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2nd International Conference on Image Processing, computer Vision and Machine Learning, ICICML 2023

作者： Duan, Xiusheng Sun, Guohua Cao, Jingya Zhang, Yuyang Yang, Jingchao Han, Duyu Hebei Polytechnic Institute Department of Artificial Intelligence and Big Data Shijiazhuang China Hebei Provincial University Road Traffic Perception Intelligent Application Technology Research and Development Center Shijiazhuang China Shijiazhuang Tiedao University School of Mechanical Engineering Shijiazhuang China Northeastern University Faculty of Robot Science and Engineering Shenyang China Hebei Jiaotong Vocational and Technical College Shijiazhuang China

ISBN: (纸本)9798350331417

In recent years, military science and technology has been developed rapidly, and new equipments has been equipped in the army. Augmented Reality (AR) technology provides the possibility to solve the problem of equipments operation training. But in the training process, One of the essential technologies is how to location and identify the operation keys. Obviously, the sample set of equipments operation keys is the basis of key recognition, and a certain scale of sample set is an indispensable element to ensure the performance of the recognition model. On the basis of manually establishing a small sample set, this paper puts forward a mechanism based on interactive automatic sample labeling and sample set expanding, the target recognition model was updated by incremental learning method at the same time. © 2023 IEEE.

关键词： data annotation incremental learning interaction sample augmentation

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Convolutional Neural Network and Deep One-Class Support Vector Machine with Imbalanced dataset for Anomaly Network Traffic Detection

Convolutional Neural Network and Deep One-Class Support Vect...

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International Conference on Cyber Security, Privacy and Networking, ICSPN 2022

作者： Chui, Kwok Tai Gupta, Brij B. Chi, Hao Ran Zhao, Mingbo Hong Kong International Center for AI and Cyber Security Research and Innovations & Department of Computer Science and Information Engineering Asia University Taichung413 Taiwan Lebanese American University Beirut1102 Lebanon Instituto de Telecomunicações Aveiro3810-193 Portugal School of Information Science & Technology Donghua University Shanghai200051 China

ISBN: (纸本)9783031220173

Anomaly detection of network traffic is important for real-time network monitoring and management. The research challenges for anomaly network traffic detection (ANTD) are attributable to the nature of highly imbalanced dataset of abnormal samples and poor generalization. Generating additional training data or undersampling does not perform well with highly imbalanced dataset. The drives to the common formulation of ANTD as an one-class classification problem. Convolution neural network is utilized for feature extraction. This is followed by a deep one-class support vector machine classifier with customized kernel via multiple kernel learning to address the issue of generalization and overfitting. The deep architecture leverages the performance of traditional support vector machine to a large extent. Performance evaluation reveals that the proposed algorithm achieves accuracy of 97.5%. Ablation studies show that our algorithm enhances the accuracy by 2.52–15.2%. Compared with existing work, our algorithm enhances the accuracy by 3.07%. Several future research directions are discussed for further exploration and analysis. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Support vector machines

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Flickr30K-CFQ: A Compact and Fragmented Query dataset for Text-image Retrieval

arXiv

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arXiv 2024年

作者： Liu, Haoyu Song, Yaoxian Wang, Xuwu Zhu, Xiangru Li, Zhixu Song, Wei Li, Tiefeng Research Center for Intelligent Robotics Zhejiang Lab China Department of Engineering Mechanics Center for X-Mechanics Zhejiang University China Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China

With the explosive growth of multi-modal information on the Internet, unimodal search cannot satisfy the requirement of Internet applications. Text-image retrieval research is needed to realize high-quality and efficient retrieval between different modalities. Existing text-image retrieval research is mostly based on general vision-language datasets (e.g. MS-COCO, Flickr30K), in which the query utterance is rigid and unnatural (i.e. verbosity and formality). To overcome the shortcoming, we construct a new Compact and Fragmented Query challenge dataset (named Flickr30K-CFQ) to model text-image retrieval task considering multiple query content and style, including compact and fine-grained entity-relation corpus. We propose a novel LLM-based Query-enhanced method using prompt engineering based on LLM. Experiments show that our proposed Flickr30-CFQ reveals the insufficiency of existing vision-language datasets in realistic text-image tasks. Our LLM-based Query-enhanced method applied on different existing text-image retrieval models improves query understanding performance both on public dataset and our challenge set Flickr30-CFQ with over 0.9% and 2.4% respectively. Our project can be available anonymously in https://***/view/Flickr30K-cfq. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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The Power of Bamboo: On the Post-Compromise Security for Searchable Symmetric Encryption 30

The Power of Bamboo: On the Post-Compromise Security for Sea...

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30th Annual Network and Distributed System Security Symposium, NDSS 2023

作者： Chen, Tianyang Xu, Peng Picek, Stjepan Luo, Bo Susilo, Willy Jin, Hai Liang, Kaitai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering China Cluster and Grid Computing Lab School of Computer Science and Technology China Huazhong University of Science and Technology Wuhan430074 China Digital Security Group Radboud University Nijmegen Netherlands Department of EECS Institute of Information Sciences The University of Kansas LawrenceKS United States Institute of Cybersecurity and Cryptology School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft2628 CD Netherlands

ISBN: (纸本)1891562835

Dynamic searchable symmetric encryption (DSSE) enables users to delegate the keyword search over dynamically updated encrypted databases to an honest-but-curious server without losing keyword privacy. This paper studies a new and practical security risk to DSSE, namely, secret key compromise (e.g., a user’s secret key is leaked or stolen), which threatens all the security guarantees offered by existing DSSE schemes. To address this open problem, we introduce the notion of searchable encryption with key-update (SEKU) that provides users with the option of non-interactive key updates. We further define the notion of post-compromise secure with respect to leakage functions to study whether DSSE schemes can still provide data security after the client’s secret key is compromised. We demonstrate that post-compromise security is achievable with a proposed protocol called "Bamboo". Interestingly, the leakage functions of Bamboo satisfy the requirements for both forward and backward security. We conduct a performance evaluation of Bamboo using a real-world dataset and compare its runtime efficiency with the existing forward-and-backward secure DSSE schemes. The result shows that Bamboo provides strong security with better or comparable performance. © 2023 30th Annual Network and Distributed System Security Symposium, NDSS 2023. All Rights Reserved.

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Evaluation of Chest CT-Scan Anomaly Detection Models Using Principal Component Analysis

Evaluation of Chest CT-Scan Anomaly Detection Models Using P...

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Information Technology (ACIT)

作者： Ala’ Alyabrodi Shrouq Al-Daja MohammadNoor Injadat Abdallah Moubayed Ali Elrashidi Cyber Security Department Zarqa University Zarqa Jordan Data Science & AI Department Zarqa University Zarqa Jordan Computer Engineering Department and Interdisciplinary Research Center for Intelligent Secure Systems King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Department of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Machine learning-based improvements in anomaly detection, visualization, and segmentation are made possible by the growing digitization of medical imaging, which reduces the workload for medical specialists. Nevertheless, trustworthy labeled data is necessary for supervised machine learning and is sometimes expensive, time-consuming, or impossible to get. Consequently, techniques that need either little labeling (unsupervised methods) or partial labeling (semi-supervised methods) have been used increasingly been used. One approach that can be used to tackle medical imaging tasks like segmentation and classification is anomaly detection, which makes use of both unsupervised and semi-supervised methods. This study investigates the anomaly detection capabilities of deep learning and machine learning models trained on chest CT scans. To adopt this approach, firstly, by evaluating several feature extraction techniques (Principal Component Analysis (PCA), Autoencoder, and variance threshold), PCA achieves the best results on the selected dataset. After that, different machine learning and deep learning models (Isolation Forest, K-means, feed-forward neural networks, and convolutional neural networks) are assessed to detect anomalies using PCA for dimensionality reduction. The experiments show that the NN-Feedforward model with PCA outperformed the other models, also attains remarkably low false alarm rate, demonstrating its effectiveness in reducing false positives and precisely detecting anomalies.

关键词： Deep learning Image segmentation Forestry Feature extraction Feedforward neural networks Labeling Information technology Anomaly detection Principal component analysis Biomedical imaging

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Enhancing Encrypted Internet Traffic Classification Through Advanced data Augmentation Techniques

arXiv

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arXiv 2024年

作者： Zion, Yehonatan Aharon, Porat Dubin, Ran Dvir, Amit Hajaj, Chen Department of Computer Science Ariel Cyber Innovation Center Ariel University Israel Dept. of Computer and Software Eng. Ariel Cyber Innovation Center Ariel University Israel Department of Industrial Engineering and Management Data Science Artificial Intelligence Research Center Ariel University Israel

The increasing popularity of online services has made Internet Traffic Classification a critical field of study. However, the rapid development of internet protocols and encryption limits usable data availability. This paper addresses the challenges of classifying encrypted internet traffic, focusing on the scarcity of open-source datasets and limitations of existing ones. We propose two data Augmentation (DA) techniques to synthetically generate data based on real samples: Average augmentation and MTU augmentation. Both augmentations are aimed to improve the performance of the classifier, each from a different perspective: The Average augmentation aims to increase dataset size by generating new synthetic samples, while the MTU augmentation enhances classifier robustness to varying Maximum Transmission Units (MTUs). Our experiments, conducted on two well-known academic datasets and a commercial dataset, demonstrate the effectiveness of these approaches in improving model performance and mitigating constraints associated with limited and homogeneous datasets. Our findings underscore the potential of data augmentation in addressing the challenges of modern internet traffic classification. Specifically, we show that our augmentation techniques significantly enhance encrypted traffic classification models. This improvement can positively impact user Quality of Experience (QoE) by more accurately classifying traffic as video streaming (e.g., YouTube) or chat (e.g., Google Chat). Additionally, it can enhance Quality of Service (QoS) for file downloading activities (e.g., Google Docs). © 2024, CC BY-NC-ND.

关键词： Quality of service

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Criteria-based Unsupervised Feature Selection Techniques: A Comparative Study Over Chest CT Scan

Criteria-based Unsupervised Feature Selection Techniques: A ...

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Information Technology (ACIT)

作者： Shrouq Al-Daja Ala’ Alyabrodi Abdallah Moubayed MohammadNoor Injadat Ali Elrashidi Data Science & AI Department Zarqa University Zarqa Jordan Cyber Security Department Zarqa University Zarqa Jordan Computer Engineering Department Interdisciplinary Research Center for Intelligent Secure Systems King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Department of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

research on classifying chest CT scans as normal or abnormal using machine learning and deep learning has garnered significant attention. To address this, various feature selection (FS) methods are employed to reduce dimensionality and identify key features. This paper evaluates criteria tailored to unsupervised binary datasets, such as feature subsets, redundancy, and diversity, by comparing Mutual Information-Based Methods (MI) with Clustering-Based Feature Selection (Agglomerative Clustering) ones. Additionally, it examines Graph Structure, Spectral Properties, and Quality criteria by comparing Graph-Based Methods with Spectral Feature Selection-be (SPEC). For feature reduction, Agglomerative Clustering may offer a slight advantage due to better handling of diversity and redundancy, though the difference is minimal. If these aspects are crucial, Agglomerative Clustering might be preferred. Conversely, for Graph-Based vs. SPEC, SPEC Method is more suitable for feature reduction due to its coherent feature selection and focus on capturing significant patterns related to medical interventions.

关键词： Deep learning Computed tomography Termination of employment Feature extraction Information technology

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Mobile edge fog, Blockchain Networking and Computing-A survey

Mobile edge fog, Blockchain Networking and Computing-A surve...

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Computational Intelligence, Communication Technology and Networking (CICTN), International Conference on

作者： Ramiz Salama Fadi Al-Turjman Shruti Bhatla Divya Mishra Department of Computer Engineering AI and Robotics Institute Research Center for AI and IoT Near East University Nicosia Mersin Turkey Artificial Intelligence Engineering Dept. AI and Robotics Institute Near East University Nicosia Mersin Turkey Research Center for AI and IoT Faculty of Engineering University of Kyrenia Kyrenia Mersin Turkey Department of Computer Science and Engineering Graphic Era Hill University Dehradun ABES Engineering College Ghaziabad

Mobile edge computing (MEC) and blockchain technology are two rapidly evolving areas in computer science that have the potential to significantly impact the design and operation of distributed systems. MEC involves the deployment of computing resources and services at the edge of the network, closer to the end users, enabling lowlatency, high-bandwidth applications such as augmented reality and machine learning. Blockchain technology, on the other hand, is a distributed database that allows for secure, transparent, and immutable record-keeping, with potential applications in a wide range of industries. In this project, we explore the integration of MEC and blockchain technology for the management of distributed computing resources at the edge of the network. We review the state of the art in these two areas, including existing projects and prototypes, and describe our materials and method for evaluating their potential for integration. Our results show that the combination of MEC and blockchain has the potential to enable new applications and services that require both low latency and secure, decentralized data management. We conclude by summarizing the main contributions of our work and discussing future directions for research in this area. Our findings have implications for the design and operation of distributed systems, and have the potential to enable new applications and services in a wide range of industries.

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Correction of Read Biases Induced by Complex Reference Genome Regions for Improving Copy Number Variation Detection Using a Gaussian Mixture Model

Correction of Read Biases Induced by Complex Reference Genom...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wang, Xuwen Chang, Zhili Wang, Shenjie Liu, Ruoyu Liu, Yuqian Zhu, Xiaoyan Shao, Yang Lai, Xin Yang, Shuanying Wang, Jiayin Second Affiliated Hospital Xi'an Jiaotong University Department of Respiratory Medicine Xi'an710003 China Xi'an Jiaotong University School of Computer Science and Technology Xi'an710049 China Xi'an Jiaotong University Shaanxi Engineering Research Center of Medical and Health Big Data Xi'an710049 China Nanjing Geneseeq Technology INC. China

ISBN: (纸本)9798350386226

Copy number variations are crucial in cancer research, but their detection through next-generation sequencing is often hindered by read biases, particularly in complex genomic regions. Existing bias-correction methods address common issues like GC content but often fail in regions with repetitive sequences or segmental duplications, leading to false-positive CNVs. We propose refMask, a hybrid Gaussian model-based method that dynamically identifies low-confidence regions in the reference genome, correcting read biases and improving CNV detection accuracy. By integrating features from hg38 and T2T genomes, refMask tailors a custom blacklist for each sequencing sample, enhancing the reliability of CNV detection across diverse conditions. Our method provides a more accurate and flexible solution compared to current fixed blacklists, offering improved performance in challenging genomic regions. © 2024 IEEE.

关键词： Copy number variation Gaussian model Next-generation sequencing Read biase

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Compact Visual data Representation for Green Multimedia - A Human Visual System Perspective

arXiv

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arXiv 2024年

作者： Chen, Peilin Fang, Xiaohan Wang, Meng Wang, Shiqi Ma, Siwei Department of Computer Science City University of Hong Kong Hong Kong The School of Data Science Lingnan University Hong Kong National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing100871 China

The Human Visual System (HVS), with its intricate sophistication, is capable of achieving ultra-compact information compression for visual signals. This remarkable ability is coupled with high generalization capability and energy efficiency. By contrast, the state-of-the-art Versatile Video Coding (VVC) standard achieves a compression ratio of around 1,000 times for raw visual data. This notable disparity motivates the research community to draw inspiration to effectively handle the immense volume of visual data in a green way. Therefore, this paper provides a survey of how visual data can be efficiently represented for green multimedia, in particular when the ultimate task is knowledge extraction instead of visual signal reconstruction. We introduce recent research efforts that promote green, sustainable, and efficient multimedia in this field. Moreover, we discuss how the deep understanding of the HVS can benefit the research community, and envision the development of future green multimedia technologies. © 2024, CC BY.

关键词： Green development

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