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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Akrami, Zinatullah Singh Bhathal, Gurjit Punjabi University Computer Science and Engineering Department Patiala India

ISBN: (纸本)9798350381931

Cloud computing enhances defense, public sectors, innovation, and infrastructure. The Korean Military plans to adopt cloud computing for its defense system, but current security measures are inadequate. A secure architecture is necessary, which aligns with U.S. military standards and addresses Korean defense system requirements in a cloud environment. Cloud computing presents cost, efficiency, and scalability benefits but also security concerns. This paper proposes a risk management framework including self-assessment, provider assessment, risk assessment, third-party review, and continuous monitoring. By using this framework, providers can understand user needs and build *** businesses lack online exposure for their products/services. This article proposes cloud-based organic product catalogs as a solution for enabling electronic ordering and transactions with micro *** e-commerce stores struggle with trust due to security risks and inadequate compliance monitoring. This research develops an adaptive assurance model using legislation, ISO standards, policies, security, and availability to enhance trustworthiness. The model employs intelligent cooperative rating techniques and the empirical survey validates its effectiveness. Findings inform the improvement of e-commerce assurance models. © 2024 IEEE.

关键词： ISO Standards

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Novel Skin Disease Prediction Using computer Vision Algorithms 8th

Novel Skin Disease Prediction Using Computer Vision Algorith...

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8th International Conference on Information System Design and Intelligent Applications, ISDIA 2024

作者： Sreekumar, Sruthi Paul, Rohan Thomas Sand, Madhav Dilip, Golda Department of Computer Science and Engineering SRMIST Vadapalani Chennai India

ISBN: (纸本)9789819748945

Skin disease detection is essential in healthcare, and traditional diagnosis by dermatologists is time-consuming and subjective. computer vision algorithms have been developed to automate skin disease detection using skin lesion images, but variations in skin tone, lighting, and lesion appearance make it challenging. A deep learning-based approach that combines CNNs and advanced computer vision algorithms is proposed and trained on a large dataset of skin lesion images. This approach can revolutionize skin disease diagnosis and treatment, leading to earlier treatment, improved patient outcomes and reduced healthcare costs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning

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Dehazing using Generative Adversarial Network - A Review

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SN computer science 2025年第1期6卷 1-17页

作者： Khatun, Amina Mostafiz, Rafid Shorif, Sumaita Binte Uddin, Mohammad Shorif Hadi, Md. Abdul Department of Computer Science and Engineering Jahangirnagar University Dhaka Bangladesh Institute of Information Technology Noakhali Science and Technology University Noakhali Bangladesh Department of Computer Science: Information Technology University of Nebraska Omaha Omaha United States

Dehazing is a difficult process in computer vision that seeks to improve the clarity and excellence of pictures taken under cloudy, foggy, and rainy circumstances. The Generative Adversarial Network (GAN) has been a viable method for removing haze from photos in recent years. This is because GAN can understand intricate data patterns and provide high-quality outcomes. This paper provides a thorough examination of the most advanced strategies in dehazing utilizing GAN. The study examines the various elements utilized in GAN-based dehazing, including generator and discriminator architectures, loss functions, and training strategies. It also explores the evaluation metrics employed to assess the effectiveness of GAN-based dehazing methods. It also examines the datasets often used to train and evaluate these models. This research concludes by examining prospective avenues for future study in the domain of dehazing, employing GAN to tackle the obstacles of real-time dehazing, managing intricate scenarios with various atmospheric conditions, and enhancing the resilience of GAN-based dehazing models. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Atmospheric haze Dehazing Generative Adversarial Network (GAN) Performance evaluation metrics

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Quantitative Risk Forecasting using Value at Risk (VaR): A Case Study of Adani Power 3

Quantitative Risk Forecasting using Value at Risk (VaR): A C...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Parthasarthy, Srikrshna Rangayyan, Sanika M. Dhruva, G. Jamadagni, Suresh Pes University Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350369083

In the contemporary financial landscape, managing and hedging risk remains an indispensable aspect of portfolio management. Value at Risk (VaR) is a widely accepted measure for quantifying and understanding the downside risk of various financial assets. This study attempts to model the financial risk of the Adani Power stock over a five-year horizon (2019 - 2024) that witnessed a tumultuous period for the company, attributable to the breakout of the Russia-Ukraine War and the publication of the Hindenburg Report. It incorporates statistical techniques such as Historical VaR, Parametric VaR and Monte Carlo Simulations to estimate the underlying financial risk. Subsequently, risk is forecasted using time-series and deep-learning approaches (such as LSTM and Bi-LSTM) to illustrate the viability of using computational approaches to quantify and extrapolate financial risk. In such volatile sectors, accurate risk forecasting is inherently essential for ensuring operational stability, managing financial uncertainties, and optimizing investment decisions amid dynamic market conditions. Through empirical analysis and statistical validation, this study contributes to the academic research on risk management in emerging markets with a focus on Adani Power. © 2024 IEEE.

关键词： Deep Learning Financial Modelling Risk Stock Price Time-series Forecasting Value at Risk

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HQL: An Augmented Q-learning Approach to Turn-based Heterogeneous Multi-Agent Systems 8

HQL: An Augmented Q-learning Approach to Turn-based Heteroge...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Luitel, Yugesh Upadhyaya Koirala, Arpan Shrestha, Aatish Poudel, Abhijeet Kathmandu University Department of Computer Science and Engineering Dhulikhel Nepal

ISBN: (纸本)9798350376425

As a remote game originating from the roots of Nepal, Baghchal has witnessed limited exploration and consequently, the strategic aspect of the game remains underdeveloped. The game can be characterized as a heterogeneous multi-agent turn-based zero-sum game that satisfies the markov property and represents a domain that has not been fully explored. This paper provides a solution to this domain through an attempt to master the game of Baghchal using reinforcement learning. The paper introduces an augmented Q-learning approach, termed Heterogeneous Q-learning (HQL), to incorporate the nature of the game. The HQL approach is utilized to train two distinctly different agents to play Baghchal. An assessment of these agents competing against each other suggests that optimal game-play often leads to a draw, with approximately 52.6% of the 2001 games resulting in this outcome. In a systematic evaluation of over 34,000 games against randomized opponents, both agents exhibit incremental improvements throughout the training process. Notably, the tiger agent learns to obtain perfect score early in the training, while, the goat agent is able to eliminate its losses only towards the end. © 2024 IEEE.

关键词： Multi agent systems

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SRNG: An Efficient Decentralized Approach for Secret Random Number Generation 6

SRNG: An Efficient Decentralized Approach for Secret Random ...

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6th IEEE International Conference on Blockchain and Cryptocurrency, ICBC 2024

作者： Barakbayeva, Togzhan Cai, Zhuo Goharshady, Amir Kafshdar Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

ISBN: (纸本)9798350316742

Many blockchain protocols and applications require access to a reliable source of distributed random numbers. This has led to the recent interest in the study of distributed random number generation (RNG) and randomness beacons. Numerous approaches have been proposed in the literature, using different cryptographic techniques and working under different assumptions. A problem that has recently been studied is that of generating secret random numbers. There is a natural usecase for this. Suppose a casino CASSIE wishes to offer its gambling games as a smart contract. It is not viable to generate a fresh distributed random number for each bet. Instead, a secret random number should be generated at predefined intervals, e.g. each day, and used as a seed to create the randomness for the whole day. This seed should only be known to CASSIE. Moreover, at the end of the day, CASSIE should be able to disclose the seed and prove that there was no tampering. In this work, we propose a simple and novel distributed random beacon protocol that generates distributed random numbers while preserving secrecy. The generated random number can be used in DeFi applications, such as decentralized casinos, for some time, until it is published along with proof that it is indeed the output of our random beacon. In addition to achieving the desired secrecy property, our approach is also efficient and requires the same amount of computation and communication as non-secret random beacons. Our protocol can easily be implemented as a smart contract. © 2024 IEEE.

关键词： Random number generation

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Heart Health Forecasting with Machine Learning Techniques

Heart Health Forecasting with Machine Learning Techniques

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Juneja, Sonam Mahmud, Sadik Nandi, Sourov Kumar Anjum, Md Tanvir Chandigarh University Department of Computer Science and Engineering Gharuan India

ISBN: (纸本)9798350375237

This research study explores the field of machine learning-based heart health forecasting. Through the integration of datasets sourced from the UCI Machine Learning Repository and Kaggle, we amalgamated a comprehensive dataset comprising 14 columns and 1025 observations. Employing a meticulous preprocessing regimen, we ensured data integrity by eliminating null values, conducting one-hot encoding for categorical variables, and normalizing the dataset. Seven well-known machine learning models were assessed in this study. Strict evaluation metrics were used on these models, including precision, sensitivity, specificity, and accuracy. Our research clarifies the subtleties of each model's performance and offers insights into its suitability and effectiveness for heart health forecasting. © 2024 IEEE.

关键词： Predictive analytics

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Evaluation of Ensemble Learning Models for Hardware-Trojan Identification at Gate-level Netlists

Evaluation of Ensemble Learning Models for Hardware-Trojan I...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Negishi, Ryotaro Togawa, Nozomu Waseda University Department of Computer Science and Communications Engineering Japan

ISBN: (纸本)9798350324136

IoT (Internet-of-Things) devices are tremendously widespread in our daily lives and these devices are very often outsourced to third-party companies to save cost. However, it is pointed out that the risk to insert malicious circuitry, called hardware Trojans (HTs), much increases there. The methods using machine learning for detecting HTs at gate-level netlists have been proposed, and those based on ensemble learning models are considered the most effective among them. This paper evaluates the performance of HT detection at gate-level netlists using various machine learning models based on ensemble learning, including random forest, XGBoost, LightGBM, and CatBoost. In particular, we optimize HT features for each machine-learning model and perform HT detection for various gate-level netlists, including intellectual property core netlists. The detailed HT detection results are thoroughly summarized and compared. © 2024 IEEE.

关键词： ensemble learning hardware security hardware Trojan machine learning netlist

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Combinatorial Approach for Factorization of Variance and Entropy in Spin Systems 35

Combinatorial Approach for Factorization of Variance and Ent...

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35th Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2024

作者： Chen, Zongchen Department of Computer Science and Engineering University at Buffalo United States

We present a simple combinatorial framework for establishing approximate tensorization of variance and entropy in the setting of spin systems (a.k.a. undirected graphical models) based on balanced separators of the underlying graph. Such approximate tensorization results immediately imply as corollaries many important structural properties of the associated Gibbs distribution, in particular rapid mixing of the Glauber dynamics for sampling. We prove approximate tensorization by recursively establishing block factorization of variance and entropy with a small balanced separator of the graph. Our approach goes beyond the classical canonical path method for variance and the recent spectral independence approach, and allows us to obtain new rapid mixing results. As applications of our approach, we show that: 1. On graphs of treewidth t, the mixing time of the Glauber dynamics is (Equation presented), which recovers the recent results of Eppstein and Frishberg [EF21] with improved exponents and simpler proofs;2. On bounded-degree planar graphs, strong spatial mixing implies Oe(n) mixing time of the Glauber dynamics, which gives a faster algorithm than the previous deterministic counting algorithm by Yin and Zhang [YZ13]. Copyright © 2024 by SIAM.

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PGV: An Edge Operator Based Random Point Localization Algorithm for Eye Tracking System 11

PGV: An Edge Operator Based Random Point Localization Algori...

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11th International Conference on Information Technology, computer and Electrical engineering, ICITACEE 2024

作者： Al-Ikhsan, Hamdani Bachtiar, Fitra Abdurrachman Faculty of Computer Science Brawijaya University Informatics Department Malang Indonesia

ISBN: (纸本)9798350389289

Eye tracking is an important variable and offers benefits in Human-computer interaction (HCI) which is often complex and impractical in practice. Eye tracking implementation usually requires specialized devices, technology, or setup to work. On top of that, challenges like face obstructions like glasses or masks, differences in lighting, distance, and angles add another layer to the implementation difficulty. A novel random point detector algorithm called Pixel Gradient Value (PGV) is proposed which is an interest point detector-like algorithm to be used for eye-tracking purposes, which works by calculating the gradient value of a pixel in a given neighborhood by using an edge detection operator like Sobel edge detector and Canny edge detector as its basis. PGV is image-agnostic, it is capable of covering a wide amount of eye-tracking scenarios while running on a minimum setup. PGV is then used in a series of Fully Convolutional Network (FCN) machine learning system used for classification and point localization of the pupil center point. The proposed model is able to achieve higher results compared to the state-of-the-art result of em ax ≤ 0.025 value of 96.04, em ax ≤ 0.05 value of 98.71 and em ax ≤ 0.01 value of 99.19. © 2024 IEEE.

关键词： Convolution

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