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A high-performance and endurable SSD cache for parity-based RAID

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Frontiers of Computer science 2019年第1期13卷 16-34页

作者： Chu LI Dan FENG Yu HUA Fang WANG Wuhan National Lab for Optoelectronics Key Laboratory of Information Storage System(School of Computer Science and TechnologyHuazhong University of Science and Technology) Ministry of Education of ChinaWuhan 430074China

Solid-state drives (SSDs)have been widely used as caching tier for disk-based RAID systems to speed up dataintensive ***,traditional cache schemes fail to effectively boost the parity-based RAID storage sys- tems (e.g.,RAID-5/6),which have poor random write performance due to the small-write ***'s worse,intensive cache writes can wear out the SSD quickly,which causes performance degradation and cost *** this article,we present the design and implementation of KDD, an efficient SSD-based caching system which Keeps Data and Deltas in *** write requests hit in the cache, KDD dispatches the data to the RAID storage without updating the parity blocks to mitigate the small write penalty, and compactly stores the compressed deltas in SSD to reduce the cache write traffic while guaranteeing reliability in case of disk *** addition,KDD organizes the metadata partition on SSD as a circular log to make the cache persistent with low *** evaluate the performance of KDD via both simulations and prototype *** results show that KDD effectively reduces the small write penalty while extending the lifetime of the SSD-based cache by up to 6.85 times.

关键词： solid-state drive (SSD) parity-based RAID small write problem write endurance

Delay Analysis of Routing Protocols for WSN

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Delay Analysis of Routing Protocols for WSN

International Symposium on Networks, Computers and Communications (ISNCC)

作者： Rahil Bensaid Adel Ben Mnaouer Hatem Boujemaa ENIG National Engineering School of Gabes Tunisia Faculty of Engineering Applied Science and Technology Canadian University Dubai UAE College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia UCAR COSIM Lab. Higher School of Communications of Tunis Tunisia

In this paper, we explore the benefits of cooperative diversity for Wireless Sensor Network (WSN). Relays are uniformily distributed in $L$ hops and $N$ branches between the sensor nodes SN and the base station BS. Three cooperative routing protocols are considered, namely: one hop, suboptimal and optimal routing protocols. One hop routing chooses the best relay in each hop. Optimal routing selects the best path between SN and BS. Suboptimal routing decomposes the network into $K$ subnetworks and selects successively the best path in each one. We derive the outage probability, the Packet Error Probability (PEP) and the queening delays spent in the system for each protocol.

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RoKEPG: RoBERTa and Knowledge Enhancement for Prescription Generation of Traditional Chinese Medicine

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arXiv 2023年

作者： Pu, Hua Mi, Jiacong Lu, Shan He, Jieyue School of Computer Science and Engineering Key Lab of Computer Network and Information Integration MOE Southeast University Jiangsu Nanjing210018 China Nanjing Fenghuo Tiandi Communication Technology Co. Ltd Jiangsu Nanjing211161 China

Traditional Chinese medicine (TCM) prescription is the most critical form of TCM treatment, and uncovering the complex nonlinear relationship between symptoms and TCM is of great significance for clinical practice and assisting physicians in diagnosis and treatment. Although there have been some studies on TCM prescription generation, these studies consider a single factor and directly model the symptom-prescription generation problem mainly based on symptom descriptions, lacking guidance from TCM knowledge. To this end, we propose a RoBERTa and Knowledge Enhancement model for Prescription Generation of Traditional Chinese Medicine (RoKEPG). RoKEPG is firstly pre-trained by our constructed TCM corpus, followed by fine-tuning the pre-trained model, and the model is guided to generate TCM prescriptions by introducing four classes of knowledge of TCM through the attention mask matrix. Experimental results on the publicly available TCM prescription dataset show that RoKEPG improves the F1 metric by about 2% over the baseline model with the best results. © 2023, CC BY.

关键词： Diagnosis

Dynamic Resource Management in Integrated NOMA Terrestrial-Satellite Networks using Multi-Agent Reinforcement Learning

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arXiv 2023年

作者： Nauman, Ali Alshahrani, Haya Mesfer Nemri, Nadhem Othman, Kamal M. Aljehane, Nojood O. Maashi, Mashael Dutta, Ashit Kumar Assiri, Mohammed Khan, Wali Ullah Department of Information and Communication Engineering Yeungnam University Korea Republic of Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Information Systems College of Science & Art at Mahayil King Khalid University Saudi Arabia Department of Electrical Engineering College of Engineering Umm Al-Qura University Makkah Saudi Arabia Department of Computer Science Faculty of Computers and Information Technology University of Tabuk Tabuk Saudi Arabia Department of Software Engineering College of Computer and Information Sciences King Saud University Po Box 103786 Riyadh11543 Saudi Arabia Department of Computer Science and Information System College of Applied Sciences AlMaarefa University Riyadh11597 Saudi Arabia Department of Computer Science College of Sciences and Humanities- Aflaj Prince Sattam bin Abdulaziz University Aflaj16273 Saudi Arabia University of Luxembourg Luxembourg City1855 Luxembourg

The integration of terrestrial and satellite wireless communication networks offers a practical solution to enhance network coverage, connectivity, and cost-effectiveness. Moreover, in today’s interconnected world, connectivity’s reliable and widespread availability is increasingly important across various domains. This is especially more crucial for applications like the Internet of Things (IoT), remote sensing, disaster management, and bridging the digital divide. However, allocating the limited network resources efficiently and ensuring seamless handover between satellite and terrestrial networks present significant challenges. Therefore, this study introduces a resource allocation framework for integrated satellite-terrestrial networks to address these challenges. The framework leverages local cache pool deployments and non-orthogonal multiple access (NOMA) to reduce time delays and improve energy efficiency. Our proposed approach utilizes a multi-agent enabled deep deterministic policy gradient algorithm (MADDPG) to optimize user association, cache design, and transmission power control, resulting in enhanced energy efficiency. The approach comprises two phases: User Association and Power Control, where users are treated as agents, and Cache Optimization, where the satellite (Bs) is considered the agent. Through extensive simulations, we demonstrate that our approach surpasses conventional single-agent deep reinforcement learning algorithms in addressing cache design and resource allocation challenges in integrated terrestrial-satellite networks. Specifically, our proposed approach achieves significantly higher energy efficiency and reduced time delays compared to existing methods. This research highlights the importance and addresses the need for efficient resource allocation and cache design in integrated terrestrial-satellite networks, paving the way for enhanced connectivity and improved network performance in various applications. Copyright © 2023, The Aut

关键词： Resource allocation

DCET-Net: Dual-Stream Convolution Expanded Transformer for Breast Cancer Histopathological Image Classification

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DCET-Net: Dual-Stream Convolution Expanded Transformer for B...

2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Zou, Ying Chen, Shannan Sun, Qiule Liu, Bin Zhang, Jianxin Dalian University Key Lab of Advanced Design and Intelligent Computing Ministry of Education Dalian China Dalian University of Technology School of Information and Communication Engineering Dalian China Dalian China Dalian Minzu University School of Computer Science and Engineering Dalian China

ISBN: (纸本)9781665401265

Researches on breast cancer histopathological image classification have achieved a great breakthrough using deep backbones of Convolutional Neural Networks (CNNs) in recent years. However, due to the inductive bias of locality, CNNs are unable to effectively extract the global feature information of breast cancer histopathological images, limiting the improvement of the classification results. To overcome this shortcoming, this paper reasonably introduces an extra backbone stream of a pure transformer, which consists of a self-attention mechanism to capture global receptive fields of histopathological images, thereby compensating the locality characteristic of CNNs backbone. Based on two backbone streams of CNN and transformer, a dual-stream network called DCET-Net is proposed, which considers local features and global ones simultaneously, and progressively combines them from these two streams to form the final representations for classification. DCET-Net is extensively evaluated on the representative BreakHis histopathological image dataset, and experimental results demonstrate that it is highly competitive with the state-of-the-art CNN methods in breast cancer histopathological image classification task. © 2021 IEEE.

关键词： Convolutional neural networks

LARGE LANGUAGE MODELS FOR FORECASTING AND ANOMALY DETECTION: A systemATIC LITERATURE REVIEW

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arXiv 2024年

作者： Su, Jing Jiang, Chufeng Jin, Xin Qiao, Yuxin Xiao, Tingsong Ma, Hongda Wei, Rong Jing, Zhi Xu, Jiajun Lin, Junhong AT&T Center for Virtualization Southern Methodist University DallasTX United States Department of Computer Science and Engineering Ohio State University ColumbusOH United States Department of Computer Science The University of Texas at Austin AustinTX United States Department of Information System Universidad Internacional Isabel I de Castilla Burgos Spain Department of Computer & Information Science & Engineering University of Florida GainesvilleFL United States Academy for Advanced Interdisciplinary Studies Peking University Beijing China School of Computer Science Carnegie Mellon University PittsburghPA United States Department of Electrical and Computer Engineering University of Southern California Los AngelesCA United States Electrical Engineering & Computer Science Department Massachusetts Institute of Technology CambridgeMA United States

This systematic literature review comprehensively examines the application of Large Language Models (LLMs) in forecasting and anomaly detection, highlighting the current state of research, inherent challenges, and prospective future directions. LLMs have demonstrated significant potential in parsing and analyzing extensive datasets to identify patterns, predict future events, and detect anomalous behavior across various domains. However, this review identifies several critical challenges that impede their broader adoption and effectiveness, including the reliance on vast historical datasets, issues with generalizability across different contexts, the phenomenon of model hallucinations, limitations within the models’ knowledge boundaries, and the substantial computational resources required. Through detailed analysis, this review discusses potential solutions and strategies to overcome these obstacles, such as integrating multimodal data, advancements in learning methodologies, and emphasizing model explainability and computational efficiency. Moreover, this review outlines critical trends that are likely to shape the evolution of LLMs in these fields, including the push toward real-time processing, the importance of sustainable modeling practices, and the value of interdisciplinary collaboration. Conclusively, this review underscores the transformative impact LLMs could have on forecasting and anomaly detection while emphasizing the need for continuous innovation, ethical considerations, and practical solutions to realize their full potential. Copyright © 2024, The Authors. All rights reserved.

关键词： Forecasting

AIDE: Attack Inference Based on Heterogeneous Dependency Graphs with MITRE ATT&CK

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AIDE: Attack Inference Based on Heterogeneous Dependency Gra...

IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Weidong Zhou Chunhe Xia Nan Feng Xinyi Pan Tianbo Wang Xiaojian Li Beijing Key Lab. of Network Technology Beihang University Beijing China Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing Guangxi Normal University Guilin China School of Software & Microelectronics Peking University Beijing China School of Cyber Science and Technology Beihang University Beijing China School of Computer Science and Engineering Guangxi Normal University Guilin China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

The MITRE ATT&CK Framework provides a rich and actionable repository of adversarial tactics, techniques, and procedures (TTPs). By leveraging the associations among these techniques, we can infer unobserved techniques based on those have been observed. Current machine learning inference methods based on small-scale data face the following problems: "logical dependencies missing" and "complex associations missing." The former means that current association methods rely solely on the statistical dependencies between attack techniques while ignoring the logical dependencies involved in executing those techniques. The latter indicates that current inference methods simplify the complex associations between technologies based on statistical assumptions, failing to adequately capture the long-range and intricate real-world relationships between techniques. To address these problems, we propose an Attack Inference model based on heterogeneous DEpendency graphs (AIDE). First, we extract three types of logical dependencies: CVE, Pattern, and Conditional, which allows us to construct a heterogeneous dependency graph among the techniques, thus addressing the "logical dependencies missing" problem. Second, we employ Relation Graph Convolution Neural Networks (RGCN) to aggregate heterogeneous dependencies into representation vectors of the techniques. Finally, we utilize the sequence inference model Decoder, which excels at capturing implicit contextual associations, to infer unknown attacks, thus addressing the "complex associations missing" problem. Experiments conducted on real datasets demonstrate that our model significantly outperforms state-of-the-art baseline models in both hits@k and map@k metrics.

关键词： Adaptation models Aggregates Refining Machine learning Vectors Threat assessment Real-time systems Decoding Security Context modeling

Deep Neural Networks based Meta-Learning for Network Intrusion Detection

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arXiv 2023年

作者： Sohail, Anabia Ayisha, Bibi Hammed, Irfan Zafar, Muhammad Mohsin Alquhayz, Hani Khan, Asifullah Pattern Recognition Lab Department of Computer & Information Sciences Pakistan Institute of Engineering & Applied Sciences Nilore Islamabad45650 Pakistan Department of Computer Science Faculty of Computing and Artificial Intelligence Air University E-9 Islamabad Pakistan Faculty of Computer Science and Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and Technology Topi District Swabi Khyber Pakhtunkhwa23640 Pakistan Department of Computer Science and Information College of Science in Zulfi Majmaah University Al-Majmaah11952 Saudi Arabia Pakistan Institute of Engineering & Applied Sciences Nilore Islamabad45650 Pakistan

The digitization of different components of industry and inter-connectivity among indigenous networks have increased the risk of network attacks. Designing an intrusion detection system to ensure security of the industrial ecosystem is difficult as network traffic encompasses various attack types, including new and evolving ones with minor changes. The data used to construct a predictive model for computer networks has a skewed class distribution and limited representation of attack types, which differ from real network traffic. These limitations result in dataset shift, negatively impacting the machine learning models' predictive abilities and reducing the detection rate against novel attacks. To address the challenges, we propose a novel deep neural network based Meta-Learning framework;information FUsion and Stacking Ensemble (INFUSE) for network intrusion detection. First, a hybrid feature space is created by integrating decision and feature spaces. Five different classifiers are utilized to generate a pool of decision spaces. The feature space is then enriched through a deep sparse autoencoder that learns the semantic relationships between attacks. Finally, the deep Meta-Learner acts as an ensemble combiner to analyze the hybrid feature space and make a final decision. Our evaluation on stringent benchmark datasets and comparison to existing techniques showed the effectiveness of INFUSE with an F-Score of 0.91, Accuracy of 91.6%, and Recall of 0.94 on the Test+ dataset, and an F-Score of 0.91, Accuracy of 85.6%, and Recall of 0.87 on the stringent Test-21 dataset. These promising results indicate the strong generalization capability and the potential to detect network attacks. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

Artificial Compound Eyes Visual Imaging system Using Organic Photodetector and Bionic Micro-Lens Array

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Artificial Compound Eyes Visual Imaging System Using Organic...

IEEE International Conference on Micro Electro Mechanical systems

作者： Jiachuang Wang Xiawei Yue Shuai Wei Fangyu Zhao Nan Qin Tiger H. Tao State Key Laboratory of Transducer Technology Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China 2020 X-Lab Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China School of Graduate Study University of Chinese Academy of Sciences Beijing China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai China Neuroxess Co. Ltd. (Jiangxi) Nanchang China Guangdong Institute of Intelligence Science and Technology Zhuhai China Tianqiao and Chrissy Chen Institute for Translational Research Shanghai China

We report a bionic artificial compound eye imaging system based on quantum dots doped film and MEMS fabrication process. The device meets the requirements of ultrathin flexible and extensive curvature bending, integrating bionic organic photodetector and micro-lens array at the microscale. Biomimetic eye pixels exhibit significant discriminative response to different target shapes and light sources. Meanwhile, it fulfills photoelectric response, performance stability and obstacle avoidance in a comprehensive scenario. It suggests that this bionic compound eye equipped unmanned omnidirectional vehicle has great potential in visual imaging system.

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