In this paper, we present the StarNEig library for solving dense nonsymmetric standard and generalized eigenvalue problems. The library is built on top of the StarPU runtime system and targets both shared and distributed memory machines. Some components of the library have support for GPU acceleration. The library currently applies to real matrices with real and complex eigenvalues and all calculations

Hundreds of millions of Chinese people have become social network users in recent years, and aligning the accounts of common Chinese users across multiple social networks is valuable to many inter‐network applications, for example, cross‐network recommendation and cross‐network link prediction. Many methods have explored the proper ways of utilizing account name information into aligning the common

Realistic modeling of mechanical behavior of soft tissue has been recognized as an essential part for medical Internet of thing for minimal invasive surgery (MIS) training simulator. Therefore, the blockchain‐based constitutive model is crucial for mechanical response of soft tissue modeling. In this article, based on the Ogden second order model, a novel hyperplastic model was presented to describe

Software Defined Networks (SDN) is now the leading framework for the existing network infrastructure. Increasing Internet traffic leads to attract SDN infrastructure in large networks like enterprise or data centers by using logical centralize control concept. This abstraction, flexibility, and agility enable the network managers to view the global picture of the network and flow the traffic in an

Cloud computing is undeniably becoming the main computing and storage platform for today's major workloads. From Internet of things and Industry 4.0 workloads to big data analytics and decision‐making jobs, cloud systems daily receive a massive number of tasks that need to be simultaneously and efficiently mapped onto the cloud resources. Therefore, deriving an appropriate task scheduling mechanism

Semantic segmentation is a hot topic in computer vision and various deep learning networks are designed to achieve higher accuracy on that by fully exploring the capability of neural networks. This paper aims to address the issue and proposes the substructures with novelty for popular networks. Meanwhile, we present a cross‐channel structure, which simultaneously reduces parameter while the kernel

Exorbitant resources (computing and memory) are required to train a deep neural network (DNN). Often researchers deploy an approach that uses distributed parallel training to acquire larger models faster on GPUs. This approach has its detriments, though; on one hand, a GPU's expanded capacity to compute also produces bigger bottlenecks in inter‐GPU's communications during model training, and multi‐GPU

Wearable technology is no longer science fiction. Thanks to the growing capability in the production chain to miniaturize complex electronics, a wide variety of gadgets that can be worn or included in dresses and accessories have emerged. These smart gadgets can collect data about the physical condition of the user and/or the environment providing the basis for innovative and valuable services. The

Energy is one of the most important objectives for optimization on modern heterogeneous high‐performance computing (HPC) platforms. The tight integration of multicore CPUs with accelerators such as graphical processing units (GPUs) and Xeon Phi coprocessors in these platforms presents several challenges to the optimization of multithreaded data‐parallel applications for energy. In this work, the problem

Structure Query Language Injection Attack is among the top 10‐security threats that can be used on the web application to cause severe damage or gain unauthorized data access to the application server. Many reports have indicated an average of 64% of global websites are at risk of being attack by SQL injection, and many of the top companies have experienced thousands of attacks attempts through SQL

Nowadays, public gatherings and social events are an integral part of a modern city life. To run such events seamlessly, it requires real time mining and monitoring of causally related events so that the management can make informed decisions and take appropriate actions. The automatic detection of event causality from short text such as tweets could be useful for event management in this context.

The increase of security incidents brings a challenge to the cloud computing security. Intrusion detection technologies have been applied to protect information in cloud from being compromised, and complicated learning‐based detection methods have been used to improve the performance of intrusion detection systems. Higher quality and well‐formed samples are crucial to the performance of detection algorithm

The multimedia contents shared on social network reveal public sentimental attitudes toward specific events. Therefore, it is necessary to conduct sentiment analysis automatically on abundant multimedia data posted by the public for real‐world applications. However, approaches to single‐modal sentiment analysis neglect the internal connections between textual and visual contents, and current multimodal

The accurate assembly of the system matrix is an important step in any code that solves partial differential equations on a mesh. We either explicitly set up a matrix, or we work in a matrix‐free environment where we have to be able to quickly return matrix entries upon demand. Either way, the construction can become costly due to nontrivial material parameters entering the equations, multigrid codes

This article proposes a random‐forest based A2Cloud framework to match scientific applications with Cloud providers and their instances for high performance. The framework leverages four engines for this task: PERF engine, Cloud trace engine, A2Cloud‐ext engine, and the random forest classifier (RFC) engine. The PERF engine profiles the application to obtain performance characteristics, including the

Social media information can effectively improve the performance of personalized recommendation model. However, the feedback information in the social media which can accurately reflect users' implicit preferences is often ignored by most existing methods. To improve the users' experience and reduce the push of unwelcome information, in this article, we propose a new social recommendation algorithm

The Internet of Things is composed of sensor and actuator devices. Devices have to be securely accessed by subjects. In this article, we take the capability‐based access control (CBAC) model where a subject is issued a capability token to manipulate a device by a device owner. In the CBAC model, information which a subject is not allowed to get may illegally flow to the subject. Hence, the operation

At present, 96% of the resources available into the World‐Wide‐Web belongs to the Deep Web , which is composed of contents that are not indexed by search engines. The Dark Web is a subset of the Deep Web , which is currently the favorite place for hiding illegal markets and contents. The most important tool that can be used to access the Dark Web is the Tor Browser . In this article, we propose a bottom‐up

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

Most of the existing global positioning system (GPS) spoofing detection schemes are vulnerable to the generative GPS spoofing attack, or require additional auxiliary equipment and extensive signal processing capabilities, leading to defects such as low real‐time performance and large communication overhead which are not available for the unmanned aerial vehicle (UAV, also known as drone) system. Therefore

The use of multi‐chip modules (MCM) and/or multi‐socket boards is the most suitable approach to increase the computation density of servers while keep chip yield attained. This article introduces a new coherence protocol suitable, in terms of complexity and scalability, for this class of systems. The proposal uses two complementary ideas: (1) A mechanism that dissociates complexity from performance

1 BACKGROUND The purpose of this special issue is to collect a selection of representative research articles that were primarily presented at the Eighth Workshop on Data‐Intensive Computing in the Clouds, held in conjunction with SC'17. In particular, this annual workshop brings together domain scientists, researchers, scholars, vendors, and practitioners from the complementary fields of data science

The formation of haze depends on the complex evolution behavior of SO2, NO2, and CO. We explore the influence of surrounding urban oxides on haze formation in Beijing. From the perspective of time evolution, multifractal detrended cross‐correlation analysis (MF‐DCCA) method quantitatively shows that all oxides reveal persistent cross‐correlations (h xy : 0.640 sim; 0.995) in the short term and long

Workload prediction has been widely researched in the literature. However, existing techniques are per‐job based and useful for service‐like tasks whose workloads exhibit seasonality and trend. But cloud jobs have many different workload patterns and some do not exhibit recurring workload patterns. We consider job‐pool‐based workload estimation, which analyzes the characteristics of existing tasks'

Due to the continuous appearance of safety fault accidents in the practice process, operation safety has become the central task of various operation and management tasks of the power grid. Therefore, to establish a line overload identification and data control model for the power system, we first defined the vulnerability of complex power systems based on the analysis of each line and node. For finding

Due to the rapid development of the cloud computing environment, it is widely accepted that cloud servers are important for users to improve work efficiency. Users need to know servers' capabilities and make optimal decisions on selecting the best available servers for users' tasks. We consider the process of learning servers' capabilities by users as a multiagent reinforcement learning process. The

Software‐defined networking (SDN) is an emerging paradigm in enterprise networks because of its flexible and cost‐effective nature. By decoupling control and data plane, SDN can provide various defense solutions for securing futuristic networks. However, the architectural design and characteristics of SDN attract several severe attacks. Distributed denial of service (DDoS) is considered as a major

Feature extraction is a critical technology that affects the accuracy of face recognition. However, certain features are highly related to changes in face are difficult to extract because of the influences of individual differences and illumination. Therefore, features can accurately describe the changes in face are urgently required. For this reason, this article proposes a feature extraction method

Cloud storage is a mode of online storage in which data are stored on multiple virtual servers usually hosted by third parties. Cloud service providers manage and operate data storage as services. The major concern in cloud storage is the integrity of outsourced data. Many public auditing schemes which authorize the third party auditor (TPA) to check the integrity of outsourced data have been proposed

The ray‐tracing algorithm is very costly regarding time complexity and while many techniques have been conceived over the years with the purpose of accelerating its execution, one stands out: parallelism exploitation of ray‐triangle intersection operations. In this sense, field‐programmable gate arrays (FPGAs) have plenty resources to run specialized accelerators that execute multiple operations in

The Golub‐Kahan bidiagonalization is widely used in the singular value decomposition of rectangular matrices and has been generalized to an iterative solver for symmetric indefinite linear systems with a two‐by‐two block structure. In this work, we present a scalability study of this generalized solver as implemented in a recent release of the parallel numerical library PETSc (Portable, Extensible

Multidimensional community discovering in heterogeneous social networks is an important issue. Many approaches have been proposed for community discovering in heterogeneous networks. However, they have focused only on topological properties of these networks, ignoring the embedded semantic information. As the solution to this information glut limit, we propose, in this article, a new multidimensional

With the hot issues such as smart city and ecological city put forward, the development of intelligence and informatization of urban space has been established, especially the Internet of Things. With the wide application of location‐based social networks, users can share their location of interest in location. By analyzing users' historical geographic information, location service recommendation can

Balancing the workload of sophisticated simulations is inherently difficult, since we have to balance both computational workload and memory footprint over meshes that can change any time or yield unpredictable cost per mesh entity, while modern supercomputers and their interconnects start to exhibit fluctuating performance. We propose a novel lightweight balancing technique for MPI+X to accompany

Most existing quality of service (QoS) control algorithms of Web applications take into account Web Server or database connections which can be released immediately. However, many applications are deployed on virtual machines (VMs) or even Spot VMs elastically rented from public Clouds. To save costs, interval‐priced VMs are not released until the ends of rented intervals. Such delays of control effects

The security threats from malicious web pages have become a hot topic for cyber security. One goal pursued by current research is to identify malicious web pages quickly, accurately, and efficiently. Considering the high detection costs and potential dimensionality curse of malicious webpage detection, in this article, we proposes a detection framework based on feature optimization and hybrid classification

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

Image‐enhancement algorithms, for example, median filtering algorithms, Gaussian filtering algorithms, and the multiscale Retinex (MSR) algorithm, are widely used in unmanned aerial vehicle (UAV) image processing to resolve the problems of poor clarity, insufficient contrast, and weak adaptability of the aerial images. Aiming to improve the low efficiency of processing a large volume of UAV images

Qualitative spatio‐temporal reasoning is an important problem in artificial intelligence and has been widely and successfully applied in geographic information system and spatio‐temporal database. Currently, action features can be found in spatio‐temporal domain and the existing spatio‐temporal formalisms are not suitable for dealing with dynamic spatio‐temporal knowledge. Thus, how to represent and

Cyber‐physical systems (CPSs) include time‐critical embedded systems where strict timing constraints are considered. These specific CPSs are dealing with close loop control functions, they are interfacing to sensors and actuators and have to fulfill safety critical real‐time constraints. To guarantee the timing correctness, a lot of work introduced the strict‐periodicity constraint. The latter is of

In view of the contradiction between the increasingly extensive application of single‐chip microcomputer technology in home appliances, digital products, medical equipment, automobile industry, aerospace and other fields, and the long learning cycle of the technology, which is not easy to master, a new single‐chip microcomputer teaching platform for high school students is designed. The teaching platform

Dual‐hop 60 GHz wireless networks which support relay‐assisted dual‐hop transmission have been widely adopted in the recent years, aiming to prolong communication distance and bypass obstacles in 60 GHz band. However, it is very challenging to perform link scheduling in such dual‐hop architecture while considering several factors, i.e., reducing network power consumption, avoiding overloaded APs/relays

This work is a part of the global tendency to use modern computing systems for modeling the phase‐field phenomena. The main goal of this article is to improve the performance of a parallel application for the solidification modeling, assuming the dynamic intensity of computations in successive time steps when calculations are performed using a carefully selected group of nodes in the grid. A two‐step

The biomechanical analysis of the pushing kick in the conventional free combat was aimed at the crowds with different ages and different types of skeletal muscles. The analysis precision was low. Therefore, this article put forward the biomechanical analysis of push‐kick of free combat based on 3D image. Three‐dimensional image technique and Hill equation were used to construct the biomechanical model

Data consistency is a critical topic in distributed systems. In existing consistency models, causal consistency has attracted a significant amount of attention because it can satisfy high‐performance requirements even in the presence of network partitions. At present, most of the causal consistency models face a tradeoff between throughput and update visibility. Simultaneously, they cannot take full

Scheduling is a widely used method in parallel computing, which assigns tasks to compute resources of a parallel environments. In this article, we consider independent parallel tasks to be scheduled onto a heterogeneous execution platform consisting of a set of multicores of different architecture. Each parallel task has an internal potential parallelism which allows a parallel execution on any multicore

In this article, an improved Lorenz system was constructed, in the first stage, a nonlinear term in general Lorenz system was replaced by the sum of term of exponential function and term of the square of single variable, then the dynamic characteristics of the improved Lorenz system were analyzed through the analysis on the phase diagrams and its Lyapunov exponents, and it is proved that the new system

Cloud bursting is a concept originating from the hybrid cloud computing paradigm. During workload spikes, the local resources of the private cloud are supplemented by resources in the public cloud. This technique could also be applied in a fog computing environment, in order to handle workload fluctuations, by offloading applications to the cloud. Toward this direction, in this article, we propose

In order to improve the ability of inheritance and protection of national sports, the association data mining model of inheritance and protection of national sports is proposed based on feature extraction of association rules. In this article, a new neural fuzzy normal form system is designed, which can perform both feature analysis and SI(sine) integration. The time series analysis model of the inheritance

Video‐based hand gesture recognition plays an important role in human‐computer interaction (HCI). Recent advanced methods usually add 3D convolutional neural networks to capture the information from both spatial and temporal dimensions. However, these methods suffer the issue of requiring large‐scale training data and high computational complexity. To address this issue, we proposed an attentive feature

Effectively obtaining the location and direction of the ship target is an important prerequisite for maritime traffic management and marine accident rescue. Thanks to the rapid development of the target detection methods based on deep learning, this article proposed a ship target detection method for multiresolution synthetic aperture radar (SAR) images based on improved region convolution neural network

With the emergence of cloud storage security, how to ensure the security of cloud storage has become a research focus. The typical searchable encryption model consists of a single cloud service provider and has some unsolvable drawbacks. In this article, a new searchable encryption scheme in multi‐cloud was proposed based on blockchain. We first define a system model in multiple clouds and combine

Sentiment analysis is one of the major tasks of natural language processing, in which attitudes, thoughts, opinions, or judgments toward a particular subject has been extracted. Web is an unstructured and rich source of information containing many text documents with opinions and reviews. The recognition of sentiment can be helpful for individual decision makers, business organizations, and governments

Social network analysis (SNA) has attracted a lot of attention in several domains in the past decades. It can be of 2‐folds: one is content‐based, and another one is structured‐based analysis. Link prediction is one of the emerging research problems, which comes under structured‐based analysis that deals with predicting the missing link, which is likely to appear in the future. In this article, the

In the age of the Internet of Things (IoT), large numbers of sensors and edge devices are deployed in various application scenarios; Therefore, collaborative learning is widely used in IoT to implement crowd intelligence by inviting multiple participants to complete a training task. As a collaborative learning framework, federated learning is designed to preserve user data privacy, where participants

One of the most critical properties that must be ensured to have a secure electronic voting is verifiability. Political parties, observers, and especially voters want to be able to verify that all eligible votes are cast as intended and counted as cast without compromising votes secrecy or voters privacy. Over the past few decades, an important number of e‐voting protocols attempt to deal with this

The generative adversarial network is widely used in image generation, and the generation of images with different styles is applied to underwater image enhancement. The existing underwater image generative adversarial network does not realize color correction when processing underwater images Therefore, we propose an improved generative adversarial network for image color restoration. Firstly, the

As the policies of a software‐defined networking (SDN) network can be updated dynamically and often at a high pace, conflicts between policies can easily occur. Due to the large number of switches and heterogeneous policies within a typical SDN network, detecting those conflicts is a laborious and challenging task. This article presents three main contributions. First, we devise an offline method for

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

Sentiment analysis can extract information from many text sources such as reviews, news, and blogs; then it classifies them based on their polarity. Moreover, big data is produced via mobile networks and social media. Applications of sentiment analysis on big data are used as a way of classifying the opinions into diverse sentiment. Accordingly, performing sentiment analysis on big data can be helpful