Data warehouses are very large databases and play key role in intelligent decision making in enterprises. The bitmap join indexes selection problem is crucial in the data warehouse physical design and known to be NP-hard. All the existing methods that solve this problem use single objective function and static query workload during the optimization. In the present work, we propose a multi-objective

Different techniques for estimating the execution time of parallel applications have been studied for the last 25 years. These approaches have proposed different methods for predicting the performance behaviour of applications. Most of these methods rely on analysing one or more of the following aspects: system workload, application structure, platform system, and the computing resources that the application

The major reason for using a simulator, instead of a real test-bed, is to enable repeatable evaluation of large-scale cloud systems. CloudSim, the most widely used simulator, enables users to implement resource provisioning, and management policies. However, CloudSim does not provide support for: (i) interactive online services; (ii) platform heterogeneities; (iii) virtual machine migration modelling;

Parallel Programming skills may require a long time to acquire. “Think in parallel” is a skill that requires time, effort, and experience. In this work, we propose to facilitate the students’ learning process in parallel programming by using instant messaging. Our aim was to find out whether students’ interaction through instant messaging tools is beneficial for the learning process. In order to do

It is critical to determine whether the brain state of an epilepsy patient is indicative of a possible seizure onset; thus, appropriate therapy or alarm may be delivered in time. Successful seizure prediction relies on the capability of accurately separating the preictal stage from the interictal stage of ictal electroencephalography (EEG). With the booming of brain e-health technologies, there exists

Let H be a connected subgraph of a given graph G. The H-structure connectivity of G is the cardinality of a minimal set \({\mathcal {F}}\) of subgraphs of G such that every element in \({\mathcal {F}}\) is isomorphic to H, and the removal of all the elements of \({\mathcal {F}}\) will disconnect G. The H-substructure connectivity of graph G is the cardinality of a minimal set \({\mathcal {F}}'\) of

To improve the accuracy of retinal vessel segmentation, a retinal vessel segmentation algorithm for color fundus images based on back-propagation (BP) neural network is proposed according to the characteristics of retinal blood vessels. Four kinds of green channel image enhancement results of adaptive histogram equalization, morphological processing, Gaussian matched filtering, and Hessian matrix filtering

Detection of threat objects concealed in passenger clothing and baggage poses a challenge to aviation security. At present, the detection technology is capable of detecting the presence of threats from the scanned images yet requires the involvement of human in determining what type of threat and where it is located. Deep learning-based object detection technique has not been successfully implemented

Bayesian neural networks (BNNs) are a promising method of obtaining statistical uncertainties for neural network predictions but with a higher computational overhead which can limit their practical usage. This work explores the use of high-performance computing with distributed training to address the challenges of training BNNs at scale. We present a performance and scalability comparison of training

With the rapid increase in amount of network encrypted traffic and malware samples using encryption to evade identification, detecting encrypted malicious traffic presents challenges. The quality of the encrypted traffic sampling method directly affects the result of malware detection, but most existing machine learning methods for sampling flow-based encrypted traffic data are inherently inaccurate

This paper explores the nature of scientific research traffic on the Korea research environment open network. Based on these investigations, we propose a scalable design and algorithm for the science demilitarization zone (DMZ). The proposed design allows users to share a data-transfer node (DTN), which is essential but costly equipment in the Science DMZ. The proposed iterative greedy algorithm attempts

Non-intrusive loading monitoring (NILM) is a load analyzing algorithm that performs the energy dis-aggregation of power load for the smart meter technology. NILM is a highly valuable application due to its cost effectiveness, but it is a very challenging research because the noisy low-level features are not easily distinguishable when multiple appliances are used together. This paper proposes a deep

In order to reduce the loss of information of the majority class samples in the resampling process, combining the distribution of class samples and the characteristics of ensemble learning algorithm, in this paper, a two-level selective ensemble learning algorithm for imbalanced datasets is proposed. Firstly, the algorithm under-samples the majority class samples and constructs multiple training subsets

Traffic prediction is a vital part of intelligent transportation systems. The ability of traffic risk prediction is of great significance to prevent traffic accidents and reduce the damages in a proactive way. Because of the complexity, uncertainty and dynamics of spatiotemporal dependence of traffic flow, accurate traffic state prediction becomes a challenging issue. Most neural networks are compute

In this work, we propose a highly scalable parallel double binned ghost particle (DBGP) algorithm for direct-forcing immersed boundary spectral element method for multiphase flow simulations. In particular, the DBGP algorithm is designed to obtain fully distributed data storage and scalable data transfer across hundreds of thousands of processors. The proposed algorithm uses a queen and worker data

It is of great significance and practical application value to extract land-cover type accurately. However, the input data usually used in classification such as reflectance data or vegetation index are very simple and quantitative remote sensing products are rarely used. In this paper, a multi-spectral land-cover classification method based on deep learning is proposed. Using the excellent detail

Quantum computing is a reality that presents challenges to computer engineering students and practitioners. It has been claimed that it is possible to effectively teach quantum computing to undergraduate students without a physics background by means of computer programming. With this statement in mind, this study starts with a comparison and tests of different quantum programming environments: Qiskit

Cloud computing is one of the most popular distributed environments, in which, multiple powerful and heterogeneous resources are used by different user applications. Task scheduling and resource provisioning are two important challenges of cloud environment, called cloud resource management. Resource management is a major problem especially for scientific workflows due to their heavy calculations and

Feature selection is one of the significant steps in classification tasks. It is a pre-processing step to select a small subset of significant features that can contribute the most to the classification process. Presently, many metaheuristic optimization algorithms were successfully applied for feature selection. The genetic algorithm (GA) as a fundamental optimization tool has been widely used in

The strength of many security protocols lies on the computational intractability of the integer factorization and discrete logarithm problems. Currently, the best-known techniques employed are number field sieve (NFS) family of algorithms. They come under the class of sub-exponential time algorithms. This class of algorithms comprises of multiple steps. The relation collection (sieving step) is one

Nowadays internet-based applications collect and distribute large datasets, which are mostly modeled by pertinent massive graphs. One solution to process such massive graphs is summarization. There are two kinds of graphs, stationary and stream. There are several algorithms to summarize stationary graphs; however, no comprehensive method has been devised to summarize stream graphs. This is because

The rapid increase in the number of cores on chips forced the designers to invent new communication methods such as Network-on-Chip (NoC) paradigm. Advances in integrated circuit fabrications even allowed three-dimensional NoC (3D-NoC) implementations. 3D-NoCs have more advantages than their 2D counterparts such as lower area, higher throughput, better performance, and less energy consumption. However

As the infrastructure of high-performance computing, the data center network plays an important role. As network failures occur frequently, data center networks demand highly performed, robust, and energy-efficient failure recovery mechanisms. Despite process, the existing work still has a huge scope to improve to satisfy these requirements. The backup-based failure recovery schemes reserve backup

The paper describes a heuristic algorithm for solving a generalized Hermitian eigenvalue problem fast. The algorithm searches a subspace for an approximate solution of the problem. If the approximate solution is unacceptable, the subspace is expanded to a larger one, and then, in the expanded subspace a possibly better approximated solution is computed. The algorithm iterates these two steps alternately

Interest in real-time systems has grown considerably over recent years, primarily due to significant increase in the use of smart technologies and latency-sensitive applications such as cloud gaming, audio/video streaming, and smart homes. Significant work has been done on resource mapping in the cloud environment, and a number of promising results have been established accordingly where the focus

The rise of high-resolution and high-throughput sequencing technologies has driven the emergence of such new fields of application as precision medicine. However, this has also led to an increase in the storage and processing requirements for the bioinformatics tools, which can only be provided by high-performance and massive data processing infrastructures. Such technologies allow the development

Cloud-edge collaboration architecture, which combines edge processing and centralized cloud processing, is suitable for placement and caching of streaming media. A cache-aware scheduling model based on neighborhood search is proposed. The model is divided into four sub-problems: job classification, node resource allocation, node clustering, and cache-aware job scheduling. Firstly, jobs are categorized

During the last decade, Big Data has emerged as a powerful alternative to address latent challenges in scalable data management. The ever-growing amount and rapid evolution of tools, techniques, and technologies associated to Big Data require a broad skill set and deep knowledge of several domains—ranging from engineering to business, including computer science, networking, or analytics among others—which

The buildup of huge data within business intelligence is essential because such data includes complete conceptual and technological stack in addition to raw and processed data, data management, and analytics. Evaluating Data Quality Model Based-In-Use has gained more ground since business value could be only estimated in its used context. Despite the numerous data quality models used for regular data

Cloud-based scientific workflow systems can play an important role in the development of cost-effective bioinformatics analysis applications. There are differences in the cost control and performance of many kinds of servers in heterogeneous cloud data centers for bioinformatics workflows running, which can lead to imbalance between operational/maintenance management costs and quality of service of

With the proliferation of technology, a system of connected and interconnected devices, henceforth referred to as Internet of Things, is emerging as a viable method for automated interactions between users and environment in day-to-day life. However, such proliferation leads to an impractical task with respect to interactions among humans and devices. The major reason behind this impractical task is

Cognitive agents are typically utilized in autonomous systems for automated decision making. With the widespread use of autonomous systems in complex environments, the need for real-time cognitive agents is essential. Cognitive agents are more capable when they are able to process larger amounts of information to make more informed and intelligent decisions. The solution search space for cognitive

Cloud computing represents the latest technology that has revolutionized the world of business. It is a promising solution giving companies the possibility of remotely storing their data and accessing services whenever they are needed and at a lower cost. However, outsourcing IT resources also brings risks, especially for sensitive information in terms of security and privacy, since all data and resources

Quantum dot cellular automata (QCA)-based demultiplexer or DeMUX is a basic module of nanocommunication and nanocomputation, like a multiplexer. However, the design and analysis of demultiplexer using QCA have been neglected by researchers, unlike multiplexer. This article proposed and analyzed a simple and optimized QCA-based single-layered demultiplexer only using two majority gates, one inverter

The high performance, high efficiency, and low cost of Commercial Off-The-Shelf (COTS) devices make them attractive for applications with strict reliability constraints. Today, COTS devices are adopted in HPC and safety-critical applications such as autonomous driving. Unfortunately, the cheap natural boron widely used in COTS chip manufacturing process makes them highly susceptible to thermal (low

Cloud computing is the fastest emerging technology that proposes several resources under various pricing strategies that are specified based on temporal constraints. The main aim of cloud computing is to enhance the performance level and minimize operating costs. Thus, organizations looking towards optimizing their spending on IT infrastructure find such pricing strategies very attractive, especially

Continuous scaling-up of high-performance computing systems has brought challenges to the debugging and tuning of large-scale parallel programs. Firstly, to locate bugs in a program or tune its performance, programmer often needs to execute the program in a specified scale repeatedly, which consumes massive resources; secondly, due to the extensively used job scheduling systems, programmers can only

Quantum-dot cellular automata (QCAs) are one of the most significant state-of-the-art technologies that have exhibited the potential to replace the complementary metal oxide semiconductor. QCA offers a variety of benefits over its conventional counterpart, including size, latency, and energy consumption. Meanwhile, multiplexers are crucial to the design of arithmetic and logic circuits, and NOT-AND

Cloud computing is attractive to business owners and allows enterprises to start from the small and increase resources only when there is a rise in service demand, but cloud may become expensive. Fog computing has many advantages, and it is suited for the applications whereby real time is very important, but fog resources may also be highly limited. The cloud and fog computing may perform tasks together

Reliability analysis is one of the crucial issues for any scalable optical interconnection network. Torus is a highly scalable optical interconnect for data centre networks. The traditional torus network has XY routing algorithm. We have proposed a novel optimised routing algorithm. This paper focuses on the time-dependent and time-independent analysis for both terminal and broadcast reliabilities

A many-core parallel approach of the multilevel fast multipole algorithm (MLFMA) based on the Athread parallel programming model is presented on the homegrown many-core SW26010 CPU of China. In the proposed many-core implementation of MLFMA, the data access efficiency is improved by using data structures based on the structure of array. The adaptive workload distribution strategies are adopted on different

In scientific computing and cryptography, there are many applications that involve large integer multiplication, which is a time-consuming operation. To reduce the computational complexity, number theoretic transform is widely used, wherein the multiplication can be performed in the frequency domain with reduced complexity. However, the speed performance of large integer multiplication is still not

The introduction of elastic optical networks (EONs) leads to new challenges, such as the problem of routing, modulation level, and spectrum assignment. In the literature, the best among the shortest routes (BSR) algorithm was proposed as fixed and fixed-alternate routing for EONs, but without considering physical impairments. Therefore, call requests were handled not taking into account the different

In order to study the problems of inadequate maintenance measures, inappropriate maintenance time, and unreasonable use of funds in asphalt pavement maintenance of Highway in China, the maintenance of highway pavement is taken as the research object in this study, and a prediction model is established for preventive maintenance performance of highway by using neural network. Firstly, the performance

Balancing energy–performance trade-offs for smartphone processor operations is undergoing intense research considering the challenges with the evolving technology of mobile computing. However, to guarantee energy-efficient processor operation, layout, and architecture, it is necessary to identify and integrate optimization techniques and parameters influencing energy–performance trade-off in various

Data science applications have become widespread as a means to extract knowledge from large datasets. Such applications are often characterized by highly heterogeneous and irregular data access patterns, thus often being referred to as big data applications. Such characteristics make the application execution quite challenging for existing software and hardware infrastructures to meet their resource

Collection of web behavior relies on self-report and web logs in daily life. The self-report is derived on memory and can seem ambiguous for analyzing web behavior. It is necessary to quantitatively measure these web behaviors to see the changes over time. Individuals tied by the same emotion become a group, and its members synchronize with each other. Therefore, this study is to propose a web behavior

One of the most challenging issues in the big data research area is the inability to process a large volume of information in a reasonable time. Hadoop and Spark are two frameworks for distributed data processing. Hadoop is a very popular and general platform for big data processing. Because of the in-memory programming model, Spark as an open-source framework is suitable for processing iterative algorithms

Dr. Feng Wang’s second affiliation is missing in the original article. Both affiliations are given here.

Cloud computing maps tasks to resources in a scalable fashion. The scheduling is an NP-hard problem; thus, the scheduler chooses one solution from among many. This is the reason why finding the best optimal solution, especially at a high scale of the system, is not possible. Applying metaheuristic algorithms to find a near-to-optimal solution, not the best one, could be the right approach. Dragonfly

Graph eigenvalues play a fundamental role in controlling structural properties which are critical considerations in the design of supercomputing interconnection networks, such as bisection bandwidth, diameter, and fault tolerance. This motivates considering graphs with optimal spectral expansion, called Ramanujan graphs, as potential candidates for interconnection networks. In this work, we explore

Augmented reality surgery has not been successfully implemented in dental implant surgery due to the negative impact of an incorrect implant placement. This research aimed to improve the convergence between computed tomography derived teeth model and real-time stereo view of patient’s teeth to provide high registration accuracy. Enhanced iterative closest point algorithm is proposed to reduce the error

Internet of things (IoT) has become a new era of communication technology for performing information exchange. With the immense increment of usage of smart devices, IoT services become more accessible. To perform secure transmission of data between IoT network and remote user, mutual authentication, and session key negotiation play a key role. In this research, we have proposed an ECC-based three-factor

Like other emerging fields, Stream Processing Engines (SPEs) pose several challenges to the researchers such as resource awareness, dynamic configurations, heterogeneous clusters, and load balancing. All of these aspects play a major role in the job scheduling process. Inefficiency in any of them causes problems for achieving the maximum throughput. SPEs must contemplate other aspects like resource

The new era of the Internet of Things is driving the evolution of conventional vehicle ad hoc networks into the Internet of vehicles (IoV). Radio frequency identification (RFID) is a reliable and advanced instrument used for automated data collection, processing and tracking, which is widely used in IoV. One of the important features of an RFID system is its ability to search for a particular tag among

Social media platforms have simplified the sharing of information, which includes news as well, as compared to traditional ways. The ease of access and sharing the data with the revolution in mobile technology has led to the proliferation of fake news. Fake news has the potential to manipulate public opinions and hence, may harm society. Thus, it is necessary to examine the credibility and authenticity

By increasing the internet of things (IoT) applications, multi-channel wireless sensor networks (WSNs) are widely used as a promising technology. Due to power constraints in WSNs, increasing the network’s lifetime is a challenging issue that must be satisfied in any design, obviously medium-access control (MAC) and routing protocols. In this paper, we proposed a joint MAC and routing mechanism that

For a connected graph \(G=(V,E)\), two spanning trees \(T_1\) and \(T_2\) of G are said to be a pair of completely independent spanning trees (or a dual-CIST for short) if for any two vertices \(u,v\in V\), the paths joining u and v in the two trees have no common vertex except for u and v. Although the existence of a dual-CIST in the underlying graph of a network has the practical application of protection

Deep learning algorithms have yielded remarkable results in medical diagnosis and image analysis, besides their contribution to improvements in a number of fields such as drug discovery, time-series modelling and optimisation methods. With regard to the analysis of histopathologic breast cancer images, the similarity of those images and the presence of healthy and tumourous tissues in different areas

Wireless body area networks are a technology for remote medical care. Because of the limited energy of the sensors, one of the problems of long-distance medical care is the high energy consumption when sending information of sensors to the sink. Choosing the proper route when sending information to the sink will reduce energy consumption and increase network lifetime. The current paper used the shuffled