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Measuring code maintainability with deep neural networks

Frontiers of computer science 2023年第6期17卷 61-75页

作者： Yamin HU Hao JIANG Zongyao HU School of Computer Science and Technology Beijing Institute of TechnologyBeijing 100081China School of Artificial Intelligence Anhui UniversityHefei 230601China

The maintainability of source code is a key quality characteristic for software *** approaches have been proposed to quantitatively measure code *** approaches rely heavily on code metrics,e.g.,the number of Lines of Code and McCabe’s Cyclomatic *** employed code metrics are essentially statistics regarding code elements,e.g.,the numbers of tokens,lines,references,and branch ***,natural language in source code,especially identifiers,is rarely exploited by such *** a result,replacing meaningful identifiers with nonsense tokens would not significantly influence their outputs,although the replacement should have significantly reduced code *** this end,in this paper,we propose a novel approach(called DeepM)to measure code maintainability by exploiting the lexical semantics of text in source *** leverages deep learning techniques(e.g.,LSTM and attention mechanism)to exploit these lexical semantics in measuring code *** key rationale of DeepM is that measuring code maintainability is complex and often far beyond the capabilities of statistics or simple ***,DeepM leverages deep learning techniques to automatically select useful features from complex and lengthy inputs and to construct a complex mapping(rather than simple heuristics)from the input to the output(code maintainability index).DeepM is evaluated on a manually-assessed *** evaluation results suggest that DeepM is accurate,and it generates the same rankings of code maintainability as those of experienced programmers on 87.5%of manually ranked pairs of Java classes.

关键词： code maintainability lexical semantics deep learning neural networks

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Abnormal event detection in surveillance videos through LSTM auto-encoding and local minima assistance

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Discover Internet of Things 2025年第1期5卷 1-25页

作者： Sengonul, Erkan Samet, Refik Abu Al-Haija, Qasem Alqahtani, Ali Alsemmeari, Rayan A. Alghamdi, Bandar Alturki, Badraddin Alsulami, Abdulaziz A. Department of Computer Engineering Ankara University Ankara06100 Turkey Department of Cybersecurity Faculty of Computer & Information Technology Jordan University of Science and Technology PO Box 3030 Irbid22110 Jordan Department of Networks & Communications Engineering College of Computer Science & Information Systems Najran University Najran61441 Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

Abnormal event detection in video surveillance is critical for security, traffic management, and industrial monitoring applications. This paper introduces an innovative methodology for anomaly detection in video data, encompassing three primary stages: preprocessing, feature learning, and anomaly detection. We employ background subtraction and noise reduction during preprocessing to refine the data. The feature-learning stage involves training an LSTM autoencoder to capture the essential features of normal video sequences. For anomaly detection, we map video data to a lower-dimensional space (latent code) and compare it against the distribution of codes from normal sequences. We determine regularity scores and identify local minima points exceeding a specified threshold while scrutinizing shadows between adjacent maxima to confirm and pinpoint anomalies. When tested on the CUHK Avenue and UMN datasets, our methodology demonstrated performance with AUCs of 93.8% and 94.1%, respectively, outperforming several baseline models. Our results show the high precision of our method that can detect anomalies, highlighting its potential advantages that it can achieve for enhancing systems of surveillance. © The Author(s) 2025.

关键词： Noise abatement

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Identity-Preserving Adversarial Training for Robust Network Embedding

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Journal of computer science & technology 2024年第1期39卷 177-191页

作者：岑科廷沈华伟曹婍徐冰冰程学旗 Data Intelligence System Research Center Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 101480China Beijing Academy of Artificial Intelligence Beijing 100000China Chinese Academy of Sciences Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China

Network embedding,as an approach to learning low-dimensional representations of nodes,has been proved extremely useful in many applications,e.g.,node classification and link ***,existing network embed-ding models are vulnerable to random or adversarial perturbations,which may degrade the performance of network em-bedding when being applied to downstream *** achieve robust network embedding,researchers introduce adversari-al training to regularize the embedding learning process by training on a mixture of adversarial examples and original ***,existing methods generate adversarial examples heuristically,failing to guarantee the imperceptibility of generated adversarial examples,and thus limit the power of adversarial *** this paper,we propose a novel method Identity-Preserving Adversarial Training(IPAT)for network embedding,which generates imperceptible adversarial exam-ples with explicit identity-preserving *** formalize such identity-preserving regularization as a multi-class classification problem where each node represents a class,and we encourage each adversarial example to be discriminated as the class of its original *** experimental results on real-world datasets demonstrate that our proposed IPAT method significantly improves the robustness of network embedding models and the generalization of the learned node representations on various downstream tasks.

关键词： network embedding identity-preserving adversarial training adversarial the example

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Geometric-like imaginarity:Quantification and state conversion

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science China(Physics,Mechanics & Astronomy) 2025年第2期68卷 13-22页

作者： Meng-Li Guo Bo Li Shao-Ming Fei School of Science East China University of TechnologyNanchang 330006China School of Computer and Computing Science Hangzhou City University Hangzhou 310015China School of Mathematical Sciences Capital Normal UniversityBeijing 100048China

From the perspective of resource-theoretic approach,this study explores the quantification of imaginary in quantum *** propose a well defined measure of imaginarity,the geometric-like measure of *** with the usual geometric imaginarity measure,this geometric-like measure of imaginarity exhibits smaller decay difference under quantum noisy channels and higher *** applications,we show that both the optimal probability of state transformations from a pure state to an arbitrary mixed state via real operations,and the maximal probability of stochastic-approximate state transformations from a pure state to an arbitrary mixed state via real operations with a given fidelity f,are given by the geometric-like measure of imaginarity.

关键词： quantum resource theory imaginarity geometric-like channels state transformations

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Automated breast nuclei feature extraction for segmentation in histopathology images using Deep-CNN-based gaussian mixture model and color optimization technique

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Multimedia Tools and Applications 2025年 1-27页

作者： Murmu, Anita Kumar, Piyush Computer Science and Engineering National Institute of Technology Patna Ashok Rajpath Bihar Patna800005 India

Hematoxylin and eosin (H&E) staining are the key sources for identifying breast cancer patterns with different colors and shapes of nuclei cells for segmenting histopathology nucleus images. In nucleus cells, the color categorization of hematoxylin is stained blue, whereas cytoplasmic cells of eosin are stained pink. This color dissimilarity in the nucleus cells indicates different structures of the cellular and cancer-specific cells, which need to detect on time is a challenge for accurate diagnosis. To overcome this issue, a Deep Convolution Neural Network (Deep-CNN) based Unet framework with Transfer Learning (TL) is proposed for segmenting histopathology breast cancer images. Specifically, the Deep-CNN framework has been used to extract features automatically from input images, and select the features by using the TL technique for minimizing the target tasks to train it faster. Furthermore, a deep convolutional-based Gaussian Mixture Model (GMM) is used to normalize the shapes and merge the nucleus cell pixels for color optimization. The proposed method has been tested on Kaggle’s 2018 Data science Blow histopathology breast image dataset. The experimental results of the proposed scheme have been evaluated by performance evaluation matrices, namely accuracy, Jaccard coefficient, Dice Similarity Coefficient (DSC), Intersection over Union (IoU), and F1-score. The proposed approach outperforms compared with different state-of-the-art models on the same dataset. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolution

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An anonymous authentication and secure data transmission scheme for the Internet of Things based on blockchain

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Frontiers of computer science 2024年第3期18卷 183-197页

作者： Xingxing CHEN Qingfeng CHENG Weidong YANG Xiangyang LUO State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Institute of Information Science and TechnologyZhengzhou 450001China Henan Key Laboratory of Grain Photoelectric Detection and Control Henan University of TechnologyZhengzhou 450001China Henan Province Key Laboratory of Cyberspace Situation Awareness Zhengzhou Institute of Information Science and TechnologyZhengzhou 450001China

With the widespread use of network infrastructures such as 5G and low-power wide-area networks,a large number of the Internet of Things(IoT)device nodes are connected to the network,generating massive amounts of ***,it is a great challenge to achieve anonymous authentication of IoT nodes and secure data *** present,blockchain technology is widely used in authentication and s data storage due to its decentralization and ***,Fan et *** a secure and efficient blockchain-based IoT authentication and data sharing *** studied it as one of the state-of-the-art protocols and found that this scheme does not consider the resistance to ephemeral secret compromise attacks and the anonymity of IoT *** overcome these security flaws,this paper proposes an enhanced authentication and data transmission scheme,which is verified by formal security proofs and informal security ***,Scyther is applied to prove the security of the proposed ***,it is demonstrated that the proposed scheme achieves better performance in terms of communication and computational cost compared to other related schemes.

关键词： Internet of Things blockchain authentication data transmission

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ZeroMark: Towards Dataset Ownership Verification without Disclosing Watermarks 38

ZeroMark: Towards Dataset Ownership Verification without Dis...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Guo, Junfeng Li, Yiming Chen, Ruibo Wu, Yihan Liu, Chenxi Huang, Heng Department of Computer Science Institute of Health Computing University of Maryland College Park United States College of Computing and Data Science Nanyang Technology University Singapore

High-quality public datasets significantly prompt the prosperity of deep neural networks (DNNs). Currently, dataset ownership verification (DOV), which consists of dataset watermarking and ownership verification, is the only feasible solution to protect their copyright by preventing unauthorized use. In this paper, we revisit existing DOV methods and find that they all mainly focused on the first stage by designing different types of dataset watermarks and directly exploiting watermarked samples as the verification samples for ownership verification. As such, their success relies on an underlying assumption that verification is a onetime and privacy-preserving process, which does not necessarily hold in practice. To alleviate this problem, we propose ZeroMark to conduct ownership verification without disclosing dataset-specified watermarks. Our method is inspired by our empirical and theoretical findings of the intrinsic property of DNNs trained on the watermarked dataset. Specifically, ZeroMark first generates the closest boundary version of given benign samples and calculates their boundary gradients under the label-only black-box setting. After that, it examines whether the given suspicious method has been trained on the protected dataset by performing a hypothesis test, based on the cosine similarity measured on the boundary gradients and the watermark pattern. Extensive experiments on benchmark datasets verify the effectiveness of our ZeroMark and its resistance to potential adaptive attacks. The codes for reproducing our main experiments are publicly available at GitHub. © 2024 Neural information processing systems foundation. All rights reserved.

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Leveraging machine learning for intelligent agriculture

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Discover Internet of Things 2025年第1期5卷 1-21页

作者： Sowmya, B.J. Meeradevi, A.K. Supreeth, S. Pradeep Kumar, D. Ravi Kumar, B.N. Rohith, S. Mishra, Divyansh Koushik, Abhishek Patil, Ankit U. Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru560054 India Department of Artificial Intelligence and Machine Learning M S Ramaiah Institute of Technology Bengaluru560054 India School of Computer Science and Engineering REVA University Bengaluru560064 India Department of Computer Science Ramaiah Institute of Technology Bengaluru560054 India Department of Information Science and Engineering BMS Institute of Technology & Management Bengaluru560119 India Department of Electronics and Engineering Nagarjuna College of Engineering and Technology Bengaluru562164 India

Agriculture, the backbone of many economies, faces challenges like lack of information, outdated practices, and limited access to technology, hindering farmer productivity. This work proposes a user-friendly, multilingual platform leveraging Generative AI to address farmers' diverse needs. The platform encompasses various features to enhance agricultural practices. An LLM-powered Government Scheme Advisor functions as a multilingual chatbot offering intelligent guidance on government agricultural schemes and subsidies. The Disease Detection module utilizes AI technology for real-time identification and treatment recommendations, minimizing crop diseases and yield losses. The Soil Testing Centre feature locates nearby soil testing centers, providing essential information based on geographical data to assist farmers in optimizing soil quality. A Crop Recommendation feature employs Machine Learning algorithms to offer personalized crop recommendations, considering various factors and aiding informed decision-making. The Crop Planning Tool, with its intuitive user interface, simplifies planning planting schedules and managing resources. Additionally, the platform includes an MSP Center Locator to find nearby Minimum Support Price (MSP) centers based on location. By integrating these innovative solutions, this platform bridges the gap between conventional agricultural techniques and contemporary technology, equipping farmers with the resources and expertise essential for advancing productivity and sustainability. Multilingual support ensures accessibility for a wider audience, breaking down language barriers and promoting inclusivity in the agricultural sector. This work proposes an innovative, multilingual platform powered by Generative AI to address these issues. Key features include an LLM-driven chatbot for government scheme guidance, AI-based real-time disease detection, and location-based tools for soil testing and MSP center identification. Additionally, the platf

关键词： Smart agriculture

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A privacy and compliance in regulated anonymous payment system based on blockchain

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Journal of Ambient Intelligence and Humanized computing 2024年第8期15卷 3141-3157页

作者： Elfadul, Issameldeen Wu, Lijun Elhabob, Rashad Elkhalil, Ahmed School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China School of Computer and Information Engineering Qilu Institute of Technology Shandong Jinan250200 China Faculty of Computer Science and Information Technology Karary University Khartoum Sudan

Decentralized Anonymous Payment Systems (DAP), often known as cryptocurrencies, stand out as some of the most innovative and successful applications on the blockchain. These systems have garnered significant attention in the financial industry due to their highly secure and reliable features. Regrettably, the DAP system can be exploited to fund illegal activities such as drug dealing and terrorism. Therefore, governments are increasingly worried about the illicit use of DAP systems, which poses a critical threat to their security. This paper proposes Privacy and Compliance in Regulated Anonymous Payment System Based on Blockchain (PCRAP), which provides government supervision and enforces regulations over transactions without sacrificing the essential idea of the blockchain, that is, without surrendering transaction privacy or anonymity of the participants. The key characteristic of the proposed scheme is using a ring signature and stealth address to ensure the anonymity of both the sender and receiver of the transaction. Moreover, a Merkle Tree is used to guarantee government supervision and enforce regulations. Our proposed scheme satisfies most of the stringent security requirements and complies with the standards of secure payment systems. Additionally, while our work supports government regulations and supervision, it guarantees unconditional anonymity for users. Furthermore, the performance analysis demonstrates that our suggested scheme still remains applicable and effective even when achieving complete anonymity. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Blockchain

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CLASSIFICATION OF DIABETES USING ENSEMBLE MACHINE LEARNING TECHNIQUES

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Scalable computing 2024年第4期25卷 3172-3180页

作者： Ashisha, G.R. Mary, X Anitha Raja, J Mahimai Electronics and Instrumentation Engineering Karunya Institute of Technology and Sciences Coimbatore India Robotics Engineering Karunya Institute of Technology and Sciences Coimbatore India Computer Science Engineering Karunya Institute of Technology and Sciences Coimbatore India

Diabetes is a widespread chronic condition that impacts people all over the globe and requires a clear and timely diagnosis. Untreated diabetes leads to retinopathy, nephropathy, and damage to the nervous system. In this context, Machine Learning (ML) might be used to detect health problems early, diagnose them, and track their progress. Ensemble techniques are a promising approach that combines many classifiers to improve forecast accuracy and resilience. This study investigates the categorization of diabetes using an ensemble machine learning technique known as a voting classifier. Using a variety of classifiers, including Light Gradient Boosting Machine (LightGBM), Gradient Boost classifier (GBC), and Random Forest (RF). The predictions are aggregated using voting methods to get a final classification result. The research is carried out using two benchmarking datasets: the Pima Indian Diabetes Dataset (PIDD) and the German Dataset. The Boruta technique is used to choose the best attributes from the datasets, while the Random Over Sampling approach balances the range of classes and eliminates abnormal data using the interquartile range approach. The findings showed that the combination of the Boruta feature selection algorithm and ensemble Voting Classifier performed better for both PIDD and German datasets with an accuracy of 93% and 90% respectively. These algorithms are evaluated and the maximum accuracy is produced using the combination of the Boruta feature selection algorithm and ensemble Voting Classifier. This research helps medical professionals in the early prediction of diabetes, reducing physician’s time. © 2024 SCPE.

关键词： Forecasting

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