Abstract The BLAST server in the National Center for Biotechnology Information in the USA receives tens of thousands of queries per day on average. However, the service is always the same for every query even though query lengths vary significantly. In fact, the lengths of a large portion of protein sequences are less than 500. On the other hand, the hit detection process consumes the most of the execution

Abstract The growing data have brought tremendous pressure for query processing and storage, so there are many studies that focus on using GPU to accelerate join operation, which is one of the most important operations in modern database systems. However, existing GPU acceleration join operation researches are not very suitable for the join operation on big data. Based on this, this paper speeds up

Abstract Energy-efficient computing is especially important in the field of high-performance computing (HPC) on supercomputers. Therefore, automated optimization of energy efficiency during the execution of a compute-intensive program is desirable. In this article, a framework for the automatic improvement of the energy efficiency on NVIDIA GPUs (graphics processing units) using dynamic voltage and

Abstract The collaborative filtering (CF) technique has been widely utilized in recommendation systems due to the precise prediction of users' interests. Most prior CF methods adapted overall ratings to make predictions by collecting preference information from other users. However, in real applications, people’s preferences usually vary with time; the traditional CF could not properly reveal the change

Abstract This paper proposes a novel three-dimensional guidance method based on clock synchronization algorithms, creatively solving the problems of randomized change, non-connection and even communication outage caused by packet loss and delay in the co-guidance of loitering munitions. The proposed method is characterized by fast convergence of the aspect angle rate in the longitudinal plane, and

Abstract Hierarchical architecture of Wireless Network on Chips (WNoCs) composes of wired level and wireless level. In this architecture, subnetworks in wired level are connected by wireless. One of the main reliability challenges in the wired level of WNoC architecture is the crosstalk fault. Crosstalk fault occurs among the long and adjacent wires of wired level of WNoC due to tandem data traversal

Abstract Unstructured big data processing requires efficient computational styles to rapidly analyze continuously changing data. Incremental processing is a promising technique to update search results without re-computing the whole process. One of the most important mining algorithms is PageRank which necessitates incremental processing and iterative computations to keep the mining results up-to-date

Abstract Ontology, as a semantic representation of a shared conceptualization, makes knowledge machine-readable and easy to spread. One of its typical applications is used to develop e-learning systems with Educational Ontology. Ontology can help students master knowledge architecture of required subjects and make scattered courseware more systematic. A big challenge is how to construct Educational

Abstract Cooperative multiple task assignment problem is an essential issue in the collaboration of multiple unmanned aerial vehicles (UAVs). Consensus-based bundle algorithm (CBBA) is a decentralized task assignment method that only considers homogeneous agents and independent tasks. Thus, we develop an extended CBBA with task coupling constraints (CBBA-TCC) in this paper to solve the multi-task assignment

Abstract Mining frequent itemset is considered as a core activity to find association rules from transactional datasets. Among the different well-known approaches to find frequent itemsets, the Apriori algorithm is the earliest proposed. Many attempts have been made to adopt the Apriori algorithm for large-scale datasets. But the bottlenecks associated with Apriori like/such as repeated scans of the

Abstract Iterative and time-step methods are spread far and wide in several mathematics and physics domains. At the same time, modern computers include multicore CPUs along with GPUs, so it is important to use all their computing capabilities for their efficient use. Aiming to improve performance of this kind of numerical methods, we introduce in this work a new heterogeneous parallelism CPU + GPU

Abstract The increasing usage of mobile cloud computing leads the cloud to become a hotbed of crime. Cloud storage is different from previous data storage in digital forensics. In this paper, we concentrate on gathering evidence from mobile iOS devices and the types of data remnants that can be found on iOS devices for different user scenarios and device states. We develop several steps for experimenting

Abstract In radiotherapy planning, which involves optimization, efforts to produce better (more effective and at the same time with lesser adverse effects) patient radiation plans must trade-off with higher computing times needed to achieve this goal. Computing times is the key (but not only) factor in radiotherapy planning, which is always performed in clinical workflows regimes. ‘Win–win’ is when

Abstract Markov decision processes provide a formal framework for a computer to make decisions autonomously and intelligently when the effects of its actions are not deterministic. This formalism has had tremendous success in many disciplines; however, its implementation on platforms with scarce computing capabilities and power, as it happens in robotics or autonomous driving, is still limited. To

Abstract Virtualization technology is important for servers that compose cloud data centers. The current trend is to consolidate servers to manage them easily and reduce hardware and power consumption in data centers. However, performance degradation is inherent to virtualization technology and is caused by the hypervisor and overhead due to the consolidation of several virtual servers inside a physical

Abstract The accuracy and performance of deep neural network models become important issues as the applications of deep learning increase. For example, the navigation system of autonomous self-driving vehicles requires very accurate deep learning models. If a self-driving car fails to detect a pedestrian in bad weather, the result can be devastating. If we can increase the model accuracy by increasing

Abstract A significant amount of research in the field of job scheduling is carried out in Hadoop. However, there is still need for research to overcome some challenges regarding scheduling jobs in Hadoop clusters. There are various factors affecting the performance of scheduling policies like data volume (storage), data source format (different data), speed (data rate), security and privacy, cost

Abstract Network embedding technologies that transform the nodes of a network into a low-dimensional vector space have many various potential applications such as node classification, community detection and Internet public opinion analysis and so forth. Most existing approaches for network embedding are calculated by utilizing the topology structure of a target network which simply describes the social

Abstract High-utility itemset mining is a prominent data-mining technique where the profit or weight of itemsets plays a crucial role in defining meaningful patterns. High average-utility itemset (HAUI) mining is an advancement over high-utility itemset mining, which introduces an unbiased measure called average utility to associate the utility of itemsets with their length. Several existing HAUI mining

Abstract In the past few decades, the demand for high-performance computing has been increasingly growing. These systems usually employ many processing elements working together via interconnection networks to solve complicated problems. Due to the efficiency and cost productivity, multistage interconnection networks (MINs) are recommended for use in such systems. At the same time, shuffle-exchange

Abstract An efficient integration of Internet of Things (IoT) and cloud computing techniques accelerates the evolution of next-generation smart environments (e.g., smart homes, buildings, cities). The advanced modern cloud networking architecture also helps to efficiently host, manage and optimize the IoT services in smart environments. In this paper, we have considered an “IoT-Cloud” environment where

Abstract Improving the energy efficiency while guaranteeing quality of services (QoS) is one of the main challenges of efficient resource management of large-scale data centers. Dynamic virtual machine (VM) consolidation is a promising approach that aims to reduce the energy consumption by reallocating VMs to hosts dynamically. Previous works mostly have considered only the current utilization of resources

Abstract The NAND flash memories have endurance issues in a high-performance computing (HPC) environment because of their blocks’ limited lifetime. Hence, the NAND flash block allocation and reclamation policies have a significant impact on its lifetime improvement. To address this problem, the scoring-based method is proposed in this paper, which employs priority-based victim block and active block

Abstract Most modern parallel programs are written with the moldable property. However, most existing parallel computing systems treat such parallel programs as rigid jobs for scheduling, resulting in two drawbacks. The first is inflexibility and inefficiency in processor allocation, leading to resource fragmentation and thus poor performance. The second is about usage inconvenience, requiring users

Abstract Inverter and majority gates are considered as two important primitive gates for designing logical circuits in the quantum-dot cellular automata (QCA) technology. Up to now, many QCA layouts have been introduced for three-input majority gates, most of which are not robust against the QCA defects and so they are prone to faults. In this paper, we propose an efficient fault-tolerant 3-input majority

Abstract Connections of cyber-physical system (CPS) components are gradually increasing owing to the introduction of the Industrial Internet of Things (IIoT). IIoT vulnerability analysis has become a major issue because complex skillful cyber-attacks on CPS systems exploit their zero-day vulnerabilities. However, current white box techniques for vulnerability analysis are difficult to use in real heterogeneous

Abstract This article describes the implementation of a dynamic scheduler for loading distribution between a hardware accelerator RTL and a CPU software task. The basic composition of a Xilinx-Zynq SoC device is a processing system (PS), coupled with FPGA programmable logic (PL). The two sections are connected via a number of Advanced eXtensible Interfaces. Hardware accelerators are mechanisms whereby

Abstract Stroke is one of the leading causes of death as well as results in a massive economic burden for society. Stroke is a cerebrovascular disease mainly divided into two types: ischemic stroke and hemorrhagic stroke, which, respectively, refer to the partial blockage and bleeding inside brain blood vessels. Both stroke types lead to nutrient and oxygen deprivation in the brain, which ultimately

Abstract Cloud computing systems are splitting compute- and data-intensive jobs into smaller tasks to execute them in a parallel manner using clusters to improve execution time. However, such systems at increasing scale are exposed to stragglers, whereby abnormally slow running tasks executing within a job substantially affect job performance completion. Such stragglers are a direct threat towards

Abstract When a drone flies along with the waypoint flight plan, rerouting has to pay a cost to change its route in real time through the Internet. The inflight rerouting cost estimation algorithm can predict the rerouting cost as a function of the flight direction and the speed of the drone. Previous studies proved that the rerouting cost estimation would apply to the cases where the entire waypoint

Abstract Wireless sensor network is effective for data aggregation and transmission in IoT environment. Here, the sensor data often contain a significant amount of noises or redundancy exists, and thus, the data are aggregated to extract meaningful information and reduce the transmission cost. In this paper, a novel data aggregation scheme is proposed based on clustering of the nodes and extreme learning

Abstract Heterogeneous cluster systems consisting of CPUs and different kinds of accelerators have become mainstream in HPC. Programming such systems is a difficult task and requires addressing manifold challenges that stem from the intricate composition of such systems and peculiarities of scientific applications. A broad range of obstacles preventing efficient execution have to be considered and

Abstract A hierarchical approach for autotuning linear algebra routines on heterogeneous platforms is presented. Hierarchy helps to alleviate the difficulties of tuning parallel routines for high-performance computing systems. This paper analyzes the application of the hierarchical approach at both the hardware and software levels, using the basic matrix multiplication and the Strassen multiplication

Abstract This paper presents a new opportunistic scheduling and resource consolidation system based on an economic model related to different service level agreements (SLAs) classes. The goal is to address the problem of companies that manage a private infrastructure of machines, i.e., a cloud platform and would like to optimize the scheduling of several requests submitted online by users. For the

Abstract Currently, all online social networks (OSNs) are considered to follow a power-law distribution. In this paper, the degree distribution for multiple OSNs has been studied. It is seen that the degree distributions of OSNs differ moderately from a power law. Lognormal distributions are an alternative to power-law distributions and have been used as best fit for many complex networks. It is seen

Abstract Development in photonic integrated circuits (PICs) provides a promising solution for on-chip optical computation and communication. PICs provides the best alternative to traditional networks-on-chip (NoC) circuits which face serious challenges such as bandwidth, latency and power consumption. Integrated optics have substantiated the ability to accomplish low-power communication and low-power

Abstract Previous studies have developed pseudo-random number generators, where a pseudo-random number is not perfectly random but is practically useful. In this paper, we propose a new system for pseudo-random number generation. Recurrent neural networks with long short-term memory units are used to mimic the appearance of a given sequence of irrational number (e.g., pi), and these are intended to

Abstract Support vector machine (SVM) is a renowned machine learning technique, which has been successfully applied to solve many practical pattern classification problems. One of the difficulties in successful implementation of SVM is its different parameters (i.e., kernel parameter(s), penalty parameter (C) and the features available in the dataset), which should be well adjusted during the training

Abstract Minimum-cost flow problems widely exist in graph theory, computer science, information science, and transportation science. The network simplex algorithm is a fast and frequently used method for solving minimum-cost flow problems. However, the conventional sequential algorithms cannot satisfy the requirement of high-computational efficiency for large-scale networks. Parallel computing has

Abstract The analysis of complex biological datasets beyond DNA scenarios is gaining increasing interest in current bioinformatics. Particularly, protein sequence data introduce additional complexity layers that impose new challenges from a computational perspective. This work is aimed at investigating GPU solutions to address these issues in a representative algorithm from the phylogenetics field:

Abstract Building sentiment analysis resources is a fundamental step before developing any sentiment analysis model. Sentiment lexicons are one of these critical resources. However, many non-English languages suffer from a severe shortage of these resources and lexicons. This study proposes an integrated framework for extracting domain-based polarity words from unannotated massive non-English corpus

Abstract Our paper aims at learning from imbalance data based on ensemble learning. At the stage, the main solution is to combine under-sampling, oversampling or cost sensitivity learning with ensemble learning. However, these feature space-based methods fail to reflect the transformation of distribution and are usually accompanied with high computational complexity and risk of overfitting. In this

Abstract We propose that clusters interconnected with network topologies having minimal mean path length will increase their processing speeds. We approach our heuristic by constructing clusters of up to 32 nodes having torus, ring, Chvatal, Wagner, Bidiakis and optimal topology for minimal mean path length and by simulating the performance of 256 nodes clusters with the same network topologies. The

Abstract In this study, we investigated the problem of scheduling streaming applications on a heterogeneous cluster environment and, based on our previous work, developed the maximum throughput scheduler algorithm (MT-Scheduler) for streaming applications. The proposed algorithm uses a dynamic programming technique to efficiently map the application topology onto the heterogeneous distributed system

Abstract A basic heterogeneous parallel Red–Black successive over-relaxation (SOR) implement, the mono-color floating-point scheme, was developed on graphics processing units (GPU) with OpenCL platform. Designed in fine granularity, compact data structure, and stencil function, a concise mapping relationship was created to implicitly describe the complex rules for searching neighbor elements, which

Abstract Force-directed algorithm is one of the most commonly used methods for visualization of 2D graphs. These algorithms can be applied to a plethora of applications such as data visualization, social network analysis, crypto-currency transactions, and wireless sensor networks. Due to their effectiveness in visualization of topological data, various force-directed algorithms for 2D graphs were proposed

Abstract Weather forecasting, especially snowfall prediction, was critical in the 2018 Winter Olympics, where the accuracy of the predictions was of key importance for the planning of the different Olympic events. It was a significant challenge for the authors to meet the requirements in time and forecast resolution, while doing their best to be as competitive as possible. All the forecasts were obtained

Abstract The temporal and spatial characteristics of users are involved in most Internet of Things (IoT) applications. The spatial and temporal movement patterns of users are the most direct manifestation of the temporal and spatial characteristics. The user’s interests, activities, experience and other characteristics are reflected by mobile mode. In view of the low clustering efficiency of moving

Abstract Deep neural networks are currently trained under data-parallel setups on high-performance computing (HPC) platforms, so that a replica of the full model is charged to each computational resource using non-overlapped subsets known as batches. Replicas combine the computed gradients to update their local copies at the end of each batch. However, differences in performance of resources assigned

Abstract Image registration is a common task in remote sensing, consisting in aligning different images of the same scene. In the particular case of hyperspectral images, the exploitation not only of the spatial information contained in the image but also of the spectral information helps to improve the registration. An example of registration method exploiting all the information contained in the

Abstract Cloud computing has gained huge attention over the past decades because of continuously increasing demands. There are several advantages to organizations moving toward cloud-based data storage solutions. These include simplified IT infrastructure and management, remote access from effectively anywhere in the world with a stable Internet connection and the cost efficiencies that cloud computing

Abstract Cloud computing is attractive mostly because it allows companies to increase and decrease available resources, which makes them seem limitless. Although cloud computing has many advantages, there are still several issues such as unpredictable latency and no mobility support. To overcome these problems, fog computing extends communication, storage, and computation toward the edge of network

Abstract Using extremely large number of processing elements in computing systems leads to unexpected phenomena, such as different efficiencies of the same system for different tasks, that cannot be explained in the frame of the classical computing paradigm. The introduced simple non-technical model enables to set up a frame and formalism needed to explain the unexpected experiences around supercomputing

Abstract In this paper, we construct a novel algorithm for solving non-smooth composite optimization problems. By using inertial technique, we propose a modified proximal gradient algorithm with outer perturbations, and under standard mild conditions, we obtain strong convergence results for finding a solution of composite optimization problem. Based on bounded perturbation resilience, we present our

Abstract The moth search algorithm (MS) is a novel intelligent optimization algorithm based on moth population behavior, which can solve many problems in different fields. However, the algorithm is easy to fall into local optimization when solving complex optimization problems. This study develops a new hybrid moth search-fireworks algorithm (MSFWA) to solve numerical and constrained engineering optimization

Abstract The coexistence of parallel applications in shared computing nodes, each one featuring different Quality of Service (QoS) requirements, carries out new challenges to improve resource occupation while keeping acceptable rates in terms of QoS. As more application-specific and system-wide metrics are included as QoS dimensions, or under situations in which resource-usage limits are strict, building

Abstract Contextual information in complex scenarios is critical for accurate object detection. Existing state-of-the-art detectors have greatly improved detection performance with the use of contexts around objects. However, these detectors consider the local and global contexts separately, which limits the improvement in detection accuracy. In this paper, we propose a pyramid context learning module

Abstract The globalization of the economy and trade has made the transportation industry flourish, and the traffic demand is growing. Under this trend, energy consumption is increasing and environmental pollution is becoming more and more serious, so the development of “low-carbon transportation” is inevitable. Intermodality is a green transportation method that reduces transportation costs, shortens

Abstract Nowadays, deep learning is one of the popular research areas of the computer sciences, and many deep networks have been proposed to solve artificial intelligence and machine learning problems. Residual networks (ResNet) for instance ResNet18, ResNet50 and ResNet101 are widely used deep network in the literature. In this paper, a novel ResNet-based signal recognition method is presented. In

Abstract Over the past few decades, there has been a surge of interest of using swarm intelligence (SI) in computer-aided optimization. SI algorithms have demonstrated their efficacy in solving various types of real-world optimization problems. However, it is impossible to find an optimization algorithm that can obtain the global optimum for every optimization problem. Therefore, researchers extensively