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Prevention of Security Attacks in Cloud computing 6

Prevention of Security Attacks in Cloud Computing

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6th International Conference on Information Systems and computer Networks, ISCON 2023

作者： Gaur, Kashish Gaur, Kaamya Diwakar, Manoj Singh, Prabhishek Pandey, Neeraj Kumar Graphic Era Deemed to Be University Dehradun India Bennett University School of Computer Science Engineering and Technology Greater Noida India Dit University School of Computing Dehradun248009 India

ISBN: (纸本)9798350346961

With rising popularity of social platforms like Google, Amazon, Microsoft, etc., there have been several incidents of cloud cyber-attacks that have been reported in the past few years. End-to-end security have become one of the biggest challenges in the field of cloud computing in recent days. Although there have been several claimed advantages that come from cloud computing models, what needs to be noticed is the loss of control in security over the cloud-based assets [9]. In this paper, we have analyzed the models to deal with cloud-related attacks and with the ways of improving cloud security. We have studied and analyzed each one of the proposals and have finalized one of them as the one model, that is comparatively better than the rest.. © 2023 IEEE.

关键词： Cloud security

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Across Images and Graphs for Question Answering 40

Across Images and Graphs for Question Answering

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Wen, Zhenyu Qian, Jiaxu Qian, Bin Yuan, Qin Qin, Jianbin Xuan, Qi Yuan, Ye Institute of Cyberspace Security Zhejiang University of Technology Hangzhou China Binjiang Cyberspace Security Institute of ZJUT Hangzhou China University of Science and Technology of China Department of Electronic Engineering and Information Science China School of Computing Newcastle University Newcastle Upon Tyne United Kingdom School of Computer Science and Technology Beijing Institute of Technology Beijing China School of Computer Shenzhen University Shenzhen China

ISBN: (纸本)9798350317152

Cross-source query serves as a proxy for scene understanding to support many web applications such as rec-ommendation systems, e-commerce, and e-learning applications. In this paper, we propose SVQA that semantically combines the knowledge from available images and graphs to answer the complex question. To this end, we design a graph-based method to unify various data sources into one representation. We then develop a complex question parse method that utilizes the structure of languages to transform the query into a query graph. A graph query engine that performs the query graph over the unified data source while optimizing the query process. To evaluate the proposed system, we build a vanilla dataset called MVQA and show that the state-of-the-art (SOTA) VQA models fail to perform our task. The comprehensive evaluations show that the proposed SVQA is able to reason implicit relationships over multiple images and external knowledge to correctly answer a complex query. We hope that our first attempt provides researchers with a fresh taste of multimodal data analysis. © 2024 IEEE.

关键词： Query processing

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Generative Positive-Unlabeled Classification for Hunting Small Open Reading Frames

Generative Positive-Unlabeled Classification for Hunting Sma...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Chen, Jie Liao, Wenbin Wen, Du Li, Jianqiang Wang, Fangzhong Shenzhen University College of Computer Science and Software Engineering China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology China Tianjin University Laboratory of Synthetic Microbiology School of Chemical Engineering & Technology China

ISBN: (纸本)9798350337488

The annotation of Open Reading Frames (ORFs) is a crucial step in gene annotation, as it precisely delineates the specific regions of expressed genes. However, small Open Reading Frames (smORFs), in comparison to ORFs, are shorter in length, exhibit lower expression abundance, and are more challenging to predict. Particularly in the presence of noise in prokaryotic data and limited availability of positive sample data, the difficulty of prediction is amplified. Therefore, it is necessary to study smORF prediction methods. However, current machine learning models use limited data for modeling and overlook the existence of undiscovered positive samples within the negative samples. Additionally, they do not incorporate prior knowledge that can be calibrated to enhance the 3-nt periodicity. This work utilizes a multimodal VAE for data dimensionality reduction and employs a GAN to generate latent vectors for data augmentation. It incorporates PU learning to leverage unknown samples and combines Riboseq data from experiments with and without antibiotic treatment. Additionally, an adversarial training mechanism is employed to enhance the model's robustness. © 2023 IEEE.

关键词： Class Imbalance Data Generation Gene Annotation Positive-Unlabeled Learning

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Multi-Controller Architecture Design and Features in Software - Defined Networking (SDN)

Multi-Controller Architecture Design and Features in Softwar...

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Mobile computing and Sustainable Informatics (ICMCSI), International Conference on

作者： V. Prabhu P. Balamurugan Department of Computer Science and Engineering School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chengalpattu Tamilnadu India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chengalpattu Tamilnadu India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Networking paradigm known as “Software-Defined Networking” (SDN) offers more flexibility with network management and is fast gaining popularity. Separating the control plane from the data plane is largely responsible for this success. In SDN-enabled networks, a centralized controller is used to translate high-level network application requirements into low-level directives that are issued to the forwarding devices. Such the controller requires limited ability to react to the request for flow processing generated by forwarding devices. Whenever an SDN-enabled network expands in terms of size or as a network traffic rises, flow processing demands can't be able to fulfill by a single centralized controller. This causes a controller's performance to decline, which eventually results in a malfunctioning SDN-enabled network. Implementing a multi-controller architecture, in which a number of controllers cooperate to manage a sizable amount of network traffic and flow processing requirements, would be a successful strategy. The introduction of a multi-controller architecture and a consideration of the problems with its scalability, coherence as well as accessibility make up the core contribution of this study. Additionally, it identifies areas where study in this area could still be done.

关键词： Performance evaluation Scalability Process control computer architecture Telecommunication traffic Coherence Software Reliability Software defined networking Mobile computing

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EdgeMove: Pipelining Device-Edge Model Training for Mobile Intelligence 23

EdgeMove: Pipelining Device-Edge Model Training for Mobile I...

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32nd ACM World Wide Web Conference, WWW 2023

作者： Dong, Zeqian He, Qiang Chen, Feifei Jin, Hai Gu, Tao Yang, Yun School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing Technologies Swinburne University of Technology Australia School of Information Technology Deakin University Australia School of Computing Macquarie University Australia National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781450394161

Training machine learning (ML) models on mobile and Web-of-Things (WoT) has been widely acknowledged and employed as a promising solution to privacy-preserving ML. However, these end-devices often suffer from constrained resources and fail to accommodate increasingly large ML models that crave great computation power. Offloading ML models partially to the cloud for training strikes a trade-off between privacy preservation and resource requirements. However, device-cloud training creates communication overheads that delay model training tremendously. This paper presents EdgeMove, the first device-edge training scheme that enables fast pipelined model training across edge devices and edge servers. It employs probing-based mechanisms to tackle the new challenges raised by device-edge training. Before training begins, it probes nearby edge servers' training performance and bootstraps model training by constructing a training pipeline with an approximate model partitioning. During the training process, EdgeMove accommodates user mobility and system dynamics by probing nearby edge servers' training performance adaptively and adapting the training pipeline proactively. Extensive experiments are conducted with two popular DNN models trained on four datasets for three ML tasks. The results demonstrate that EdgeMove achieves a 1.3 × -2.1 × speedup over the state-of-the-art scheme. © 2023 ACM.

关键词： Internet of things

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SynchroLearn: Master Deadlock and Synchronization Through Interactive Learning

SynchroLearn: Master Deadlock and Synchronization Through In...

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International Conference on computing and Networking Technology (ICCNT)

作者： G Bhavya Shreya Bhanot M Srinidhi Samreen Sidhu Sajitha Krishnan Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science & Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Today, educational tools have become necessary for learners to learn well and improve their knowledge. These tools complement knowledge as they can make it more vivid and add some simulation or game into the learning process. It is self-learning since the students can learn from any place and it also enables the students to go further on content that may interest them, and which may help them in the understanding of the concept. This paper takes the idea of developing SynchroLearn, which is an educational visualization tool developed with the help of HTML, CSS, and JavaScript that enables the teaching of deadlock handling techniques effectively for both the developers and the students. To expound on techniques such as the Producer-Consumer problem, Peterson’s method, Test-and-Set Lock, and the Dining Philosophers problem, an online tool known as SynchroLearn helps in providing the learner an actual time visualization. These strategies present several ways of handling resources, and with that, the management of their utilization process, so that the processes could be controlled in terms of what resources they require, which in turn, helps to solve and prevent such cases of deadlocks. This approach helps to learn complex relationships as well as to understand the interactions between the different processes, identify the deadlock situations, and use the essential methods for their resolution. They bring out the idea linking theoretical proficiencies and practical application by demonstrating them as simple-to-follow graphics which are therefore easier to implement.

关键词： Visualization Education Process control Games System recovery Synchronization

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An Experimental Investigation on Unsupervised Text Summarization for Customer Reviews

An Experimental Investigation on Unsupervised Text Summariza...

引用

2022 International Conference on Machine Learning and Data engineering, ICMLDE 2022

作者： Manojkumar, V.K. Mathi, Senthilkumar Gao, Xiao-Zhi Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India School of Computing University of Eastern Finland Kuopio Finland

People generally turn to websites such as Yelp to find reviews for the food/restaurant before trying it first-hand. However, some reviews are so long and ambiguous that users get confused about whether the review appreciates or disparages the food. Here, websites might want to employ summarization techniques so that the crux of the study is well understood in just a sentence. The process of compressing the information/data while preserving its idea is text summarization. The present work's main objective is to empirically investigate the suitable algorithm for summarizing reviews so that users do not need to spend time reading through the entire study. The performance of the conducted investigations is analyzed with the metrics such as cosine similarity, the score of the bilingual evaluation understudy (precision), and recall-oriented understudy for gisting evaluation analysis. The experiments show that the text summarization technique, LexRank outperforms other methods with the precision and recall values of 0.586 and 0.346, respectively. © 2022 Elsevier B.V.. All rights reserved.

关键词： Websites

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Robust Sensor Fusion Algorithms for IoT

Robust Sensor Fusion Algorithms for IoT

引用

2024 International Conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Subramanian, Sakthitharan Lokeshwari, Gandrakoti Jagadeesan, S. Leela, S. Pratheep, V.G. Venkatasudhahar, Murugesan School of Computing Faculty of Engineering and Technology Srm Institute of Science and Technology Department of Computational Intelligence Tamil Nadu Chennai603203 India Geethanjali College of Engineering and Technology Department of Cybersecurity Telangana Hyderabad501301 India Department of Computer Science and Engineering Tamil Nadu Erode638052 India Sri Eshwar College of Engineering Department of Computer Science and Business Systems Tamil Nadu Coimbatore641202 India Kongu Engineering College Department of Mechatronics Engineering Tamil Nadu Perundurai638060 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Mechanical Engineering Tamil Nadu Chennai600062 India

ISBN: (纸本)9798331543617

Sensor fusion is a critical component of the IoT ecosystem, as it combines a variety of data streams from multiple sensors to enhance the accuracy, reliability, and real-time decision-making. This research concentrates on the development of robust sensor fusion algorithms that are capable of effectively managing the heterogeneity, noise, and uncertainty that are inherent in data from Internet of Things (IoT) sensors. Our primary focus is on the ability of these algorithmic methodologies to manage data from multi-modal sensors. We investigate algorithms that are based on deep learning, Bayesian inference, and Kalman filtering. The proposed solutions aim to improve the performance of systems in sectors that are influenced by the Internet of Things (IoT), including healthcare, smart cities, industrial automation, and autonomous cars. This investigation investigates the scalability, energy efficiency, and computational burden of the Internet of Things (IoT) in the context of limited resources. Through simulations and practical applications, we demonstrate the enhancement of data precision, defect tolerance, and overall system dependability by these robust sensor fusion techniques. © 2024 IEEE.

关键词： Kalman filters

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Sorting the Digital Stream: Big Data-driven Insights into Email Classification for Spam and Ham Detection

Sorting the Digital Stream: Big Data-driven Insights into Em...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Shah, Syed Attique Arputham, Emil Anthony Ahmed, Awais Farah, Mohamed Ben Shah, Attal Aziz, Abdul Birmingham City University STEAMhouse School of Computing and Digital Technology BirminghamB47RQ United Kingdom University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan China Faculty of Ict Buitems Department of Computer Engineering Quetta Pakistan Universidad de Zaragoza Aragon Institute of Engineering Research Zaragoza50018 Spain

ISBN: (纸本)9798350324457

In contemporary email communication, the ever- expanding volume of digital correspondence has ushered in an era where big data plays a pivotal role in addressing the challenge of distinguishing between legitimate (ham) and unsolicited (spam) emails. The primary objective of this paper is the meticulous identification and establishment of criteria for the discrimination between ham and spam emails. To achieve this, the study harnesses data from three distinct datasets, aiming to identify common attributes shared across all emails, irrespective of their classification, while concurrently devising methodologies for precise spam detection. Central to this endeavor is the evaluation of the effectiveness of feature selection techniques, specifically Chi-Square and Pearson Correlation, in elevating the accuracy of email classification. The investigation extends to assessing how the combination of these feature selection techniques with the broader machine learning framework can be optimized. This optimization entails the application of diverse preprocessing techniques to the datasets, all designed to amplify the precision of email classification. Furthermore, this research scrutinizes the performance evaluation metrics employed in the assessment of email classifiers. By conducting comprehensive experiments, the study identifies optimal classifiers based on rigorous evaluation metrics. This contributes valuable insights to the toolkit of techniques for proficient email classification within the realm of big data analysis. © 2023 IEEE.

关键词： Artificial Neural Network Classification Data Pre-processing Decision Tree Email Spam Detection Feature Selection Machine Learning Naive Bayes

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DS-PPO: A Reinforcement Learning Method for Wargaming 10

DS-PPO: A Reinforcement Learning Method for Wargaming

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10th International Conference on computer and Communications, ICCC 2024

作者： Yu, Bo Li, Shun Wang, Wei Liu, Dongying Meng, Yuanze Shenyang Institute of Computing Technology Chinese Academy of Science Shenyang China University of Chinese Academy of Sciences Shenyang China School of Computer Science and Engineering Northeastern University Shenyang China

Wargame environments present unique challenges for reinforcement learning algorithms due to their large-scale state-action spaces, environmental asymmetry, and high random-ness. To address these challenges, this paper proposes DS-PPO, an improved reinforcement learning algorithm. DS-PPO incorpo-rates dual state input and action masking mechanisms to enhance the agent's perception of the environment and prevent invalid actions. Additionally, a redesigned loss function promotes policy ex-ploration and reduces instability. Experimental results demon-strate that DS-PPO outperforms other algorithms, achieving a win rate of 56.15%. © 2024 IEEE.

关键词： Action Masking DS-PPO Reinforcement Learning Wargame Simu-lation

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