Search

Recently, many image encryption algorithms have been proposed. Part of them failed to pass the cryptanalysis test and were found to be unsecure. Some others were more secure and could resist the known cryptanalysis methods. Many of those recent schemes followed a well-known model that consists of encrypting the image's pixels using a variety of techniques and methods. Many algorithms were claimed to be secure and performant to be used in real applications. This paper investigates this claim by performing a comparative analysis between a chaotic algorithm recently proposed and the standard general purpose AES cryptosystem in a web application context. The experimental tests conducted on both algorithms in this paper proved that the pixel-wise algorithms are not suitable for this kind of applications. [ABSTRACT FROM AUTHOR]

Because of the limitations of current PCB detection methods, such as low detection accuracy and high rate of false and missed detection, a PCB defect detection algorithm based on improved RetinaNet is proposed, that is, RetinaNet-line. Firstly, the algorithm uses PVT v2B2-Hi as the backbone network of the whole model, which can not only solve the deep feature redundancy caused by the residual network in RetinaNet but also improve the feature extraction ability of the network. Then, the CARAFE up-sampling module is used to enrich the semantic features of defective targets and promote the discrimination ability of the algorithm to defective targets. Finally, HSV color space is used to process the input feature image, and the color distribution is expanded by random perturbation in H, S, and V channels, with the data enhancement completed. In this paper, experiments are conducted on the publicly shared PCB defect detection dataset from the Intelligent Open Laboratory of Peking University, and the results show that the improved algorithm achieves 94.2% in the mAP index, which is better than other networks. [ABSTRACT FROM AUTHOR]

The overwhelming growth of cloud computing has introduced new challenges for the cloud infrastructure. Many cloud service providers (CSPs) have been adopting renewable energy (RE) sources to increase profitability and reduce carbon emissions. Therefore, recent literature focuses on managing cloud infrastructure with RE sources in addition to traditional non-renewable energy (NRE) sources whenever required. However, these works aim to maximize the usage of RE or minimize the cost without considering the uncertainty (UN). This paper presents three user request-based scheduling algorithms, namely UN-based future-aware best fit, UN-based round-robin, and UN-based highest available renewable first, by managing the UN of RE and NRE. These algorithms consider two types of UN, namely UN-RE and UN-NRE, concerning the user request. The proposed algorithms undergo a rigorous simulation process with 200 to 2000 user requests and 20 to 200 datacenters and are compared using overall cost, UN cost and time, and the number of used RE resources. [ABSTRACT FROM AUTHOR]

IoT networks can provide many benefits and opportunities, although their implementation poses challenges. Cloud-only storage of IoT data would be very costly and time-consuming. In this paper, a new scheme is proposed for caching IoT content on the edge with SDN-based processing capability. The proposed scheme considers a global SDN controller, which coordinates cache decisions across the entire IoT network. The Moth-Flame Optimization-Cluster Head Selection (MFO-CHS) algorithm is used to cluster devices where the selected cluster heads send the IoT data to the edge nodes for caching. In addition, by utilizing edge caching capabilities and using MFO to select and cache the appropriate contents on edge nodes, the proposed Moth-Flame Optimization-Edge Caching (MFO-EC) algorithm can provide data with lower latency on upcoming requests. Caching can also help ensure reliability and availability since intermittent connections and power limitations affect IoT devices. Caching decisions regarding IoT characteristics are not made intelligently in the default caching scheme for maximizing device longevity and managing the possibility that content producers may become unreachable. This scheme considers several metrics, and the proposed moth-flame optimization (MFO) algorithms, MFO-CHS, and MFO-EC algorithms, which are nature-inspired paradigms for the edge caching problem called "MFO-SDN-EC", Moth-Flame Optimization-Software-defined Networking -Edge Caching, are used to select the best options regarding considered criteria and improve the QoS in the SDN based IoT environment. Based on simulations, our proposed caching method can reduce energy consumption by 45%, decrease the average response time by 49%, and also increase cache-hit rates. Furthermore, our results demonstrate our algorithm's superiority over several current approaches in terms of assessment measures. [ABSTRACT FROM AUTHOR]

The large amount of data generated every day makes necessary the re-implementation of new methods capable of handle with massive data efficiently. This is the case of Association Rules, an unsupervised data mining tool capable of extracting information in the form of IF-THEN patterns. Although several methods have been proposed for the extraction of frequent itemsets (previous phase before mining association rules) in very large databases, the high computational cost and lack of memory remains a major problem to be solved when processing large data. Therefore, the aim of this paper is three fold: (1) to review existent algorithms for frequent itemset and association rule mining, (2)to develop new efficient frequent itemset Big Data algorithms using distributive computation, as well as a new association rule mining algorithm in Spark, and (3) to compare the proposed algorithms with the existent proposals varying the number of transactions and the number of items. To this purpose, we have used the Spark platform which has been demonstrated to outperform existing distributive algorithmic implementations. [ABSTRACT FROM AUTHOR]

Due to easier access, improved performance, and lower costs, the use of cloud services has increased dramatically. However, cloud service providers are still looking for ways to complete users' jobs at a high speed to increase profits and reduce energy consumption costs. To achieve such a goal, many algorithms for scheduling problem have been introduced. However, most techniques consider an objective in the scheduling process. This paper presents a new hybrid multi-objective algorithm, called SMO_ACO, for addressing the scheduling problem. The proposed SMO_ACO algorithm combines Spider Monkey Optimization (SMO) and Ant Colony Optimization (ACO) algorithm. Additionally, a fitness function is formulated to tackle 4 objectives of the scheduling problem. The proposed fitness function considers parameters like schedule length, execution cost, consumed energy, and resource utilization. The proposed algorithm is implemented using the Cloud Sim toolkit and evaluated for different workloads. The performance of the proposed technique is verified using several performance metrics and the results are compared with the most recent existing algorithms. The results prove that the proposed SMO_ACO approach allocates resources efficiently while maintaining cloud performance that increases profits. [ABSTRACT FROM AUTHOR]

Fog Computing paradigm that provisions low-latency computing services at the edge network, is a bonanza for supply chain computing resources in Internet of Things (IoT) applications. In different scenarios such as smart homes/healthcare systems, multiple IoT applications are distributed simultaneously in cloud and fog nodes to provide different IoT-based services. In addition, each program requires resources and has its desired quality of service (QoS) which should be met. One of the key challenges in fog computing environment is how to efficiently allocate resources to IoT applications because inefficient resource allocation leads to burdening providers high costs and it lowers down the delivered QoS to users. Since the majority of IoT applications are time-sensitive, the low delay and near physically allocated resources improve the amount of delivered QoS. Therefore, the resource clustering algorithms with the lowest distance error rate and the lowest delay as a consequence are favorable. The aim is to reduce clustering errors and improve the overall performance of the system. This paper formulates resource allocation to IoT applications in heterogeneous 4-layered fog platforms to an optimization problem. To solve this problem, a fusion approach incorporating a genetic algorithm (GA) and the k-means clustering approach is presented. Firstly, it utilizes the k-means approach and Jaccard measurement to cluster fog nodes with a minimum clustering rate. Then, the resources of fog clusters are allocated to IoT devices with the minimum error rate by incorporating GA algorithm. This selection of processing nodes in a fog layer helps to minimize latency and allows applications to access resources simultaneously. The simulation results in extensive scenarios prove the superiority of the proposed algorithm against other successful meta-heuristic approaches in terms of the objective function and lowest error/delay rate. [ABSTRACT FROM AUTHOR]

Cloud computing has experienced extraordinary development across a wide range of industries by giving customers the flexibility to employ computing resources as needed. The task scheduling problem is one of several major challenges in cloud computing, and it should be scheduled effectively to minimize makespan and maximize resource utilization. Therefore, this paper put forward an improved scheduling efficiency algorithm called Enhanced Marine Predator Algorithm (EMPA). Firstly, task scheduling model with makespan and resource utilization is constructed. Secondly, each individual represents a result of task scheduling, and the purpose of algorithms is to find the optimal scheduling result, therefore the operator of WOA, nonlinear inertia weight coefficient and golden sine strategy are introduced into Marine Predator Algorithm. In the simulation experiment, EMPA is compared with Grey Wolf Optimizer (GWO), Sine Cosine Algorithm (SCA), Particle Swarm Optimization (PSO), and Whale Optimization Algorithm (WOA) under different number of tasks in synthetic datasets and GoCJ datasets.The experimental results show that the EMPA algorithm has more advantages in terms of makespan, degree of imbalance, and resource utilization. [ABSTRACT FROM AUTHOR]

In this paper, we consider the matter of on-line clustering of hyperplanes within the presence of bounded noise. This is often a challenging problem that involves the segmentation of the data and the estimation of the hyperplanes parameters. The proposed algorithm consists in two successive steps. At whenever, the first step allows to assign the current data point to the most appropriate hyperplane and therefore the second step realizes an update of the parameters of the hyperplane that contains the data point. The second step springs from an Outer Bounding Ellipsoid type algorithm suitable for on-line parameters estimation within the presence of bounded noise. The performance of our algorithm is proven using synthetic and real data. [ABSTRACT FROM AUTHOR]

Numerous data centers have adopted heterogeneous computing server to accelerate the processing speed with various applications. However, as the growth of efficiency, the issue of high power consumption is increasingly becoming crucial. Traditional researchers focus on the developing strategies for power efficiency that neglects the importance of power prediction that substantially impacts on energy planning on data center. Different to the previous work, this paper utilizes the advantage of Artificial Neural Network (ANN) to design and implement an improved algorithm of power consumption prediction on heterogeneous computing server with various types of jobs. First, the impact of multi-type of jobs has been taken into account, i.e., CPU-intensive, memory-intensive, I/O-intensive and GPU-intensive. We collect the data trace from these jobs by a set of benchmarks. Then, based on the collected data trace, power prediction algorithm has been established, so that developing an improved ANN-based power consumption prediction algorithm with CNN-BiLSTM neural network and attention mechanism. Specifically, one dimensional CNN is utilized to perform local feature extraction and dimension reduction on the input, the BiLSTM network is adopted to extract high-level features from the above results. The miscellaneous features extracted are filtered out by using attention mechanism. Last, the experiment has been conducted, and the peculiarity for four types of jobs has been discussed. Compared with the latest proposed ANN-based prediction power algorithms, our proposed algorithm has better performance in prediction accuracy on power consumption. [ABSTRACT FROM AUTHOR]

Clustering is an important technique for data mining and machine learning. In this paper, a novel clustering algorithm, scale division and region growing (SRG), is proposed, which is based on scale division and region growing. This method uses the division-and-merge strategy, which consists of two primary phases, namely the division phase and the merging phase. In the division phases, the data is mapped to a two-dimensional space, and the clustering problem is transformed to an image processing problem. Then the image is divided into different scales to adaptively find the appropriate scale. After finding the appropriate scale, the region growing is carried out under this scale and the multiple sub-clusters are created. In the merging phases, the sub-clusters are evaluated and merged to form the final clusters in the dataset. The proposed algorithm is tested on 15 benchmark datasets including both the synthetic datasets and the real datasets, and it is found that SRG can obtain better clustering results and has higher accuracy than other typical clustering algorithms. Moreover, the experimental results on noisy datasets show that the proposed algorithm has good robustness. [ABSTRACT FROM AUTHOR]

As the performance of Radio Frequency Identification (RFID) devices is susceptible to the influence of the surrounding environment, making the original data collected by RFID devices with uncertain, of which missed data and redundant data are the main source of uncertainty data, these uncertainty data will seriously affect the quality of RFID upper layer applications. Therefore, to solve the uncertainty in RFID data, the original data must be cleaned. For the shortcomings of tag dynamics detection in the traditional data cleaning algorithm named Statistical Smoothing for Unreliable RFID data (SMURF), an adaptive sliding window-based data cleaning algorithm for RFID single-tag is proposed. The method takes into account the influence of tag speed on tag integrity judgment, and divides sliding sub-windows to accurately detect tag changes and then reasonably adjusts the sliding window size. In addition, considering that the average reading rate of tags is affected by the collision between multiple tags, an RFID multi-tag cleaning method based on twice-tag number estimation is proposed. The method accurately estimates the number of tags by the twice-tag estimation method, controls the read period by the Markov chain, and reduces the occurrence of multiple tag collisions by using an unequal time slot optimization method. Experimental results show that the proposed method in this paper can form a complete set of RFID data stream cleaning algorithms, which effectively reduces the uncertain data and improves the data accuracy. [ABSTRACT FROM AUTHOR]