Search

Cloud storage service has been in full swing in the industry. Delta synchronization technology as a key technology of cloud storage services has not made a key breakthrough. Almost all the existing researches are based on the synchronization process proposed by the Rsync algorithm and mix some optimization appropriately, but the particularity of cloud storage service is not fully considered. This paper proposes a new incremental synchronization method SimpleSync, which makes use of the characteristic that the server does not actively modify the backup files in the cloud storage service, removes the redundant steps in Rsync, and enables the synchronization between the client and the server only through a single communication. Besides, according to the server‐side synchronization request processing logic, this paper puts forward the design idea of parallel processing with the Flink framework, to the best of our knowledge, for the first time. After the server receives the synchronization request, SimpleSync first puts it into Kafka for buffering and then uses Flink to process the synchronization request in parallel. In the experimental part, a large number of experiments are designed to compare SimpleSync with other delta synchronization algorithms. Experimental results show that SimpleSync has obvious advantages in synchronization performance. Meanwhile, experiments show that SimpleSync has correctness. [ABSTRACT FROM AUTHOR]

Summary: The visibility of the image is degraded by haze components which are affected in image during image acquisition process. The haze removal process can be categorized into haze reduction from single image and haze reduction from multiple images. The second case consumes more time and the complexity of algorithm is high. In order to overcome such limitations, this paper proposes an efficient methodology for haze reduction in single acquired image. In this paper, haze detection and removing methodology is proposed using Gaussian pyramidal decomposition. This proposed methodology is tested on both indoor and outdoor haze affected images. The performance of this proposed haze removal algorithm is analyzed with respect to average running time and peak signal to noise ratio, mean square error, mean absolute error, Entropy, and normalized histogram intersection coefficient. [ABSTRACT FROM AUTHOR]

The monitor concept provides a structured and flexible high-level programming construct to control concurrent accesses to shared resources. It has been widely used in a concurrent programming environment for implicitly ensuring mutual exclusion and explicitly achieving process synchronization. This paper proposes an extension to the monitor construct for detecting runtime errors in monitor operations. Monitors are studied and classified according to their functional characteristics. A taxonomy of concurrency control faults over a monitor is then defined. The concepts of a monitor event sequence and a monitor state sequence provide a uniform approach to history information recording and fault detection. Rules for detecting various types of faults are defined. Based on these rules, fault-detection algorithms are developed. A prototypical implementation of the proposed monitor construct with runtime fault detection mechanisms has been developed in Java. We shall briefly report our experience with and the evaluation of the robust monitor prototype. Copyright © 2005 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Using multiple independent networks (also known as rails) is an emerging technique which is being used to overcome bandwidth limitations and enhance fault tolerance of current high-performance parallel computers. In this paper, we present and analyze various algorithms to allocate multiple communication rails, including static and dynamic allocation schemes. An analytical lower bound on the number of rails required for static rail allocation is shown. We also present an extensive experimental comparison of the behavior of various algorithms in terms of bandwidth and latency. We show that striping messages over multiple rails can substantially reduce network latency, depending on average message size, network load and allocation scheme. The methods compared include a static rail allocation, a basic round-robin rail allocation, a local-dynamic allocation based on local knowledge and a dynamic rail allocation that reserves both communication endpoints of a message before sending it. The last method is shown to perform better than the others at higher loads: up to 49% better than local-knowledge allocation and 37% better than the round-robin allocation. This allocation scheme also shows lower latency and it saturates at higher loads (for long enough messages). Most importantly, this proposed allocation scheme scales well with the number of rails and message size. In addition we propose a hybrid algorithm that combines the benefits of the local-dynamic allocation for short messages with those of the dynamic algorithm for large messages. Copyright © 2003 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Summary: The sorting efficiency of the warehouse management and handling robots (automated guided vehicles [AGVs]) in an unmanned warehouse affects the delivery and lead time of orders. Studying the collaborative sorting of multiple AGVs can significantly improve work efficiency. The goal of this paper is to minimize the overall shortest transportation path and time while ensuring the completion of all orders and corresponding recycling or return tasks. This paper conducts experiments on global path planning for multiple AGVs based on the A* algorithm, both without considering collision and deadlock situations, and considering collision and deadlock situations. The results show that without considering collision and deadlock situations, the method of matching fixed storage locations and picking stations can improve overall efficiency, while considering collision and deadlock problems will result in reduced efficiency. In the future, the model will be further optimized taking into account actual conditions to improve overall efficiency. [ABSTRACT FROM AUTHOR]

Recently, the notion of a smart city, which includes smart well‐being, smart transit, and smart society, has attracted much attention due to its impact on people's quality of living. Data in smart cities are characterized by variety, velocity, volume, value, and veracity that are the well‐known characteristics of big data. The fast pace expanding of IoT devices and sensors in smart cities generates a huge volume of data that can help decision‐makers and managers in city management. The aim of this article is to wholly and systematically review big data handling approaches in smart cities, in which we analyze research efforts published between 2013 and February 2021, where these techniques are categorized based on their algorithms and architectures. Further, the main ideas, evaluation techniques, tools, evaluation metrics, algorithm types, advantages, and disadvantages are explored. Additionally, essential evaluation factors are introduced in which scalability and availability by 16%, time by 15% and accuracy by 11% are more in focus, and finally, some of the challenges, open issues, and future trends that are valuable for further research are suggested in big data handling approaches in smart cities. [ABSTRACT FROM AUTHOR]

Summary: Lightweight cryptography is a rapidly developing research field. Its main goal is to provide security for devices with fewer resources. These limited‐resource devices implement reliable ciphers that use very little power and computation. The lightweight cipher should be built for high performance while using the fewest resources possible, such as memory and power. In this article, we compare block ciphers and several other stream ciphers based on criteria such as input size, output size, structure employed, key size, number of rounds, vulnerable attacks, chip area, gate equivalent, memory use, throughput, and security features. Moreover, this article provides a detailed analysis comparing all cryptographic algorithms and their use in day‐to‐day life activities. This paper also discusses some lightweight ciphers, stream ciphers, and hybrid ciphers. Moreover, it shows the cryptanalysis of some block ciphers like DES. [ABSTRACT FROM AUTHOR]

They evaluate these optimizations in different multicore and GPU architectures, investigating the impact of different APIs on the performance, energy efficiency, and portability of the code. In the fifth contribution, entitled "Energy efficiency and portability of oil and gas simulations on multicore and graphics processing unit architectures", Serpa et al.5 propose three optimizations for an oil and gas application, reverse time migration (RTM), which reduce the floating-point operations by changing the equation derivatives. Moreover, the Brute Force algorithm running on CPU + GPU architecture has greater energy efficiency, reaching at least 1.79× more operations per energy consumption than other algorithms on different architectures explored in the work. [Extracted from the article]

Summary: Summary Software‐Defined Networking (SDN) is an emerging network paradigm that has gained significant traction from many researchers to address the requirement of current data centers. Although central control is the major advantage of SDN, it is also a single point of failure if it is made unreachable by a Distributed Denial of Service (DDoS) attack. Despite the large number of traditional detection solutions that exist currently, DDoS attacks continue to grow in frequency, volume, and severity. This paper brings an analysis of the problem and suggests the implementation of four machine learning algorithms (SVM, MLP, Decision Tree, and Random Forest) with the purpose of classifying DDoS attacks in an SDN simulated environment (Mininet 2.2.2). With this goal, the DDoS attacks were simulated using the Scapy tool with a list of valid IPs, acquiring, as a result, the best accuracy with the Random Forest algorithm and the best processing time with the Decision Tree algorithm. Moreover, it is shown the most important features to classify DDoS attacks and some drawbacks in the implementation of a classifier to detect the three kinds of DDoS attacks discussed in this paper (controller attack, flow‐table attack, and bandwidth attack). [ABSTRACT FROM AUTHOR]

Summary: In this paper, a new technique is suggested for best electric load forecasting of South Korea. The proposed study is based on an improved metaheuristic methodology. This paper proposes a new improved version of Support Vector Machine for Regression (SVR) base on the proposed algorithm. The algorithm is based on a modified version of Crow Search Algorithm (ACSA), where, by combining by the SVR, generates an efficient classifier. Although, different drawbacks of using original CSA are stated, the proposed ACSA method provides a proper modification for resolving of these shortcomings. The forecasting model has been then performed to a historical data from South Korea to indicate the algorithm efficiency and the results are compared with some other methods, including SVR‐CGA, SVR‐CAS, ANN, and simple Regression to show the suggested method performance. Final results show that MAPD, MSE, and RMSE values of the test data with lower values for South, North, Center, and East, provided better results based on the proposed SVR‐ACSA based on different values of center and kernel of the SVR. Experimental results on the case study shows that the minimum MAPD has been added which is happened in Northern regional is 1.1062, 1.1336, 1.1065, 1.02, 2.23, 1.96, and 1.49 for SVR‐ACSA, SVR‐CGA, SVR‐CAS, ANN, Regression, LSTM‐RNN, and ELM which indicates the method's higher efficiency. [ABSTRACT FROM AUTHOR]

Smoke has a very bad effect on the outdoor vision system. Not only are the videos with poor visual effects obtained, but also the quality and structure of the videos are reduced. In this paper, we propose a video smoke removal method based on low‐rank tensor completion via spatial‐temporal continuity constraint. The proposed method is based on the smoke mixing model and consider the sparseness of smoke and the global and local consistency of clean video. Then, the optimal solution of the smoke removal algorithm model is quickly realized by the Alternating Direction Method of Multiplier. Finally, we evaluate the experiment results of real‐world data and simulated data from the visual effects and objective indicators. And the experiment results show that our proposed algorithm can achieve better smoke removal results. [ABSTRACT FROM AUTHOR]

Summary: Cloud computing and smart gadgets are the need of smart world these days. This often leads to latency and irregular connectivity issues in many situations. In order to overcome this issue, an emerging technique of fog computing is used for cloud and smart devices. A decentralized computing infrastructure in which all the elements, that is, storage, compute, data and the applications in use, are passed in an efficient and logical place between cloud and the data source, is called Fog computing. The cloud computing and services are generally extended by fog computing, which brings the power and advantages of data creation and data analysis at the network edge. Real‐time location based services and applications with mobility support are enabled due to the physical proximity of users and high speed internet connection to the cloud. Fog computing is promoted with leveraging load balancing techniques so as to balance the load which is done in two ways, that is, static load balancing and dynamic load balancing. In this paper, different load balancing algorithms are discussed and their comparative analysis has been carried out. Round Robin load balancing is the simplest and easiest load balancing technique to be implemented in fog computing environments. The major problem of Source IP Hash load balancing algorithm is that each change can redirect to anyone with a different server, and thus, is least preferred in fog networks. The mechanisms to make energy efficient load balancing are also considered as the part of this paper. [ABSTRACT FROM AUTHOR]

Summary: Convolutional neural networks (CNNs) have shown remarkable advantages in a wide range of domains at the expense of huge parameters and computations. Modern CNNs still tend to be more complex and larger to achieve better inference accuracy. However, the complex and large structures of CNNs could slow down the inference speed. Recently, Compressing the convolutional weights to be sparse by pruning the unimportant parameters has been demonstrated as an efficient way to reduce the computations of CNNs. On the other hand, field‐programmable gate arrays (FPGAs) have been a popular hardware platform to accelerate CNN inference. In this paper, we propose an algorithm/hardware co‐optimized method for accelerating CNN inference on FPGAs. For the algorithm, we take advantage of unstructured and structured parameter sparsifying methods to achieve high sparsity and keep the regularity of convolutional weights. Correspondingly, hardware‐friendly index representations of sparse convolutional weights are proposed. For the hardware architecture, we propose row‐wise input‐stationary dataflow, which is tightly coupled with the algorithm. A row‐wise computing engine (RConv Engine) is proposed, which is based on the dataflow. Inside the RConv Engine, the scalar‐vector structure is applied to implement the basic processing elements (PEs). To flexibly calculate the feature map with various sizes, the PEs are organized in a 2D structure with two work modes. The experimental results demonstrate that our co‐optimized method implements high sparsity of convolutional weights, and the computing engine achieves high computation efficiency. Compared with other accelerators, our co‐optimized method implements a 10.9×$$ \times $$ speedup on FPS at most with the highest sparsity of convolutional weights and negligible accuracy loss. [ABSTRACT FROM AUTHOR]

Summary: IOTA is a distributed ledger technology with a new structure called Tangle, which offers high scalability, no fees, and near‐instant transfers for the internet‐of‐things (IoT) networks. The most important issue of IOTA is consensus achievement, which is handled by a single node acting as a coordinator. A single and default coordinator node exposes IOTA to the single point of failure and incomplete distribution issues. In this paper, a novel algorithm for selecting multiple coordinators to participate in the consensus process at milestones is proposed (i.e., MCS algorithm) to overcome the problem of a single coordinator in IOTA network. The MCS algorithm is applied in a two‐layered architecture including IoT devices (i.e., Layer 1) and fog nodes (i.e., Layer 2). We define and formulate four different properties as metrics to select multiple coordinators in this architecture. Moreover, a collusion list is defined to decrease the risk of collusion between fog nodes in the network. Experimental results show that using multiple fog nodes as coordinators in IOTA network can improve the average response time and average throughput while does not considerably sacrifice the total cost and system utilization in comparison with the use of a single default coordinator in IOTA network. [ABSTRACT FROM AUTHOR]

Summary: Clustering ensemble is a popular approach for identifying data clusters that combines the clustering results from multiple base clustering algorithms to produce more accurate and robust data clusters. However, the performance of clustering ensemble algorithms is highly dependent on the quality of clustering members. To address this problem, this paper proposes a member enhancement‐based clustering ensemble (MECE) algorithm that selects the ensemble members by considering their distribution consistency. MECE has two main components, called heterocluster splitting and homocluster merging. The first component estimates two probability density functions (p.d.f.s) estimated on the sample points of an heterocluster and represents them using a Gaussian distribution and a Gaussian mixture model. If the random numbers generated by these two p.d.f.s have different probability distributions, the heterocluster is then split into smaller clusters. The second component merges the clusters that have high neighborhood densities into a homocluster, where the neighborhood density is measured using a novel evaluation criterion. In addition, a co‐association matrix is presented, which serves as a summary for the ensemble of diverse clusters. A series of experiments were conducted to evaluate the feasibility and effectiveness of the proposed ensemble member generation algorithm. Results show that the proposed MECE algorithm can select high quality ensemble members and as a result yield the better clusterings than six state‐of‐the‐art ensemble clustering algorithms, that is, cluster‐based similarity partitioning algorithm (CSPA), meta‐clustering algorithm (MCLA), hybrid bipartite graph formulation (HBGF), evidence accumulation clustering (EAC), locally weighted evidence accumulation (LWEA), and locally weighted graph partition (LWGP). Specifically, MECE algorithm has the nearly 23% higher average NMI, 27% higher average ARI, 15% higher average FMI, and 10% higher average purity than CSPA, MCLA, HBGF, EAC, LWEA, and LWGA algorithms. The experimental results demonstrate that MECE algorithm is a valid approach to deal with the clustering ensemble problems. [ABSTRACT FROM AUTHOR]

Summary: This paper presents the on‐chip evolution system of combinational logic circuits by a new hybrid algorithm known as improved genetic‐simulated annealing algorithm (IGASA). IGASA is based on the concept and principles of genetic algorithm (GA) and simulated annealing (SA). The main idea is to combine GA's global search ability with the advantage of SA's fast convergence to reach an optimal solution. In this paper, a new mutation operation and a repeated annealing (RA) process are introduced to overcome the premature convergence of the standard GA and the large number of searches in the later period. An elitist strategy to enhance convergence characteristic of the proposed algorithm is incorporated to better speed up the evolution process. In addition, the new algorithm is implemented in a new evolvable platform using Intel Cyclone V FPGA with an embedded ARM microprocessor. Comparison of the performance of the improved algorithm to the pure GASA algorithm and standard GA is presented on a number of case studies. The experiment results demonstrate that feasible circuits are always achieved by the IGASA algorithm unlike with other algorithms and the number of generations required is less. [ABSTRACT FROM AUTHOR]

Big services are collections of interrelated web services across virtual and physical domains, processing Big Data. Existing service selection and composition algorithms fail to achieve the global optimum solution in a reasonable time. In this paper, we design an efficient quality of service‐aware big service composition methodology using a distributed co‐evolutionary algorithm. In our proposed model, we develop a distributed NSGA‐III for finding the optimal Pareto front and a distributed multi‐objective Jaya algorithm for enhancing the diversity of solutions. The distributed co‐evolutionary algorithm finds the near‐optimal solution in a fast and scalable way. [ABSTRACT FROM AUTHOR]

Fog computing is a novel, decentralized and heterogeneous computing environment that extends the traditional cloud computing systems by facilitating task processing near end‐users on computing resources called fog nodes. These diverse and resource‐constrained fog devices process a large volume of tasks generated by various fog applications. These tasks are generated by various applications, some of which may be latency‐sensitive, while others may tolerate some degree of delay in their normal functions. Task scheduling determines when a task should be allocated to a computing resource and how long that task can occupy the assigned resource. The majority of task scheduling algorithms focus on prioritizing the latency‐sensitive tasks only, which results in the long waiting time for the other type of tasks. Hence, these priority‐based schedulers cause task starvation for less important tasks while achieving delay‐optimal results for latency‐sensitive tasks. As a result, in this paper, we propose MQP, a multi‐queue priority‐based preemptive task scheduling approach that achieves a balanced task allocation for those applications that can tolerate a certain amount of processing delay and the latency‐sensitive fog applications. At run‐time, the MQP algorithm categorizes tasks as short and long based on their burst time. MQP algorithm maintains a separate task queue for each task category and dynamically updates the time slot value for preemption. The proposed technique's major purpose is to reduce response time for those data‐intensive applications in the fog computing environment, which include both latency‐sensitive tasks and tasks which are less latency‐sensitive, thereby addressing the starvation problem for less latency‐sensitive tasks. A smart traffic management case study is created to model a scenario with both latency‐sensitive short and less latency‐sensitive long tasks. We implement the MQP algorithm using iFogSim and confirm that it reduces the service latencies for long tasks. Simulation results show that the MQP algorithm allocates tasks to a fog device more efficiently and reduces the service latencies for long tasks. The average value of percentage reduction in the latency across all experimental configurations achieved is 22.68% and 38.45% in comparison to First Come‐First Serve and shortest job first algorithms. [ABSTRACT FROM AUTHOR]

Summary: The fulfillment of non‐functional requirements like timing or energy consumption is of utmost importance in many embedded systems and respective applications. Especially, with the introduction of multi‐core architectures, the ability to predict non‐functional execution qualities becomes more and more difficult, as multiple concurrent application programs may interfere in execution when typically sharing all the resources. In this paper, we advocate a novel parallel computing paradigm called invasive computing that allows to isolate application programs on multi‐core targets. For a presented case study of a cyber‐physical real‐time control system, we show that invasive computing enables composability that in fact allows to characterize and analyze each application program statically and independent from each other. More specifically, it is shown that a distributed object detection algorithm for controlling an inverted pendulum and implemented on a heterogeneous invasive multi‐processor SoC (MPSoC) is able to provide real‐time guarantees as well as reliability requirements on demand. [ABSTRACT FROM AUTHOR]

In the production and construction of industry, safety accidents caused by unsafe behaviors of staff often occur. In a complex construction site scene, due to improper operations by personnel, huge safety risks will be buried in the entire production process. The use of deep learning algorithms to replace manual monitoring of site safety regulations is a powerful guarantee for sticking to the line of safety in production. First, the improved YOLO v3 algorithm is used to output the predicted anchor box of the target object, and then pixel feature statistics are performed on the anchor box, and the weight coefficients are respectively multiplied to output the confidence of the standard wearing of the helmet in each predicted anchor box area, according to the empirical threshold determine whether workers meet the standards for wearing helmets. Experimental results show that the helmet wearing detection algorithm based on deep learning in this paper increases the feature map scale, optimizes the prior dimensional algorithm of specific helmet dataset, and improves the loss function, and then combines image processing pixel feature statistics to accurately detect whether the helmet is worn by the standard. The final result is that mAP reaches 93.1% and FPS reaches 55 f/s. In the helmet recognition task, compared to the original YOLO v3 algorithm, mAP is increased by 3.5% and FPS is increased by 3 f/s. It shows that the improved detection algorithm has a better effect on the detection speed and accuracy of the helmet detection task. [ABSTRACT FROM AUTHOR]

Summary: Most of networks in real world obviously present dynamic characteristics over time, and the community structure of adjacent snapshots has a certain degree of instability and temporal smoothing. Traditional Temporal Trade‐off algorithms consider that communities found at time t depend both on past evolutions. Because this kind of algorithms are based on the hypothesis of short‐term smoothness, they can barely find abnormal evolution and group emergence in time. In this paper, a Dynamic Community Detection method based on an improved Evolutionary Matrix (DCDEM) is proposed, and the improved evolutionary matrix combines the community structure detected at the previous time with current network structure to track the evolution. Firstly, the evolutionary matrix transforms original unweighted network into weighted network by incorporating community structure detected at the previous time with current network topology. Secondly, the Overlapping Community Detection based on Edge Density Clustering with New edge Similarity (OCDEDC_NS) algorithm is applied to the evolutionary matrix in order to get edge communities. Thirdly, some small communities are merged to optimize the community structure. Finally, the edge communities are restored to the node overlapping communities. Experiments on both synthetic and real‐world networks demonstrate that the proposed algorithm can detect evolutionary community structure in dynamic networks effectively. [ABSTRACT FROM AUTHOR]

Summary: Data Deduplication is the foremost technique used for data compression by removing redundant data. It is broadly utilized for cloud storage server, which reduces the un‐utilized memory space and optimized bandwidth. The confidentiality of sensitive information maintains during Deduplication detection. The Efficient Hash Function–based Duplication Detection (EHFDD) algorithm is proposed to encrypt the content before outsourcing in cloud environment. This paper is an attempt to enhance the data security and notify the issues of approved data Deduplication detections. Furthermore, it also presents several novel Deduplication improvisations for helping certified duplicate verification in hybrid cloud. The EHFDD method enables duplicate detection scheme with minimal overhead compared to existing functions. Based on experimental evaluations, proposed EHFDD algorithm reduces the average delay in 28.7 milliseconds, memory utilization in 8%, computation time in 457 milliseconds, communications overhead in 900 milliseconds, and improves the success rate in 3.13%. [ABSTRACT FROM AUTHOR]

Summary: The paper presented here entails technique that is based upon Sorensen's similarity coefficient as well as the Sorensen‐ Single Linkage Clustering (SLC) algorithm, which essentially is a hybrid clustering approach that has been deployed here to show issue related to cell formation during the process of cellular engineering. In the following paper and study that has been presented here, the suggested hybrid algorithm comparison has been drawn versus the existing clustering algorithms and their performances have been compared specifically the MOD‐SLC modified single linkage or SLC also known as the single linkage clustering algorithm. The Sorensen‐SLC technique and the inferences drawn on the same show that it performs comparatively better when compared with the MOD‐SLC and SLC algorithms. Additionally, the computation that is necessary for this hybrid algorithm is absolutely a bare minimal and the computation processing is effective and easy. [ABSTRACT FROM AUTHOR]

Traditional fault diagnosis methods assume that the rolling bearing fault samples are precise data. However, this assumption may be wrong when there are problems of measurement uncertainty etc. Due to this, this paper proposes an interval native Bayes uncertain fault diagnosis method based on the firefly algorithm. First, the interval fault vibration signals are decomposed by intrinsic time scale decomposition, and several proper rotation components (PRC) are obtained. Features of PRC, such as interval kurtosis etc., are extracted as fault samples. Then, an interval native Bayes uncertain fault diagnosis method is designed for these uncertain rolling bearing interval features. Conventionally, the fault diagnosis method utilizes the same interval features to distinguish different fault types. However, each type of fault has its own distinctive classification accuracy for different features. Thus, this paper uses the firefly algorithm to extract different optimal interval fault features for different fault types. Experimental results show: (i) the proposed interval native Bayes method can be effectively applied in the interval fault diagnosis of rolling bearing under measurement uncertainty conditions. (ii) Compared with two traditional methods which extract the same fault features for all fault types, the new method can obtain higher classification accuracy. [ABSTRACT FROM AUTHOR]

Summary: Load balancing is a crucial feature of cloud computing that evenly distributes workload between servers, network interfaces, and hard drives. Because of dynamic computing over the internet, cloud computing suffers from request overloading. To address this challenge, this paper proposes a new deep learning‐based deadline constrained (DL‐DC) load balancing technique. The proposed DL‐DC technique will improve resource utilization, reduce cost, latency, and response time, as well as balance load between servers and improve reliability. The proposed DL‐DC technique will direct traffic to a load balancer, which will forward the load with a deadline to a deep Inception ResNet. This network considers some parameters, such as sticky session, content‐based and instance health check and efficiently predicts the schedule for the task. Finally, a predicted task schedule has been derived using the DL‐DC model which is used to distribute the task to the virtual machine. The proposed DL‐DC load balancing algorithm is compared with other existing algorithms such as QMPSO, DQTS, and ACSO in terms of cost, makespan, response time, transmission time and task migration. The proposed method achieves up to 21.08% low response time, 27.3% decrease in make span, 25.5% decrease in task migration, and 38.9% decrease in cost respectively. [ABSTRACT FROM AUTHOR]

The aim of this paper is to show that Kahan's and Gill‐Møller compensated summation algorithms that allow to achieve high accuracy of summing long sequences of floating‐point numbers can be efficiently vectorized and parallelized using Intel AVX‐512 intrinsics together with OpenMP constructs in order to utilize SIMD extension of modern multicore processors. Numerical experiments show that the new implementations of the algorithms achieve much better accuracy than ordinary summation in both double and single precision and their performance is comparable with the performance of the ordinary summation algorithm optimized automatically. The vectorized Gill‐Møller algorithm is faster than the vectorized Kahan's algorithm. However, in case of single precision, the accuracy of the Gill‐Møller algorithm is worse than Kahan's but it can be fixed by the use of mixed‐precision. Then the accuracy of both compensated summation algorithms is the same and the Gill‐Møller algorithm is still faster than Kahan's. [ABSTRACT FROM AUTHOR]

Real‐time stream data processing has gained high importance with the rapid rise of big data trends in different areas such as social media, finance, business, science, and bioinformatics. Stream data can be characterized as fast, unstable, and big data sets. Due to these properties of stream data, it cannot be processed effectively with traditional algorithms. Just like stream data processing, clustering is also a difficult task. However, researchers have attempted to classify stream data by modifying traditional algorithms or designing new ones. So, in previous studies, incremental methods were used for clustering the stream data. This paper highlights the need to develop an efficient real‐time clustering algorithm for data streams in the presence of concept high drift and an adaptive algorithm for different dimensions. The proposed clustering algorithm, SubtStream, combines decremental (subtractive property) and incremental (additivity property) strategies to overcome the high drift. It also introduces a new dimension‐based approach to adopt the dimension change. We use three radius parameters, Predefined User Parameter, Proactive Adaptive Parameter, and Reactive Adaptive Parameter, to achieve adaptability. The proposed method, SubtStream, showed better performance on synthetic and real data sets. [ABSTRACT FROM AUTHOR]

With advancements in science and technology, cloud computing is the next big thing in the industry. Cloud cryptography is a technique that uses encryption algorithms to secure data. The significant advantage of cloud storage is no difficulty to get to, diminished equipment, low protection, and fixing cost so every association is working with the cloud. Encryption is the process of encoding information to prevent unauthorized access. Nowadays, we desire to secure the information that is to be stored in our computer or transmitted utilizing the internet against attacks. The cryptographic method depends on their response time, confidentiality, bandwidth, and integrity. Furthermore, security is a significant factor in cloud computing for ensuring client data is placed on the safe mode in the cloud. Our research paper compares the efficiency, usage, and utility of available cryptography algorithms. Evaluation results suggest which algorithm is better for which type of data and environment. [ABSTRACT FROM AUTHOR]

Summary: In recent years, multilabel learning has been applied to a lot of application areas and is yet a challenging task. In multilabel learning, an instance often belongs to multiple class labels simultaneously. The labels usually have correlations with others, and mining label correlations is helpful to enhance the multilabel classification performance. Aiming at increasing the accuracy of prediction, Label embedding (LE) is an important technique, and conducive to extracting label information for multilabel learning. In this paper, we present a novel multilabel learning approach via exploiting label correlations, which can be naturally extended to tackle feature selection problem. First, to obtain the discriminative features shared by all labels, the proposed algorithm learns a latent space by employing deep canonical correlation analysis. Then we exploit label correlations by enforcing predictions on similar labels to be similar, thereby improving the prediction performance. Results on several multiple datasets illustrate that the proposed algorithm has the advantages on multilabel classification and feature selection. [ABSTRACT FROM AUTHOR]

Summary: Lung cancer has become one of the major diseases that seriously threaten human health. Early diagnosis of lung cancer is very important for the quality of lung cancer patients. Researching computer‐assisted systems for lung cancer recognition is one of the effective means to help physicians quickly diagnose lung cancer. Therefore, this paper studies the multimodal recognition algorithm for lung images. This algorithm uses CT images, PET images, and PET/CT images of lung tumors as the experimental objects and uses convolutional neural network to realize lung tumor recognition. Based on the recognition of lung tumors by convolutional neural networks, an integrated convolutional neural network was used to identify lung tumors to improve the recognition accuracy and reduce the training time. The experimental results show that the convolutional neural network can effectively identify the lung tumor images. The number of iterations and batch size in the training process will have an impact on the recognition of lung tumors. The integrated convolutional neural network designed is superior to single convolutional neural network in terms of recognition accuracy and time consumption. [ABSTRACT FROM AUTHOR]

Summary: Welders are widely used in machinery manufacturing and machining industry. In the process of electric welding, accidents are often caused by some unsafe behaviors. This paper focuses on the intelligent recognition method of welding flame, which can reduce the accidents of welding flame to a certain extent. Due to the difficulties in observing workers on job sites, developing an algorithm for intelligent identification of unsafe factors is significant. Firstly, Hue,Saturation and Value (HSV) color space with strong robustness is selected to segment the mark image with threshold value, and the region of interest in which the welding fame is located is segment; secondly, the Progressive Probabilistic Hough transform is used to detect the region of interest, judge the number of existing lines, and finally determine whether there is welding flame in this region. In this way, the welding flame can be better detected, which can reduce the workload of managers and is beneficial to the supervision of the management personnel. [ABSTRACT FROM AUTHOR]

Summary: The software‐as‐a‐service layer of cloud computing has evolved much interest among the researchers in the academic and industry worlds. It is based on service‐oriented architectures and web service (WS) technology, which are used to offer a remotely accessible application. When a single WS is unable to satisfy all the customer's requirements, a WS composition (WSC) method is applied to connect together various available WSs for building distributed application, using discovery, compatibility checking, selection, and deployment steps. This paper has two‐fold contributions: The major one is the proposition of a new algorithm based on minimally unsatisfiable subset formal algorithm to compose WSs. This contribution ensures a faster WSC, which is prominent in an online context. The second contribution is the proposition of a new penalty‐based genetic algorithm to identify near‐optimal solutions whenever an exact one is not identified using our formal algorithm. Indeed, in some cases, many available WSs provide overlapping/identical functionality; in this context, the choice of participated services is given using quality of service. This work proposes a WSC ensuring to find out an exact or a near‐optimal solution according to the user need. A set of experimentations is applied to validate our contributions. [ABSTRACT FROM AUTHOR]

Summary: Impala system is an open source, analytic MPP database for Apache Hadoop. Impala system uses a query execution scheduling scheme that assigns near‐equal bytes retrieval tasks for different hosts to ensure system load balance. However, such “load balance” cannot guarantee a short response time for Impala system, when there are original loads in the system. Traditional query execution scheduling methods require either some assumptions or particular architecture, which cannot be directly used in Impala system. In this paper, we present a query execution scheduling scheme for Impala system. If the query fetches data from a single table, the scheme exploits the maximum flow algorithm. If the query fetches data from multiple tables, the scheme employs a cost‐based algorithm with heuristic pruning rules. In addition, we propose a cost model for Impala system, which considers parallel execution, communication cost, and cluster load. The performance of the proposed scheme is evaluated by the TPC‐DS benchmark, and experimental results show that the scheme can reduce the query response time by 10%‐30%. [ABSTRACT FROM AUTHOR]

In recent years, graph‐based data clustering algorithms have become popular as they perform connectivity‐based rather than centroid‐based partitioning. Methods related to minimum spanning tree (MST)‐based data clustering are types of graph‐based algorithms that can recognize arbitrary shapes of clusters by eliminating inconsistent edges from MST graphs. In all MST‐based data clustering algorithms, definition of inconsistent edges is the main problem that needs to be addressed. The longest edges in MST graphs are considered as inconsistent edges under ideal conditions. Nevertheless, outliers often exist in real‐world tasks, which makes the longest edges inaccurate cluster separation indicators. In this paper, we propose a new data clustering algorithm using MST and a critical distance method. The proposed algorithm solves the main issue of MST‐based data clustering, namely identifying and removing inconsistent edges to obtain clusters even in the event that the dataset contains some outliers. It begins by constructing the MST over a given weighted graph based on Euclidean distance and then splits up the graph into clusters by eliminating inconsistent edges using critical distance as a threshold. Integration of the advantages of both MST and critical distance methodology to obtain optimal clusters is the main contribution of this work. The conducted experimental analysis and results using different datasets prove that our proposed clustering algorithm yields better overall performance compared with the most common data clustering algorithms. Taking the Liver and Tumor datasets as an example, the proposed algorithm outperforms all other clustering algorithms with clustering accuracy equal to 0.579 and 0.660, respectively. [ABSTRACT FROM AUTHOR]

Path planning is an essential step for mobile robots. This paper presents a novel shape parameter search‐ (SP‐Search) based path planning algorithm for mobile robots using quintic trigonometric Bézier curve and its two shape parameters. The proposed search algorithm recursively divides the shape parameter space into a grid and uses a multi‐objective function to determine the shape parameter values. The multi‐objective function is expressed by the sum of four terms multiplied by coefficients: the integral of the squared jerk, the integral of the squared curvature, the mean of minimum distances between the predefined skeleton and quintic trigonometric Bézier path, and the arclength. Optimal shape parameters can be determined according to the required cases by the user using four coefficient values. Experimental results illustrate the feasibility of the proposed SP‐Search algorithm. Also, we have developed another intuitive algorithm, the Brute Force Grid Search algorithm, which is the brute force approach for shape parameter search to compare the performance of the proposed SP‐search algorithm for path planning problems as a baseline. The experimental results show that the SP‐Search algorithm finds better shape parameter values with a considerably small number of searched points. [ABSTRACT FROM AUTHOR]

The population size has a great impact on the performance of the Differential Evolution (DE) algorithm, but in many classic DE algorithms, the population size is usually determined by the user based on the experience value, and it remains unchanged during the evolution process, which greatly affect the performance of DE. To this end, a dynamic population reduction differential evolution algorithm (DPSHADE) that combines linear and nonlinear strategy piecewise functions is proposed. The algorithm uses a dynamic population size reduction method to dynamically adjust the population size during operation, and construct a combination of linear and nonlinear piecewise functions for dynamic scale adaptive adjustment. In this paper, we proposed the DPSHADE algorithm and is compared with the four traditional algorithms in the CEC2017 benchmark set. The experimental results show that DPSHADE performs better in overall performance, which is significant better than the performance of SHADE. [ABSTRACT FROM AUTHOR]

Summary: In this paper, we introduce a method for reasoning with words based on hedge algebra using linguistic cognitive map (핃ℂ필). Our model consists of a set of vertices and edges whose values are linguistic variables that are constrained by a linguistic lattice. The algorithm for the system studied consists of fuzzification, reasoning, and defuzzification. Further, the total effect between any two vertices in the 핃ℂ필 is computed. [ABSTRACT FROM AUTHOR]

Witnessing broad energy‐critical applications of barrier coverage in mobile and wireless sensor networks, emerging practical applications have recently brought a new barrier coverage model which uses sink‐based mobile sensors for covering a given barrier with the aim of prolonging the lifespan of the coverage. In the model, a set of sink stations were distributed on the plane in which each sink can emit mobile sensors with an identical radius. The task is to cover a given line barrier with the emitted mobile sensors, aiming to minimize the maximum movement of the sensors so as to prolong the shortest lifespan among the sensors in coverage. In this paper, we first devise an algorithm for optimally solving the problem based on the properties of the structures called movement parity and tangent equilibrium points between the sinks. Then based on a more sophisticated geometric property of optimum solutions, we improve the runtime to a linear runtime O(k) which attains the possibly optimum runtime of the problem for k being the number of sinks. At last, numerical experiments are carried out to demonstrate the practical performance gain of our algorithms against baselines in literature. [ABSTRACT FROM AUTHOR]

Cloud computing is a technology that is being used by scientists for the execution of huge scale workflows as it has various features such as availability, faster, cheaper, elasticity, and high accessibility. In addition, the users are charged on a pay per use basis which makes it more efficient to be used by various clients. Scheduling workflows in the cloud is a difficult task as it takes into account the dependencies of workflows along with quality of service (QoS) demands. The problem becomes more challenging when there are multiple QoS objectives which are often disagreeing to each other. A lot of research has been done to minimize makespan and execution cost under deadline and budget constraints. This article presents a workflow scheduling algorithm based on Jaya to minimize them both and returns solutions according to the weights a user gives. The algorithm has been compared with Min‐Min, Max‐Min, Round Robin, heterogeneous earliest finish time first, first come first serve, minimum completion time, dynamic heterogeneous earliest finish time first in WorkflowSim. The results are compared based on both cost and time. In the end, a trade‐off between the objectives has been shown. Results show that Jaya performs much better than other algorithms when averaged over several iterations. [ABSTRACT FROM AUTHOR]

Summary: Crowdsourcing is now one of the most important and transformative paradigms, with great success in a variety of application tasks. Crowdsourcing obtains knowledge and information to solve cognitive or intelligence‐intensive tasks from an evolving group of participants via the Internet. Unfortunately, providing a hard privacy guarantee and query optimization is incompatible when a higher task acceptance rate needs to be accomplished and this case is common in most existing crowdsourcing solutions. The state of art systems suffered from different complexities such as lack of crowdsourcing optimization techniques, increased cost, latency, security, and scalability issues. In this paper, we have proposed a crowdsourcing model to optimize the cost and latency, issues that occur while query optimization using the Moth Flame and Tunicate Swarm Algorithm (MF‐TSA). The TSA algorithm is added to the MF algorithm to enhance its exploitation capability and yield fast convergence. The data privacy concerns of the worker and the requestor are addressed using homomorphic encryption that simultaneously enhances the efficiency of the crowdsourcing framework. The main aim of this work is to optimize the cost and latency for query plan selection along with security. Initially, the homomorphic encryption model is used to encrypt the data. In query design, two kinds of crowd‐controlled administrators, that is, Crowd Powered Selection (CSelect) and Crowd Powered Join (CJoin) are connected for assessing query. The proposed framework utilizes MF‐TSA to optimize the selection and join queries with low cost and latency. Finally, the experimental results demonstrate better query optimization performance than other existing algorithms such as sequential, parallel, and CrowdOp. [ABSTRACT FROM AUTHOR]

Summary: Web scraping is the process of extracting data from web pages and it is an essential part for the generation of datasets. Currently the field is dominated by capable commercial applications, however, there is always a need for web crawling and web scraping applications for custom projects. Developing fit for purpose tools for retrieving and structuring data from web services, cloud systems, and big data is a challenging task. Based on empirical studies, some of the challenges include structural issues, formatting/ presentation, availability, denial of service, size, and information fetching problems with browsers. Additionally, the data become inaccessible after the structure/template of the website changes for example, after the website update. Thus the dataset cannot be updated in the future without manually modifying the parameters of the Web Scraper. In this paper we propose an algorithm capable of autocorrecting the template (web scraping parameters) used for locating the target data and dealing with some common empirical problems. This is very useful in case there is a need for updating the dataset later, as usually, websites tend to change their pages. Moreover, we introduce an implementation of the algorithm via a tool developed for extracting data from the unity asset store. The tool can capture and store data in XML format. The tool extracted a total of 46 785 (40 611 3D and 6174 2D) items, with 35 successful first retries, 11 second retries and 5 fails. [ABSTRACT FROM AUTHOR]

Different nature‐inspired meta‐heuristic algorithms have been proposed to solve optimization problems. One of these algorithms is called social spider optimization (SSO) algorithm. Spiders' natural behaviors have inspired them to find the bait position by detecting vibrations in their web. Although the SSO algorithm has good accuracy in achieving optimal solutions, it suffers from a low convergence rate. In this paper, we attempted to improve SSO by changing its motion and mating parameters. To provide a practical example of using the new proposed algorithm, we based it on multi‐objective opposition‐based SSO, named MOPSSO. We used this algorithm in a feature selection process for analyzing text psychology, which is a multi‐objective problem. Textual psychology analysis is used in various fields, including collecting and analyzing people's views on various products, topics, social and political events. After selecting features, in order to classify the text, we used a new hybrid method that hybrids fuzzy C‐MEANS data clustering technique, a decision tree (DT), and Naïve Bayes (NB). Experimental results show that the improved SSO algorithm performs better than SSO, social spider algorithm, and CMA‐ES algorithms. Additionally, the performance of the proposed hybrid classification method is better than those of NB and DT. [ABSTRACT FROM AUTHOR]

Summary: In the present trend, availability of data increases more and more in all the fields in more complex way. It is very difficult for handling in an effective way with best scalability for proper decision making of knowledge extraction. K‐Means algorithm is the most familiar extensive old‐style partitioned and faster clustering algorithm than other clustering methods. But it is very subtle for initial centroids and it can be simply surrounded. So, in need of effective optimum centroid with faster clustering, this paper proposed Rainfall Flow Optimization (RFFO) based on K‐Means algorithm. RFFO is a new flood optimization technique like other optimization methods such as PSO, ACO, BCO, and so forth. RFFO is based on the nature of rainfall flow with various environments and its behavior of flow from shallow to depth. Scientific calculations are also used to find flow of data from one location to another location by its present location of waterfall, its velocity, and its most neighboring flow depth. This water storage mainly depends on nearest storage location, maximum depth, size of the storage area, and condition of the storage location. RFFO has some of the unique features than other optimization methods like speed of flow, total quantity of flow, location of flow, and so forth, that determines the optimal centroid. The performance of this proposed RFFO technique is measured with Accuracy, Jaccard Coefficient, Random Coefficient, and also compared with other existing methods using medical data set to cluster risk factor of heart disease with 300 data set. [ABSTRACT FROM AUTHOR]

Summary: This paper introduces several budget‐aware algorithms to deploy scientific workflows on Infrastructure as a Service Cloud platforms, where users can request Virtual Machines (VMs) of different types, each with specific cost and computing resources. We use a realistic application/platform model with stochastic task weights, and VMs communicating through a Cloud storage. We extend two well‐known algorithms, MinMin and HEFT, and make scheduling decisions based upon machine availability and remaining budget. During the mapping process, the budget‐aware algorithms make conservative assumptions to avoid exceeding the initial budget; we further improve the results with refined versions that aim at rescheduling some tasks onto faster VMs, thereby spending any budget fraction leftover by the first allocation. These refined variants are much more time‐consuming than the former algorithms, so there is a trade‐off to find in terms of scalability. We report an extensive set of simulations with workflows from the Pegasus benchmark suite. Most of the time, our budget‐aware algorithms succeed in achieving efficient makespans while enforcing the given budget, and this despite the uncertainty in task weights. [ABSTRACT FROM AUTHOR]

Summary: In this paper, we propose a composite nonlinear multiset canonical correlation projections (CNMCPs) framework where orthogonal constraints are imposed in each set. This makes CNMCP capable of learning uncorrelated low‐dimensional features with minimum redundancy in Hilbert space. With the CNMCP framework, we further present a particular algorithm called multikernel multiset canonical correlations or mKMCC, which introduces different weights into multiple nonlinear functions in all views. An alternating iterative optimization is designed for computational solution. Numerous experimental results on practical datasets have demonstrated the effectiveness and robustness of mKMCC, in contrast with existing kernel correlation learning approaches. [ABSTRACT FROM AUTHOR]

SUMMARY: At present, disputes caused by medical expenses are widespread. How to use information means to provide accurate prediction of medical expenses for serious illnesses has become a research hotspot. Gastric cancer is a common cancer. The key of its medical expenses prediction lies in the mining of repeated structures and the statistics of repeated execution times. In the existing process mining methods, repeated nodes are regarded as the same nodes, and only counted once. This paper changes the original dynamic‐service‐flow‐net into a dynamic‐medical‐path‐net by taking the repetition times of nodes into account. Then αtj algorithm and TNC algorithm are proposed to build the dynamic‐medical‐path‐net system to predict the medical expenses. A medical evaluation model is proposed to evaluate each alternative schemes comprehensively in order to get the best medical scheme, and then the predictive medical expense would be obtained. The proposed method has about 25% improved to the conventional methods. [ABSTRACT FROM AUTHOR]

Summary: In scenes high in visual complexity, the identification of a moving object can be affected by changes in scale and occlusion factors during the tracking process, resulting in reduced tracking accuracy. Accordingly, to address the problem of low accuracy, a multiscale fast correlation filtering tracking algorithm based on a feature fusion model is proposed in the present work with the aim of reducing the poor tracking effects caused by occlusion discrimination and scale changes in complex scenes. The object's grayscale (GRAY) feature, histogram of oriented gradient (HOG) feature, and color name (CN) feature are reduced to dimensions and fused to form a feature matrix. Moreover, a hierarchical principal component analysis (HPCA) algorithm is used to extract visually salient features and reconstruct the feature matrix under real‐time conditions, the correlation filtering position is trained, the number of dimensions is effectively reduced, and the feature fusion matrix is used to train the multiscale fast correlation filtering, with the result that the object's position and scale can be accurately predicted. The proposed algorithm is then compared with five popular correlation filtering tracking algorithms. Experimental results demonstrate that its average tracking speed reaches a reasonable frames/second velocity; moreover, it can also achieve promising object tracking results on the OTB benchmark data sets. The tracking accuracy is superior to that of the other five correlation filtering tracking algorithms when applied to scenes featuring object occlusion and changes in scale. The proposed algorithm also exhibits better robustness and improved performance under real‐time conditions in sophisticated scenarios, including scale variation, deformation, fast motion, occlusion, and so on. [ABSTRACT FROM AUTHOR]