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23rd IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing Workshops, CCGridW 2023

作者： Beerman, Jack Berent, David Falter, Zach Bhunia, Suman Miami University Department of Computer Science and Software Engineering OxfordOH45056 United States

ISBN: (纸本)9798350302080

In April, 2021 a ransomware attack occurred on Colonial Pipeline. The details of this attack point to the hacking group DarkSide taking advantage of the design flaws in the Colonial Pipeline network. After extensive research, the specificity of this attack was related to VPN access through an unused account. In order to regain control of their systems, Colonial Pipeline paid the attackers. This act has only created an incentive for similar attacks across the nation. The events of this attack have impacted both the United States, where the company is located and the world in a very negative way. This paper analyzes the attack with published data and provides a detailed attack methodology. From the attack methodology the focus then shifts into the impacts that an attack of this caliber had, on both the company, the United States, and the world. We then outline possible defense strategies against this type of ransomware attack, analyzing what could have been done to prevent this attack from happening. In addition, we also detail how companies can prevent future attacks of this caliber. Finally we wrap up or findings and detail the key takeaways of the entire attack. © 2023 IEEE.

关键词： Virtual private networks

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No-reference Image Contrast Quality Assessment based on the Degree of Uniformity in Probability Distribution

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IEIE Transactions on Smart Processing and Computing 2022年第2期11卷 85-91页

作者： Kim, Wonvin Yim, Changhoon Department of Computer Software Korean Bible University Seoul Korea Republic of Department of Computer Science and Engineering Konkuk University Seoul Korea Republic of

We propose a novel contrast-based no-reference image quality assessment (NR-IQA) method. The proposed method quantifies the degree of uniformity in the probability distribution of the pixel values of an image to measure contrast quality without the need for a reference image. To quantify the degree of symmetric uniformity in the distribution of pixel values for image contrast assessment, we divide the intensity levels into left and right ranges recursively at each depth as a binary tree. This symmetric hierarchical recursive calculation of the degree of uniformity for contrast measurement provides a good metric for NR-IQA. Experimental results show that the proposed method is more consistent with the levels of contrast decrement and is superior in assessing contrast-enhanced and dehazed images compared with state-of-the-art and classic NR-IQA methods. The proposed method is more suited to real-time applications due to its lower computational complexity compared with previous methods. © 2022 The Institute of Electronics and Information Engineers.

关键词： Probability distributions

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Optimizing auditory brainstem response detection through NSGA-II guided feature selection

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Neural Computing and Applications 2025年第15期37卷 8925-8943页

作者： Majidpour, Jafar Hassanzadeh, Hiwa Khezri, Edris Arabi, Hossein Department of Software Engineering University of Raparin Kurdistan Region Rania Iraq Department of Computer Science University of Raparin Kurdistan Region Rania Iraq Department of Computer Engineering Islamic Azad University Boukan Branch Boukan Iran Division of Nuclear Medicine and Molecular Imaging Department of Medical Imaging Geneva University Hospital Geneva1211 Switzerland

Hearing-impaired people undergo auditory brainstem response (ABR) testing to assess their peripheral auditory nerve system. Audiologists apply diagnostic labels to ABR data using reference-based indicators such as peak latency, waveform shape, amplitude, and others. ABR test scoring errors may invalidate auditory nerve system integrity results. Machine learning, especially deep learning, may reduce human error in ABR analysis. This work offers a complete methodology to handle ABR analysis problems. First, extract ABR test images from PDF reports to create a dataset. The next step uses Elastic Distortion approach to create high-quality ABR images while conserving data. ResNet-50 deep learning extracts ABR image characteristics in the third phase. The fourth step finds the most relevant features using a trainable multi-objective non-dominated sorting genetic algorithm II optimizer. Finally, ABR images are classified using machine learning algorithms. The proposed model achieved significant performance metrics: an accuracy of 99.46%, precision of 99.33%, specificity of 99.57%, sensitivity of 99.57%, F1-score of 99.43%, and a mean squared error of 0.0054 when using a support vector machine classifier with both augmented and original images. These findings demonstrate the effectiveness of trainable feature selection and high-quality ABR images in improving ABR analysis accuracy. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Mean square error

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Enhancing Ropes for Collaborative Text Editing

Enhancing Ropes for Collaborative Text Editing

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2023 Congress in computer Science, computer engineering, and Applied Computing, CSCE 2023

作者： Sandal, Semih Ovatman, Tolga Aselsan Inc Software Engineering Department Ankara Turkey Istanbul Technical University Department of Computer Engineering Istanbul Turkey

ISBN: (纸本)9798350327595

Rope is a tree-based data structure providing performance enhancements in string concatenation operations in applications such as text editors. Even though collaborative text editing has become one of the major applications in cloud environments, utilizing ropes in collaborative text editing is rarely discussed in current literature. In this study, we enhance the Rope data structure for collaborative text editing. Firstly we discuss possible issues that Ropes may suffer in collaborative text editing and propose enhancements that adapt Ropes to such environments. During this adaptation we pay special attention to keep Ropes' immutable nature for the insert operations and balance the tree by following AVL-tree-style and Red-Black-Tree-style methods. We also provide a performance comparison of those balancing approaches. In our results, it can be seen that the AVL-Rope and Red-Black-Rope are slower in the concatenation operation due to balancing, but faster in index search operation and insert operation than the unbalanced Rope. © 2023 IEEE.

关键词： Balanced trees Collaborative text editing Rope data structure String processing

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Tidal-Tree-Mem: Toward Read-Intensive Key-Value Stores With Tidal Structure Based on LSM-Tree

Tidal-Tree-Mem: Toward Read-Intensive Key-Value Stores With ...

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作者： Ma, Chenlin Yang, Hao Wu, Shangyu Wang, Yi Mao, Rui Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China City University of Hong Kong Department of Computer Science Hong Kong Shenzhen University College of Computer Science and Software Engineering The Shenzhen Institute of Computing Sciences Shenzhen518060 China

The log-structured merge-tree (LSM-tree)-based key-value store has been widely adopted by many large-scale data storage applications for its excellent write performance. However, such write performance gains mainly come from scarifying read performance due to the leveled and log-structured intrinsic characteristics of the LSM-tree. Therefore, the critical challenge of the existing LSM-tree is how to improve the read efficiency by reducing read amplification. This article for the first time proposes Tidal-tree-Mem, a novel data structure where data flow inside the LSM-tree-like Tidal waves. First, a floating strategy is proposed to allow frequently accessed files at the bottom of the LSM-tree to move to higher positions, reducing read amplification. Second, a stretching strategy is proposed to vary the shape of the LSM-tree to adapt to workloads with different characteristics. To evaluate the performance of Tidal-tree-Mem, we conduct a series of experiments using standard benchmarks from YCSB. The experimental results show that Tidal-tree-Mem can effectively reduce read amplification and the overall latency by over 71.94% and 47.34%, respectively, compared with representative schemes. © 1982-2012 IEEE.

关键词： Benchmarking

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A Vulnerability Accessibility Analysis Framework for software Supply Chain 10

A Vulnerability Accessibility Analysis Framework for Softwar...

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10th International Symposium on System Security, Safety, and Reliability, ISSSR 2024

作者： Xiao, Lei Gao, Zhenyu Xu, Qizhen Chang, Le Zhu, Shunzhi Xiamen University of Technology College of Computer and Information Engineering Xiamen361024 China Software Security Company Chengdu610041 China

ISBN: (纸本)9798350362930

The security issues brought by third-party components in software are becoming increasingly prominent. However, current component analysis tools only analyze the existence of the vulnerabilities introduced by components, resulting in much false positives. In this paper, we present a vulnerability accessibility analysis framework to analyze whether these vulnerabilities would be actually called by the project. © 2024 IEEE.

关键词： Supply chains

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Parameter-Efficient Federated Cooperative Learning for 3D Object Detection in Autonomous Driving

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IEEE Internet of Things Journal 2025年第12期12卷 20314-20325页

作者： Chi, Fangyuan Wang, Yixiao Nasiopoulos, Panos Leung, Victor C.M. University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada Toronto AI Lab LG Electronics TorontoONM5V 1M3 Canada Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China

In the rapidly evolving field of autonomous driving, accurately detecting and understanding dynamic environments remains a challenge. Federated learning (FL) offers a promising approach by integrating decentralized models from multiple Connected Autonomous Vehicles (CAVs) to enhance the performance of Deep Learning (DL) based object detection methods. However, traditional FL faces hurdles such as extensive data synchronization requirements, limited data variance, and high communication costs. This paper introduces a federated cooperative learning framework that addresses these challenges by combining data from both CAVs and Roadside Units (RSUs). The framework combines local cooperative perception with global federated learning through a parameter-efficient federated learning adapter (PEFLA) and a lazy communication (LazyComm) strategy, improving DL-based object detection capabilities across diverse driving scenarios while significantly reducing bandwidth requirements. We also present a novel multi-agent-multi-town dataset V2X-Fed, specifically developed to validate the effectiveness of our approach under various conditions. Notably, our framework retains 97.51% of the detection accuracy achieved by full-model federated learning, while utilizing only 1.4% of the bandwidth typically required, demonstrating substantial improvements over conventional federated learning strategies. This study underscores the potential of our tailored approach to substantially enhance autonomous vehicle technologies with minimal resource utilization. © 2014 IEEE.

关键词： Bandwidth

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MR Image Quality Assessment via Enhanced Mamba: A Hybrid Spatial-Frequency Approach

MR Image Quality Assessment via Enhanced Mamba: A Hybrid Spa...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Lou, Yiwei Xu, Dexuan Zhang, Rongchao Zhang, Jiayu Cao, Yongzhi Wang, Hanpin Huang, Yu Key Laboratory of High Confidence Software Technologies Peking University Ministry of Education China Peking University School of Computer Science Beijing China Peking University School of Software and Microelectronics Beijing China Peking University National Engineering Research Center for Software Engineering Beijing China

ISBN: (纸本)9798350386226

Magnetic resonance (MR) image quality assessment plays a crucial role in disease diagnosis and data analysis. Existing methods typically treat the data as images and process them with convolutional networks, thereby ignoring the sequential characteristics of MR data. In this paper, we propose a hybrid spatial-frequency network (HSFNet) for MR image quality assessment, which extracts MR image quality features in both spatial and frequency domains. Specifically, within each data domain, information from local images and global sequences is iteratively integrated by applying a Mamba-based cascading processing module for multiple times. Extensive experiments on both T1-weighting and T2-weighting MR datasets demonstrate the proposed method's effectiveness and generalization ability in comparison with state-of-the-art MR image quality assessment methods. © 2024 IEEE.

关键词： Image quality

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Gray-box Adversarial Attack of Deep Reinforcement Learning-based Trading Agents 22

Gray-box Adversarial Attack of Deep Reinforcement Learning-b...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Ataiefard, Foozhan Hemmati, Hadi University of Calgary Electrical and Software Engineering Calgary Canada York University Electrical Engineering and Computer Science Toronto Canada

ISBN: (纸本)9798350345346

In recent years, deep reinforcement learning (Deep RL) has been successfully implemented as a smart agent in many systems such as complex games, self-driving cars, and chat-bots. One of the interesting use cases of Deep RL is its application as an automated stock trading agent. In general, any automated trading agent is prone to manipulations by adversaries in the trading environment. Thus studying their robustness is vital for their success in practice. However, typical mechanism to study RL robustness, which is based on white-box gradient-based adversarial sample generation techniques (like FGSM), is obsolete for this use case, since the models are protected behind secure international exchange APIs, such as NASDAQ. In this research, we demonstrate that a 'gray-box' approach for attacking a Deep RL-based trading agent is possible by trading in the same stock market, with no extra access to the trading agent. In our proposed approach, an adversary agent uses a hybrid Deep Neural Network as its policy consisting of Convolutional layers and fully-connected layers. On average, over three simulated trading market configurations, the adversary policy proposed in this research is able to reduce the reward values by 214.17%, which results in reducing the potential profits of the baseline by 139.4%, ensemble method by 93.7%, and an automated trading software developed by our industrial partner by 85.5%, while consuming significantly less budget than the victims (427.77%, 187.16%, and 66.97%, respectively). © 2023 IEEE.

关键词： Adversarial At-tacks Automated Trading Deep Reinforcement Learning Robustness

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Preprocessing Method for Color-Adaptive Pix2Pix Generative Neural Network Data

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Journal of Institute of Control, Robotics and Systems 2023年第6期29卷 521-526页

作者： Lee, Hayoung Son, Bong-Ki Lee, Jaeho Department of IT Media Engineering Duksung Women’s University Korea Republic of Department of Computer Science and Engineering Seowon University Korea Republic of Department of Software Duksung Women’s University Korea Republic of

Adversarial generative neural networks (GANs) have received increased research interest in recent years, and they have been applied in various fields, such as image generation, transformation, and restoration. Pix2Pix, a popular GAN model, trains data in which input and output data are paired. However, because this method is based on sketches, it cannot consider colors once the training is completed. Not only to apply colors requires coloring on edges of all training data, which requires high effort to build data, but also it is difficult to automatically generate edges. In this paper, we proposed a method that considers colors and performs the automatic generation of edges required for Pix2Pix. The proposed method extracts edge data by using the canny edge detection algorithm and constructs color edge and color background data by using the extracted edge data. The applicability of the proposed method was verified by conducting experiments in the fashion field. © ICROS 2023.

关键词： Color

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