of biochemical sensing and detection.

关键词： polarization refractive index

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Machine Learning is All You Need: A Simple Token-Based Approach for Effective Code Clone Detection

Machine Learning is All You Need: A Simple Token-Based Appro...

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International Conference on Software Engineering (ICSE)

作者： Siyue Feng Wenqi Suo Yueming Wu Deqing Zou Yang Liu Hai Jin Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab Jinyinhu Laboratory Wuhan China School of Cyber Science and Engineering HUST Wuhan China Nanyang Technological University Singapore School of Computer Science and Technology HUST Wuhan China

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

As software engineering advances and the code demand rises, the prevalence of code clones has increased. This phenomenon poses risks like vulnerability propagation, underscoring the growing importance of code clone detection techniques. While numerous code clone detection methods have been proposed, they often fall short in real-world code environments. They either struggle to identify code clones effectively or demand substantial time and computational resources to handle complex clones. This paper introduces a code clone detection method namely Toma using tokens and machine learning. Specifically, we extract token type sequences and employ six similarity calculation methods to generate feature vectors. These vectors are then input into a trained machine learning model for classification. To evaluate the effectiveness and scalability of Toma, we conduct experiments on the widely used BigCloneBench dataset. Results show that our tool outperforms token-based code clone detectors and most tree-based clone detectors, demonstrating high effectiveness and significant time savings.

关键词： Codes Scalability Computational modeling Cloning Machine learning Detectors Feature extraction

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Short mid-infrared watt-level all-fiber nonlinear pulse compressor above 100-MHz pulse repetition rate

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High Power Laser Science and Engineering 2023年第1期11卷 107-113页

作者： Jingcheng Shang Chao Mei Shengzhi Zhao Yizhou Liu Kejian Yang Chun Wang Tao Li Tianli Feng School of Information Science and Engineering Shandong Provincial Key Laboratory of Laser Technology and ApplicationShandong UniversityQingdaoChina Key Laboratory of Laser&Infrared System(Shandong University) Ministry of EducationQingdaoChina Institute of Novel Semiconductors State Key Laboratory of Crystal MaterialsShandong UniversityJinanChina Beijing Engineering and Technology Center for Convergence Networks and Ubiquitous Services SCCEUniversity of Science and Technology BeijingBeijingChina

We firstly report a 2-μm all-fiber nonlinear pulse compressor based on two pieces of normal dispersion fiber(NDF),which enables a high-power scaling ability of watt-level and a high pulse compression ratio of *** the NDFbased all-fiber nonlinear pulse compressor,the 450-fs laser pulses with a repetition rate of 101.4 MHz are compressed to 35.1 fs,corresponding to a 5.2 optical oscillation cycle at the 2-μm wavelength *** output average power reaches 1.28 W,which is believed to be the highest value never achieved from the previous 2-μm all-fiber nonlinear pulse compressors with a high pulse repetition rate above 100 *** dynamic evolution of the ultrafast pulse inside the all-fiber nonlinear pulse compressor is numerically analyzed,matching well with the experimental results.

关键词： few-optical-cycle pulses high pulse repetition rate nonlinear pulse compression

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Large Model Collaborative Optimization Enhanced by Mobile Consumer Electronics Constructed Federated ML

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IEEE Transactions on Consumer Electronics 2024年

作者： Bu, Chao Li, Jianlong Wang, Jinsong Tianjin University of Technology Key Laboratory of Computer Vision and System of Ministry of Education School of Computer Science and Engineering Tianjin300384 China Tianjin University of Technology Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology School of Computer Science and Engineering Tianjin300384 China

With the rapid development of internet of everything, current Mobile Consumer Electronics (MCEs) can support complex computing due to their specialized data-handling capacity. Thus, federated Machine Learning (ML) and edge computing are integrated to leverage such capacity of MCEs to enhance large model training. This paper studies motivations of all participants that take part in large model collaborative optimization from a global perspective, and proposes the positive promotion pattern that collaborates MCEs, Edge computing Servers (ECSs), and the centralized computing center to support large model collaborative optimization by introducing the idea of computing economic. We firstly propose the novel three-layered framework for large model collaboratively optimizing based on MCEs constructed federated ML. Secondly, we introduce the economic factor into the large model optimization by devising algorithms to assess consumptions of all participants and dynamically adjust incentive benefits according to the qualities of trained models. Finally, we design the scheme to encourage more MCEs to train local models by adaptively adjusting the ECS pricing strategy with the ECS future economic benefit trend considered. Experimental results validate that our proposed approach is able to optimize the global accuracy and improve the economic benefit more efficiently than the state of the art. © 1975-2011 IEEE.

关键词： Mobile edge computing

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Towards Effective and Efficient Error Handling Code Fuzzing Based on Software Fault Injection

Towards Effective and Efficient Error Handling Code Fuzzing ...

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IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

作者： Kang Chen Ming Wen Haoxiang Jia Rongxin Wu Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology (HRUST) Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security Jin YinHu Laboratory Wuhan China School of Informatics Xiamen University Xiamen China School of Computer Science and Technology HUST Wuhan China Cluster and Grid Computing Lab

ISBN: (数字)9798350330663

ISBN: (纸本)9798350330670

Software systems often encounter various errors or exceptions in practice, and thus proper error handling code is essential to ensure the reliability of software systems. Unfortunately, error handling code is often bug-prone, while sufficiently testing them is challenging as such code often cannot be triggered under normal conditions. Motivated by this, recent studies have proposed to leverage software fault injection (SFI) based fuzzing to discover potential bugs in complicated error handling code. Despite the promising results achieved, their effectiveness and efficiency are still compromised in practice due to the huge search space of error sites, inadequate fuzzing guidance, and the overhead induced by context-sensitive SFI. To achieve effective and efficient testing of error handling code, this study presents AFL-FI, which first utilizes a similarity-based method to identify suspicious error sites, and then incorporates the idea of error site coverage to guide the fuzzing process. Finally, the design of lightweight context-sensitive SFI enables AFL-FI to execute test cases efficiently. We evaluate AFL-FI on eight large-scale open-source projects, and the results show that it can outperform existing state-of-the-art fuzzing tools significantly in terms of branch code coverage. More importantly, AFL-FI has discovered 13 previously unknown bugs, and all of them have been confirmed while 12 of them have been fixed. Besides, our evaluation also demonstrates that all the key designs of AFL- F I are effective that contribute significantly to its overall performance.

关键词： Codes Computer bugs Fuzzing Software systems Software reliability Testing

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Why Does Little Robustness Help? A Further Step Towards Understanding Adversarial Transferability

Why Does Little Robustness Help? A Further Step Towards Unde...

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IEEE Symposium on Security and Privacy

作者： Yechao Zhang Shengshan Hu Leo Yu Zhang Junyu Shi Minghui Li Xiaogeng Liu Wei Wan Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security School of Cyber Science and Engineering Huazhong University of Science and Technology School of Information and Communication Technology Griffith University School of Software Engineering Huazhong University of Science and Technology Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology

ISBN: (数字)9798350331301

ISBN: (纸本)9798350331318

Adversarial examples for deep neural networks (DNNs) are transferable: examples that successfully fool one white-box surrogate model can also deceive other black-box models with different architectures. Although a bunch of empirical studies have provided guidance on generating highly transferable adversarial examples, many of these findings fail to be well explained and even lead to confusing or inconsistent advice for practical *** this paper, we take a further step towards understanding adversarial transferability, with a particular focus on surrogate aspects. Starting from the intriguing "little robustness" phenomenon, where models adversarially trained with mildly perturbed adversarial samples can serve as better surrogates for transfer attacks, we attribute it to a trade-off between two dominant factors: model smoothness and gradient similarity. Our research focuses on their joint effects on transferability, rather than demonstrating the separate relationships alone. Through a combination of theoretical and empirical analyses, we hypothesize that the data distribution shift induced by off-manifold samples in adversarial training is the reason that impairs gradient *** on these insights, we further explore the impacts of prevalent data augmentation and gradient regularization on transferability and analyze how the trade-off manifests in various training methods, thus building a comprehensive blueprint for the regulation mechanisms behind transferability. Finally, we provide a general route for constructing superior surrogates to boost transferability, which optimizes both model smoothness and gradient similarity simultaneously, e.g., the combination of input gradient regularization and sharpness-aware minimization (SAM), validated by extensive experiments. In summary, we call for attention to the united impacts of these two factors for launching effective transfer attacks, rather than optimizing one while ignoring the other, and emphasize the

关键词： Training Privacy Buildings Closed box Minimization Data augmentation Robustness

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