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arXiv 2024年

作者： Li, Na Zhou, Chunyi Gao, Yansong Chen, Hui Fu, Anmin Zhang, Zhi Yu, Shui School of Cyberspace Science and Engineering Nanjing University of Science and Technology Nanjing210094 China State Key Laboratory of Integrated Services Networks Xidian University Xi’an710071 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Data61 CSIRO CanberraACT2601 Australia Department of Computer Science and Software Engineering University of Western Australia PerthWA6009 Australia School of Computer Science University of Technology Sydney SydneyNSW2007 Australia

Personal digital data is a critical asset, and governments worldwide have enforced laws and regulations to protect data privacy. Data users have been endowed with the 'right to be forgotten' of their data. In the course of machine learning (ML), the forgotten right requires a model provider to delete user data and its subsequent impact on ML models upon user requests. Machine unlearning emerges to address this, which has garnered ever-increasing attention from both industry and academia. While the area has developed rapidly, there is a lack of comprehensive surveys to capture the latest advancements. Recognizing this shortage, we conduct an extensive exploration to map the landscape of machine unlearning including the (fine-grained) taxonomy of unlearning algorithms under centralized and distributed settings, debate on approximate unlearning, verification and evaluation metrics, challenges and solutions for unlearning under different applications, as well as attacks targeting machine unlearning. The survey concludes by outlining potential directions for future research, hoping to serve as a guide for interested scholars. Copyright © 2024, The Authors. All rights reserved.

关键词： Taxonomies

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Research on Seedling Grading of Platycladus Orientalis Based on Fuzzy C-Means Clustering Algorithm Improved by Genetic Sparrow Family Search Optimization

SSRN

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SSRN 2023年

作者： Liu, Yang Zhou, Juan Wang, Juan Di, Bao Wang, Chao Ren, ZhenHui College of Mechanical and Electrical Engineering Hebei Agricultural University Baoding071001 China Department of Computer Application Engineering Hebei Software Institute Baoding071000 China College of Horticulture Hebei Agricultural University Baoding071001 China College of Information Science and Technology Hebei Agricultural University Baoding071001 China

Platycladus orientalis has high ecological and economic value, but the quality of Platycladus orientalis seedlings varies greatly. The original quality grading standard is relatively simple and the grading boundary is not clear. It is of great significance to use the in-situ Electrical Impedance Spectroscopy (EIS) technique for nondestructive testing of Platycladus orientalis roots and the improved fuzzy C-means clustering algorithm for scientific and accurate grading of Platycladus orientalis seedlings combined with morphological *** a result, five parameters were found reflecting the quality of Platycladus orientalis seedlings, including phase angle at 4000Hz and tangent value at 400kHz, which could reflect the quality of roots. An Fuzzy C-means (FCM) clustering algorithm improved by genetic sparrow family search optimization (GSFS-FCM) was proposed. Through the test of standard functions and typical datasets, it was found that the number of clusters of the algorithm could be determined automatically. Although the running time is increased, the number of iterations is reduced by more than 12%, and the clustering error rate is reduced by more than 8%. Finally, eight months old Platycladus orientalis seedlings were divided into six grades with the GSFS-FCM *** the present study it can be concluded that among the Platycladus orientalis seedlings root EIS parameters measured by the instrument in situ, phase angle at 4000Hz and tangent value at 400kHz can be used to represent the quality of the roots. GSFS-FCM algorithm can effectively and automatically grading the seedlings of Platycladus orientalis without specifying the number of clusters. The algorithm has fewer iterations and higher accuracy. © 2023, The Authors. All rights reserved.

关键词： Grading

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FireXnet: an explainable AI-based tailored deep learning model for wildfire detection on resource-constrained devices

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Fire Ecology 2023年第1期19卷 1-19页

作者： Ahmad, Khubab Khan, Muhammad Shahbaz Ahmed, Fawad Driss, Maha Boulila, Wadii Alazeb, Abdulwahab Alsulami, Mohammad Alshehri, Mohammed S. Ghadi, Yazeed Yasin Ahmad, Jawad Faculty of Engineering and Technology Multimedia University Melaka Malaysia School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh UK Department of Cyber Security Pakistan Navy Engineering College NUST Karachi Pakistan Robotics and Internet of Things Laboratory Prince Sultan University Riyadh Saudi Arabia RIADI Laboratory National School of Computer Sciences University of Manouba Manouba Tunisia Department of Computer Science College of Computer Science and Information Systems Najran University Najran Saudi Arabia Department of Software Engineering Computer Science Al Ain University Abu Dhabi United Arab Emirates

Forests cover nearly one-third of the Earth’s land and are some of our most biodiverse ecosystems. Due to climate change, these essential habitats are endangered by increasing wildfires. Wildfires are not just a risk to the environment, but they also pose public health risks. Given these issues, there is an indispensable need for efficient and early detection methods. Conventional detection approaches fall short due to spatial limitations and manual feature engineering, which calls for the exploration and development of data-driven deep learning solutions. This paper, in this regard, proposes 'FireXnet', a tailored deep learning model designed for improved efficiency and accuracy in wildfire detection. FireXnet is tailored to have a lightweight architecture that exhibits high accuracy with significantly less training and testing time. It contains considerably reduced trainable and non-trainable parameters, which makes it suitable for resource-constrained devices. To make the FireXnet model visually explainable and trustable, a powerful explainable artificial intelligence (AI) tool, SHAP (SHapley Additive exPlanations) has been incorporated. It interprets FireXnet’s decisions by computing the contribution of each feature to the prediction. Furthermore, the performance of FireXnet is compared against five pre-trained models — VGG16, InceptionResNetV2, InceptionV3, DenseNet201, and MobileNetV2 — to benchmark its efficiency. For a fair comparison, transfer learning and fine-tuning have been applied to the aforementioned models to retrain the models on our dataset. The test accuracy of the proposed FireXnet model is 98.42%, which is greater than all other models used for comparison. Furthermore, results of reliability parameters confirm the model’s reliability, i.e., a confidence interval of [0.97, 1.00] validates the certainty of the proposed model’s estimates and a Cohen’s kappa coefficient of 0.98 proves that decisions of FireXnet are in considerable accordance with t

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Robustness Assessment of Hyperspectral Image Cnns Using Metamorphic Testing

SSRN

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SSRN 2022年

作者： Bouchoucha, Rached Ben Braiek, Houssem Khomh, Foutse Bouzidi, Sonia Zaatour, Rania Department of Computer Engineering and Software Engineering Polytechnique MontrealQCH3T 1J4 Canada Department of Computer Engineering and Software Engineering National Institute of Applied Science and Technology Tunis1080 Tunisia

Remote sensing has proven its utility in many critical domains, such as medicine, military, and ecology. Recently, we have been witnessing a surge in the adoption of deep learning (DL) techniques by the remote sensing community. DL-based classifiers, such as convolutional neural networks (CNNs), have been reported to achieve impressive predictive performances reaching 99\% of accuracy when applied to hyperspectral images (HSIs), a high-dimensional type of remote sensing data. However, these deep learners are known to be highly sensitive to even slight perturbations of their high-dimensional raw inputs. In real-world contexts, concerns can be raised about how robust they really are against corner-case scenarios. When HSI classifiers are applied in safety-critical applications, ensuring an adequate level of robustness is crucial to prevent unexpected system behaviors. Yet, there are few studies dealing with their robustness, nor are RGB-testing methods able to cover the HSI-specific challenges. This led us to propose a systematic testing method to assess the robustness of the CNNs trained to classify HSIs. First, we elaborate domain-specific metamorphic transformations that simulate naturally-occurring distortions of remote sensing HSIs. Then, we leverage metaheuristic search algorithms to optimize the fitness of synthetically-distorted inputs to stress the weaknesses of the on-testing CNN, while remaining in compliance with domain expert requirements, in order to preserve the semantic of the generated inputs. Relying on our metamorphic testing method, we assess the robustness of established and novel CNNs for HSI classification, and demonstrate their failure, on average, in 25\% of the produced test cases. Furthermore, we fine-tuned the tested CNNs on training data augmented with these failure-revealing metamorphic transformations. Results show that the fined-tuning successfully fixed at least 90\% of the CNN weaknesses, with less than 1\% of degradation in the origi

关键词： Remote sensing

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Optimizing CDN Architectures: Multi-Metric Algorithmic Breakthroughs for Edge and Distributed Performance

arXiv

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arXiv 2024年

作者： Absur, Md. Nurul Saha, Sourya Nova, Sifat Nawrin Nasif, Kazi Fahim Ahmad Nasib, Md. Rahat Ul Department of Computer Science City University of New York New York United States Department of Computer Science Chalmers University of Technology Gothenburg Sweden College of Computing and Software Engineering Kennesaw State University Georgia United States Samsung Austin Research Center AustinTX United States

A Content Delivery Network (CDN) is a powerful system of distributed caching servers that aims to accelerate content delivery, like high-definition video, IoT applications, and ultra-low-latency services, efficiently and with fast velocity. This has become of paramount importance in the post-pandemic era. Challenges arise when exponential content volume growth and scalability across different geographic locations are required. This paper investigates data-driven evaluations of CDN algorithms in dynamic server selection for latency reduction, bandwidth throttling for efficient resource management, real-time Round Trip Time analysis for adaptive routing, and programmatic network delay simulation to emulate various conditions. Key performance metrics, such as round-trip time (RTT) and CPU usage, are carefully analyzed to evaluate scalability and algorithmic efficiency through two experimental setups: a constrained edge-like local system and a scalable FABRIC testbed. The statistical validation of RTT trends, alongside CPU utilization, is presented in the results. The optimization process reveals significant trade-offs between scalability and resource consumption, providing actionable insights for effectively deploying and enhancing CDN algorithms in edge and distributed computing environments. © 2024, CC BY-NC-ND.

关键词： Scalability

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Robustface Recognition by Fusing Fuzzy Type 2 Induced Multiple Facial Fused Image 4

Robustface Recognition by Fusing Fuzzy Type 2 Induced Multip...

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4th IEEE International Conference on Computing, Power and Communication Technologies, GUCON 2021

作者： Ghosh, Manas Dey, Aniruddha Shaw, Rabindra Nath Ghosh, Ankush RCCIIT Department of Master of Computer Application Kolkata India MAKAUT Department of Information Technology Kolkata India Galgotias University Department of EC and Communication Engineering India School of Engineering and Applied Science Neotia University Kolkata India

ISBN: (纸本)9781728199511

This paper projects a new decision making model for face recognition from the original image fused with their true and partial diagonal images by integrating the type-2 fuzzy set based approach to mitigate the factors that pretend the face recognition accuracy. The G2DFLD based feature vectors corresponding to a test image are given input to neural network based classifier trained with the feature vectors of the fused images to generate the merit weights with respect to different classes (subjects) under consideration. A new scheme has been introduced in the present approach to generate a score by employing a fuzzy type-2 set based treatment. These scores with respect to each of the classes under consideration are rendered from the feature vectors of the test image and those of the diagonally fused training samples. For each class, the score is fused weighted by the corresponding merit weights to generate the concluding score. These class-wise concluding scores are deliberated in recognizing the test face image. Faces from the well-known databases (gallery) with varied pose, illumination and occlusion are used to evaluate the performance of the model. It has been found that our model exhibits more accurate classification performance than existing similar kind of image level fusion method. © 2021 IEEE.

关键词： Face recognition

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Deep Global Distance Estimation Hashing for Image Retrieval

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IEEE Transactions on Big Data 2025年

作者： Xu, Mengfei Luo, Bowen Xu, Jianfeng Ding, Feng The School of Software Nanchang University Jiangxi330047 China Department of Computer Science University of Regina ReginaSKS4S 0A2 Canada Nanchang Key Laboratory for Innovative Application of Information Technology China

Hashing technology has an excellent high dimensional visual information encoding ability. It can map distance information from high dimensional to low dimensional Hamming space, which is widely used in large-scale image nearest neighbor search tasks. Most recent studies, such as pair-wise and triplet-wise, use local positional relations to construct hashing functions. These learning methods, which are only based on local information, will lead to an incoherent feature representation, especially for semantic similarity features. Therefore, we propose a global distance estimation hashing (DEH). The DEH establishes a deep hashing model by constructing global distance relations that can define a distance between every paired category. Thus the distance relations constructed by DEH include much more detailed information. In other words, DEH focuses on global distance relations and makes the division of boundaries have excellent performance, especially for those samples with subtle changes. After the mapping, the model quantization approach effectively decreases the quantization loss caused by transitioning from the real-valued space to the Hamming space. Numerous experiments show that our DEH achieves excellent results on CIFAR-10, NUS-WIDE and ImageNet. These achievements not only validate the effectiveness of DEH in general image hashing retrieval tasks but also demonstrate its significant advantages in handling the large-scale multimedia retrieval systems. © 2015 IEEE. All rights reserved.

关键词： Content based retrieval

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Dependency-Aware Hybrid Task Offloading in Mobile Edge Computing Networks 27

Dependency-Aware Hybrid Task Offloading in Mobile Edge Compu...

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27th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2021

作者： Ming, Zhao Li, Xiuhua Sun, Chuan Fan, Qilin Wang, Xiaofei Leung, Victor C. M. School of Big Data and Software Engineering Chongqing University Chongqing China TKLAN College of Intelligence and Computing Tianjin University Tianjin China College of Computer Science and Software Engineering Shenzhen University Shenzhen China The University of British Columbia Department of Electrical and Computer Engineering Vancouver Canada

ISBN: (纸本)9781665408783

With the rapid increase of data in mobile edge computing (MEC) networks, mobile devices (MDs) have been generating many computation-latency-sensitive tasks. As the MDs are limited by resources in terms of storage, computation, and bandwidth, part of tasks have to be offloaded to the edge of mobile networks or the remote cloud for more efficient processing. Hence, task offloading plays a vital role in this scene. Existing works about task offloading mainly aim at one-shot task offloading and rarely consider the dependencies of tasks. In this paper, we focus on minimizing the maximum delay of processing a series of tasks with dependencies in MEC networks, which supports device-to-device communications. Specifically, we consider task offloading under a hybrid scenario with a small base station (SBS) deployed with an edge server (ES) and several MDs which generate several tasks with dependencies. Then we model the tasks to a weighted directed acyclic graph (DAG) and formulate the optimization problem as minimizing the critical path of the weighted DAG. To tackle this NP-hard problem, we propose a heuristic scheme to iteratively optimize the delay of paths of the weighted DAG under the constraints of the ES. To evaluate the proposed scheme, we perform numerical experiments with different numbers of tasks. Simulation results demonstrate that the proposed scheme outperforms other schemes in terms of reducing the system delay and saving the energy consumption of the MDs. © 2021 IEEE.

关键词： Optimization

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Incremental Instance Segmentation for Cluttered Baggage Threat Detection

Incremental Instance Segmentation for Cluttered Baggage Thre...

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IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and applications

作者： Ammara Nasim Divya Velayudhan Abdelfatah Hassan Ahmed Taimur Hassan Samet Akcay Muhammad Usman Akram Naoufel Werghi Department of Computer and Software Engineering National University of Sciences and Technology Pakistan Department of Electrical Engineering and Computer Science KUCARS and C2PS Khalifa University UAE Department of Electrical and Computer Engineering Abu Dhabi University Abu Dhabi UAE R&D Division Intel Corporation UK

Identification of contraband items from highly oc-cluded baggage of air travelers is a challenging task even for human experts with very high experience. Many researchers have been working rigorously to develop computer vision-based techniques for baggage screening through X-ray images. Nu-merous machine learning and deep learning-based frameworks have been proposed by researchers in the last two decades. However, all of these techniques face limitations in segmenting prohibited items from highly occluded and cluttered baggage. In this paper, we propose a novel framework based on semantic segmentation to automatically detect concealed prohibited items from X-ray baggage scans. Furthermore, to detect different overlapping instances of the same contraband item, we propose an instance-aware segmentation model that enables the semantic segmentation model to identify multiple instances of the same threat category through incremental learning without requiring additional overhead. The proposed framework is computationally lighter compared to other similar approaches as it requires min-imal training examples and leverages previous knowledge. The proposed model has outperformed state-of-the-art instance seg-mentation techniques when tested on publicly available GDXray and SIXray datasets, giving mean average precision scores of 0.50 and 0.47, respectively. In addition, the proposed framework leads other instance segmentation baseline models in terms of mean inference time.

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RAT: A Refactoring-Aware Traceability Model for Bug Localization

RAT: A Refactoring-Aware Traceability Model for Bug Localiza...

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International Conference on software engineering (ICSE)

作者： Feifei Niu Wesley K. G. Assunção LiGuo Huang Christoph Mayr-Dorn Jidong Ge Bin Luo Alexander Egyed State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Institute for Software Systems Engineering Johannes Kepler University Linz Austria Department of Computer Science and Engineering Southern Methodist University Dallas Texas USA

A large number of bug reports are created during the evolution of a software system. Locating the source code files that need to be changed in order to fix these bugs is a challenging task. Information retrieval-based bug localization techniques do so by correlating bug reports with historical information about the source code (e.g., previously resolved bug reports, commit logs). These techniques have shown to be efficient and easy to use. However, one flaw that is nearly omnipresent in all these techniques is that they ignore code refactorings. Code refactorings are common during software system evolution, but from the perspective of typical version control systems, they break the code history. For example, a class when renamed then appears as two separate classes with separate histories. Obviously, this is a problem that affects any technique that leverages code history. This paper proposes a refactoring-aware traceability model to keep track of the code evolution history. With this model, we reconstruct the code history by analyzing the impact of code refactorings to correctly stitch together what would otherwise be a fragmented history. To demonstrate that a refactoring aware history is indeed beneficial, we investigated three widely adopted bug localization techniques that make use of code history, which are important components in existing approaches. Our evaluation on 11 open source projects shows that taking code refactorings into account significantly improves the results of these bug localization techniques without significant changes to the techniques themselves. The more refactorings are used in a project, the stronger the benefit we observed. Based on our findings, we believe that much of the state of the art leveraging code history should benefit from our work.

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