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Fuzzy Deduplication Scheme Supporting Pre-verification of Label Consistency 1

17th International Conference on Provable and Practical Security, ProvSec 2023

作者： Tang, Zehui Zeng, Shengke Li, Tao Cheng, Shuai Zheng, Haoyu School of Computer and Software Engineering Xihua University Chengdu610039 China State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China School of Computer Science Qufu Normal University Rizhao276825 China

ISBN: (数字)9783031455131

ISBN: (纸本)9783031455124

Efficiently and securely removing encrypted redundant data with cross-user in the cloud is challenging. Convergent Encryption (CE) is difficult to resist dictionary attacks for its deterministic tag. Server-aided mechanism is against such attacks while it may exist collusion. Focus on multimedia data, this paper proposes an efficient and secure fuzzy deduplication system without any additional servers. We also propose a notion of pre-verification of label consistency to compensate for the irreparable post-verification loss. Compared with other fuzzy deduplication schemes, our work has apparent advantages in deduplication efficiency and security based on a natural data set. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Cryptography

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Improving Alzheimer's Disease Diagnosis on Brain MRI Scans with an Ensemble of Deep Learning Models 1

Improving Alzheimer's Disease Diagnosis on Brain MRI Scans w...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Reza, Md. Sazid Jahangir Kabir, Mir Md. Rakib Mollah, Md. Abdur Rajshahi University of Engineering and Technology Department of Computer Science and Engineering Rajshahi Bangladesh Td School University of Technology Sydney Data Science and Innovation Sydney Australia

ISBN: (纸本)9798350330915

Alzheimer's disease (AD) is a widespread neurolog-ical condition affecting millions globally. It gradually advances, leading to memory loss, cognitive deterioration, and a substantial decline in overall quality of life for those affected. AD patients experience memory decline, eroding cherished memories and straining relationships, while daily tasks become challenging. Numerous investigations have been conducted in this field, as the timely identification of Alzheimer's disease at its initial stage is of the utmost importance. A major limitation in this field is the predominant emphasis on using single fine-tuned CNN architecture or comparing pre-trained and custom CNN models for Alzheimer's detection, often on small datasets, which neglects a more comprehensive approach. Using smaller datasets can negatively impact deep learning modeling accuracy due to overfitting, limited representation, and poor generalization. This study addresses the current research problems and proposes an ensemble approach that combines predictions from various pre-trained models, including DenseNet-121, EfficientNet B7, ResNet-50, VGG-19, and also from a Custom CNN. The model averaging ensemble method was applied, a subset of the Stacking Ensemble, to two ADNI datasets, with dataset-I being the larger. The goal was to assess the efficacy of this ensemble approach for accurate multiclass classification on ADNI datasets, where it successfully identified all classes despite differing sample volumes. A vast experiment was conducted on two distinct and widely recognized real-world datasets, resulting in accuracies of 99.96% and 98.90% respectively. Finally, the outcome of the research compared with recent research findings demonstrates the potential of our approach in advancing Alzheimer's disease detection by outperforming other benchmark approaches by a significant margin. © 2023 IEEE.

关键词： ADNI DenseNet-121 EfficientNet B7 Ensemble Model Averaging Predictions ResNet-50 VGG-19

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A New Approach for Iris Segmentation Based on U-Net 6

A New Approach for Iris Segmentation Based on U-Net

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6th International Conference on computer Network, Electronic and Automation, ICCNEA 2023

作者： Yang, Yan Wang, Jianguo Xi'an Technological University School of Computer Science and Engineering Xi'an China Xi'an Technological University Research Institute of Artificial Intelligence and Data Science Xi'an China

ISBN: (纸本)9798350305388

The concept of iris segmentation was created to increase the accuracy of iris recognition. Past recognition methods used the entire eye image directly for recognition classification, which led to poor recognition results. For the sake of sovling this problem, this paper proposes the ResU-Net (RU-Net) model, which can guide the network to learn more features that distinguish between iris and non-iris pixels. First, based on the U-Net, the backbone network model is changed to ResNet50 in this paper. This has the advantage of reducing the number of parameters and network complexity, and improving the learning capability. For the sake of solving the problem of sample imbalance between iris region and background region, this paper introduces the Focal Loss loss function. focal loss can effectively deal with the case of sample category imbalance and make the network focus more on the pixels that are difficult to classify. In this paper, the proposed RU-Net model is experimentally evaluated on the CASIA-Iris-Thousand dataset. The experimental results demonstrate that RU-Net achieves significant improvements on NIR iris images, reaching 96.22% MIoU and 98.19% MPA. This indicates that the RU-Net method outperforms other representative iris segmentation methods and has better segmentation capability. © 2023 IEEE.

关键词： Pixels

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New results on quantum boomerang attacks

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Quantum Information Processing 2023年第4期22卷 1-27页

作者： Zou, Hongkai Zou, Jian Luo, Yiyuan College of Computer and Data Science Fuzhou University Fuzhou China Key Lab of Information Security of Network Systems Fuzhou University Fuzhou China School of Information Sciences and Technology Huizhou University Huizhou China

At SAC 2021, Frixons et al. proposed quantum boomerang attacks that can effectively recover the keys of block ciphers in the quantum setting. Based on their work, we further consider how to quantize the generic boomerang attacks proposed by Biham et al. at FSE 2002, so as to obtain more generic quantum boomerang attacks. Similar to Frixons et al.’s work, we only consider quantum key recovery attacks in the single-key setting. With the help of some famous quantum algorithms, this paper presents two methods to convert the attacks of Biham et al. into some new quantum key recovery attacks. In order to proof our methods, we apply our new ideas to attack Serpent-256 and ARIA-196. To sum up, for Serpent-256, we give valid 9-round and 10-round quantum key recovery attacks respectively. The quantum time complexity of 9-round and 10-round of Serpent-256 is 2 115.43 and 2 126.6 respectively. Furthermore, we show a valid quantum key attack on 6-round ARIA-196 which has a time complexity of 2 89.8 with negligible memory. The time complexity of the above quantum attacks are better than the corresponding classical attacks and quantum generic key recovery attack via Grover’s algorithm. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Quantum theory

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Sustainability Forecasting for Deep Learning Packages 31

Sustainability Forecasting for Deep Learning Packages

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31st IEEE International Conference on Software Analysis, Evolution and Reengineering, SANER 2024

作者： Han, Junxiao Wang, Yunkun Liu, Zhongxin Bao, Lingfeng Liu, Jiakun Lo, David Deng, Shuiguang School of Computer & Computing Science Hangzhou City University Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China Zhejiang University The State Key Laboratory of Blockchain and Data Security Hangzhou China School of Computing and Information Systems Singapore Management University Singapore Singapore

ISBN: (纸本)9798350330663

Deep Learning (DL) technologies have been widely adopted to tackle various tasks. In this process, through software dependencies, a multi-layer DL supply chain (SC) is formed, with DL frameworks acting as the root, DL packages acting as the bridge nodes, and downstream DL projects acting as the periphery. However, most Open Source Software (OSS) projects may fail. Considering the crucial position of DL packages in the DL SC, to foster the sustainable development of DL SCs and DL packages, we aim to forecast the long-term sustainability of DL packages. Here, sustained activity is adopted as the main proxy of sustainability, and the sustainability status is classified as 'sus-tainable' or 'dormant'. Relatedly, a DL package is considered as 'sustainable' if it has sustained activity in its last 12 months. Otherwise, it is deemed as 'dormant'. To this end, we propose an approach that begins with obtaining longitudinal features for each DL package in each month. Then, we develop a model to forecast the sustainability of DL packages by incorporating the longitudinal features, which can aptly predict sustainability with an accuracy of up to 0.81. Subsequently, an interpretable module is developed to interpret the determinants (i.e., important features) that impact the sustainability of DL packages. Finally, we generate sustainability trajectories for each DL package to better understand the monthly changes of their sustainability status. Our findings uncover that for most DL packages, fewer but more centralized developers and a balanced collaboration are more likely to help sustain the DL packages. Furthermore, although some DL packages are sustainable, their sustainability trajectories present statistically decreasing trends over time. Based on the findings, we shed light on the dynamic sustainability of DL packages, highlight future research directions, and provide practical suggestions to DL package maintainers, developers, users, and software engineering researchers. ©

关键词： Open source software

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Machine Learning Approach for Onion Leaf Disease Detection: A Case Study in Maharashtra, India 15

Machine Learning Approach for Onion Leaf Disease Detection: ...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Amondkar, Madhavi Bhoite, Sachin Vidyalankar School of Information Technology Department of Information Technology & Data Science Mumbai India Dr. Vishwanath Karad MIT World Peace University Department of Computer Science & Applications Pune India

ISBN: (纸本)9798331300579

Maharashtra is a remarkable state in India that is a major producer of onions, accounting for a sizeable portion of the country's total production. An ideal climate for onion growth is provided in areas like Nashik, Ahmednagar, and Pune Despite its importance, growing onions is filled with difficulties, the most significant of which being the frequency of illnesses that damage onion crops. Serious risks to onion production include diseases which are downy mildew, purple blotch, bacterial blight, white rot & basal rot which result in output losses and financial hardship for farmers. Machine learning models have become extremely effective tools for managing and forecasting agricultural crop diseases such as tomato, corn, grape etc. While nearly all crop leaf diseases may be predicted by these algorithms, very little or none has been done for onions in Maharashtra. There is almost no research done focusing on the onion disease found in different parts of Maharashtra. The purpose of the study is to assist farmers in Maharashtra in recognizing various diseases during the cultivation of onions by utilizing a machine learning model that combines CNN with image classification. Using a dataset of plant leaf diseases, this model ran with 95% accuracy. Additionally, given that the suggested model may be trained on any kind of leaf disease picture dataset and assist in disease type prediction, it can be suggested to work on an onion disease dataset as well. © Grenze Scientific Society, 2024.

关键词： basal rot CNN leaf disease machine learning Maharashtra onion disease purple blotch

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A Survey of Integrating Generative Artificial Intelligence and 6G Mobile Services: Architectures, Solutions, Technologies and Outlooks

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IEEE Transactions on Cognitive Communications and Networking 2025年第3期11卷 1334-1356页

作者： Liu, Yi-Jing Du, Hongyang Xu, Xinyi Zhang, Ruichen Feng, Gang Cao, Bin Niyato, Dusit Kim, Dong In Jamalipour, Abbas Letaief, Khaled B. Tafazolli, Rahim University of Electronic Science and Technology of China National Key Lab on Wireless Communications Chengdu China University of Hong Kong Department of Electrical and Electronic Engineering Pok Fu Lam Hong Kong Nanyang Technological University College of Computing and Data Science Singapore Singapore Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of University of Sydney School of Electrical and Computer Engineering Sydney Australia Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong University of Surrey Institute for Communication Systems School of Computer Science and Electronic Engineering United Kingdom

Generative artificial intelligence (GenAI) is rapidly driving a new phase of artificial intelligence revolution, marked by various applications such as ChatGPT, Sora and DeepSeek. With powerful capabilities in content generation, personalization and user interaction, GenAI can drive significant enhancements in various mobile services to satisfy individual user preferences. Meanwhile, mobile services offer substantial support for accessibility, functionality and intelligence of GenAI applications. However, current research on integrating GenAI and mobile services remains nascent, lacking necessary theoretical and technical support. In this survey, we break down a new vision to guide research on integrating GenAI and mobile services, aiming to improve 6G intelligence levels and enable 6G mobile networks to support the integration of GenAI and mobile services effectively. We begin by examining the necessity of integrating GenAI with 6G mobile services and exploring potential learning architectures of GenAI models within mobile networks. Next, we analyze potential solutions from two perspectives: how 6G mobile networks empower GenAI and how GenAI enables mobile services. We subsequently explore a promising use case, along with essential features and techniques, to facilitate the integration of GenAI and mobile services. Finally, we discuss future research directions, aiming at realizing intelligent 6G networks. © 2015 IEEE.

关键词： Generative adversarial networks

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Emotional Reactivity Classification Using Artificial Intelligence Based on Twitter in Bahasa Indonesia 3

Emotional Reactivity Classification Using Artificial Intelli...

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3rd IEEE International Conference on Social sciences and Intelligence Management, SSIM 2023

作者： Paramita, Greta V. Andangsari, Esther W. Kemala, Ade P. Ali, Moondore M. Putri, Trisa M. Puspita, Katarina I. Bina Nusantara University Faculty of Humanities Psychology Department Jakarta Indonesia Data Science Program School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798350327786

Twitter is one of Indonesia's most widely used social media to express positive and negative emotions. Negative emotions are referred to as emotional reactivity, an example of which is depression. To detect emotional reactivity, Artificial Intelligence (AI) was used. With deep learning pretrained model methods (IndoBERT and IndoBERTweet), AI successfully classified emotional reactivity. AI satisfactorily detected emotional reactivity on Twitter. © 2023 IEEE.

关键词： Deep learning

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Image based and Point Cloud based Methods for 3D View Reconstruction in Real-time Environment 14

Image based and Point Cloud based Methods for 3D View Recons...

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2024 IEEE 14th Annual Computing and Communication Workshop and Conference, CCWC 2024

作者： Agrawal, Arya Sharma, Teena Verma, Nishchal K. Vasikarla, Shantaram Vellore Institute of Technology School of Computer Science and Engineering Vellore India Indian Institute of Technology Mehta Family School of Data Science and Artificial Intelligence Guwahati India Indian Institute of Technology Dept. of Electrical Engineering Kanpur India California State University Dept. of Computer Science NorthridgeCA United States

ISBN: (纸本)9798350360134

3D view reconstruction in a real-time unknown environment poses several challenges to vision-based applications. Herein, this paper outlines real-time experimental results and analysis of 3D view reconstruction methods. Since real-time experimentation requires inputs from different sensors, this paper outlines the results of methods from the following categories: image based methods and point cloud based methods. This paper explains the detailed steps involved in 3D view reconstruction followed by an explanation of the methods. It provides detailed discussions, advantages, limitations, and empirical results, along with presenting differences between algorithms. For analysis of experimental results, two benchmarked datasets i.e., KITTI 3D benchmark suite and ShapeNet are considered. Furthermore, the paper explores potential future scopes, including the utilization of generative models, unsupervised learning, advanced sensors, and many more. © 2024 IEEE.

关键词： Network coding

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Identifying change patterns of API misuses from code changes

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science China(Information sciences) 2021年第3期64卷 69-87页

作者： Wenjian LIU Bihuan CHEN Xin PENG Qinghao SUN Wenyun ZHAO School of Computer Science Fudan University Shanghai Key Laboratory of Data Science Fudan University Shanghai Institute of Intelligent Electronics & Systems

Library or framework APIs are difficult to learn and use, leading to unexpected software behaviors or bugs. Hence, various API mining techniques have been introduced to mine API usage patterns about the co-occurring of API calls or pre-conditions of API calls. However, they fail to mine patterns about an API call itself(e.g., whether the arguments of the API call are correctly set and whether the API is suitably chosen over other similar APIs). To bridge this gap, we propose Cpam to identify change patterns(in the form of a pair of APIs before and after code changes) to fix API misuses, using historical code changes. Given a set of target APIs and a corpus of open-source projects, Cpam first selects the commits that potentially fix API misuses from the corpus, then extracts changes to API misuses in each selected commit, and finally identifies change patterns of API misuses. We implement Cpam for Java, and conduct large-scale evaluation,targeting Java SE APIs and using a corpus of 1162 Java projects. Our experimental results demonstrate Cpam's effectiveness and efficiency. By applying identified change patterns to bug detection, we find 44 new bugs, and 18 of them have been confirmed and fixed.

关键词： API misuses API mining AST differencing change patterns bug fixing

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