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Early Diagnosis of Lung Tumors for Extending Patients’ Life Using Deep Neural Networks

Computers, Materials & Continua 2023年第7期76卷 993-1007页

作者： A.Manju R.Kaladevi Shanmugasundaram Hariharan Shih-Yu Chen Vinay Kukreja Pradip Kumar Sharma Fayez Alqahtani Amr Tolba Jin Wang Department of Computer Science and Engineering SRM Institute of Science and TechnologyRamapuramChennaiIndia Department of Computer Science and Engineering Saveetha Engineering CollegeChennaiIndia Department of Computer Science and Engineering Vardhaman College of EngineeringHyderabadIndia Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin 64002Taiwan Intelligence Recognition Industry Service Research Center National Yunlin University of Science and TechnologyYunlin 64002Taiwan Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjabIndia Department of Computing Science University of AberdeenAberdeenUK Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh 12372Saudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadh 11437Saudi Arabia School of Computer&Communication Engineering Changsha University of Science&TechnologyChangshaChina

The medical community has more concern on lung cancer *** experts’physical segmentation of lung cancers is time-consuming and needs to be *** research study’s objective is to diagnose lung tumors at an early stage to extend the life of humans using deep learning ***-Aided Diagnostic(CAD)system aids in the diagnosis and shortens the time necessary to detect the tumor *** application of Deep Neural Networks(DNN)has also been exhibited as an excellent and effective method in classification and segmentation *** research aims to separate lung cancers from images of Magnetic Resonance Imaging(MRI)with threshold *** Honey hook process categorizes lung cancer based on characteristics retrieved using several *** this principle,the work presents a solution for image compression utilizing a Deep Wave Auto-Encoder(DWAE).The combination of the two approaches significantly reduces the overall size of the feature set required for any future classification process performed using *** proposed DWAE-DNN image classifier is applied to a lung imaging dataset with Radial Basis Function(RBF)*** study reported promising results with an accuracy of 97.34%,whereas using the Decision Tree(DT)classifier has an accuracy of 94.24%.The proposed approach(DWAE-DNN)is found to classify the images with an accuracy of 98.67%,either as malignant or normal *** contrast to the accuracy requirements,the work also uses the benchmark standards like specificity,sensitivity,and precision to evaluate the efficiency of the *** is found from an investigation that the DT classifier provides the maximum performance in the DWAE-DNN depending on the network’s performance on image testing,as shown by the data acquired by the categorizers themselves.

关键词： Lung tumor deep wave auto encoder decision tree classifier deep neural networks extraction techniques

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Improving the Performance of DNN-based software Services using Automated Layer Caching

arXiv

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arXiv 2022年

作者： Abedi, Mohammadamin Ioannou, Yani Jamshidi, Pooyan Hemmati, Hadi Electrical and Software Engineering University of Calgary Canada Computing Science and Engineering University of South Carolina United States

Deep Neural Networks (DNNs) have become an essential component in many application domains including web-based services. A variety of these services require high throughput and (close to) real-time features, for instance, to respond or react to users' requests or to process a stream of incoming data on time. However, the trend in DNN design is toward larger models with many layers and parameters to achieve more accurate results. Although these models are often pre-trained, the computational complexity in such large models can still be relatively significant, hindering low inference latency. Implementing a caching mechanism is a typical systems engineering solution for speeding up a service response time. However, traditional caching is often not suitable for DNN-based services. In this paper, we propose an end-to-end automated solution to improve the performance of DNN-based services in terms of their computational complexity and inference latency. Our caching method adopts the ideas of self-distillation of DNN models and early exits. The proposed solution is an automated online layer caching mechanism that allows early exiting of a large model during inference time if the cache model in one of the early exits is confident enough for final prediction. One of the main contributions of this paper is that we have implemented the idea as an online caching, meaning that the cache models do not need access to training data and perform solely based on the incoming data at run-time, making it suitable for applications using pre-trained models. Our experiments results on two downstream tasks (face and object classification) show that, on average, caching can reduce the computational complexity of those services up to 58% (in terms of FLOPs count) and improve their inference latency up to 46% with low to zero reduction in accuracy. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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Reversible Data Hiding With Secret Encrypted Image Sharing and Adaptive Coding

引用

IEEE Internet of Things Journal 2025年第13期12卷 23933-23945页

作者： Fang, Guangtian Wang, Feng Zhao, Chenbin Qin, Chuan Chang, Ching-Chun Chang, Chin-Chen Fujian University of Technology College of Computer Science and Mathematics Fuzhou350118 China Fujian University of Technology College of Computer Science and Mathematics Key Laboratory of Big Data Mining and Applications Fuzhou350118 China Monash University Faculty of IT Department of Software Systems and Cybersecurity VIC3800 Australia Ministry of Education School of Cyber Science and Engineering Key Laboratory of Aerospace Information Security and Trusted Computing Wuhan430072 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Feng Chia University Information and Communication Security Research Center Taichuang 40724 Taiwan Feng Chia University Department of Information Engineering and Computer Science Taichung40724 Taiwan

To ensure the security of image information and facilitate efficient management in the cloud, the utilization of reversible data hiding in encrypted images (RDHEI) has emerged as pivotal. However, most existing RDHEI schemes suffer from lower security and limited embedding capacity. To tackle these challenges, we propose a reversible data hiding with secret encrypted image sharing and adaptive coding scheme. Specifically, in the encryption phase, we introduce an improved secret sharing (SS) encryption method based on the chinese remainder theorem for polynomials (CRTP). This method not only improves the security of encrypted images but also vacates a larger room for embedding. In the embedding phase, we introduce an adaptive coding embedding approach using XOR preservation (XORP) and huffman coding, which provides high embedding capacity. Experimental results and security analysis demonstrate that our proposed encryption method achieves optimal values in security indicators for encrypted images, such as information entropy, histograms, NPCR and UACI. The proposed embedding method is superior to some state-of-the-art schemes in terms of embedding capacity. Furthermore, in datasets BOSSBase and BOWS2, the average embedding rates of the proposed embedding approach can reach 2.1745 bpp and 2.0656 bpp, respectively. © 2025 IEEE.

关键词： Cryptography Encryption Security Data mining Adaptive coding Correlation Cloud computing Polynomials Image coding Streaming media

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$\mathrm{E}_{\gamma}$-Mixing Time

$\mathrm{E}_{\gamma}$-Mixing Time

引用

IEEE International Symposium on Information Theory

作者： Behnoosh Zamanlooy Shahab Asoodeh Mario Diaz Flavio P. Calmon Department of Computing and Software McMaster University Canada IIMAS Universidad Nacional Autónoma de México Mexico School of Engineering and Applied Sciences Harvard University USA

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

We investigate the mixing times of Markov kernels under $\mathsf{E}_{\gamma}-\mathbf{divergence}$ . We demonstrate that the zero-error $\mathsf{E}_{\gamma}- \mathbf{mixing}$ time, for any $\gamma > 1$ , of irreducible and aperiodic Markov chains, is bounded, a property that is not shared by the TV-mixing time. We further obtain upper bounds on the $\mathsf{E}_{\gamma}-\mathbf{mixing}$ times for a broad family of contractive Markov kernels via a new non-linear strong data processing inequality for the $\mathsf{E}_{\gamma}- \mathbf{divergence}$ . We apply our results to derive new bounds for the local differential privacy guarantees offered by the sequential application of a privacy mechanism to data.

关键词： Differential privacy Upper bound Data processing Kernel Information theory

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Relation-Based Multi-Teacher Knowledge Distillation

Relation-Based Multi-Teacher Knowledge Distillation

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Yutian Wang Yi Xu School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Chengdu China Laboratory of Intelligent Collaborative Computing University of Electronic Science and Technology of China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Traditional knowledge distillation adopts one teacher model to instruct the training of a lightweight student model. To improve the performance of knowledge distillation, multi-teacher knowledge distillation utilizing multi-party knowledge to guide model learning was proposed. However, existing methods mainly use the summation-result of teacher models of a single sample to integrate multi-party knowledge directly, ignoring the relation knowledge among samples. In this paper, we propose a novel multi-teacher knowledge distillation method, which utilizes the data relation knowledge to allocate weights for teachers adaptively. To obtain the data relation knowledge, we design output-based strategy and feature-based strategy, which would help to allocate more weight for the teacher who has better learned the data relation knowledge. Extensive experiments have demonstrated the performance and efficiency of our proposed multi-teacher knowledge distillation method.

关键词： Training Adaptation models Neural networks

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SmartOracle: Generating Smart Contract Oracle via Fine-Grained Invariant Detection

arXiv

引用

arXiv 2024年

作者： Su, Jianzhong Chen, Jiachi Fang, Zhiyuan Lin, Xingwei Tang, Yutian Zheng, Zibin School of Software Engineering Sun Yat-sen University China College of Computer Science and Technology Zhejiang University China School of Computing Science University of Glasgow United Kingdom

As decentralized applications (DApps) proliferate, the increased complexity and usage of smart contracts have heightened their susceptibility to security incidents and financial losses. Although various vulnerability detection tools have been developed to mitigate these issues, they often suffer poor performance in detecting vulnerabilities, as they either rely on simplistic and general-purpose oracles that may be inadequate for vulnerability detection, or require user-specified oracles, which are labor-intensive to create. In this paper, we introduce SmartOracle, a dynamic invariant detector that automatically generates fine-grained invariants as application-specific oracles for vulnerability detection. From historical transactions, SmartOracle uses pattern-based detection and advanced inference to construct comprehensive properties, and mines multi-layer likely invariants to accommodate the complicated contract functionalities. After that, SmartOracle identifies smart contract vulnerabilities by hunting the violated invariants in new transactions. In the field of invariant detection, SmartOracle detects 50% more ERC20 invariants than existing dynamic invariant detection and achieves 96% precision rate. Furthermore, we build a dataset that contains vulnerable contracts from real-world security incidents. SmartOracle successfully detects 466 abnormal transactions with an acceptable precision rate 96%, involving 31 vulnerable contracts. The experimental results demonstrate its effectiveness in detecting smart contract vulnerabilities, especially those related to complicated contract functionalities. Copyright © 2024, The Authors. All rights reserved.

关键词： Smart contract

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Optimization of Memory March X Test Algorithm Based on Nuclear Safety Level DCS System Platform 1

引用

9th International Conference on Energy engineering and Environmental engineering, ICEEEE 2022

作者： Bai, Yongfei Wu, Zhiqiang Liu, Jie Jiang, Wei Zhong, Ke Han, Wenxing Chen, Zhi School of Computer Science University of South China Hengyang421200 China Nuclear Power Institute of China Chengdu610000 China Intelligent Equipment Software Evaluation Engineering Technology Research Center of Hunan Hengyang China CNNC Key Laboratory on High Trusted Computing Hunan Hengyang China

ISBN: (数字)9783031302336

ISBN: (纸本)9783031302329

At present, the testing algorithm of nuclear power safety level DCS system for memory is GALPAT algorithm. This algorithm has a high time complexity and low fault coverage for memory. During the testing process, additional storage space is required, and the security of additional memory cannot be guaranteed. Based on the memory fault testing theory, this paper analyzes and studies the March X algorithm. It is found that the algorithm can not meet the coverage requirements for memory faults when applied to the nuclear power safety level DCS system. A March X+ algorithm is proposed. The algorithm can meet the index requirements of the nuclear power safety level DCS system for memory testing algorithms, and the fault detection rate reaches more than 95%, And it innovatively proposes to save the data to the register during the detection process, which does not require additional memory and improves the security. To verify the feasibility of the algorithm designed in this paper, the algorithm test time and functionality were verified in the main control PBIST module under a nuclear power safety level DCS system platform. The results show that the proposed test algorithm has certain advantages in test time and memory fault coverage. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Nuclear energy

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Risk-Driven Architecture Modeling for Automated Driving Systems with Extended Sysml

SSRN

引用

SSRN 2024年

作者： Li, Hang Du, Dehui Zhao, Jiangnan Nie, Jihui Sun, Xingzhe Software Engineering Institute East China Normal University Shanghai200062 China Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai200062 China

Automated Driving Systems (ADS) is a kind of AI-enabled Cyber Physical Systems (CPS) with AI components such as environment perception and decision-making for execution. As the sophistication of these systems continues to evolve, the imperative of ensuring their safety becomes increasingly critical. However, architecture modeling for ADS is still a challenging problem, especially, how to model the risk and safety during the process of architecture modeling. This necessitates a deeper exploration of modeling methodology tailored to the domain of ADS. To address these issues, we propose a risk-driven architecture modeling approach by extended SysML integrating critical risk and safety concepts from ISO 26262 and ISO 21448 standard in ADS field. Addressing vital dimensions of system architecture, including requirements, system structures, and interactions between components, our approach endeavors to imbue SysML with representations of risk and safety specific to ADS. Essentially, this study leverages Model-Based Systems engineering (MBSE) for ADS, an approach that supports the risk and safety modeling of AI-enabled systems. Moreover, a specific modeling tool prototype has been developed, serving as a practical enabler corresponding to our proposed approach. Notably, the feasibility of our approach has been validated through a case study based on Apollo platform. © 2024, The Authors. All rights reserved.

关键词： Decision making

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Towards Evidence-Based Cybersecurity Assessment of Programmable Systems to Ensure the Protection of Critical IT Infrastructure 12

Towards Evidence-Based Cybersecurity Assessment of Programma...

引用

12th IEEE International Conference on Intelligent Data Acquisition and Advanced computing Systems: Technology and Applications, IDAACS 2023

作者： Illiashenko, Oleg Kharchenko, Vyacheslav Odarushchenko, Oleg National Aerospace University 'KhAI' Department of Computer Systems and Networks Kharkiv61070 Ukraine Institute of Information Science and Technologies 'Alessandro Faedo' - Isti Cnr Software Engineering & Dependable Computing Laboratory Pisa56124 Italy Research and Production Enterprise Rpc Radics Llc Kropyvnytskyi25009 Ukraine

ISBN: (纸本)9798350358056

The paper is devoted to developing scientific principles, methods, means, and information technology of model-oriented verification and evidence-based assessment using functional safety and cybersecurity cases for programmable systems of critical applications (PSCA). In particular, information and control systems important for the safety of nuclear power plants (NPPs), aerospace systems, railway domains, etc., developed using Field Programmable Gate Arrays (FPGAs) and hardware platforms are reviewed. The goal is to ensure the guaranteed completeness and reliability of its functional safety and cybersecurity assessment by developing and implementing a set of formal and semi-formal methods and tools that consider defects of different nature - physical, design, trojans, and vulnerabilities that can be attacked and lead to a fatal system failure, which results in damaging the critical IT infrastructure. The methods shown are based on integrating algebraic, tabular, graph models, and case assessment methodology. The methods are implemented as appropriate technologies for evidence-based verification and evaluation of PSCA. For formal methods, a prototype of a translator of Very High-Speed Integrated Circuits Hardware Description Language (VHDL) code is developed into an algebra of behaviors. It provides evidence-based verification and a framework and tools to design reports on assessing the cybersecurity and functional safety of programmable systems. © 2023 IEEE.

关键词： critical IT infrastructure cybersecurity FPGA functional safety programmable systems of critical applications

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Middle School Architect: Programmatic Modeling and Construction via a 3D Graphics Platform in Elm

Middle School Architect: Programmatic Modeling and Construct...

引用

Emerging Trends in Information Technology and engineering (ic-ETITE), Conference on

作者： Anurag Parcha Sanjana Mishra Christopher Kumar Anand Jagalingam Pushparaj Simon Fraser University Burnaby BC Canada Computing and Software McMaster University Hamilton ON Canada School of Computer Science and Engineering Vellore Institute of Technology Vellore TN India

Today, school students are first introduced to geom-etry and advanced mathematical concepts by plotting a graph in 2D and they struggle with 3D concepts since current technologies are either too difficult to grasp or too simple with only drag-drop functionalities disconnected from coordinates. This article presents a 3D graphics library and an interactive, educational system for visualization of a 3D environment where planks of Cross Laminated Timber (CLT) can be manipulated by geometric and mathematical functions to produce a 3D building. Throughout, there is a focus on functions, 3D geometry and other underlying mathematics, and links are made with the world of computing, specifically computer graphics, to increase students' interest and confidence. Activities built on this library simultaneously meet multiple curricular objectives: mathematics (including 3D geometry), programming (in Elm, a web program-ming language used commercially), and sustainable development (using a negative embodied carbon building material).

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