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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Wang, Yi He, Jiajian Sun, Kaoyi Dong, Yunhao Chen, Jiaxian Ma, Chenlin Zhou, Amelie Chi Mao, Rui College of Computer Science and Software Engineering Shenzhen University China Hong Kong Baptist University Department of Computer Science Hong Kong

ISBN: (纸本)9798350317152

As the most common data structure for key-value stores, LogStructured Merge Tree (LSM-tree) can eliminate random write operations and keep acceptable read performance. However, write stall and write amplification introduced by the leveled compaction of LSM-tree significantly degrade the system performance. The emerging non-volatile memory (NVM) provides byte-addressable access and low-latency data persistence. Integrating DIMM-interface NVM in the design of the LSM-tree can potentially alleviate the write stall and write amplification issue, as the access speed of NVM is several orders of magnitude faster than hard disk drives or flash memory-based solid-state drives. This hybrid storage should be carefully designed, requiring new architectural and key-value structural support. This paper presents ZigZagDB, an NVM-enabled data man-agement scheme for LSM-tree-based key-value stores. ZigZagDB adds additional layers of key-value stores and uses non-volatile memory as the storage media to hold these additional layers of data. The newly designed key-value stores alternately access the data from either SSD or NVM. This 'ZigZag' shape of storage collaboration and synchronization can benefit write efficiency and space utilization. By utilizing the NVM with very limited capacity, the redesigned organization of LSM-tree can effectively solve the write stall and write amplification issue. We demonstrate the viability of the proposed ZigZagDB using a set of extensive experiments. Experimental results show that ZigZagDB can significantly reduce the write amplification and boost the throughput in comparison with representative schemes. © 2024 IEEE.

关键词： Flash memory

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Spam detection with fasttext based features

Spam detection with fasttext based features

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2024 Innovations in Intelligent Systems and Applications Conference, ASYU 2024

作者： Karadeniz, Talha Tokdemir, Gül Maraş, H. Hakan Department of Software Engineering Çankaya University Ankara Turkey Department of Computer Engineering Çankaya University Ankara Turkey Department of Computer Programming Çankaya University Ankara Turkey

ISBN: (纸本)9798350379433

Fasttext is a powerful word representation method that creates word representations based on vectors of character n-grams. In this work, we propose a method that utilizes fasttext features for a novel feature engineering model for the spam detection problem. In the feature engineering method, the combination of average, mean of second derivative;mean peak and standard deviation of fasttext features are computed. Finally, tf-idf features are also considered for the modeling process. The success of each feature engineering technique is measured and reported. The combination of the five feature extraction methods, tested on two spam detection datasets, yielded promising results with an accuracy of 0.978 on e-mail spam detection and an accuracy of 0.986 on sms spam classification. © 2024 IEEE.

关键词： Support vector machines

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Secure Batch Deduplication without Dual Servers in Backup System

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IEEE Transactions on Dependable and Secure Computing 2024年 1-13页

作者： Zheng, Haoyu Zeng, Shengke Li, Hongwei Li, Zhijun Xihua College Xihua University Chengdu China school of Computer and Software Engineering Xihua University Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China MIG Group Cisco Systems Canada Co. Ottawa ON Canada

Cloud storage provides highly available and low cost resources to users. However, as massive amounts of outsourced data grow rapidly, an effective data deduplication scheme is necessary. This is a hot and challenging field, in which there are quite a few researches. However, most of previous works require dual-server fashion to be against brute-force attacks and do not support batch checking. It is not practicable for the massive data stored in the cloud. In this paper, we present a secure batch deduplication scheme for backup system. Besides, our scheme resists the brute-force attacks without the aid of other servers. The core idea of the batch deduplication is to separate users into different groups by using short hashes. Within each group, we leverage group key agreement and symmetric encryption to achieve secure batch checking and semantically secure storage. We also extensively evaluate its performance and overhead based on different datasets. We show that our scheme saves the data storage by up to 89.84%. These results show that our scheme is efficient and scalable for cloud backup system and can also ensure data confidentiality. IEEE

关键词： Semantics

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LeaderKV: Improving Read Performance of KV Stores via Learned Index and Decoupled KV Table 40

LeaderKV: Improving Read Performance of KV Stores via Learne...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Wang, Yi Yuan, Jianan Wu, Shangyu Liu, Huan Chen, Jiaxian Ma, Chenlin Qin, Jianbin Shenzhen University College of Computer Science and Software Engineering China City University of Hong Kong Department of Computer Science Hong Kong

ISBN: (纸本)9798350317152

Log-structured merge-tree (LSM-tree) is a storage architecture widely used in key-value (KV) stores. To enhance the read efficiency of LSM-tree, recent works utilize the learned index to learn the mapping between keys and locations. However, in existing learned-index-aided KV stores, inefficient design of the learned index and disk access significantly impact the read performance. How to design a learned KV store to improve index efficiency and minimize disk access remains a critical problem. This paper presents LeaderKV, a read-optimized LSM-tree-based KV store. LeaderKV employs decoupled KV tables (DK-Table) and efficient learned indexes for data retrieval. DKTables are storage files in Leader Kvbecause they avoid reading irrelevant data in collaboration with learned indexes during queries. A learned index called Leader is proposed to accelerate data retrieval within DKTable. Leader is composed of precise models and approximate models. A redirect mechanism is designed to reduce the cost of mispredictions in Leader. We integrate DKTable and Leader into LeaderKV and demonstrate its effectiveness using a variety of datasets and workloads. Experimental results show that LeaderKV significantly improves the read performance compared to representative schemes. © 2024 IEEE.

关键词： Efficiency

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Traffic sign detection algorithm based on improved YOLOv5 4

Traffic sign detection algorithm based on improved YOLOv5

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4th International Conference on computer Vision and Pattern Analysis, ICCPA 2024

作者： Ma, Chi Li, Qinrong Hu, Hui Li, Jingyan Guo, Qiang School of Computer Science and Engineering Huizhou University Huizhou516007 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

ISBN: (纸本)9781510682528

This paper introduces an enhanced YOLOv5 algorithm tailored for real-world traffic sign detection applications. Through the incorporation of Coordinate Attention after the SPPF module of the YOLOv5 backbone, the YOLOv5 neck pays more attention to key areas in the image and preserves accurate positional information. In response to the fact that traffic signs are mostly small targets, the PAN and FPN in the original algorithm's neck network are upgraded by substituting BiFPN for the previous feature fusion method, improving the algorithm's ability to detect traffic signs of different sizes, specifically targeting improved detection accuracy for small-scale targets. To validate the effectiveness of these modifications, we compared our improved model with other object detection model on the TT100K dataset and conducted ablation experiments. The experiments result revealed that the enhanced algorithm achieved an mAP of 94.2%, surpassing the original YOLOv5 model by 6.9%. The detection speed was 49.2 FPS, meeting the real-time requirements. © 2024 SPIE.

关键词： Traffic signals

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Robust Vision Transformer Model Against Adversarial Attacks in Medical Image Classification 47

Robust Vision Transformer Model Against Adversarial Attacks ...

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47th International Conference on Telecommunications and Signal Processing, TSP 2024

作者： Kanca, Elif Gulsoy, Tolgahan Avas, Selen Kablan, Elif Baykal Karadeniz Technical University Department of Software Engineering Trabzon Turkey Karadeniz Technical University Department of Computer Engineering Trabzon Turkey

ISBN: (纸本)9798350365597

Recent advancements in deep learning have signif-icantly enhanced the rapid and precise classification of medical images. Vision transformers, an advanced model, have started replacing CNN s in several medical image tasks. However, recent research indicates that these sophisticated models are susceptible to adversarial attacks generated by various methods. This paper proposes a ViT-based model to bolster the robustness of Vision Transformers against such attacks. Extensive experiments across diverse datasets demonstrate that the ViT model attains higher accuracy on clean images compared to the proposed model. However, the proposed classification model exhibits greater re-silience against adversarial attacks. Across all datasets and attack methods, the proposed model achieves the lowest accuracy of 66.67%, contrasting with 0% for the ViT model. These findings underscore the paramount importance of constructing robust models for medical image classification. © 2024 IEEE.

关键词： Deep learning

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OCRBench: on the hidden mystery of OCR in large multimodal models

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Science China(Information Sciences) 2024年第12期67卷 23-35页

作者： Yuliang LIU Zhang LI Mingxin HUANG Biao YANG Wenwen YU Chunyuan LI Xu-Cheng YIN Cheng-Lin LIU Lianwen JIN Xiang BAI School of Artificial Intelligence and Automation Huazhong University of Science and Technology School of Electronic and Information Engineering South China University of Technology Microsoft Research School of Computer & Communication Engineering University of Science and Technology Beijing Institute of Automation Chinese Academy of Sciences School of Software Engineering Huazhong University of Science and Technology

Large models have recently played a dominant role in natural language processing and multimodal vision-language learning. However, their effectiveness in text-related visual tasks remains relatively unexplored. In this paper, we conducted a comprehensive evaluation of large multimodal models, such as GPT4V and Gemini, in various text-related visual tasks including text recognition, scene text-centric visual question answering(VQA), document-oriented VQA, key information extraction(KIE), and handwritten mathematical expression recognition(HMER). To facilitate the assessment of optical character recognition(OCR) capabilities in large multimodal models, we propose OCRBench, a comprehensive evaluation benchmark. OCRBench contains 29 datasets, making it the most comprehensive OCR evaluation benchmark available. Furthermore, our study reveals both the strengths and weaknesses of these models, particularly in handling multilingual text, handwritten text, non-semantic text, and mathematical expression *** importantly, the baseline results presented in this study could provide a foundational framework for the conception and assessment of innovative strategies targeted at enhancing zero-shot multimodal *** evaluation pipeline and benchmark are available at https://***/Yuliang-Liu/Multimodal OCR.

关键词： large multimodal model OCR text recognition scene text-centric VQA document-oriented VQA key information extraction handwritten mathematical expression recognition

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Multimodal Classifier for Disaster Response 2nd

Multimodal Classifier for Disaster Response

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2nd International Conference on Advanced engineering, Technology and Applications, ICAETA 2023

作者： Alqaraleh, Saed Sirin, Hatice Computer Engineering Department Hasan Kalyoncu University Gaziantep Turkey Software Engineering Department Hasan Kalyoncu University Gaziantep Turkey

ISBN: (纸本)9783031509193

Data obtained from social media has a massive effect on making correct decisions in time-critical situations and natural disasters. Social media content generally consists of messages, images, and videos. In situations of disasters, using multimedia files such as images can significantly help in understanding the damage caused by disasters compared to using text only. In other words, the exact situation and the effect of disaster are better understood using visual data. So far, researchers widely use text datasets for building efficient disaster management systems, and a limited number of studies have focused on using other content, such as images and videos. This is due to the lack of available multimodal datasets. We addressed this limitation in this work by introducing a new Turkish multimodal dataset. This dataset was created by collecting disaster-related Turkish texts and their related images from Twitter. Then, by three evaluators and the majority voting, each sample was annotated as a disaster or not a disaster. Next, multimodal classification studies were carried out with the late fusion technique. The BERT embedding approach and a pre-trained LSTM model are used to classify the text, and a pre-trained CNN model is used for the visual content (images). Overall, concatenating both inputs in a multimodal learning architecture using late fusion achieved an accuracy of 91.87% compared to early fusion, which achieved 86.72%. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Disaster Management Image Classification Multimodal Classifier Turkish language Tweet Text Classification

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Efficient Defense Against Model Stealing Attacks on Convolutional Neural Networks 22

Efficient Defense Against Model Stealing Attacks on Convolut...

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

作者： Khaled, Kacem Dhaouadi, Mouna De Magalhaes, Felipe Gohring Nicolescu, Gabriela Polytechnique Department of Computer Engineering and Software Engineering Montreal Canada University of Montreal Department of Computer Science and Operations Research Canada

ISBN: (纸本)9798350345346

Model stealing attacks have become a serious concern for deep learning models, where an attacker can steal a trained model by querying its black-box API. This can lead to intellectual property theft and other security and privacy risks. The current state-of-the-art defenses against model stealing attacks suggest adding perturbations to the prediction probabilities. However, they suffer from heavy computations and make impracticable assumptions about the adversary. They often require the training of auxiliary models. This can be time-consuming and resource-intensive which hinders the deployment of these defenses in real-world applications. In this paper, we propose a simple yet effective and efficient defense alternative. We introduce a heuristic approach to perturb the output probabilities. The proposed defense can be easily integrated into models without additional training. We show that our defense is effective in defending against three state-of-the-art stealing attacks. We evaluate our approach on large and quantized (i.e., compressed) Convolutional Neural Networks (CNNs) trained on several vision datasets. Our technique outperforms the state-of-the-art defenses with a ×37 faster inference latency without requiring any additional model and with a low impact on the model's performance. We validate that our defense is also effective for quantized CNNs targeting edge devices. © 2023 IEEE.

关键词： Deep learning Model stealing attacks Privacy Quantization Security

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Feature Selection Using Automatic Programming Methods in Hypertension Risk Prediction 8

Feature Selection Using Automatic Programming Methods in Hyp...

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8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024

作者： Yagmurcu, Merve Arslan, Sibel Sivas Cumhuriyet University Computer Engineering Department Sivas Turkey Sivas Cumhuriyet University Software Engineering Department Sivas Turkey

ISBN: (纸本)9798331531492

Hypertension is a condition where the pressure in the blood vessels is higher than normal. It can lead to serious problems such as heart attack, stroke, heart failure, kidney disease and vision problems. Therefore, early diagnosis and treatment is important to find appropriate treatment strategies for the disease. In this study, automatic programming (AP) methods, were used and compared to analyze the risk of hypertension. These methods are Artificial Bee Colony Programming developed from the behavior of honeybees, Genetic Programming (GP) inspired by genetic selection and Immune Plasma Programming (IPP) based on immune plasma therapy. According to the performance evaluations obtained from the methods, GP and IPP were the most successful methods with test success rates of 0.91% and 0.89% respectively. In future research, Due to the success of the AP methods, we aim to develop different versions for health problems. © 2024 IEEE.

关键词： Genetic programming

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