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Multi-Objective Task Scheduling in Cloud Environments using Dynamic Velocity-Adaptive Particle Swarm Optimization

Multi-Objective Task Scheduling in Cloud Environments using ...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Kumar, Ravi Vardhan, Manu Natinal Institute of Technology Dept. of Computer Science and Engineering Raipur India

ISBN: (纸本)9798350373783

In the realm of cloud computing, tackling the multi-objective task scheduling problem means finding the best way to assign tasks to virtual machines, taking into account conflicting goals like minimizing execution time, load balance and cost. This intricate task demands smart algorithms to strike a balance between these competing objectives, ensuring the most efficient use of resources and meeting user expectations in the cloud. Different optimization techniques, such as Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), have been explored to handle this challenge and optimize task scheduling in the cloud environment. In classical PSO, particles can move at varying speeds in each iteration. In this study, we apply Velocity Stagnation Particle Swarm Optimization (VSPSO) variants where velocity stagnation applied during particles movement. This approach provides particles with a third movement option, enabling them to maintain the same velocity as in the previous iteration. VSPSO has a greater capacity to balance the exploration and exploitation. VSPSO adjusts the initial term of the PSO velocity equation to prevent premature convergence. Furthermore, the population of VSPSO is split into two swarms to ensure *** the inherent capabilities of PSO, VSPSO algorithm dynamically updates particle velocities to attain 5-6% total execution time 6-10% reduced cost and optimal balance load. Through meticulous comparisons with other algorithms, including Genetic Algorithm and Ant Colony Optimization, this study scrutinizes the effectiveness of our enhanced PSO approach. The overarching goal is to push the boundaries of multi-objective task scheduling in cloud computing by attaining minimal execution time, reduce cost and optimal balance load on each virtual machines. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Impact of Human-in-the-Loop Paradigm in AI Labeling and Annotation 8

Impact of Human-in-the-Loop Paradigm in AI Labeling and Anno...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Davda, Meeth Jitendra Sindhu, D.V. R V College of Engineering Dept. of Computer Science Bangalore India

ISBN: (纸本)9798331505462

Incorporating human expertise directly into artifi-cial intelligence workflows, a concept known as the Human-in-the-Loop (HITL) paradigm, has proven to be a crucial approach in improving the processes of AI data labeling and annotation. This survey paper delves into the impact of the HITL paradigm on the quality and efficiency of data annotation in AI systems, emphasizing the synergistic benefits of combining human insights with automated techniques. This paper reviews a variety of recent methodologies that integrate human evaluative input with automated systems, showcasing the enhancement of data accu-racy and system reliability across several applications, including healthcare, autonomous vehicles, and language processing. It classifies various HITL techniques, evaluates the applicability of each and offers potential solutions. © 2024 IEEE.

关键词： Active learning

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Physical Layer Authentication via Underwater Acoustic Multipath Channel Crafting

Physical Layer Authentication via Underwater Acoustic Multip...

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OCEANS 2024 - Halifax, OCEANS 2024

作者： Eccher, Davide Casari, Paolo University of Trento Dept. of Information Engineering and Computer Science Trento Italy

ISBN: (纸本)9798331540081

With the increasing number of actors in the under-water environment and the development of new applications, such as large-scale monitoring and autonomous underwater vehicle control, securing underwater communications is becoming a primary necessity. Security was not prioritized in the past due to the constraints of underwater acoustic communications, which cannot sustain the overhead of typical cryptographic techniques. In this paper, we propose a method to authenticate a network device by exploiting the physical properties of the acoustic channel. In particular, our method hinges on the uniqueness and quasi-reciprocity of the channel, from which the authenticator (Alice) node can extract several parameters such as the number of multi path channel components, their delay and amplitude. These values are similar on both ends of a link between Alice and a legitimate transmitter (Bob), and can be used as a seed to craft a new artificial channel, that is then applied to transmissions from Bob to Alice. With this procedure, Alice can distinguish Bob from an impersonating attacker (Eve), given a previous message exchange history. Eve can try to bypass the protocol by estimating the channel parameters and by trying to replicate Bob's signal by crafting a similar channel. In our tests, we observe that the estimation error for Eve, caused by her wrong channel estimates, becomes significant even for short distances betwen Eve and Bob. This error results in a discrepancy between the signal generated by Eve and the one expected by Alice, and reveals Eve as an attacker. © 2024 IEEE.

关键词： Underwater acoustics

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ConfigMaster: An Interactive Solution for System Management 8

ConfigMaster: An Interactive Solution for System Management

引用

8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Ganapathi, Ananya Sivakumar, Parikshith Divya, K.V. Elango, Aditya Gupta, Hardik Amrita School of Computing Dept. of Computer Science Engineering Bengaluru India

ISBN: (纸本)9798331505462

The 'ConfigMaster: An interactive solution for system management' is a simple utility written using Bash scripting. It is designed for Unix-like systems. The tool lets users easily configure system settings and do administrative tasks without having to remember complex commands. The tool minimizes user's work by automating tasks. The tool can be used even by users with very little technical knowledge, as it has a simple menu-based interface to manage things like network settings, software packages, and wireless connections directly from the terminal. By automating common tasks, the tool makes system management more efficient and accessible. By automating common tasks, the tool makes system management more efficient and accessible. It's very useful to simplify system configuration on Unix-like systems using scripting languages like Bash. 'ConfigMaster' aims to offer a smooth user experience with the incorporation of core management principles from an operating system, such as disk scheduling, process synchronization, and memory management etc. Being flexible itself, this tool will bring further improvements to make effective responses to emerging user needs. The framework being flexible, it can be expanded to add new features to configure under different scenarios. By integrating essentials operating system operations, 'ConfigMaster' enables users to efficiently perform system configurations along with promoting ease of use. © 2024 IEEE.

关键词： Wi-Fi

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PraaKrum: A Practical Byzantine-Resilient Federated Learning Algorithm 16

PraaKrum: A Practical Byzantine-Resilient Federated Learning...

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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Kukreja, Prabhleen Mahendran, V. Indian Institute of Technology Tirupati Dept. of Computer Science and Engineering India

ISBN: (纸本)9798350383119

Federated Learning (FL) is considered as a suitable paradigm for intelligent data analytics over Internet of Thing (IoT) devices. While the data-privacy preserving feature of FL is useful, the lack of data auditing abilities of the participating IoTs creates a potential attack surface. To this end, a potential attacker can perform data/model poisoning attacks on the FL. To protect from such attacks, a popular theoretically proven algorithm for FL namely, Krum [1] was proposed. From an implementation perspective, the Krum algorithm needs to be provided with apriori information such as the total number of attackers in each round, which is impractical. It is not feasible to know, in advance, which of the participating IoT clients are attackers. Furthermore, in the presence of mobile Parameter Server (PS) which stays in the WiFi coverage area consisting of stationary clients for an unknown time, the implementation of Krum algorithm in such practical setting is not investigated, to the best of our knowledge. With an aim to provide a practical implementation of a resilient FL framework, for the first time, this paper investigates the sensitivity of the attackers information towards the robustness offered by the Krum-based FL algorithm. The insights gained from the sensitivity analysis are used to develop a novel and Practical algorithm of Krum (PraaKrum) for FL for a WiFi network with mobile PS. The developed analysis is helpful in both understanding the key resilient characteristics offered by the Krum algorithm and providing the research community with a guideline towards the practical implementation of the attacker-resilient Krum-based FL algorithm in a mobile wireless setting. © 2024 IEEE.

关键词： Byzantine-resilience Federated learning insensitivity result Krum algorithm WiFi wireless IoT

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Hybrid Model of Fireworks Algorithm and Deep Learning for Drought Prediction using Satellite Data 9

Hybrid Model of Fireworks Algorithm and Deep Learning for Dr...

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9th IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2023

作者： Paudel, Ankit Bhavana, B.N. Anchalia, Aniketh Kakati, Anirudh Chaudhari, Shilpa Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350334395

Many meta heuristic optimization techniques have been developed to address exceedingly difficult optimization issues in numerous disciplines. Fireworks Algorithm (FWA) was proposed as a Swarm Intelligence Optimization technique, mimicking fire work explosions. This paper proposes using FWA in the field of Agricultural Drought Assessment. Droughts are complex natural disasters that need efficient mitigation techniques. Latest research in existing drought assessment tools suggest utilizing Convolutional Neural Network (CNN) models, often hybridized with other optimization strategies. Considering the four vegetation indices Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Atmospherically Resistant Vegetation Index (ARVI) and Soil Adjusted Vegetation Index (SAVI), a hybridized model that integrates FWA into CNN is proposed. This enhanced model can give up to 94% accuracy with very minimal loss for prediction of future drought. This model improves efficiency significantly. © 2023 IEEE.

关键词： Drought

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Prompt-Based Domain Incremental Learning with Modular Classification Layer 27

Prompt-Based Domain Incremental Learning with Modular Classi...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Wang, Boyu Ma, Yue Qiu, Qinru Dept. of Electrical Engineering and Computer Science Syracuse University United States

ISBN: (纸本)9781643685489

Continual learning is referred to as machine learning model's ability to learn a sequence of tasks or data over time without forgetting previously learned knowledge. In particular, we focus on domain incremental learning where the model trained on one domain, e.g., real-life photos, has to be augmented to accommodate data from new domains, e.g., cartoon images. Typical incremental learning relies on rehearsal-based methods, which store trained samples in a buffer and replay them during training alongside new data. This results in significant memory overhead and raise concerns about data privacy. Recently, prompt-based methods address these challenges and outperform them by utilizing the pre-trained Vision Transformer (ViT) and replacing the replay buffer with a prompt pool. However, existing prompt-based models fail to capture domain-specific knowledge and perform poorly in domain incremental learning. In this paper, we propose "domain prompt incremental learning via dynamic neural network", which combines the advantages of architecture-based and prompt-based methods. Specifically, our framework maintains two types of prompt: a instance-level prompt that improves the model's generalization ability is shared across all input samples;and a domain prompt that encodes domain-specific knowledge is assigned for each task. Furthermore, a separated classification head is trained for each domain so that the model has a pre-trained ViT and an ensemble of classification layers, one for each domain. The experimental results shows that our approach outperforms state-of-the-art methods by 2.3% in average on two domain incremental learning (DIL) benchmarks. © 2024 The Authors.

关键词： Contrastive Learning

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Early Prediction of Diabetes using Several Machine Learning Algorithms 7

Early Prediction of Diabetes using Several Machine Learning ...

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Rexie, J.A.M. Santhosh, P. Solomon, P. Nikhil Vishnu, P. Atul Dept. of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350397253

One of the main issues diabetes poses to the medical profession globally is that its consequences are escalating swiftly. Elevated blood glucose levels cause diabetes, also referred to as diabetes mellitus or simply diabetes. On the basis of physical and chemical exams, a number of standard approaches can be used to diagnose diabetes. But, Doctors face a difficult task in predicting diabetes that affects the kidneys, eyes, heart, nerves, feet and other parts of the *** diagnosis analyses illness prognosis and diagnosis using a doctor's training and expertise, although this might be vulnerable to error. Machine learning and data science approaches have the potential to enrich other scientific disciplines by providing new perspectives on well-known issues. One such initiative is to assist in making predictions based on medical data. 'Machine learning' describes the processes by which computers tend to learn from experience, and is a recent area of data science. The aim of this research is to develop a system to accurately diagnose diabetes in patients at an early stage by comparing the results of several machine learning algorithms. Use four different supervised machine learning techniques: Random Forests RF, Logistic LR Regression,Decision DT Trees and (SVM) Support Vector Machines. The main objective of this research project is to develop a prognostic tool for early detection and prediction of *** model is also deployed into a web application using Python Flask, and the web application is built using HTML and CSS. Anyone can input features into the web application, and the model - which was previously developed using machine learning techniques - will then predict whether or not they will be diagnosed with diabetes. © 2023 IEEE.

关键词： Diagnosis

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Securely Transmit Data over Long Distances Using Quantum Key Distribution Based on E91 Protocol 1

Securely Transmit Data over Long Distances Using Quantum Key...

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1st IEEE International Conference on Networking and Communications, ICNWC 2023

作者： RajKumar, V. Priyadharshini, G. Dept. of Computer Science and Engineering TamilNadu India

ISBN: (纸本)9798350336009

At its core, quantum computing is a fastdeveloping technology that has the potential to process massive volumes of data at high speeds. Some factoring issues may be difficult for the classical computer to solve because of the nature of the factoring. Quantum computing data connections are carried out using optical fiber at a distance as short as possible. While using the E91 protocol for quantum key distribution in a wireless network communication channel, we may extend the distance over which keys are sent. How to design a wireless communication channel for use in quantum computing applications. Using this technology, we are now investigating the development of a broadcast channel that will enhance the data transmission range while maintaining high security and minimizing time consumption. In our technique, we employed polarization multiplexing to communicate across a broadcast channel satellite connection, which allowed us to share data between the transmitter and receiver at the same time. The transfer key is protected by the use of polarization multiplexing and the E91 protocol, both of which are implemented. In a security network, it is used to create an extended communication channel distance between two nodes. In the end, we can meet our goal in this study by comparing the existing method to the performance analysis shown in a graph. © 2023 IEEE.

关键词： Quantum cryptography

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Gait Recognition: A Cross - Dataset Evaluation of Deep Learning Models

Gait Recognition: A Cross - Dataset Evaluation of Deep Learn...

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2024 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2024

作者： Singloo, Hemal Kumar, Manoj Namdev, Simran Sonbawne, Kinsukh Suni Indoria, Nishant Vit Bhopal University Dept. Computer Science and Engineering Sehore India

ISBN: (纸本)9798350348460

This research paper investigates the effectiveness of deep learning models for gait recognition using a variety of data, including gait phase data and sensor data. This study evaluates the performance of convolutional neural networks (CNN) and long-term memory networks (LSTM), focusing on comparative analysis across multiple datasets to gain insight and contribute to different data analyses. The gait level dataset contains important movement patterns as a main area of research. Additionally, sensor-based data identifies various information needs, and complete analysis, for greater understanding and use. These comprehensive experimental results provide several observations on the variability and performance of CNNs and LSTMs across various datasets. The applicability of these models in recognizing activities in different products is presented, and their advantages and limitations in model recognition, display, and feature extraction are seen. This study distinguishes between different types of deep learning models and demonstrates their suitability for handling different types of data. These results lead to decisions regarding model selection and application, contributing to advances in analytics, biometrics, and human recognition applications. © 2024 IEEE.

关键词： CNN Deep Learning Algorithms Gait data LSTM Machine Learning

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