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Relay-projection microscopic telescopy

Light(science & Applications) 2025年第4期14卷 1143-1155页

作者： Wenjun Yi Shuyue Zhu Meicheng Fu Nan Gu JunliQi Siyu Liu Mengjun Zhu Ping Wang Xin Chen Yi Zhang Hongyu Zhang Yao Xu Junyi Du Peng Xiong Zhaohua Dong Luobing Dong Qiong Liu Xiujian Li College of Science National University of Defense TechnologyChangshaChina Xi’an Satellite Control Center Xi’anChina School of Information and Electronics Engineering Hunan City UniversityYiyangChina College of Electronic Science National University of Defense TechnologyChangshaChina The School of Computer Science and Technology Xidian UniversityXi’anChina Senior Department of Otolaryngology Head and Neck Surgery the 6th Medical Center of Chinese PLA General HospitalChinese PLA Medical SchoolBeijingChina

The fundamental trade-off between spatial resolution and imaging distance poses a significant challenge for current imaging techniques,such as those used in modern biomedical diagnosis and remote ***,we introduce a new conceptual method for imaging dynamic amplitude-phase-mixed objects,termed relay-projection microscopic telescopy(rPMT),which fundamentally challenges conventional light collection techniques by employing non-line-ofsight light collection through square-law relay-projection *** successfully resolved tiny features measuring 2.76μm,22.10μm,and 35.08μm for objects positioned at distances of 1019.0 mm,26.4 m,and 96.0 m,respectively,from single-shot spatial power spectrum images captured on the relay screen;these results demonstrate that the resolution capabilities of rPMT significantly surpass the Abbe diffraction limit of the 25 mm-aperture camera lens at the respective distances,achieving resolution improvement factors of 7.9,25.4,and *** rPMT exhibits long-distance,wide-range,high-resolution imaging capabilities that exceed the diffraction limit of the camera lens and the focusing range limit,even when the objects are obscured by a scattering *** rPMT enables telescopic imaging from centimeters to beyond hundreds of meters with micrometer-scale resolution using simple devices,including a laser diode,a portable camera,and a diffusely reflecting *** contemporary high-resolution imaging techniques,our method does not require labeling reagents,wavefront modulation,synthetic receive aperture,or ptychography scanning,which significantly reduce the complexity of the imaging system and enhance the application *** method holds particular promise for in-vivo label-free dynamic biomedical microscopic imaging diagnosis and remote surveillance of small objects.

关键词： light collection techniques Imaging Distance Non Line Sight Relay Projection Microscopic Telescopy remote sensingherewe imaging techniquessuch biomedical diagnosis Spatial Resolution

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IoT-Based Healthcare Monitoring System 1

IoT-Based Healthcare Monitoring System

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1st International Conference on Emerging Technologies for Dependable Internet of Things, ICETI 2024

作者： Al-Hadi, Marwa Abdulrahman Al-Gaphari, Ghaleb Hamoud Al-Baltah, Ibrahim Ahmed Julian, Francisco Bacallao Al-Hadi, Ahmed Abdulrahman Department of Computer Science Faculty of Computer Science and IT Sana’a University Sana’a Yemen Department of Information Technology Faculty of Computer Science and IT Sana’a University Sana’a Yemen Faculty of Science and Engineering Al-Hikma University Sana’a Yemen Cardiac-Center Military Hospital Yemen Department of Science and Technologies Faculty of Engineering and Technology Mohamed Boudiaf University M'sila Algeria Department of Industrial Electronic and Automatic Engineering Faculty of Higher Polytechnic Technology Burgos University Burgos Spain

ISBN: (纸本)9798331533557

In an era when healthcare was becoming increasingly crucial, many developing nations, including Yemen, struggled to provide basic medical services. Nearly half of Yemen's population lacked access to adequate healthcare, with the situation being even more dire in rural and remote areas. While sectors such as industry, agriculture, and environmental science rapidly embraced technological advancements, healthcare systems in these regions lagged, with limited research addressing affordable and accessible solutions. This gap was further highlighted by the global healthcare crisis and the urgent need for innovative, cost-effective approaches. In response, this study outlined the creation and deployment of a cost-effective health monitoring system utilizing Internet of Things (IoT) technology to address these challenges. The system was designed to monitor vital signs, specifically heart rate (HR), using an Arduino Uno and ECG sensor, at a meager total cost of $27.25. It provided real-time monitoring for situations requiring immediate intervention. Power consumption tests demonstrated that the system operated efficiently, consuming between 1.061 and 1.35 watts, making it practical and affordable. Additionally, the potential integration of deep learning techniques promises to enhance the system's accuracy and efficiency. Although this study focused on IoT-based health monitoring, its potential extended beyond this, offering broader implications for future healthcare technology solutions. © 2024 IEEE.

关键词： Electronic health record

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A Data Consistency Insurance Method for Smart Contract

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computers, Materials & Continua 2023年第9期76卷 3783-3795页

作者： Jing Deng Xiaofei Xing Guoqiang Deng Ning Hu Shen Su Le Wang Md Zakirul Alam Bhuiyan School of Computer Science and Cyber Engineering Guangzhou UniversityGuangzhou510006China Information Network Engineering and Reasearch Center South China University of TechnologyGuangzhou510640China Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhou510006China Department of Computer and Information Sciences Fordham UniversityNew York10458USA

As one of the major threats to the current DeFi(Decentralized Finance)ecosystem,reentrant attack induces data inconsistency of the victim smart contract,enabling attackers to steal on-chain assets from DeFi projects,which could terribly do harm to the confidence of the blockchain ***,protecting DeFi projects from the reentrant attack is very difficult,since generating a call loop within the highly automatic DeFi ecosystem could be very *** researchers mainly focus on the detection of the reentrant vulnerabilities in the code testing,and no method could promise the non-existent of reentrant *** this paper,we introduce the database lock mechanism to isolate the correlated smart contract states from other operations in the same contract,so that we can prevent the attackers from abusing the inconsistent smart contract *** to the existing resolutions of front-running,code audit,andmodifier,our method guarantees protection resultswith better *** we further evaluate our method on a number of de facto reentrant attacks observed from *** results prove that our method could efficiently prevent the reentrant attack with less running cost.

关键词： Blockchain smart contract data consistency reentrancy attack

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A Deep Graph Convolutional Network based Web API Classification Approach for Mashup Creation 25

A Deep Graph Convolutional Network based Web API Classificat...

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25th IEEE International Conferences on High Performance Computing and Communications, 9th International Conference on Data science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Qi, Lianyong Yan, Boyuan Zheng, Jiaqi Xu, Xiaolong Dou, Wanchun Zhou, Xiaokang Zhang, Jie School of Computer Science Qufu Normal University China China Nanjing University Department of Computer Science and Technology China School of Computer and Software Nanjing University of Information Science and Technology China Shiga University Faculty of Data Science Japan Riken Center for Advanced Intelligence Project Japan

ISBN: (纸本)9798350330014

Software developers create mashups of their interest by searching, selecting, and combining web Application Programming Interfaces (APIs) with diverse functionalities. However, achieving the selection of APIs relies on a crucial prerequisite: accurate classification of APIs. However, the traditional web API classification methods are often based on a single piece of API function's information such as manual annotations and description documents which decreases the accuracy of API classification results significantly, especially for the web API sharing communities with sparse data (e.g., ***). To tackle this issue, we propose BDGCN which improves some existing web API classification approaches by adding isomorphic graph of strong correlation among different web APIs on the basis of API description documents. Concretely, at the first, we integrate the information of multiple dimensions of web APIs as embedded inputs in which we then build an API-API strongly correlated isomorphic network to strengthen the association among API individuals, and to initialize the embedding vector from the description of each API by tuning the pre-trained Bidi-rectional Encoder Representation from Transformers (BERT) model. Besides, in order to smooth the impact of high-order features between neighboring nodes, a deep graph convolutional network is introduced in this paper that is consistent with the problem. In fact, we set up a categorization layer and utilize cross-entropy function to achieve accurate classification of APIs. Experimental results demonstrate the superiority of our method compared to the existing methods. © 2023 IEEE.

关键词： Classification (of information)

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Using Knowledge Graph Embeddings in Embedding Based Recommender Systems 20

Using Knowledge Graph Embeddings in Embedding Based Recommen...

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20th International Conference on Language Engineering, ESOLEC 2022

作者： Ragab, Ahmed Hussein El-Kafrawy, Passant School of Information Technology and Computer Science Computer Science Department Nile University Cairo Egypt School of Information Technology and Computer Science CIS Research Center Nile University Computer Science Department Egypt Effat Collage of Engineering Jedah Saudi Arabia

ISBN: (纸本)9781665453226

This paper proposes using entity2rec [1] which utilizes knowledge graph-based embeddings (node2vec) instead of traditional embedding layers in embedding based recommender systems. This opens the door to increasing the accuracy of some of the most implemented recommender systems running in production in many companies by just replacing the traditional embedding layer with node2vec graph embedding without the risk of completely migrating to newer SOTA systems and risking unexpected performance issues. Also, Graph embeddings will be able to incorporate user and item features which can help in solving the well-known Cold start problem in recommender systems. Both embedding methods are compared on the movie-Lens 100-K dataset in an item-item collaborative filtering recommender and we show that the suggested replacement improves the representation learning of the embedding layer by adding a semantic layer that can increase the overall performance of the normal embedding based recommenders. First, normal Recommender systems are introduced, and a brief explanation of both traditional and graph-based embeddings is presented. Then, the proposed approach is presented along with related work. Finally, results are presented along with future work. © 2022 IEEE.

关键词： Recommender systems

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Pyneapple-L: Scalable Expressive Learning-based Spatial Analysis 24

Pyneapple-L: Scalable Expressive Learning-based Spatial Anal...

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32nd ACM SIGSPATIAL International Conference on Advances in Geographic information Systems, ACM SIGSPATIAL 2024

作者： Liu, Yongyi Lee, Nicolas Kang, Yunfan Shahneh, Mohammad Reza Mahmood, Ahmed Chinnam, Vishal Rohith Sarawadekar, Aparna Vivek Oymak, Samet Sabek, Ibrahim Magdy, Amr Department of Computer Science and Engineering University of California Riverside United States Center for Geospatial Sciences University of California Riverside United States Google LLC United States Thomas Lord Department of Computer Science University of Southern California United States Department of Geography & Geographic Information Science University of Illinois Urbana-Champaign United States Electrical Engineering and Computer Science Department University of Michigan-Ann Arbor United States

ISBN: (纸本)9798400711077

This paper demonstrates Pyneapple-L, an open-source library designed to enhance scalable spatial analysis through learning-based techniques. Through collaboration with social scientists and domain experts, we identify scalability challenges inherent in conventional spatial analysis methods, particularly as the data size increases. Pyneapple-L addresses these challenges by leveraging learning-based models to offer scalable solutions. We demonstrate two modules: scalable learning of spatial hotspots along spatial networks and augmented geographically weighted regression. To showcase Pyneapple-L, we have developed a user-friendly frontend web application to interact with different datasets, algorithms, model configurations, and visualize outcomes on interactive maps that support both broad and analytical views. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Genre Classification of Bangla Poem Using Machine Learning and Deep Learning Techniques

Genre Classification of Bangla Poem Using Machine Learning a...

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2023 IEEE World AI IoT Congress, AIIoT 2023

作者： Pasha, Syed Tangim Islam, Ashraful Rahman, Mohammed Masudur Ahmed, Eshtiak Ahmed Foysal, Md. Ferdouse Zahangir Alam, Md Daffodil International University Department of Computing and Information System Bangladesh Independent University Center for Computational and Data Sciences Bangladesh Independent University Department of Computer Science and Engineering Bangladesh International Standard University Department of Computer Science and Engineering Bangladesh Daffodil International University Department of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350337617

The computational analysis of the Bangla poems is a challenging task due to the diverse linguistic, stylistic, and semantic features of the Bangla language. In this work, we prepared a dataset of 1311 Bangla poems of two separate categories: Love and Miscellaneous poem, which contain 500 and 811 poems respectively. We used word or semantic-based features to classify Bangla poems using the TF-IDF feature techniques. We used Logistic Regression, Naïve Bayes (NB), and Support Vector Machine (SVM) models for classification through machine learning, and we used Bayesian optimization techniques for hyperparameters tuning of these three models. We also used LSTM, CNN, and transformer models for this research. For the performance evaluation of the classification models, we used four evaluation metrics of precision, recall, F1-score, and accuracy. We also used the ROC-AUC curve to distinguish between all the machine learning and deep learning models. The experimental results expressed that, the transformer model achieved the highest accuracy compared to all the typical machine learning and deep learning models with an accuracy of 87%. © 2023 IEEE.

关键词： Semantics

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Automatic Bowel Preparation Assessment Using Deep Learning 26th

Automatic Bowel Preparation Assessment Using Deep Learning

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26th International Conference on Pattern Recognition, ICPR 2022

作者： Haithami, Mahmood Salah Ahmed, Amr Liao, Iman Yi Altulea, Hamid Jalab School of Computer Science University of Nottingham Semenyih Malaysia School of Computer Science University of Nottingham Nottingham United Kingdom Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia Information and Communication Technology Research Group Scientific Research Center Al-Ayen University Dhi Qar Iraq

ISBN: (纸本)9783031376597

Bowel preparation is considered a critical step in colonoscopy. Manual bowel preparation assessment is time consuming and prone to human errors and biases. Automatic Bowel evaluation using machine/deep learning is a better and efficient alternative. Most of the relevant literature have focused on achieving high validation accuracy, where private handy-picked dataset does not reflect real-environment situation. Furthermore, treating a video dataset as a collection of individual frames may produce overestimated results. This is due to the fact a video contains nearly identical consecutive frames, hence, dividing them into training and validation sets yields two similar distributed datasets. Given a public dataset, Nerthus, we show empirically a significant drop in performance when a video dataset is treated as a collection of videos (depicting the real environment/context), instead of a collection of individual frames. We propose a model that utilizes both sequence and none-sequence (spatial) information within videos. The proposed model achieved on average 83% validation accuracy across 4 validation sets, whereas, the state-of-the-art models achieved on average a range of 66%–72% validation accuracy. © 2023, Springer Nature Switzerland AG.

关键词： Deep learning

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Model Checking for Probabilistic Multiagent Systems

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Journal of computer science & Technology 2023年第5期38卷 1162-1186页

作者：付辰 Andrea Turrini 黄小炜宋磊冯元张立军 State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China Institute of Intelligent Software Guangzhou 511455China Department of Computer Science University of LiverpoolLiverpool L693BXU.K Microsoft Research Beijing 100190China Centre for Quantum Software and Information University of Technology SydneySydney 2007Australia

In multiagent systems,agents usually do not have complete information of the whole system,which makes the analysis of such systems *** incompleteness of information is normally modelled by means of accessibility relations,and the schedulers consistent with such relations are called *** this paper,we consider probabilistic multiagent systems with accessibility relations and focus on the model checking problem with respect to the probabilistic epistemic temporal logic,which can specify both temporal and epistemic ***,the problem is undecidable in *** show that it becomes decidable when restricted to memoryless uniform ***,we present two algorithms for this case:one reduces the model checking problem into a mixed integer non-linear programming(MINLP)problem,which can then be solved by Satisfiability Modulo Theories(SMT)solvers,and the other is an approximate algorithm based on the upper confidence bounds applied to trees(UCT)algorithm,which can return a result whenever *** algorithms have been implemented in an existing model checker and then validated on *** experimental results show the efficiency and extendability of these algorithms,and the algorithm based on UCT outperforms the one based on MINLP in most cases.

关键词： probabilistic multiagent system model checking uniform scheduler probabilistic epistemic temporal logic

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Unveiling vulnerable smart contracts:Toward profiling vulnerable smart contracts using genetic algorithm and generating benchmark dataset

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Blockchain(Research and Applications) 2024年第1期5卷 58-84页

作者： Sepideh HajiHosseinKhani Arash Habibi Lashkari Ali Mizani Oskui Computer Science Department of Computer Science and EngineeringYork UniversityTorontoOntario M3J1P3Canada Behavior-Centric Cybersecurity Center(BCCC) School of Information TechnologyYork UniversityTorontoOntario M3J1P3Canada Financial and Crypto Advisory of Switzerland(FiCAS AG) ZugSwitzerland

Smart contracts(SCs)are crucial in maintaining trust within blockchain ***,existing methods for analyzing SC vulnerabilities often lack accuracy and effectiveness,while approaches based on Deep Neural Networks(DNNs)struggle with detecting complex vulnerabilities due to limited data *** paper proposes a novel approach for analyzing SC *** method leverages an advanced form of the Genetic Algorithm(GA)and includes the development of a comprehensive benchmark dataset consisting of 36,670 Solidity source code *** primary objective of our study is to profile vulnerable SCs *** achieve this goal,we have devised an analyzer called SCsVulLyzer based on GAs,designed explicitly for profiling ***,we have carefully curated a new dataset encompassing a wide range of examples,ensuring the practical validation of our ***,we have established three distinct taxonomies that cover SCs,profiling techniques,and feature *** taxonomies provide a systematic classification and analysis of information,improving the efficiency of our *** methodology underwent rigorous testing through experimentation,and the results demonstrated the superior capabilities of our model in detecting *** to traditional and DNN-based approaches,our approach achieved higher precision,recall,and F1-score,which are widely used metrics for evaluating model *** all these metrics,our model showed exceptional *** customization and adaptations we implemented within the GA significantly enhanced its *** approach detects SC vulnerabilities more efficiently and facilitates robust *** promising results highlight the potential of GA-based profiling to improve the detection of SC vulnerabilities,contributing to enhanced security in blockchain networks.

关键词： Smart contracts(SCs) Vulnerability Vulnerable smart contracts Vulnerability profiling Genetic algorithm

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