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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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Detecting Poisoning Attacks in Collaborative IDSs of Vehicular Networks Using XAI and Shapley Value

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ACM Journal on Autonomous Transportation Systems 2024年第3期2卷 1-21页

作者： Ahmed Saleh Bataineh Mohammad Zulkernine Adel Abusitta Talal Halabi School of Computing Queen's University Kingston Canada and Computer Science Al Hussein Technical University Amman Jordan Queen's University Kingston Canada Department of Computer Engineering and Software Engineering Polytechnique Montréal Montreal Canada Laval University Quebec Canada

Machine learning-based Intrusion Detection Systems (IDSs) for vehicle networks can collaborate to enhance their performance by sharing crucial decisions when individual datasets lack diversity, which hinders effective model training. However, such collaborative coalitions are vulnerable to poisoning attacks, where certain members tamper with training data, leading to wrong predictions by the IDSs. The current solutions to this problem have the following issues: (1) They require accessing the training dataset of IDSs, which raises critical privacy concerns; (2) their heavy reliance on voting mechanisms may exclude clients and fail to detect malicious coalition members when attackers form the majority coalition; and (3) the verification process can potentially expose sensitive information, posing privacy risks. To address these issues and improve the resilience of collaborative IDSs against poisoning attacks, we propose a novel approach that combines XAI (Explainable AI) technology with Shapley value from cooperative game theory. XAI justifies IDS decisions, while the Shapley value identifies poisoning attacks in training datasets by capturing contradictions between explanations and decisions. We tested our implementation using a publicly available dataset and various machine learning models (e.g., RFC, SVM, and LSTM). Our results prove highly promising in detecting poisoning attacks and overcoming the flaws in existing solutions.

关键词： XAI shaply value

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Why not Just Look for Answers? Using a More Direct Way for API Recommendation

Why not Just Look for Answers? Using a More Direct Way for A...

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Asia-Pacific Conference on software engineering

作者： Changxin Liu Ling Xu Wenhan Mu Rui Qin Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University) Ministry of Education China School of Big Data and Software Engineering Chongqing University Chongqing China

ISBN: (数字)9798331534011

ISBN: (纸本)9798331534028

Automated API recommendation technology can help developers quickly find target APIs that meet their require-ments. Some retrieval-based API recommendation approaches, such as BIKER and CLEAR, adopt an indirect API recommen-dation strategy. Although these approaches have shown good performance, we observed two limitations in these approaches: (1) These approaches heavily relies on the coverage of the collected question titles proposed by developers; (2) These approaches ignore the API source code information. To overcome the two limitations, we propose an approach named AnsAPIRec (Answer- directed API Recommendation), which leverages a pre-trained model and joint-attention mechanism in this paper. In addition to the fully qualified name of the API, AnsAPIRec extracts the corresponding source code from the JDK as an additional feature. AnsAPIRec adopts a direct API recommendation strategy, which directly calculates the similarity between user queries and API features, i.e., API fully qualified name and API source code, to find the target API, alleviating the reliance on question titles. To further enhance AnsAPIRec's ability to understand the semantics of the inputs, we propose a joint-attention mechanism to learn the interdependent representations between the queries and API features. This deep semantic fusion mechanism enables AnsAPIRec to perform well in understanding user intent and API functionality. We reused the APIBench-Q dataset, containing 5885 queries for training and 653 queries for testing. The experimental result shows that AnsAPIRec achieved an MRR of 0.473, significantly outperforming the baseline approaches, BIKER and CLEAR.

关键词： Training Source coding Semantics Feature extraction Testing software engineering

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

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

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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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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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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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Studying and Recommending Information Highlighting in Stack Overflow Answers

arXiv

引用

arXiv 2024年

作者： Ahmed, Shahla Shaan Wang, Shaowei Tian, Yuan Chen, Tse-Hsun Zhang, Haoxiang Department of Computer Science University of Manitoba Canada School of Computing Queen’s University Canada Department of Computer Science and Software Engineering Concordia University Canada Huawei Canada

Context: Navigating the knowledge of Stack Overflow (SO) remains challenging. To make the posts vivid to users, SO allows users to write and edit posts with Markdown or HTML so that users can leverage various formatting styles (e.g., bold, italic, and code) to highlight the important information. Nonetheless, there have been limited studies on the highlighted information. Objective: We carried out the first large-scale exploratory study on the information highlighted in SO answers in our recent study. To extend our previous study, we develop approaches to automatically recommend highlighted content with formatting styles using neural network architectures initially designed for the Named Entity Recognition task. Method: In this paper, we studied 31,169,429 answers of Stack Overflow. For training recommendation models, we choose CNN-based and BERT-based models for each type of formatting (i.e., Bold, Italic, Code, and Heading) using the information highlighting dataset we collected from SO answers. Results: Our models achieve a precision ranging from 0.50 to 0.72 for different formatting types. It is easier to build a model to recommend Code than other types. Models for text formatting types (i.e., Heading, Bold, and Italic) suffer low recall. Our analysis of failure cases indicates that the majority of the failure cases are due to missing identification. One explanation is that the models are easy to learn the frequent highlighted words while struggling to learn less frequent words (i.g., long-tail knowledge). Conclusion: Our findings suggest that it is possible to develop recommendation models for highlighting information for answers with different formatting styles on Stack Overflow. © 2024, CC BY.

关键词： Recommender systems

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RSED: Zero-Shot Relation Triplet Extraction via Relation Selection and Entity Boundary Detection

RSED: Zero-Shot Relation Triplet Extraction via Relation Sel...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yuquan Lan Dongxu Li Yunqi Zhang Hui Zhao Gang Zhao Software Engineering Institute East China Normal University Shanghai China Shanghai Key Laboratory of Trustworthy Computing Shanghai China China Resources Pharma Comm Beijing China

Zero-shot relation triplet extraction (ZeroRTE) aims to extract relation triplets of unseen relation types from unstructured texts, with a core challenge of training models to recognize new relations without labeled data. The seminal work handles this task by leveraging pre-trained language models to generate synthetic data for new relations, which requires a laborious training procedure. Moreover, the quality of the synthetic data significantly influences the performance. We propose a model named RSED to tackle the ZeroRTE task via Relation Selection and Entity boundary Detection, which considerably simplifies the procedure and is agnostic to the synthetic data. The whole model is composed of three modules. The first relation selection module utilizes a novel relation selection modeling approach to enable the recognition of unseen relations. Such a modelling approach breaks free from the constraints of specific relation types, naturally infuses the relation semantics, and eliminates the need for synthetic data. The second relation filtering module removes irrelevant relations. The third entity boundary detection module extracts multiple entities following a set prediction paradigm. We evaluate our model on two ZeroRTE datasets. The experiment results show that our method consistently outperforms previous works.

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

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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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