The field of computer vision is predominantly driven by supervised models, which, despite their efficacy, are computationally expensive and often intractable for many applications. Recently, research has expedited alt...
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Generating financial reports from a piece of news is a challenging task due to the lack of sufficient background knowledge to effectively generate long financial reports. To address this issue, this article proposes a...
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Intrusion detection systems(IDS)are one of the most promising ways for securing data and networks;In recent decades,IDS has used a variety of categorization *** classifiers,on the other hand,do not work effectively un...
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Intrusion detection systems(IDS)are one of the most promising ways for securing data and networks;In recent decades,IDS has used a variety of categorization *** classifiers,on the other hand,do not work effectively unless they are combined with additional algorithms that can alter the classifier’s parameters or select the optimal sub-set of features for the *** are used in tandem with classifiers to increase the stability and with efficiency of the classifiers in detecting *** algorithms,on the other hand,have a number of limitations,particularly when used to detect new types of *** this paper,the NSL KDD dataset and KDD Cup 99 is used to find the performance of the proposed classifier model and compared;These two IDS dataset is preprocessed,then Auto Cryptographic Denoising(ACD)adopted to remove noise in the feature of the IDS dataset;the classifier algorithms,K-Means and Neural network classifies the dataset with adam *** classifier is evaluated by measuring performance measures like f-measure,recall,precision,detection rate and *** neural network obtained the highest classifying accuracy as 91.12%with drop-out function that shows the efficiency of the classifier model with drop-out function for KDD Cup99 *** their power and limitations in the proposed methodology that could be used in future works in the IDS area.
Blockchain is one of the emerging technologies that are applied in various fields and its application in Healthcare 4.0 is crucial to handle the vast amount of health records that are growing continuously everyday. Th...
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With the development of communication systems, modulation methods are becoming more and more diverse. Among them, quadrature spatial modulation(QSM) is considered as one method with less capacity and high efficiency. ...
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With the development of communication systems, modulation methods are becoming more and more diverse. Among them, quadrature spatial modulation(QSM) is considered as one method with less capacity and high efficiency. In QSM, the traditional signal detection methods sometimes are unable to meet the actual requirement of low complexity of the system. Therefore, this paper proposes a signal detection scheme for QSM systems using deep learning to solve the complexity problem. Results from the simulations show that the bit error rate performance of the proposed deep learning-based detector is better than that of the zero-forcing(ZF) and minimum mean square error(MMSE) detectors, and similar to the maximum likelihood(ML) detector. Moreover, the proposed method requires less processing time than ZF, MMSE,and ML.
We explore the impact of coarse quantization on matrix completion in the extreme scenario of dithered one-bit sensing, where the matrix entries are compared with random dither levels. In particular, instead of observi...
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We explore the impact of coarse quantization on matrix completion in the extreme scenario of dithered one-bit sensing, where the matrix entries are compared with random dither levels. In particular, instead of observing a subset of high-resolution entries of a low-rank matrix, we have access to a small number of one-bit samples, generated as a result of these comparisons. In order to recover the low-rank matrix using its coarsely quantized known entries, we begin by transforming the problem of one-bit matrix completion (one-bit MC) with random dithering into a nuclear norm minimization problem. The one-bit sampled information is represented as linear inequality feasibility constraints. We then develop the popular singular value thresholding (SVT) algorithm to accommodate these inequality constraints, resulting in the creation of the One-Bit SVT (OBSVT). Our findings demonstrate that incorporating multiple random dither sequences in one-bit MC can significantly improve the performance of the matrix completion algorithm. In pursuit of achieving this objective, we utilize diverse dithering schemes, namely uniform, Gaussian, and discrete dithers. To accelerate the convergence of our proposed algorithm, we introduce three variants of the OB-SVT algorithm. Among these variants is the randomized sketched OB-SVT, which departs from using the entire information at each iteration, opting instead to utilize sketched data. This approach effectively reduces the dimension of the operational space and accelerates the convergence. We perform numerical evaluations comparing our proposed algorithm with the maximum likelihood estimation method previously employed for one-bit MC, and demonstrate that our approach can achieve a better recovery performance. Authors
CWE-502 vulnerabilities have been reported over 100 times each year since 2018, comprising more than 1% of all documented vulnerabilities in 2021. However, domestic research on this topic remains scarce. This study ap...
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CWE-502 vulnerabilities have been reported over 100 times each year since 2018, comprising more than 1% of all documented vulnerabilities in 2021. However, domestic research on this topic remains scarce. This study applied expanded rules to 4 out of the 6 Rules and Recommendations in the Software engineering Institute’s computer Emergency Response Team (SEI CERT) Oracle Coding Standard for Java. To mitigate this vulnerability, the PMD ruleset was expanded by referencing the SEI CERT Coding Standard as a static analysis solution. The extended ruleset can be used by utilizing the OWASP Top 10 attack scenarios and the OWASP Deserialization Cheat Sheet. This study emphasizes the significance of deserialization vulnerabilities and aims to enhance the reliability testing and evaluation of system software with Java. Copyright 2024.
This paper provides a finite-sample analysis of a passive stochastic gradient Langevin dynamics (PSGLD) algorithm. This algorithm is designed to achieve adaptive inverse reinforcement learning (IRL). Adaptive IRL aims...
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Traffic on highways has increased significantly in the past few years. Consequently, this has caused delays for the drivers in reaching their final destination and increased the highway's congestion level. Many op...
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Efficient management of cloud resources for more resource utilization on the one hand, and reducing the makespan on the other hand, has always been an important research issue in cloud environment. Since the proper al...
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Efficient management of cloud resources for more resource utilization on the one hand, and reducing the makespan on the other hand, has always been an important research issue in cloud environment. Since the proper allocation of cloud resources is an optimization problem, the learning-based and population-based meta-heuristic methods are used for this purpose. Most learning methods have scalability problem and may not converge to the optimal solution as the problem space becomes larger, and population-based optimization methods usually need a lot of iterations. The proposed method of this paper aims to overcome the limitations of these two techniques and uses their advantages to increase cloud resource utilization and improve the execution time. A Pareto-based algorithm is also exploited to solve this bi-objective problems. Since increasing the size of population leads to increase the convergence time, a proper learning-based selecting method of population is also utilized to reduce the number of iterations. The proposed method of this paper, called multi-agent bi-objective cloud resource management for dependent tasks using Q-learning and NSGA-3 (BCRN), employs an improved version of Q-learning to reduce the makespan and enhance resource utilization. To overcome the scalability problem of Q-learning, the number of states and actions are reduced in the BCRN, which reduces the complexity of the learning process and leads to better convergence time of the learning process. Two learning agents are also utilized in the BCRN, each of which seeks to improve the objectives of the problem. The NSGA-3 (non-deterministic sorting genetic algorithm-3) algorithm is used as to address the bi-objective problem. In the NSGA-3 algorithm, the initial population is often generated randomly, which leads to more convergence time of the learning process. Using a modified bi-objective Q-learning model, the initial population of the BCRN is generated considering the both objectives of the
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