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作者： Ammar, Mostafa Abdel-Hamid, Ayman A. Rizk, Mohamed R.M. Ahmed, Magdy A. Computer Networks and Datacenter Arab Academy for Science Technology and Maritime Transport Alexandria Egypt Computer Science Department College of Computing and Information Technology Arab Academy for Science Technology and Maritime Transport Alexandria Egypt Electrical Engineering Department Faculty of Engineering Alexandria University Alexandria Egypt Computer and Systems Engineering Department Faculty of Engineering Alexandria University Alexandria Egypt

Nowadays, there is a rising demand for enterprises to migrate datacentre into public cloud. This transfer has several drivers such as decreasing datacentre operational cost and increasing scalability. Moreover, it motivated cloud providers to construct more multitenant datacentres. On the other hand, cyber attacks against IT infrastructures are becoming sophisticated. In this paper, a framework is proposed to enhance security for software defined network (SDNs)-based multitenant datacentres. A novel mechanism is introduced to only forward suspicious traffic for deep packet inspection (DPI) without affecting any other traffic. Attack graphs are used to specify all possible attack scenarios against datacentre network. Framework proof of concept prototype is implemented using a mixed emulation and simulation environment. A typical multitenant datacentre network topology is used to test and evaluate framework performance. Performance evaluation results show framework feasibility and performance against attacks while not affecting delay sensitive traffic. Copyright © 2020 Inderscience Enterprises Ltd.

关键词： Network topology

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HSV Model based Skin Color Segmentation using Uncomplicated Threshold and Logical AND Operation

HSV Model based Skin Color Segmentation using Uncomplicated ...

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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： P Ganesan B. S. Sathish L. M. I. Leo Joseph G. Sajiv R. Murugesan A. Akilandeswari S. Gomathi Dept. of Electronics and Communication Engineering Vidya Jyothi Institute of Technology Hyderabad India Dept. of Computer Science Engineering AI Sri Venkateshwara College of Engineering Bengaluru India Dept. of Electronics and Communication Engineering SR University Warangal Telangana India Saveetha School of Engineering SIMATS Chennai India Dept. of Electronics and Communication Engineering Narshima Reddy Engineering College Secunderabad India

In human, skin tone is significantly varying from one extend (darkest) to other (lightest) due to the difference in the amount of pigmentation (melanin). Even though skin color detection and segmentation is the challenging and complex task which finds applications in face recognition, human tracking, video conferencing, content based image retrieval. The variation in the pigmentation is the consequence of the exposure to the amount of solar radiation and heredity. The segmentation is the clustering process wherein the whole image is clustered into small groups according to the color or texture characteristics. The attainment of image analysis is exclusively based on the upshot of the segmentation. The proposed segmentation procedure is provocative due to the attributes like illumination, the characteristics of the image capturing device, ethnicity and individual characteristics. In skin color segmentation, pixels clustered as the skinny or non-skin pixels. The proposed method utilized HSV color model as a tool for the skin tone detection and segmentation. The outcome of the proposed system is weighed against with RGB color model. Experimental results clearly illustrated that the proposed method has a very good efficiency to segment skin color pixels.

关键词： Image segmentation Image color analysis Computational modeling Image retrieval Lighting Skin Solar radiation

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The Role of IOT in Creating SC’S through Ultra Fast Updation of the Status for Accurate Action Plan

The Role of IOT in Creating SC’S through Ultra Fast Updatio...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： S Hariharasudhan S. Subasree M. Bhuvaneswari Mekvin Victor Rintu Kumar K. Sambath Kumar Department of Information Technology Prince Shri Venkateshwara Padmavathy Engineering College Department of computer science and Engineering Nehru Institute of Engineering and Technology T.M. Palayam Tamil Nadu India Department of Biomedical Engineering Karpagam Academy of Higher Education Coimbatore TN School of Business and Management Christ University Bangalore Department of Mech Engg IES Institute of Technology and Management IES University Bhopal M.P. India Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology (Deemed to be University) Avadi Chennai Tamil Nadu

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

The idea of a smart city includes the merging of technologies and advances aimed at improving urban efficiency, scientific progress, the preservation of the environment, and social inclusion. Coined in the year 2000, the term became widely used in politics, business, management, and urban planning groups to drive tech-based changes in urban areas. It reacts to the difficulties posed by postindustrial communities handling problems such as pollution to the environment, demographic changes, population growth, health care monetary crises, and resource shortages. Beyond technical answers, the smart city idea includes non-technical innovations for healthy urban life. Particularly encouraging is the application that uses Internet of The circumstances (IoT)based sensors in healthcare, applying machine learning for effective data management. This paper discusses the application of AI-powered Ai and Wireless Sensor Networks, more commonly known as the field of health care, acting as a basic study to understand the impact of IoT in smart cities, especially in healthcare, for the sake of future research.

关键词： Technological innovation Accuracy Smart cities Sociology Medical services Machine learning Blockchains

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Deep learning for macular fovea detection based on ultra-widefield fundus images 2

Deep learning for macular fovea detection based on ultra-wid...

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2nd International Conference on Electrical, Electronics, and Information engineering, EEIE 2023

作者： Wang, Mini Han Huang, Lina Hou, Guanghui Yang, Jie Xing, Lumin Yuan, Qiting Chong, Kelvin Kam-Lung Lin, Zhiyuan Zeng, Peijin Fang, Xiaoxiao Yao, Xiaoping Li, Qingqian Liu, Jiang Lin, Chen Department of Ophthalmology & Visual Sciences Faculty of Medicine Chinese University of Hong Kong Hong Kong999077 Hong Kong The faculty of Data Science City University of Macau 999078 China Zhuhai People's Hospital Zhuhai Hospital Affiliated with Jinan University Zhuhai519000 China The Department of AI and big data application Zhuhai Institute of Advanced Technology Chinese Academy of Sciences Zhuhai519000 China Perspective Technology Group Zhuhai519000 China Jinan University Affiliated Shenzhen Eye Hospital Shenzhen518000 China Aier Eye Hospital of Zhuhai Zhuhai519000 China College of Artificial Intelligence Chongqing Industry & Trade Polytechnic Chongqing408000 China The First Affiliated Hospital Shandong First Medical University Shandong Provincial Qianfoshan Hospital Jinan250014 China Business School Hong Kong University of Science and Technology Hong Kong999077 Hong Kong Company Ltd. Guangdong Shenzhen518000 China School of Computer Beijing Institute of Technology Zhuhai Zhuhai519000 China Union Hospital of Fujian Medical University Fuzhou353000 China Department of Ophthalmology People's Hospital of Yantian District Shenzhen518000 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518000 China Department of Ophthalmology Shenzhen People's Hospital Shenzhen518000 China

ISBN: (纸本)9781510672840

Macula fovea detection is a crucial molecular biological prerequisite for screening and diagnosing macular diseases. Without early detection and proper treatment, any abnormality involving the macula may lead to blindness. However, with the ophthalmologist shortage and time-consuming artificial evaluation, neither the accuracy nor effectiveness of the diagnosis process could be guaranteed. In this project, we proposed a light-weighted deep learning model based on ultra-widefield fundus (UWF) images for macula fovea detection tasks. This study collected 2300 ultra-widefield fundus images from Shenzhen Aier Eye Hospital in China. A light-weighted method based on a U-shape network (Unet) and Fully Convolution Network (FCN) approach is implemented on 1800 (before amplifying process) training fundus images, 400 (before amplifying process) validation images, and 100 test images. Three professional ophthalmologists were invited to mark the fovea. A method from the anatomy perspective is investigated. This approach is derived from the spatial relationship between the macula fovea and optic disc center in UWF. A set of parameters of this method is set based on the experience of ophthalmologists and verified to be effective. The ultra-widefield swept-source optical coherence tomography (UWF-OCT) approach is the grounded method. Through a comparison of proposed methods, we conclude that the proposed light-weighted Unet method outperformed other methods on macula fovea detection tasks. © 2024 SPIE.

关键词： Convolution

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Multi-AMP: Detecting the antimicrobial peptides and their activities using the multi-task learning

Multi-AMP: Detecting the antimicrobial peptides and their ac...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Qiaozhen, Meng Tang, Jijun Guo, Fei Tianjin University College of Intelligence and Computing Tianjin China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Central South University School of Computer Science and Engineering Changsha China

ISBN: (纸本)9781665401265

Recently due to the broad-spectrum and high-efficiency antibacterial activity, antimicrobial peptides (AMPs) have become the best alternative to antibiotics. With the rapid increase of the antibacterial peptides, many computational methods have been developed to identify the AMPs and their specific antibacterial activities. However, most existing methods regard these two problems as independent sub-problems and ignore the correlation between tasks. In this paper, we propose a method, Multi-AMP, which utilizes multi-task learning and solves two tasks simultaneously: 1) whether a given peptide is AMP, 2) which activities it performs. The two tasks share the parameters at the bottom layers of the model and learn the specific information at the top layers. Experiments indicate that our multi-task model performs better than single-task models and two existing predictors, which can give insights to the drug discovery process. © 2021 IEEE.

关键词： Peptides

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When Neural Code Completion Models Size up the Situation: Attaining Cheaper and Faster Completion through Dynamic Model Inference

arXiv

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arXiv 2024年

作者： Sun, Zhensu Du, Xiaoning Song, Fu Wang, Shangwen Li, Li Monash University MelbourneVIC Australia State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China National University of Defense Technology Changsha China Beihang University Yunnan Key Laboratory of Software Engineering Beijing China

Leveraging recent advancements in large language models, modern neural code completion models have demonstrated the capability to generate highly accurate code suggestions. However, their massive size poses challenges in terms of computational costs and environmental impact, hindering their widespread adoption in practical scenarios. Dynamic inference emerges as a promising solution, as it allocates minimal computation during inference while maintaining the model’s performance. In this research, we explore dynamic inference within the context of code completion. Initially, we conducted an empirical investigation on GPT-2, focusing on the inference capabilities of intermediate layers for code completion. We found that 54.4% of tokens can be accurately generated using just the first layer, signifying significant computational savings potential. Moreover, despite using all layers, the model still fails to predict 14.5% of tokens correctly, and the subsequent completions continued from them are rarely considered helpful, with only a 4.2% Acceptance Rate. These findings motivate our exploration of dynamic inference in code completion and inspire us to enhance it with a decision-making mechanism that stops the generation of incorrect code. We thus propose a novel dynamic inference method specifically tailored for code completion models. This method aims not only to produce correct predictions with largely reduced computation but also to prevent incorrect predictions proactively. Our extensive evaluation shows that it can averagely skip 1.7 layers out of 16 layers in the models, leading to an 11.2% speedup with only a marginal 1.1% reduction in ROUGE-L. Copyright © 2024, The Authors. All rights reserved.

关键词： Forecasting

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Construction of Malaria Disease Prediction System using Deep Learning

Construction of Malaria Disease Prediction System using Deep...

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Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： J Sivakumar Sudhagar D Vithya V T Jaichandran R K. Narasimha Raju C. S. Sundar Ganesh Department of Electronics and Communication Engineering St.Joseph'sCollegeof Engineering Chennai Tamil Nadu India Department of Information Technology Jerusalem College of Engineering Chennai Tamil Nadu India Department of Computer Science and Engineering R.M.K. College of Engineering and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering AarupadaiVeedu Institute of Technology Vinayaka Mission's Research Foundation Paiyanoor Tamil Nadu India Department of Computer Science and Engineering Gayatri Vidya Parishad College of Engineering Visakhapatnam Andhra Pradesh India Department of Electrical and Electronics Engineering Karpagam College of Engineering Coimbatore Tamil Nadu India

ISBN: (纸本)9781665460859

Malarial fever is an infectious sickness that influences a large number of lives each year. Malarial fever is as yet a significant worldwide wellbeing trouble, with more than 3.2 billion individuals in 91 nations staying in danger of the infection. Precisely recognizing intestinal sickness from different illnesses, particularly simple malarial fever from non-malarial contaminations, stays a test. Customary analysis of malarial fever in lab requires an accomplished individual and cautious assessment to segregate solid and contaminated red platelets. Profound learning-based models have had extraordinary accomplishment in object recognition; however, the cutting-edge models have not yet been generally applied to natural picture informationIn this article, item location model recently utilized on normal pictures to distinguish cells is appliedand perceive their stages in brightfield microscopy pictures of malarial fever tainted blood This paper, is incorporated with Spotting of Malarial fever from the output images of tiny malign by employing CNN (Convolutional Neural Network) model. The datasets are collected from the Kaggle and used for testing and training. In this model, malarial parasites are discerned from infinitesimal output images with 97.76% of accuracy. In this paper, the CNN model is evaluated using Accuracy and Loss.

关键词： Training Solid modeling Renewable energy sources Software algorithms Standardization Predictive models Solids

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Parallel and Accelerated Feature Extraction of Manipulative Scene of Space Dim Target

Parallel and Accelerated Feature Extraction of Manipulative ...

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International Conference on Control, Automation and Robotics (ICCAR)

作者： Jiyang Yu Dan Huang Jinyang Li Wenjie Li Xianjie Wang Xiaolong Shi China Academy of Space Technology Beijing Institute of Spacecraft System Engineering Beijing China China Research and Development Academy of Machinery Equipment Beijing China Chongqing University of Technology Chongqing China Intelligent Equipment Research Department Chongqing Feima Innovation Research Institute Chongqing China College of Computer Science and Engineering Chongqing University of Technology Chongqing China

Aiming at the problems of difficult feature extraction and high real-time requirement for weak targets in space manipulation scenes, this paper designed a parallel stream HOG feature computing architecture based on multi-cache interaction, and adopted cell stream splitting and multi-angle interval parallel computing methods to improve the degree of parallelism. The whole architecture adopts 16-bit fixed-point base calculation, transcendental function combined with polynomial expansion and table lookup method to improve the calculation accuracy. 16 detection windows at the same time, the use of parallel computing, effectively improve the response to more than $\mathbf{1024\times 1024}$ pixels high-definition camera real-time computing applications. After testing the data with low signal-to-noise ratio, the error from theoretical calculation is less than 3%. The experimental verification shows that compared with previous designs, the proposed method occupies the same number of resources except Block RAM, and the computational efficiency is increased by at least 57.2%.

关键词：

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LVOS: A Benchmark for Long-term Video Object Segmentation

arXiv

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arXiv 2022年

作者： Hong, Lingyi Chen, Wenchao Liu, Zhongying Zhang, Wei Guo, Pinxue Chen, Zhaoyu Zhang, Wenqiang School of Computer Science Fudan University China Shanghai Key Laboratory of Intelligent Information Processing China Academy for Engineering and Technology Fudan University China

Existing video object segmentation (VOS) benchmarks focus on short-term videos which just last about 3-5 seconds and where objects are visible most of the time. These videos are poorly representative of practical applications, and the absence of long-term datasets restricts further investigation of VOS on the application in realistic scenarios. So, in this paper, we present a new benchmark dataset and evaluation methodology named LVOS, which consists of 220 videos with a total duration of 421 minutes. To the best of our knowledge, LVOS is the first densely annotated long-term VOS dataset. The videos in our LVOS last 1.59 minutes on average, which is 20 times longer than videos in existing VOS datasets. Each video includes various attributes, especially challenges deriving from the wild, such as long-term reappearing and cross-temporal similar objeccts. Moreover, we provide additional language descriptions to encourage the exploration of integrating linguistic and visual features for video object segmentation. Based on LVOS, we assess existing video object segmentation algorithms and propose a Diverse Dynamic Memory network (DDMemory) that consists of three complementary memory banks to exploit temporal information adequately. The experiment results demonstrate the strength and weaknesses of prior methods, pointing promising directions for further study. Our objective is to provide the community with a large and varied benchmark to boost the advancement of long-term VOS. Data and code are available at https:// ***/***/. Copyright © 2022, The Authors. All rights reserved.

关键词： Visual languages

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SELF-SUPERVISED VIDEO REPRESENTATION LEARNING WITH MOTION-CONTRASTIVE PERCEPTION

arXiv

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arXiv 2022年

作者： Liu, Jinyu Cheng, Ying Zhang, Yuejie Zhao, Rui-Wei Feng, Rui School of Computer Science Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University China Academy for Engineering and Technology Fudan University China

Visual-only self-supervised learning has achieved significant improvement in video representation learning. Existing related methods encourage models to learn video representations by utilizing contrastive learning or designing specific pretext tasks. However, some models are likely to focus on the background, which is unimportant for learning video representations. To alleviate this problem, we propose a new view called long-range residual frame to obtain more motion-specific information. Based on this, we propose the Motion-Contrastive Perception Network (MCPNet), which consists of two branches, namely, Motion Information Perception (MIP) and Contrastive Instance Perception (CIP), to learn generic video representations by focusing on the changing areas in videos. Specifically, the MIP branch aims to learn fine-grained motion features, and the CIP branch performs contrastive learning to learn overall semantics information for each instance. Experiments on two benchmark datasets UCF-101 and HMDB-51 show that our method outperforms current state-of-the-art visual-only self-supervised approaches. Copyright © 2022, The Authors. All rights reserved.

关键词： Video recording

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