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In blind detection, both the downlink control information (DCI) format and the physical downlink control channel format are unknown, which results in high detection complexity. If the DCI format can be determined, half of the invalid candidates will be eliminated, which can significantly reduce the detection complexity. To determine the DCI format, this letter designs a DCI metric based on a correction factor of the transition probability, which is capable of measuring the reliability of decoding paths with different number of frozen bits. Furthermore, to reduce the decoding latency, a simplified decoding algorithm is adopted in the proposed blind detection scheme, which shows good adaptivity for a variety of code lengths and code rates. Moreover, an update criterion of the DCI metric based on the simplified decoding method is proposed, which improves the detection accuracy and reduces the decoding latency. Simulation results show the advantages of the proposed method in terms of the detection performance and latency compared with the existing schemes.

We present H2DP , a holistic heterogeneity-aware data placement scheme for hybrid parallel I/O systems, which consist of HDD servers and SSD servers. Most of the existing approaches focus on server performance or application I/O pattern heterogeneity in data placement. H2DP considers three axes of heterogeneity: server performance, server space, and application I/O pattern. More specifically, H2DP determines the optimized stripe sizes on servers based on server performance, keeps only critical data on all hybrid servers and the rest data on HDD servers, and dynamically migrates data among different types of servers at run-time. This holistic heterogeneity-awareness enables H2DP to achieve high performance by alleviating server load imbalance, efficiently utilizing SSD space, and accommodating application pattern variation. We have implemented a prototype of H2DP under MPICH2 atop OrangeFS. Extensive experimental results demonstrate that H2DP significantly improve I/O system performance compared to existing data placement schemes.

As the performance gap between processors and storage devices keeps increasing, I/O performance becomes a critical bottleneck of modern high-performance computing systems. In this paper, we propose a pattern-directed and layout-aware data replication design, named PDLA, to improve the performance of parallel I/O systems. PDLA includes an HDD-based scheme H-PDLA and an SSD-based scheme S-PDLA . For applications with relatively low I/O concurrency, H-PDLA identifies access patterns of applications and makes a reorganized data replica for each access pattern on HDD-based servers with an optimized data layout. Moreover, to accommodate applications with high I/O concurrency, S-PDLA replicates critical access patterns that can bring performance benefits on SSD-based servers or on HDD-based and SSD-based servers. We have implemented the proposed replication scheme under MPICH2 library on top of OrangeFS file system. Experimental results show that H-PDLA can significantly improve the original parallel I/O system performance and demonstrate the advantages of S-PDLA over H-PDLA.

Objectives In this study, we aimed to investigate the spontaneous neural activity in the conventional frequency band (0.01−0.08 Hz) and two sub-frequency bands (slow-4: 0.027–0.073 Hz, and slow-5: 0.01–0.027 Hz) in tension-type headache (TTH) patients with regional homogeneity (ReHo) analyses. Methods Thirty-eight TTH patients and thirty-eight healthy controls (HCs) underwent resting-state functional magnetic resonance imaging (RS-fMRI) scanning to investigate abnormal spontaneous neural activity using ReHo analysis in conventional frequency band (0.01−0.08 Hz) and two sub-frequency bands (slow-4: 0.027–0.073 Hz and slow-5: 0.01–0.027 Hz). Results In comparison with the HC group, patients with TTH exhibited ReHo increases in the right medial superior frontal gyrus in the conventional frequency band (0.01−0.08 Hz). The between group differences in the slow-5 band (0.01–0.027 Hz) highly resembled the differences in the conventional frequency band (0.01−0.08 Hz); even the voxels with increased ReHo were spatially more extensive, including the right medial superior frontal gyrus and the middle frontal gyrus. In contrast, no region showed significant between-group differences in the slow-4 band (0.027–0.073 Hz). The correlation analyses showed no correlation between the ReHo values in TTH patients and VAS scores, course of disease and number of seizures per month in conventional band (0.01−0.08 Hz), slow-4 band (0.027–0.073 Hz), as well as in slow-5 band (0.01–0.027 Hz). Conclusions The results showed that the superior frontal gyrus and middle frontal gyrus were involved in the integration and processing of pain signals. In addition, the abnormal spontaneous neural activity in TTH patients was frequency-specific. Namely, slow-5 band (0.01–0.027 Hz) might contain additional useful information in comparison to slow-4 band (0.027−0.073 Hz). This preliminary exploration might provide an objective imaging basis for the understanding of the pathophysiological mechanism of TTH.

Persistent memory (PMEM) devices can achieve comparable performance to DRAM while providing significantly more capacity. This has made the technology compelling as an expansion to main memory. Rethinking PMEM as storage devices can offer a high performance buffering layer for HPC applications to temporarily, but safely store data. However, modern parallel I/O libraries, such as HDF5 and pNetCDF, are complicated and introduce significant software and metadata overheads when persisting data to these storage devices, wasting much of their potential. In this work, we explore the potential of PMEM as storage through pMEMCPY: a simple, lightweight, and portable I/O library for storing data in persistent memory. We demonstrate that our approach is up to 2x faster than other popular parallel I/O libraries under real workloads.

As an important topic in hyperspectral image (HSI) analysis, band selection has attracted increasing attention in the last two decades for dimensionality reduction in HSI. With the great success of deep learning (DL)-based models recently, a robust unsupervised band selection (UBS) neural network is highly desired, particularly due to the lack of sufficient ground truth information to train the DL networks. Existing DL models for band selection either depend on the class label information or have unstable results via ranking the learned weights. To tackle these challenging issues, in this article, we propose a Gumbel-Softmax (GS) trick enabled concrete autoencoder-based UBS framework (CAE-UBS) for HSI, in which the learning process is featured by the introduced concrete random variables and the reconstruction loss. By searching from the generated potential band selection candidates from the concrete encoder, the optimal band subset can be selected based on an information entropy (IE) criterion. The idea of the CAE-UBS is quite straightforward, which does not rely on any complicated strategies or metrics. The robust performance on four publicly available datasets has validated the superiority of our CAE-UBS framework in the classification of the HSIs.

Multicloud access control is important for resource sharing and security interoperability across different clouds, and heterogeneity of access control policy is an important challenge for cloud mashups. XAC-ML is widely used in distributed environment as a declaratively fine-grained, attribute-based access control policy language, but the policy integration of XACML lacks formal description and theory foundation. Multicloud Access Control Policy Integration Framework (MACPIF) is proposed in the paper, which consists of Attribute-based Policy Evaluation Model (ABPEM), Four-value Logic with Completeness (FLC) and Four-value Logic based Policy Integration Operators (FLPIOs). ABPEM evaluates access control policy and extends XACML decision to four-value. According to policy decision set and policy integration characteristics, we construct FLC and define FLPIOs including Intersection, Union, Difference, Implication and Equivalence. We prove that MACPIF can achieve policy monotonicity, functional completeness, canonical suitability and canonical completeness. Analysis results show that this framework can meet the requirements of policy integration in Multicloud.

In this letter, we propose an efficient blind detection scheme of polar codes compliant with the 5G standard. In the proposed scheme, the lengths and locations of the downlink-control information (DCI) are detected separately in two stages. Before entering the second stage that is dedicated to the detection of locations, at least half of the candidates are eliminated through the detection of the DCI lengths in the first stage. Specifically, a novel detection metric is used in the first stage to detect the lengths of the DCI and further exclude invalid candidates. Simulation results show that the proposed scheme outperforms the existing schemes in terms of detection accuracy and latency.

In the practical production, after the completion of a job on a machine, it may be transported between the different machines. And, the transportation time may affect product quality in certain industries, such as steelmaking. However, the transportation times are commonly neglected in the literature. In this paper, the transportation time and processing time are taken as the independent time into the flexible job shop scheduling problem. The mathematical model of the flexible job shop scheduling problem with transportation time is established to minimize the maximum completion time. The FJSP problem is NP-hard. Then, an improved genetic algorithm is used to solve the problem. In the decoding process, an operation left shift insertion method according to the problem characteristics is proposed to decode the chromosomes in order to get the active scheduling solutions. The actual instance is solved by the proposed algorithm used the Matlab software. The computational results show that the proposed mathematical model and algorithm are valid and feasible, which could effectively guide the actual production practice.

The second generation of blockchain represented by smart contracts has been developing vigorously in recent years. However, frequent smart contract vulnerability incidents pose a serious risk to blockchain ecosystem security. Since current symbol execution tools often fall into path explosion and thus lead to inefficient detection, this paper expands Mythril's framework to optimize its performance. Firstly, it finds out potential vulnerable code regions using static analysis and identifies critical paths that may have security defects. Then, aiming at the problem that traditional search algorithms cannot actively locate and explore critical paths, this paper presents a multi-objective oriented path search (MOPS) strategy based on path priority. This strategy guides dynamic symbolic execution to cover critical paths quickly, avoiding blind traversal of program execution paths. Finally, it describes security rules and proposes corresponding detection logics for different vulnerability categories. This paper analyzes over 1000 smart contracts extracted from Etherscan. Compared with existing tools based on symbolic execution, the proposed method can reduce time consumption by around 35% while ensuring the accuracy of vulnerability detection. Moreover, existing tools often issue warnings that do not actually cause financial losses. But the proposed method only concentrates on code regions related to transfer of funds, so it can reduce the false alarm rate to some extent.

In this paper, we explain the connection between information processing by complex systems and recurrent activity sequences in their dynamics. We argue that an understanding of information processing pathways in terms of these dynamical motifs is important for designing effective interventions. We then describe a recently completed study, where we video recorded 37 shared book reading (SBR) sessions, and thereafter annotated each of these sessions for 26 activities (reading the book, comments and questions, management talk by the teacher, and responses from the children). For all SBR sessions, the annotations consisted of sequences of one activity followed by another (transitions). We tested the empirical data against a null model where activities occur randomly, to identify 34 transitions that occur more frequently than by chance, and visualize these transitions that are statistically significant at the confidence level of p < 10−3 in the form of a static network. We then chose six significant transitions, and tested their extensions against the same null model to identify statistically significant length-3 sequences. This extension procedure was repeated to obtain length-4, length-5, and longer sequences until no further statistically significant extensions can be found. Finally, we organized the longest significant sequences into five families of dynamical motifs, and discuss their implications on the effectiveness of SBR.

Prefetching techniques have been studied for decades. However, there are few studies on how concurrent memory accesses may affect prefetching effectiveness. When there are multiple concurrent memory requests, we can classify them into sub-classes by analyzing the overlapping relationship. In this work, we first propose pure prefetch coverage (PPC), a novel prefetching metric that can identify an accurate prefetch coverage under the concurrent memory access model. Then we propose APAC, an adaptive prefetch framework with PPC metric that can capture the dynamics of applications and adjust the prefetching aggressiveness. Our experimental results show that the PPC metric has a higher IPC correlation compared to the conventional prefetch coverage (PC) metric. For memory-intensive single-thread benchmarks, APAC provides an average performance improvement by 17.3% and 5.9% compared to the state-of-the-art adaptive prefetch framework FDP and NST. In a multi-core system, APAC outperforms FDP and NST by 8.5% and 5.0% IPC on average, respectively.

The current study seeks to explore the impact of multimedia input at home on bilingual children's language outcomes. Two hundred and two Singaporean English-Mandarin kindergarteners' multimedia experience (i.e., the resources and the amount of multimedia input) and conventional language exposure (e.g., language use with family members) were investigated with a parental questionnaire. A series of English and Mandarin tests were conducted to assess children's proficiency (i.e., in receptive vocabulary, receptive grammar, verbal fluency) by standardized measures. Results demonstrated that the diversity of multimedia input is more important than the amount of multimedia input in promoting children's Mandarin language maintenance, while controlling for children's conventional language exposure, SES, and language aptitude. The number of multimedia sources is significantly and positively related to children's general Mandarin proficiency. In contrast, English multimedia exposure at home exerts little impact on children's general English proficiency. The findings indicate the unique contribution of multimedia diversity to children's early heritage language maintenance. The strong social relevance of the study is discussed at the end of the paper.

Optimized sensing is important for computational imaging in low-resource environments, when images must be recovered from severely limited measurements. In this paper, we propose a physics-constrained, fully differentiable, autoencoder that learns a probabilistic sensor-sampling strategy for optimized sensor design. The proposed method learns a system's preferred sampling distribution that characterizes the correlations between different sensor selections as a binary, fully-connected Ising model. The learned probabilistic model is achieved by using a Gibbs sampling inspired network architecture, and is trained end-to-end with a reconstruction network for efficient co-design. The proposed framework is applicable to sensor selection problems in a variety of computational imaging applications. In this paper, we demonstrate the approach in the context of a very-long-baseline-interferometry (VLBI) array design task, where sensor correlations and atmospheric noise present unique challenges. We demonstrate results broadly consistent with expectation, and draw attention to particular structures preferred in the telescope array geometry that can be leveraged to plan future observations and design array expansions., Comment: This paper has been accepted to the IEEE International Conference on Computational Photography (ICCP) 2020. Keywords: Computational Imaging, Optimized Sensing, Ising Model, Deep Learning, VLBI, Interferometry

In this paper, a backpropagation (BP) neural network algorithm and a multiple factor regression (MR) algorithm are presented to improve the performance of the prediction of wheat growth. By applying the BP neural network algorithm and the MR algorithm, the corresponding Leaf Area Index (LAI) and Soil Plant Analysis Development (SPAD) values can be regressed from the Thematic Mapping (TM) data. The experimental result demonstrates that the designed framework has a better performance, which can effectively predict desired parameters and provide a promising solution for the crop growth monitoring. For finding a better solution for the crop growth monitoring, the performance of the BP neural network and the MR algorithms have been investigated.

As data volumes of high-performance computing applications continuously increase, low I/O performance becomes a fatal bottleneck of these data-intensive applications. Data replication is a promising approach to improve parallel I/O performance. However, most existing strategies are designed based on the assumption that contiguous requests are being served more efficiently than non-contiguous requests, which is not necessarily true in a parallel I/O system. The reason is that the multiple-server data distribution makes the favorable accesses between contiguous requests and non-contiguous ones indeterminate. In this study, we propose CEDA, a cost-effective distribution-aware data replication scheme to better support parallel I/O systems. As logical file access information is inefficient to make replication decisions in a parallel environment, CEDA considers physical data accesses on servers in both data selection and data placement during a parallel replication process. Specifically, CEDA first proposes a distribution-aware cost model to evaluate the file request time with a given data layout, and then it carries out cost-effective data replication based on replication benefit analysis. We have implemented CEDA as a part of the MPI I/O library in light of high portability on top of the OrangeFS file system. By replaying representative benchmarks and a real application, we collected comprehensive experimental results on both HDD- and SSD-based servers and conclude that CEDA can significantly improve parallel I/O system performance.

In the age of data-driven computing, integrating High Performance Computing(HPC) and Big Data(BD) environments may be the key to increasing productivity and to driving scientific discovery forward. Scientific workflows consist of diverse applications (i.e., HPC simulations and BD analysis) each with distinct representations of data that introduce a semantic barrier between the two environments. To solve scientific problems at scale, accessing semantically different data from different storage resources is the biggest unsolved challenge. In this work, we aim to address a critical question: ”How can we exploit the existing resources and efficiently provide transparent access to data from/to both environments”. We propose iNtelligent I/O Bridging Engine(NIOBE), a new data integration framework that enables integrated data access for scientific workflows with asynchronous I/O and data aggregation. NIOBE performs the data integration using available I/O resources, in contrast to existing optimizations that ignore the I/O nodes present on the data path. In NIOBE, data access is optimized to consider both the ongoing production and the consumption of the data in the future. Experimental results show that with NIOBE, an integrated scientific workflow can be accelerated by up to 10x when compared to a no-integration baseline and by up to 133% compared to other state-of-the-art integration solutions.

Multi-tiered Parallel I/O systems that combine traditional HDDs with emerging SSDs mitigate the cost burden of SSDs while benefiting from their superior I/O performance. While a multi-tiered parallel I/O system is promising for data-intensive applications in high-performance (HPC) domains, placing data on each tier of the system to achieve high I/O performance remains a challenge. In this paper, we propose a cost-aware region-level (CARL) data placement scheme in multi-tiered parallel I/O systems. CARL divides a large file into several small regions, and then places regions on different types of servers based on region access costs. CARL includes a static policy S-CARL and a dynamic policy D-CARL. For applications whose I/O access patterns are completely known, S-CARL calculates the region costs within the entire workload duration, and uses a static data placement scheme to selectively place regions on the proper servers. To adapt to applications whose access patterns are unknown in advance, D-CARL uses a dynamic data placement scheme which migrates data among different servers within each time window. We have implemented CARL under MPI-IO library and OrangeFS parallel file system environment. Our evaluation with representative benchmarks and an application shows that CARL is both feasible and able to improve I/O performance significantly.

Fault detection and diagnosis for nonstationary processes is a difficult task since the fault signal may be buried in the nonstationary trends of variables. Most of the traditional multivariate statistical methods, such as principal component analysis and partial least squares, etc., cannot describe the relations among nonstationary variables due to their stationary assumption. The motivation of the paper is to address the fault diagnosis problem for nonstationary industrial processes. A sparse reconstruction strategy is proposed to online isolate and diagnose the faulty variables based on cointegration analysis which does not require any historical fault data. First, nonstationary variables are distinguished and separated from stationary variables so that the cointegration models are constructed to describe the long-run equilibrium relation among the nonstationary variables. Second, a faulty variables isolation strategy is formulated for online fault diagnosis by integrating least absolute shrinkage and ...

Parallel file system (PFS) is commonly used in high-end computing systems. With the emergence of solid state drives (SSDs), hybrid PFS, which consists of both HDD and SSD servers, provides a practical I/O system solution for data-intensive applications. However, most existing data layout schemes are inefficient for hybrid PFS due to their unawareness of server heterogeneities and workload changes in different parts of a file. In this study, we propose a heterogeneity-aware region-level data layout scheme, HARL, to improve the data distribution of a hybrid PFS. HARL first divides a file into fine-grained, varying sized regions according to the workload features of an application, then determines appropriate file stripe sizes on servers for each region based on the performance of heterogeneous servers. Furthermore, to further improve the performance of a hybrid PFS, we propose a dynamic region-level layout scheme, HARL-D, which creates multiple replicas for each region and redirects file requests to the proper replicas with the lowest access costs at the runtime. Experimental results of representative benchmarks and a real application show that HARL can greatly improve I/O system performance, and demonstrate the advantages of HARL-D over HARL.

Key–value stores are being widely used as the storage system for large-scale internet services and cloud storage systems. However, they are rarely used in HPC systems, where parallel file systems are the dominant storage solution. In this study, we examine the architecture differences and performance characteristics of parallel file systems and key–value stores. We propose using key–value stores to optimize overall Input/Output (I/O) performance, especially for workloads that parallel file systems cannot handle well, such as the cases with intense data synchronization or heavy metadata operations. We conducted experiments with several synthetic benchmarks, an I/O benchmark, and a real application. We modeled the performance of these two systems using collected data from our experiments, and we provide a predictive method to identify which system offers better I/O performance given a specific workload. The results show that we can optimize the I/O performance in HPC systems by utilizing key–value stores.

The study of how conversational partners align language behavior has been given much attention in psychology, linguistics and sociology. Prominent frameworks for investigating this type of cooperative behavior include dialogic resonance, the interactive alignment model, abstract structural priming, and format tying. Yet, we do not have estimates of the overall amount of discourse alignment in human conversation. Obtaining estimates is challenging because alignment is complex, occurring at many different levels, and the resources needed to manually annotate large corpora to measure all aspects of discourse alignment restrict rapid progress on the issue. However, recent NLP research has developed systems to estimate alignment at least at the lexical and syntactic levels of conversation. While such systems come with error rates and lack the precision of manual annotation, interesting findings are emerging such as a recent study reporting grammatical alignment levels in the British National Corpus (BNC) was not particularly high, possibly what would be expected by chance. This is a controversial paper. The current study therefore, replicates the study in the recently released BNC 2014 spoken, finding in this corpus syntactic similarity is also close to the alignment one would get by randomly pairing conversational turns. The study goes further, finding the most frequent similarity level between utterances is zero, and that there exist no convincing cases of abstract structural alignment. Extending the research to child language corpora, grammatical alignment between children and adults is slightly above chance, though the most frequent level of syntactic similarity is zero, with no convincing cases of an abstract structure used by a child because their adult interlocutor had primed them. The findings might mediate theory-building that assumes discourse alignment at the grammatical level is widespread throughout conversation between adults or adults and children.

Parallel file systems (PFS) are widely-used to ease the I/O bottleneck of modern high-performance computing systems. However, PFSs do not work well for small requests, especially small random requests. Newer Solid State Drives (SSD) have excellent performance on small random data accesses, but also incur a high monetary cost. In this study, we propose SLA-Cache, a Selective and Layout-Aware Cache system that employs a small set of SSD-based file servers as a cache of conventional HDD-based file servers. SLA-Cache uses a novel scheme to identify performance-critical data, and conducts a selective cache admission (SCA) policy to fully utilize SSD-based file servers. Moreover, since data layout of the cache system can also largely influence its access performance, SLA-Cache applies a layout-aware cache placement scheme (LCP) to store data on SSD-based file servers. By storing data with an optimal layout requiring the lowest access cost among three typical layout candidates, LCP can further improve system performance. We have implemented SLA-Cache under the MPICH2 I/O library. Experimental results show that SLA-Cache can significantly improve I/O throughput, and is a promising approach for parallel applications.

Hybrid parallel file systems (PFSs), which consist of solid-state drive servers (SServer) and hard disk drive servers (HServer), have recently attracted growing attention. Compared to a traditional HServer, an SServer consistently provides improved storage performance but lacks storage space. However, most current data layout schemes do not consider the differences in performance and space between heterogeneous servers and may significantly degrade the performance of the hybrid PFSs. In this article, we propose performance and space-aware (PSA) scheme, a novel data layout scheme, which maximizes the hybrid PFSs’ performance by applying adaptive varied-size file stripes. PSA dispatches data on heterogeneous file servers not only based on storage performance but also storage space. We have implemented PSA within OrangeFS, a popular PFS in the high-performance computing domain. Our extensive experiments with representative benchmarks, including IOR, HPIO, MPI-TILE-IO, and BTIO, show that PSA provides superior I/O throughput than the default and performance-aware file data layout schemes.

With the wide usage of smartphones in our daily life, new malware is emerging to compromise the mobile OS and then steal or manipulate sensitive data from mobile applications. Forensic analysis tools demand a reliable and trustworthy memory acquisition of the operating systems running on the smartphones for further digital forensic analysis. However, a compromised OS may launch denial of service attacks to prevent a valid memory acquisition by forensic examiners. In this paper, we develop a TrustZone-based memory acquisition mechanism called TrustDump that is capable of reliably and securely obtaining the RAM memory and CPU registers of the mobile OS even if the OS has crashed or been compromised. TrustDump is isolated from the mobile OS by TrustZone. Instead of using a hypervisor to ensure the isolation between the OS and the memory acquisition tool, we rely on ARM TrustZone to achieve a hardware-assisted isolation with a small trusted computing base. TrustDump can include basic online analysis modules to catch malware in an early stage. Moreover, the acquired memory and register data can be sent to a remote server through a fast Micro-USB port for real-time forensics analysis when the OS runs or a slow serial port for further forensic analysis when the OS has crashed. A trusted graphical user interface is integrated in the TrustZone to authenticate the user and prevent the misuse of our memory acquisition tool. We build a TrustDump prototype on Freescale i.MX53 QSB.

Modern High Performance Computing (HPC) applications, such as Earth science simulations, produce large amounts of data due to the surging of computing power, while big data applications have become more compute-intensive due to increasingly sophisticated analysis algorithms. The needs of both HPC and big data technologies for advanced HPC and big data applications create a demand for integrated system support. In this study, we introduce Scientific Data Processing (SciDP) to support both HPC and big data applications via integrated scientific data processing. SciDP can directly process scientific data stored on a Parallel File System (PFS), which is typically deployed in an HPC environment, in a big data programming environment running atop Hadoop Distributed File System (HDFS). SciDP seamlessly integrates PFS, HDFS, and the widely-used R data analysis system to support highly efficient processing of scientific data. It utilizes the merits of both PFS and HDFS for fast data transfer, overlaps computing with data accessing, and integrates R into the data transfer process. Experimental results show that SciDP accelerates analysis and visualization of a production NASA Center for Climate Simulation (NCCS) climate and weather application by 6x to 8x when compared to existing solutions.

Aiming at the problem of fault prediction for satellite communication system, a prediction model based on deep learning is proposed in this paper. Firstly, the equipment parameters are summed up, and then two kinds of states covering normal and abnormal situations are determined. After feature learning, self-encoding network is used to obtain new features which can characterize the deep feature of the data. Then the tagged data extracted from monitoring equipment are applied to train the prediction classifier which is the combination of deep belief network and softmax classifier. The deep belief network is composed of limited Boltzmann machine as well as BP network. BP network is used for parameters adjustment. Finally, the effects of fault prediction including the performance of model and average prediction accuracy are tested through simulation.

Parallel file systems (PFSs) are widely deployed to speed up the performance of high-performance computing (HPC) applications. In recent years, hybrid PFSs that consist of HDD-SSD servers, have attracted much attention in HPC community. However, existing data layout schemes do not well consider the characteristics of heterogeneous servers and heterogeneous access patterns, thus may experience considerable inefficiencies. In this study, we propose MHA, a migratory heterogeneity-aware data layout scheme to improve the data distribution of hybrid PFS. More specifically, to accommodate heterogeneous access patterns, MHA first migrates file data into several regions, each with similar access patterns. Then, by leveraging a data access cost model, MHA determines the appropriate stripe sizes on heterogeneous servers to get the best performance on each region. We have implemented MHA under MPI-IO library on top of OrangeFS file system. Experimental results show that MHA can significantly improve the hybrid PFS I/O system performance compared to existing data layout schemes.

Checkpointing is the predominant storage driver in today's petascale supercomputers and is expected to remain as such in tomorrow's exascale supercomputers. Users typically prefer to checkpoint into a shared file yet parallel file systems often perform poorly for shared file writing. A powerful technique to address this problem is to transparently transform shared file writing into many exclusively written as is done in ADIOS and PLFS. Unfortunately, the metadata to reconstruct the fragments into the original file grows with the number of writers. As such, the current approach cannot scale to exaflop supercomputers due to the large overhead of creating and reassembling the metadata. In this paper, we develop and evaluate algorithms by which patterns in the PLFS metadata can be discovered and then used to replace the current metadata. Our evaluation shows that these patterns reduce the size of the metadata by several orders of magnitude, increase the performance of writes by up to 40 percent, and the performance of reads by up to 480 percent. This contribution therefore can allow current checkpointing models to survive the transition from petato exascale.

Cloud computing assembles large networks of virtualised ICT services such as hardware resources (such as CPU, storage, and network), software resources (such as databases, application servers, and web servers) and applications. Big Data applications have become a common phenomenon in domain of science, engineering, and commerce. Large-scale, heterogeneous, and uncertain Big Data applications are becoming increasingly common, yet current cloud resource provisioning methods do not scale well and nor do they perform well under highly unpredictable conditions (data volume, data variety, data arrival rate, etc.). Much research effort have been paid in the fundamental understanding, technologies, and concepts related to autonomic provisioning of cloud resources for Big Data applications, to make cloud-hosted Big Data applications operate more efficiently, with reduced financial and environmental costs, reduced under-utilisation of resources, and better performance at times of unpredictable workload. Targeting the aforementioned research challenges, this special issue compiles recent advances in Autonomic Provisioning of Big Data Applications on Clouds. The special issue articles are briefly summarized.

In the present work, a new iterative selection strategy and reconstruction modeling method is proposed for fault diagnosis. The proposed algorithm can extract those informative fault directions which are responsible for the concerned alarming monitoring statistic. First, the fault effects are decomposed in two different monitoring subspaces, principal subspace (PCS) and residual subspace (RS). Then, the significance of each fault direction in both PCS and RS is evaluated by the proposed iterative fault directions selection strategy, and those informative ones are chosen for reconstruction modeling. The proposed method provide an effective way of modeling the fault effects so as to correct the fault more efficiently. Its feasibility and performance are illustrated with numerical simulation and the Tennessee Eastman (TE) benchmark process.

In addition to locality, data access concurrency has emerged as a pillar factor of memory performance. In this research, we introduce a concurrency-aware solution, the memory Sluice Gate Theory, for solving the outstanding memory wall problem. Sluice gates are designed to control data transfer at each memory layer dynamically, and a global control algorithm, named layered performance matching, is developed to match the data transfer request/supply at each memory layer thus matching the overall performance between the CPU and memory system. Formal theoretical analyses are given to show, with sufficient data access concurrency and hardware support, the memory wall impact can be reduced to the minimum. Experimental testing is conducted which confirm the theoretical findings.

Recommender systems have been widely used by online stores to suggest items of interest to users. These systems often identify a subset of items from a much larger set that best matches the user's interest. A key concern with existing approaches is overspecialization, which results in returning items that are too similar to each other. Unlike existing solutions that rely on diversity metrics to reduce similarity among recommended items, we propose using choice probability to measure the overall quality of a recommendation list, which unifies the desire to achieve both relevancy and diversity in recommendation. We first define the recommendation problem from the discrete choice perspective. We then model the problem under the multilevel nested logit model, which is capable of handling similarities between alternatives along multiple dimensions. We formulate the problem as a nonlinear binary integer programming problem and develop an efficient dynamic programming algorithm that solves the problem to optimum in O(nKSR2) time, where n is the number of levels and K is the maximum number of children nests a nest can have in the multilevel nested logit model, S is the total number of items in the item pool, and R is the number of items wanted in recommendation.

With the rapid development of air traffic management, a number of new technologies will replace today control method. In the future, aircrafts can keep appropriate interval according to control intention. And controllers will command the whole situation in real-time. On the basis of the 4D trajectory design and plan of American in air traffic management, we establish 4D trajectory model by UML and XML Schema. Then the Pub/Sub mechanism is modeled and analyzed. Through analyzing the QoS data synchronization, we can regulate data exchange which under 4D trajectory interoperability effectively.

This paper discusses land utilization and classification based on remote sensing image analysis expert system technology. Taking the bureau department area remote sensing images as the basic information, and using MAPGIS and Ecognition software, we carry on the classification of the land. The results show that remote sensing image analysis expert system technology applies to land utilization situation and land classification, which has reference value.

In this paper, a high-speed data transmission system based on FPGA (Field Programmable Gate Array) and USB 2.0 (Universal Serial Bus) technology is designed. Firstly, we introduce the hardware of the data transmission system, which includes data acquisition circuit, transmission circuit and power conversion circuit, etc. Secondly, the corresponding software design including USB firmware program, USB driver and firmware modification are described in detail. Finally, the corresponding experiment has been carried out, which show that the developed system will help to achieve the data transmission system with the characteristics of high-speed, integration and low power consumption, etc.

Data classification is the foundation for the intelligent identification and management of massive information in the internet of things. To classify the massive data accurately, an evolutionary neural network is presented. The input features and the structure of neural network are evolved simultaneously to consider their joint contribution to the performance of neural network. The sensitivity analysis is performed to guide the evolutionary algorithm to search the optimum solution. It can be seen from the experimental results that the proposed evolutionary algorithm optimized the structure of neural network and eliminate the tedious input features at the same time. The excellent classification accuracy is achieved finally.

Concurrent accesses to the shared storage resources in current HPC machines lead to severe performance degradation caused by I/O contention. In this study, we identify some key challenges to efficiently handling interleaved data accesses, and we propose a system-wide solution to optimize global performance. We implemented and tested several I/O scheduling policies, including prioritizing specific applications by leveraging burst buffers to defer the conflicting accesses from another application and/or directing the requests to different storage servers inside the parallel file system infrastructure. The results show that we mitigate the negative effects of interference and optimize the performance up to 2x depending on the selected I/O policy.

Due to the exponentially increasing size of design space of microprocessors and time-consuming simulations, predictive models have been widely employed in design space exploration (DSE). Traditional approaches mostly build a program-specific predictor that needs a large number of program-specific samples. Thus considerable simulation cost is required for each program. In this paper, we study the novel problem of transferring knowledge from the labeled samples of previous programs to help predict the responses of the new target program whose labeled samples are very sparse. Inspired by the recent advances of transfer learning, we propose a transfer learning based DSE framework TrDSE to build a more efficient and effective predictive model for the target program with only a few simulations by borrowing knowledge from previous programs. Specifically, TrDSE includes two phases: 1) clustering the programs based on the proposed orthogonal array sampling and the distribution related features, and 2) with the guidance of clustering results, predicting the responses of configurations in design space of the target program by a transfer learning based regression algorithm. We evaluate the proposed TrDSE on the benchmarks of SPEC CPU 2006 suite. The results demonstrate that the proposed framework is more efficient and effective than state-of-art DSE techniques.

The increasingly important data-intensive scientific discovery presents a critical question to the high performance computing (HPC) community - how to efficiently support these growing scientific big data applications with HPC systems that are traditionally designed for big compute applications? The conventional HPC systems are computing-centric and designed for computation-intensive applications. Scientific big data applications have growlingly different characteristics compared to big compute applications. These scientific applications, however, will still largely rely on HPC systems to be solved. In this research, we try to answer this question with a rethinking of HPC system architecture. We study and analyze the potential of a new decoupled HPC system architecture for data-intensive scientific applications. The fundamental idea is to decouple conventional compute nodes and dynamically provision as data processing nodes that focus on data processing capability. We present studies and analyses for such decoupled HPC system architecture. The current results have shown its promising potential. Its data-centric architecture can have an impact in designing and developing future HPC systems for growingly important data-intensive scientific discovery and innovation.

With the growing importance of networked embedded devices in the upcoming Internet of Things, new attacks targeting embedded OSes are emerging. ARM processors, which power over 60% of embedded devices, introduce a hardware security extension called TrustZone to protect secure applications in an isolated secure world that cannot be manipulated by a compromised OS in the normal world. LeveragingTrustZone technology, a number of memory integrity checking schemes have been proposed in the secure world to introspect malicious memory modification of the normal world. In this paper, we first discover and verify an ARM TrustZone cache incoherence behavior, which results in the cache contents of the two worlds, secure and non-secure, potentially being different even when they are mapped to the same physical address. Furthermore, code in one TrustZone world cannot access the cache content in the other world. Based on this observation, we develop a new rootkit called CacheKit that hides in the cache of the normal world and is able to evade memory introspection from the secure world. We implement a CacheKit prototype on Cortex-A8 processors after solving a number of challenges. First, we employ the Cache-as-RAM technique to ensure that the malicious code is only loaded into the CPU cache and not RAM. Thus, the secure world cannot detect the existence of the malicious code by examining the RAM. Second, we use the ARM processor's hardware support on cache settings to keep the malicious code persistent in the cache. Third, to evade introspection that flushes cache content back into RAM, we utilize physical addresses from the I/O address range that is not backed by any real I/O devices or RAM. The experimental results show that CacheKit can successfully evade memory introspection from the secure world and has small performance impacts on the rich OS. We discuss potential countermeasures to detect this type of rootkit attack.

Data volume grows dramatically in the era of big data. To save capital cost on storage hardware, datacenters currently prefer using erasure coding rather than simply replication to resist data loss. Erasure coding can provide equivalent three-way fault tolerance to HDFS's default three replication mechanism but degrades data availability for task scheduling. In an erasure-coded system, data reconstruction time will be paid while tasks access the missing blocks during MapReduce job processing. Tasks' accessing corrupt data introduces task stragglers and degrades resource utilization. To overcome these challenges, we propose a novel mechanism, Dominoes, that coordinates lightweight data states checking and job scheduling to hide such recovery penalty during job processing and enhances job throughputs. The experimental results confirm Dominoes' effectiveness and efficiency that improves job throughput by 9% to 9.7% under failure at an overhead of 2.6% for failure-free jobs.

Scalability is a key factor of the design of distributed systems and parallel algorithms and machines. However, conventional scalabilities are designed for homogeneous parallel processing. There is no suitable and commonly accepted definition of scalability metric for heterogeneous systems. Isospeed scalability is a well-defined metric for homogeneous computing. This study extends the isospeed scalability metric to general heterogeneous computing systems. The proposed isospeed-efficiency model is suitable for both homogeneous and heterogeneous computing. Through theoretical analyses, we derive methodologies of scalability measurement and prediction for heterogeneous systems. Experimental results have verified the analytical results and confirmed that the proposed isospeed-efficiency scalability works well in both homogeneous and heterogeneous environments.

Chinese digitalization process of television hasn 't achieved anticipated goals as yet. No new value-added services for consumers, dissymmetrical development between service patterns and operation patterns, and the enormous input cost, are the dominant factors. To address these issues, a new dual-mode transmission networks for DTV (DMTN) is proposed in this study, in which the primary TV network is superimposed by a complementary framework based on broad-storage TV (BSTV). The necessity of congregating hot TV resources is discussed via analyzing the scale-free properties in DMTN. At the same time, the self-organized parallel broadcasting technology (SPBT) and uniform content locator (UCL) are studied in DMTN to utilize availably transmission channels and to help consumers to communicate with each other expediently. Experiment is designed and conducted, including system structure, functionality description, experiment parameters, and experiment results. Experimental results show that DMTN can provide not only traditional TV services on schedule, but also digital interactive TV services on demand using a single channel. DMTN is able to meet many individual consumers needs for video information, change the operation pattern of broadcast and television industry, and become an effective approach to advance the national informatization in low cost.

Data access delay is a major bottleneck in utilizing current high-end computing (HEC) machines. Prefetching, where data is fetched before CPU demands for it, has been considered as an effective solution to masking data access delay. However, current client-initiated prefetching strategies, where a computing processor initiates prefetching instructions, have many limitations. They do not work well for applications with complex, non-contiguous data access patterns. While technology advances continue to increase the gap between computing and data access performance, trading computing power for reducing data access delay has become a natural choice. In this paper, we present a server-based data-push approach and discuss its associated implementation mechanisms. In the server-push architecture, a dedicated server called Data Push Server (DPS) initiates and proactively pushes data closer to the client in time. Issues, such as what data to fetch, when to fetch, and how to push are studied. The SimpleScalar simulator is modified with a dedicated prefetching engine that pushes data for another processor to test DPS based prefetching. Simulation results show that L1 Cache miss rate can be reduced by up to 97% (71% on average) over a superscalar processor for SPEC CPU2000 benchmarks that have high cache miss rates.

Many applications foreseen for exascale era should process huge amount of data. However, the IO infrastructure of current supercomputing architecture cannot be generalized to deal with this amount of data due to the need for excessive data movement from storage layers to compute nodes leading to limited scalability. There has been extensive studies addressing this challenge. Decoupled Execution Paradigm (DEP) is an attractive solution due to its unique features such as available fast storage devices close to computational units and available programmable units close to file system.In this paper we study the effectiveness of DEP for a well-known data-intensive kernel, disk-to-disk (aka out-of-core) sorting. We propose an optimized algorithm that uses almost all features of DEP pushing the performance of sorting in HPC even further compared to other existing solutions. Advantages in our algorithm are gained by exploiting programming units close to parallel file system to achieve higher IO throughput, compressing data before sending it over network or to disk, storing intermediate results of computation close to compute nodes, and fully overlapping IO with computation. We also provide an analytical model for our proposed algorithm. Our algorithm achieves 30% better performance compared to the theoretically optimal sorting algorithm running on the same testbed but not designed to exploit the DEP architecture.

Two-factor authentication has been widely used due to the vulnerabilities associated with traditional text-based password. One-time password (OTP) plays an indispensable role on authenticating mobile users to critical web services that demand a high level of security. As the smartphones are increasingly gaining popularity nowadays, software-based OTP generators have been developed and installed into smartphones as software apps, which bring great convenience to the users without introducing extra burden. However, software-based OTP solutions cannot guarantee the confidentiality of the generated passwords or even the seeds when the mobile OS is compromised. Moreover, they also suffer from denial-of-service attacks when the mobile OS crashes. Hardware-based OTP tokens can solve these security problems in the software-based OTP solutions; however, it is inconvenient for the users to carry physical tokens with them, particularly, when there are more than one token to be carried. In this paper, we present TrustOTP, a secure one-time password solution that can achieve both the flexibility of software tokens and the security of hardware tokens by using ARM TrustZone technology. TrustOTP can not only protect the confidentiality of the OTPs against a malicious mobile OS, but also guarantee reliable OTP generation and trusted OTP display when the mobile OS is compromised or even crashes. It is flexible to integrate multiple OTP algorithms and instances for different application scenarios on the same smartphone platform without modifying the mobile OS. We develop a prototype of TrustOTP on Freescale i.MX53 QSB. The experimental results show that TrustOTP has small impacts on the mobile OS and its power consumption is low.

The success of the Hadoop MapReduce programming model has greatly propelled research in big data analytics. In recent years, there is a growing interest in the High Performance Computing (HPC) community to use Hadoop-based tools for processing scientific data. This interest is due to the facts that data movement becomes prohibitively expensive, highperformance data analytic becomes an important part of HPC, and Hadoop-based tools can perform large-scale data processing in a time and budget efficient manner. In this study, we propose PortHadoop, an enhanced Hadoop architecture that enables MapReduce applications reading data directly from HPC parallel file systems (PFS). PortHadoop saves HDFS storage space, and, more importantly, avoids the otherwise costly data copying. PortHadoop keeps all the semantics in the original Hadoop system and PFS. Therefore, Hadoop MapReduce applications can run on PortHadoop without code change except that the input file location is in PFS rather than HDFS. Our experimental results show that PortHadoop can operate effectively and efficiently with the PVFS2 and Ceph file systems.

With rapidly growing computing power, ultra high-resolution Earth science simulations with a long period of time are feasible. However, it is still very challenging to distribute and analyze a huge amount of simulation results, which could be over 100TB. One key reason is that typical Earth science data are represented in NetCDF, which is not supported by the popular and powerful Hadoop Distribute File System (HDFS) and consequently cannot be analyzed with tools based on HDFS. In this paper, we propose a Hadoop-based visualization and diagnosis framework for visualizing and analyzing Earth science data. It has a data model to transform data from the format of NetCDF to CSV (Comma Separated Value) that is supported by HDFS. With this model, data can be processed with the operations such as maximize, sum, and subset through HIVE and Cloudera Impala and, therefore, typical diagnoses can be performed. In addition, the framework has a technique to visualize and diagnose HDFS-resident data with the popular visualization and diagnosis tool, IDL. To speed up this process, a concurrent reader is developed to obtain HDFS-resident data. Moreover, a dynamic reader to transfer data from a parallel file system (PFS) to HDFS is developed to efficiently visualize and diagnose PFS-resident data. The cloud resolve mode simulations are used for testing and evaluating this framework.