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25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Wang, Zihao Shi, Donghao Yang, Chenguang Si, Weiyong Li, Qinchuan School Of Mechanical Engineering Zhejiang Sci-Tech University Hangzhou China Bristol Robotics Laboratory University Of The West Of England Bristol United Kingdom School Of Computer Science And Electronic Engineering University Of Essex London United Kingdom

ISBN: (纸本)9798350340266

In the past few decades, the demand for assistive robots in the field of healthcare has steadily increased. We conducted research on an autonomous robot system based on imitation learning to assist in liver scans guided by ultrasound. Traditional procedures for liver examination using ultrasound imaging typically require a physician to slide the ultrasound probe along the patient's lower ribs for multi-angle observation. To automate this process, we employed imitation learning to capture the physician's manipulation techniques. However, the imitation generalization system often struggles to accurately extract the starting and ending points due to the obstruction of fat and skin, and the individual variations in patient anatomy lead to inconsistencies in the positions of lower ribcage. To improve the scanning process, we introduced stiffness estimation to differentiate between the abdomen and the rib, allowing real-time adjustments for optimal alignment of lower ribcage. Furthermore, due to the need for occasional physician intervention during the detection process, we implemented mechanisms for pausing and resuming the trajectory learning system to ensure continued operation. Ultimately, the proposed approach was evaluated using the Franka Panda robot system and demonstrated autonomous liver ultrasound examination in scenarios involving multiple human subjects. © 2024 IEEE.

关键词： Stiffness

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Secure RFID-Assisted Authentication Protocol for Vehicular Cloud Computing Environment

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IEEE Transactions on Intelligent Transportation Systems 2024年第9期25卷 12528-12537页

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Tariq, Tayyaba Alenazi, Mohammed J. F. Das, Ashok Kumar University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Institute for Cyber Security School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Sichuan University Key Laboratory of Data Protection and Intelligent Management Ministry of Education Chengdu610065 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518000 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology Yunlin Douliu64002 Taiwan King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500032 India

Vehicular network technology has made substantial advancements in recent years in the field of Intelligent Transportation Systems. Vehicular Cloud Computing (VCC) has emerged as a novel paradigm with a substantial increase in data exchange within Vehicular ad-hoc networks (VANETs). VCC integrates cloud computing, vehicular networking, and Internet of Things (IoT) technologies. It enables Infrastructure-to-Vehicle (I2V), Vehicle-to-Vehicle (V2V), and Vehicle-to-Device (V2D) communication. VCC optimizes vehicle, cloud infrastructure, and IoT resources while addressing significant communication security and vehicle-user privacy challenges. To address these issues, we developed an RFID-based authentication protocol for VCC based on a Henon map using a hash function. In addition, we also incorporated a Physical Unclonable Function (PUF) to resist physical tampering attacks. We validate the protocol's security formally and informally. The formal security analysis is conducted through a widely used RoR model. We use the Scyther simulation tool to verify the proposed protocol's security against various attacks. Moreover, we compare the performance of our protocol with similar existing protocols across important performance parameters such as communication and computation overheads and security attributes. The proposed protocol yields substantial improvements, demonstrating a 40.74% reduction in computation overhead and a 13.03% decrease in communication overhead as compared to related protocols, delivering both enhanced performance and resource efficiency. The analysis demonstrates its capacity to support secure communication in the VCC environment and satisfy desirable security attributes. © 2024 IEEE.

关键词： Authentication

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Inferring the Effects of Protein Variants on Protein-Protein Interactions with Interpretable Transformer Representations

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Research 2024年第2期2023卷 473-489页

作者： Zhe Liu Wei Qian Wenxiang Cai Weichen Song Weidi Wang Dhruba Tara Maharjan Wenhong Cheng Jue Chen Han Wang Dong Xu Guan Ning Lin Shanghai Mental Health Center Shanghai Jiao Tong University School of MedicineSchool of Biomedical EngineeringShanghai Jiao Tong UniversityShanghaiChina Shanghai Key Laboratory of Psychotic Disorders ShanghaiChina School of Information Science and Technology Institute of Computational BiologyNortheast Normal UniversityChangchunChina Department of Electrical Engineering and Computer Science University of MissouriColumbiaMO 65211USA Christopher S.Bond Life Sciences Center University of MissouriColumbiaMO 65211USA

Identifying pathogenetic variants and inferring their impact on protein-protein interactions sheds light on their functional consequences on *** by the availability of experimental data on the consequences of protein interaction,most existing methods focus on building models to predict changes in protein binding ***,we introduced MIPPI,an end-to-end,interpretable transformer-based deep learning model that learns features directly from sequences by leveraging the interaction data from *** was specifically trained to determine the types of variant impact(increasing,decreasing,disrupting,and no effect)on protein-protein *** demonstrate the accuracy of MIPPI and provide interpretation through the analysis of learned attention weights,which exhibit correlations with the amino acids interacting with the ***,we showed the practicality of MIPPI in prioritizing de novo mutations associated with complex neurodevelopmental disorders and the potential to determine the pathogenic and driving ***,we experimentally validated the functional impact of several variants identified in patients with such ***,MIPPI emerges as a versatile,robust,and interpretable model,capable of effectively predicting mutation impacts on protein-protein interactions and facilitating the discovery of clinically actionable variants.

关键词： consequences weights versatile

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VMCTE:Visualization-BasedMalware Classification Using Transfer and Ensemble Learning

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computers, Materials & Continua 2023年第5期75卷 4445-4465页

作者： Zhiguo Chen Jiabing Cao Engineering Research Center of Digital Forensics Ministry of EducationNanjing University of Information Science and TechnologyNanjing210044China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China

The Corona Virus Disease 2019(COVID-19)effect has made telecommuting and remote learning the *** growing number of Internet-connected devices provides cyber attackers with more attack *** development of malware by criminals also incorporates a number of sophisticated obfuscation techniques,making it difficult to classify and detect malware using conventional ***,this paper proposes a novel visualization-based malware classification system using transfer and ensemble learning(VMCTE).VMCTE has a strong anti-interference *** if malware uses obfuscation,fuzzing,encryption,and other techniques to evade detection,it can be accurately classified into its corresponding malware *** traditional dynamic and static analysis techniques,VMCTE does not require either reverse engineering or the aid of domain expert *** proposed classification system combines three strong deep convolutional neural networks(ResNet50,MobilenetV1,and MobilenetV2)as feature extractors,lessens the dimension of the extracted features using principal component analysis,and employs a support vector machine to establish the classification *** semantic representations of malware images can be extracted using various convolutional neural network(CNN)architectures,obtaining higher-quality features than traditional *** fine-tuned and non-fine-tuned classification models based on transfer learning can greatly enhance the capacity to classify various families *** experimental findings on the Malimg dataset demonstrate that VMCTE can attain 99.64%,99.64%,99.66%,and 99.64%accuracy,F1-score,precision,and recall,respectively.

关键词： Malware classification ensemble learning visualization transfer learning

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Research on Machine Unlearning Verification Based on Predictive Cross-Entropy 10

Research on Machine Unlearning Verification Based on Predict...

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10th IEEE Smart World Congress, SWC 2024

作者： Yue, Xinghui Peng, Jiale Liu, Meiqi He, Haitao Zhang, Yuqing YanShan University & National Computer Network Intrusion Prevention Center University of Chinese Academy of Sciences School of Information Science and Engineering Hebei China YanShan University School of Information Science and Engineering Hebei China YanShan University & National Computer Network Intrusion Prevention Center University of Chinese Academy of Sciences School of Information Science and Engineering Hebei Beijing China

ISBN: (纸本)9798331520861

The General Data Protection Regulation (GDPR), implemented by the European Union in 2018, set a global standard for personal data privacy protection. Among its requirements is the 'right to be forgotten' which mandates that service providers must delete a user's personal data upon the user's request to withdraw data authorization. However, deep learning networks tend to retain learned information, making them susceptible to privacy attacks. While several methods exist for achieving machine unlearning to enforce the right to be forgotten, verifying the effectiveness of machine unlearning remains challenging. In this paper, we propose the metric 'Percentage of Unlearning' (PoU), which measures the extent of machine unlearning through the change in predictive cross-entropy of the data intended to be forgotten during the unlearning process. We introduce a machine unlearning verification framework based on predictive cross-entropy, enabling users to self-verify whether their data has been deleted without solely relying on the service providers' assurances. This framework also assists service providers in ensuring complete machine unlearning while minimizing time and computational costs. We compared the PoU metric with existing verification metrics such as ACC and MIA recall. Experimental results demonstrate that, compared to existing verification methods, the PoU metric not only confirms the effectiveness of machine unlearning but also addresses the shortcomings of existing metrics by quantifying the degree of unlearning. © 2024 IEEE.

关键词： Differential privacy

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Optimizing Steering Angle Predictive Convolutional Neural Network for Autonomous Car

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computers, Materials & Continua 2022年第5期71卷 2285-2302页

作者： Hajira Saleem Faisal Riaz Asadullah Shaikh Khairan Rajab Adel Rajab Muhammad Akram Mana Saleh Al Reshan Control Automotive and Robotics Lab Affiliated lab of National Center of Robotics and Automation(NCRA HEC Pakistan)and with the Department of Computer Science and Information TechnologyMirpur University of Science and Technology(MUST)Mirpur Azad Kashmir10250Pakistan College of Computer Science and Information System Najran UniversityNajran61441Saudi Arabia College of Computer Science and Engineering University of South FloridaTampa33620United States

Deep learning techniques,particularly convolutional neural networks(CNNs),have exhibited remarkable performance in solving visionrelated problems,especially in unpredictable,dynamic,and challenging *** autonomous vehicles,imitation-learning-based steering angle prediction is viable due to the visual imagery comprehension of *** this regard,globally,researchers are currently focusing on the architectural design and optimization of the hyperparameters of CNNs to achieve the best *** has proven the superiority of metaheuristic algorithms over the manual-tuning of ***,to the best of our knowledge,these techniques are yet to be applied to address the problem of imitationlearning-based steering angle ***,in this study,we examine the application of the bat algorithm and particle swarm optimization algorithm for the optimization of the CNN model and its hyperparameters,which are employed to solve the steering angle prediction *** validate the performance of each hyperparameters’set and architectural parameters’set,we utilized the Udacity steering angle dataset and obtained the best results at the following hyperparameter set:optimizer,Adagrad;learning rate,0.0052;and nonlinear activation function,exponential linear *** per our findings,we determined that the deep learning models show better results but require more training epochs and time as compared to shallower *** show the superiority of our approach in optimizing CNNs through metaheuristic algorithms as compared with the manual-tuning *** testing was also performed using the model trained with the optimal architecture,which we developed using our approach.

关键词： Bat algorithm convolutional neural network hyperparameters metaheuristic optimization algorithm steering angle prediction

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Broadband single-polarization single-mode low confinement loss hollow core anti-resonant fiber with semi-elliptical nested tube

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Optical and Quantum Electronics 2025年第4期57卷 1-12页

作者： Lin, Wen Fei, Yihong Wang, Ning Jia, Hongzhi Engineering Research Center of Optical Instruments and Systems Ministry of Education Shanghai Key Laboratory of Modern Optical Systems School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

In this paper, a hollow-core anti-resonant optical fibre containing a semi-elliptical nested tube is proposed, which has the characteristics of single-polarization, large bandwidth, single-mode and low confinement loss. By optimizing the structural design, the polarization extinction ratio (PER) of the fiber reaches 21,0183 at a wavelength of 1.55 μm, and the y-polarized fundamental mode loss is only 0.00086 dB/m. By changing the material and thickness of the semi-ellipsoidal nested tube, a large bandwidth of 280 nm was achieved in the wavelength range of 1.51-1.78 μm, with loss limited to less than 0.001 dB/m within the bandwidth range. The PER and higher-order mode extinction ratio of 4870, 1487, respectively. The loss remains below 0.01 dB/m when the bending radius exceeds 4.5 cm. Therefore, it is expected that this optical fiber can be used in optical devices such as fiber optic sensors, fiber optic lasers and fiber optic gyroscopes. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Single mode fibers

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Radiative cooling for passive thermal management towards sustainable carbon neutrality

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National science Review 2023年第1期10卷 87-97页

作者： Jun Liang Jiawei Wu Jun Guo Huagen Li Xianjun Zhou Sheng Liang Cheng-Wei Qiu Guangming Tao Wuhan National Laboratory for Optoelectronics and Optics Valley Laboratory Huazhong University of Science and Technology Department of Electrical and Computer Engineering National University of Singapore MOE Key Laboratory of Thermo-Fluid Science and Engineering School of Energy and Power Engineering Xi'an Jiaotong University Key Laboratory of Education Ministry on Luminescence and Optical Information Technology National Physical Experiment Teaching Demonstration CenterDepartment of PhysicsSchool of Physical Science and Engineering Beijing Jiaotong University State Key Laboratory of Material Processing and Die &Mould Technology School of Materials Science and Engineering Huazhong University of Science and Technology

Photonic structures at the wavelength scale offer innovative energy solutions for a wide range of applications,from high-efficiency photovoltaics to passive cooling,thus reshaping the global energy *** cooling based on structural and material design presents new opportunities for sustainable carbon neutrality as a zero-energy,ecologically friendly cooling *** this review,in addition to introducing the fundamentals of the basic theory of radiative cooling technology,typical radiative cooling materials alongside their cooling effects over recent years are summarized and the current research status of radiative cooling materials is outlined and ***,technical challenges and potential advancements for radiative cooling are forecast with an outline of future application scenarios and development *** the future,radiative cooling is expected to make a significant contribution to global energy saving and emission reduction.

关键词： photonic structures radiative cooling energy saving and emission reduction carbon neutrality

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BASIL:Binary Anchor-Based Smart Indoor Localization

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Tsinghua science and Technology 2025年第1期30卷 1-17页

作者： Zhe Yang Yanjun Li Yufan Zhang Yun Pan Chung Shue Chen Yi-hua Zhu School of Computer Science and Technology Zhejiang University of TechnologyHangzhou 310023China College of Information Science&Electronic Engineering Zhejiang UniversityHangzhou 310027China Nokia Bell Labs Paris-Saclay CenterMassy 91300France

Indoor localization has been challenging research due to the invalidity of the global navigation satellite system in indoor *** advances in ambient assistive living have shown great power in detecting and locating persons living in their homes,especially using the ON/OFF binary *** this paper,we exploit the Bluetooth low-energy beacons as device-based binary anchors under the lowest transmission power to turn any indoor activity and facility interaction into a binary location *** binary anchors are fused with an extended Kalman filter based pedestrian dead-reckoning using a factor graph optimization,with extra constraints including the normalized magnetic loop closure which is optimized using an attenuation factor,and a rapidly-exploring random tree-based map collision *** proposed system provides a cost-effective,scalable,and robust localization for common indoor *** experimental results show an effective sub-meter precision for the long-term trajectories,and a small amount of anchors is enough for significant calibration in large scenarios.

关键词： indoor localization sensor fusion graph optimization binary anchor loop closure

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A Comprehensive Dataset of Real-scene Images for Text Detection and Recognition in Persian 14

A Comprehensive Dataset of Real-scene Images for Text Detect...

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14th International Conference on computer and Knowledge engineering, ICCKE 2024

作者： Souzanchi, Iman Rahimi, Ramin Anvari, Mohammad Ali Majidi Baniasadi, Atefeh Sadeghi, Ashkan Mohammadi, Mohammad Reza PART AI Research Center Tehran Iran School of Computer Engineering Iran University of Science and Technology Tehran Iran

ISBN: (纸本)9798331511272

— Extracting text from scene images is a widely utilized field owing to the abundance of information available in scene images and their potential utilization in computer vision applications such as self-driving cars, text translation, information extraction from invoices, shopfronts, license plate retrieval, etc. Nonetheless, this field presents challenges because of the varying fonts, styles, sizes, and other characteristics of the text. Despite the existence of numerous studies on scene text recognition for languages such as English that employ deep learning models, a major barrier to implementing these models in Persian is the lack of an appropriate and sufficient dataset both in terms of quantity and quality. This paper aims to introduce a comprehensive collection of Persian scene images obtained from diverse sources, including newspapers, magazines, books, business cards, road signs, advertising billboards, shopfronts, invoices, and scanned documents. This dataset comprises over 250k annotated text lines from 5000 images, including various lengths, fonts, and sizes that have been prepared under different conditions, including varying brightness and viewing angles. Additionally, more than 2,500,000 images of meaningful sentences have been synthesized since the annotation of real data is so expensive. In order to assess the efficacy of our dataset, a scene text recognition model was trained from existing models, and a word accuracy of 83.9% was achieved on challenging test images. ©2024 IEEE.

关键词： Image annotation

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