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The increasing gap between computing speed and storage latency leads to possible I/O bottlenecks on massively parallel computers. To mitigate this issue, hierarchical storage provides multi-tiered configurations where each tier has its own physical characteristics and associated performance. Selecting the most appropriate file placement policy on this multi-tiered storage is difficult and there is to our knowledge no tool that systematically provides statistics and metrics for optimal file policy selection. In this paper, we present FiLiP (File Lifecycle Profiler), a software which provides statistics and metrics for a better understanding of file access by applications and the consequences on file movements across hierarchical storage. After the description of FiLiP’s main features and architecture, we highlight the usefulness of our tool using three I/O intensive simulation HPC applications: NEMO, S3DIO and NAMD and a three-tiered burst buffer. [ABSTRACT FROM AUTHOR]

Companies with custom production often need to store a lot of unique tools. Amount of these tools is always increasing. That can lead to problems with storing of these tools. Storages are often overfull and unorganized. In those cases, it takes a lot of time to find specific tool, and it is an issue which generates many other wastes. Because software solutions for storage management which can resolve this problem are expensive, companies cannot afford to buy them. This problem is calling for some low-cost solutions to manage these storages. Solutions cheap and simple that every company can afford to implement. This low-cost solution could be a system of storage control, based on paper cards with simple database. This article shows how to implement the card system to unorganized storage and how can this system improve storage management, establish order and reduce waste in storage. This all is proven by a case study in glass making company at storage of wooden forms for parts made by glassblowing, that study is also included. [ABSTRACT FROM AUTHOR]

Abstract We introduce a type and effect system, for an imperative object calculus, which infers sharing possibly introduced by the evaluation of an expression, represented as an equivalence relation among its free variables. This direct representation of sharing effects at the syntactic level allows us to express in a natural way, and to generalize, widely-used notions in literature, notably uniqueness and borrowing. Moreover, the calculus is pure in the sense that reduction is defined on language terms only, since they directly encode store. The advantage of this non-standard execution model with respect to a behaviorally equivalent standard model using a global auxiliary structure is that reachability relations among references are partly encoded by scoping. Highlights • Syntactic treatment of imperative object oriented calculus. • Blocks define a hierarchical memory. • Type and effect system tracing the sharing effect due to expression execution. • Operational semantics does not require external memory but rewrites expressions. • Subject reduction enforces properties of uniqueness and safe manipulation of object graphs. [ABSTRACT FROM AUTHOR]

Highlights • A hierarchical approach decomposing a complex verification problem into simpler ones is proposed. • Use a system science tool called STAMP to help understand the nature of the control system to be verified. • A formal framework is brought up, so the overall safety property can be proved formally by proving sub-goals. • Arbitrary techniques and tools can be chosen to prove sub-goals if appropriate. Abstract Complexity is a major obstacle to apply formal verification techniques in industry. Assume-guarantee (AG) reasoning is a promising compositional verification approach to deal with complexity. Proper decomposition and assumption design is vital in AG reasoning. We are motivated by the fact that a good insight into the target system can help the formal verification. So, instead of trying to automate the AG reasoning process, an accident model called STAMP is adopted to help us get a better understanding of the safety control system to be verified and a strategy for decomposition and designing assumptions accordingly. This conceptual approach is exemplified by verifying a simplified train control system. [ABSTRACT FROM AUTHOR]

The need for novel data analysis is urgent in the face of a data deluge from modern applications. Traditional approaches to data analysis incur significant data movement costs, moving data back and forth between the storage system and the processor. Emerging Active Flash devices enable processing on the flash, where the data already resides. An array of such Active Flash devices allows us to revisit how analysis workflows interact with storage systems. By seamlessly blending together the flash storage and data analysis, we create an analysis workflow-aware storage system, AnalyzeThis. Our guiding principle is that analysis-awareness be deeply ingrained in each and every layer of the storage, elevating data analyses as first-class citizens, and transforming AnalyzeThis into a potent analytics-aware appliance. To evaluate the AnalyzeThis system, we have adopted both emulation and simulation approaches. In particular, we have evaluated AnalyzeThis by implementing the AnalyzeThis storage system on top of the Active Flash Array's emulation platform. We have also implemented an event-driven AnalyzeThis simulator, called AnalyzeThisSim, which allows us to address the limitations of the emulation platform, e.g., performance impact of using multi-core SSDs. The results from our emulation and simulation platforms indicate that AnalyzeThis is a viable approach for expediting workflow execution and minimizing data movement. [ABSTRACT FROM AUTHOR]

We present a framework called Hierarchically Interacting Logs (HIL) for constructing Flash Translation Layers (FTLs). The main goal of the HIL framework is to heal the Achilles heel —the crash recovery—of FTLs (hence, its name). Nonetheless, the framework itself is general enough to encompass not only block-mapped and page-mapped FTLs but also many of their variants, including hybrid ones, because of its compositional nature. Crash recovery within the HIL framework proceeds in two phases: structural recovery and functional recovery. During the structural recovery, residual effects due to program operations ongoing at the time of the crash are eliminated in an atomic manner using shadow paging. During the functional recovery, operations that would have been performed if there had been no crash are replayed in a redo-only fashion. Both phases operate in an idempotent manner, preventing repeated crashes during recovery from causing any additional problems. We demonstrate the practicality of the proposed HIL framework by implementing a prototype and showing that its performance during normal execution and also during crash recovery is at least as good as those of state-of-the-art SSDs. [ABSTRACT FROM AUTHOR]

The combination of the explosive growth in digital data and the demand to preserve much of these data in the long term has made it imperative to find a more cost-effective way than HDD arrays and a more easily accessible way than tape libraries to store massive amounts of data. While modern optical discs are capable of guaranteeing more than 50-year data preservation without media replacement, individual optical discs' lack of the performance and capacity relative to HDDs or tapes has significantly limited their use in datacenters. This article presents a Rack-scale Optical disc library System, or ROS in short, which provides a PB-level total capacity and inline accessibility on thousands of optical discs built within a 42U Rack. A rotatable roller and robotic arm separating and fetching discs are designed to improve disc placement density and simplify the mechanical structure. A hierarchical storage system based on SSDs, hard disks, and optical discs is proposed to effectively hide the delay of mechanical operation. However, an optical library file system (OLFS) based on FUSE is proposed to schedule mechanical operation and organize data on the tiered storage with a POSIX user interface to provide an illusion of inline data accessibility. We further optimize OLFS by reducing unnecessary user/kernel context switches inheriting from legacy FUSE framework. We evaluate ROS on a few key performance metrics, including operation delays of the mechanical structure and software overhead in a prototype PB-level ROS system. The results show that ROS stacked on Samba and FUSE as network-attached storage (NAS) mode almost saturates the throughput provided by underlying samba via 10GbE network for external users, as well as in this scenario provides about 53ms file write and 15ms read latency, exhibiting its inline accessibility. Besides, ROS is able to effectively hide and virtualize internal complex operational behaviors and be easily deployable in datacenters. [ABSTRACT FROM AUTHOR]

Highlights • It is the first attempt to develop enabling strategies of EVs for UFLS. • The hierarchical structure is proposed to enable EVs to take part in UFLS. • EVs are classified into four grades with different priority levels. • UFLS-EV, T-UFLS, and A-UFLS are compared. Abstract Due to large-scale integration of renewable energy sources into the power grid and the increasing requirements of customers for power quality, under frequency load shedding (UFLS) faces a challenge in ensuring power supply reliability. Large-scale electric vehicles (EVs) as distributed energy storage resources can provide the powerful potential for UFLS to deal with this challenge. In this paper, enabling strategies of EVs are proposed for UFLS to enhance frequency stability and simultaneously reduce power outages, where a hierarchical framework consisting of the control center (CC), EV charging stations (EVCS), and EV terminals (EVT) is developed to implement EVs’ participation in UFLS. In the EVCS, the capacity of EVs is calculated based on EV information from the EVT and uploaded to the CC, and at the same time, the dispatch from the CC is distributed among EVs in the EVCS and sent to the EVT. With the proposed strategies, load shedding can be compensated by the vehicle-to-grid power of EVs to reduce power supply interruptions as much as possible. Simulations on the IEEE 9-bus power system have shown the effectiveness of the proposed approach in reducing power outages and enhancing frequency stability. [ABSTRACT FROM AUTHOR]

The memory performance of data mining applications became crucial due to increasing dataset sizes and multi-level cache hierarchies. Recursive partitioning methods such as decision tree and random forest learning are some of the most important algorithms in this field, and numerous researchers worked on improving the accuracy of model trees as well as enhancing the overall performance of the learning process. Most modern applications that employ decision tree learning favor creating multiple models for higher accuracy by sacrificing performance. In this work, we exploit the flexibility inherent in recursive partitioning based applications regarding performance and accuracy tradeoffs, and propose a framework to improve performance with negligible accuracy losses. This framework employs a data access skipping module (DASM) using which costly cache accesses are skipped according to the aggressiveness of the strategy specified by the user and a heuristic to predict skipped data accesses to keep accuracy losses at minimum. Our experimental evaluation shows that the proposed framework offers significant performance improvements (up to 25%) with relatively much smaller losses in accuracy (up to 8%) over the original case. We demonstrate that our framework is scalable under various accuracy requirements via exploring accuracy changes over time and replacement policies. In addition, we explore NoC/SNUCA systems for similar opportunities of memory performance improvement. [ABSTRACT FROM AUTHOR]

Abstract: The cold‐chain information has characterized by the loss and dispersion according to the different collecting methods. The description for the quality decay factors of aquatic products can be defined as the multidimensional information. A series of nodes with multidimensional information are assembled to be hierarchies aiming at describing the environment conditions and locations in the supply chain. Each of the single hierarchy levels constitutes a sequence of node information in a network, which is applied as internal information analysis. The cross‐layer information structure is defined as “bridge” information which is able to record the information transmissions among every hierarchy from the point of view of the whole chain. The study has established a novel structured modeling to describe the cold chain of aquatic products based on a network‐hierarchy framework. An organized and sustainable transmission process can be built and recorded by the multidimensional attributes for the whole course of cold chain of aquatic products. In addition, seamless connections among every hierarchy are attainable by the environmental information records continuously to monitor the quality of aquatic products. The quality assessments and shelf life predictions are estimated properly as the risk control in order to monitor and trace the safety of aquatic products under the supply chain perspective. [ABSTRACT FROM AUTHOR]

This paper is arranged in three main sections: the first section is a hierarchical method based on clustering and a fuzzy membership system where the tessellated three-dimensional (3D) models are classified into their containing primitives: cylinder, cone, sphere, and flat. In the second section, automated assembly planning (AAP) is considered as the main application of our novel hierarchical primitive classification approach. The classified primitives obtained from the first section are used to define the removal directions between mating parts in an assembly model. Finally, a fuzzification method is used to express the uncertainty of the detected connections between every pair of parts. The acquired uncertainties are used in a user interaction process to approve, deny, or modify the connections with higher uncertainties. [ABSTRACT FROM AUTHOR]

A complex engineering system typically consists of a group of components/subsystems in a hierarchical structure and the system is monitored at only some, not all, of these hierarchical levels. This paper investigates a Bayesian approach to system reliability prediction using multilevel incomplete data. These data are drawn simultaneously from different component/subsystem levels within the same system, thus need to be analyzed with the consideration of their overlapping nature. In this paper, a Bayesian network model is proposed for modeling the reliability of a multilevel system, where a lower level node can only be connected to one higher level node. Through Bayesian inference, the posterior distributions of lifetime parameters and conditional probabilities in the model are obtained by combining prior beliefs with lifetime data coming from different system levels. This study is also extended to include mixed data types, i.e., both pass/fail data and lifetime data. The effectiveness of the proposed approach is illustrated in a case study. [ABSTRACT FROM PUBLISHER]

The scheduling of load balanced transactions is an emerging problem in on-demand computing system. This problem can be studied considering several parameters such as resource availability, performance, and reliability. The existing approaches to find the exact solutions for this problem are limited. This paper presents a Honey Bee Optimization (HBO)-based method to solve the mentioned problem. In this method, first, the load of the system is balanced and then the transactions are scheduled using foraging behavior of honey bees to find the optimal solutions. We also modify four known scheduling algorithms such as Ant Colony Optimization (ACO), Hierarchical Load Balanced Algorithm (HLBA), Dynamic and Decentralized Load Balancing (DLB), and Randomized to obtain transaction scheduling algorithms for the purpose of comparison with our proposed algorithm. The compared results show that the proposed algorithm performs better than the modified existing algorithms. [ABSTRACT FROM AUTHOR]

Using clustering method to detect useful patterns in large datasets has attracted considerable interest recently. The HKM clustering algorithm (Hierarchical K-means) is very efficient in large-scale data analysis. It has been widely used to build visual vocabulary for large scale video/image retrieval system. However, the speed and even the accuracy of hierarchical K-means clustering algorithm still have room to be improved. In this paper, we propose a Parallel N-path quantification hierarchical K-means clustering algorithm which improves on the hierarchical K-means clustering algorithm in the following ways. Firstly, we replace the Euclidean kernel with the Hellinger kernel to improve the accuracy. Secondly, the Greedy N-best Paths Labeling method is adopted to improve the clustering accuracy. Thirdly, the multi-core processors-based parallel clustering algorithm is proposed. Our results confirm that the proposed clustering algorithm is much faster and more effective. [ABSTRACT FROM AUTHOR]

Cloud systems have demonstrated the powerful computation and storage capability in many scientific applications. In this paper, we propose a class of scalable distributed loop self-scheduling schemes to achieve good load balancing and scalability. We implemented these schemes on a large-scale cluster and on a heterogeneous cloud system. The schemes consider the distribution of the output data, which can help reduce communication overhead and improve scalability. We evaluated the schemes using four scientific computations: Mandelbrot set, adjoint convolution, matrix multiplication and quick sort. The results show that the new schemes achieve better load balancing, better scalability and better overall performance than standard distributed loop self-scheduling schemes. [ABSTRACT FROM AUTHOR]

Multi-component based Log-Structured Merge-tree (LSM-tree) has been becoming one of the mainstream indexes. LSM-tree adopts component-by-component KV item flowing down mechanism to push each KV item from one smaller component to the adjacent larger component during compaction procedures until the KV items reach the largest component. This process incurs significant write amplification and limits the write throughput. In this paper, we propose one multi-component Skip-tree to aggressively push the KV items to the non-adjacent larger components via skipping some components and then make the KV items’ top-down move more efficient. We develop adaptive and reliable KV item movements among components. By reducing the number of steps during the flowing process from memory-resident component to the disk-resident largest component, Skip-tree can effectively reduce the write amplification and thus improve the system throughput. We design and implement one high performance key-value store, named SkipStore, based on Skip-tree. The experiments demonstrate that SkipStore outperforms the state-of-the-art open-sourced system RocksDB in Facebook by 66.5 percent under HDD and 61 percent under SSD. [ABSTRACT FROM PUBLISHER]

Radio Frequency Identification (RFID) technology already plays a major role in many areas. One of the applications is for object detection. RFID is used for effective object's identification. A good example is object detection at storage management system. It is applicable to the environment where people easily forget the places of the stored object. In the other hand, the automatic object detection will be useful for disabled people like blind person such that they need information before pick up and store object while doing their daily life activities. As a solution of these problems, this study is proposed in order to assist the disabled people by develop an intelligent storage system which has database to store and inform the position and orientation of object stored using the idea of ‘Intelligent Space’. In this article, the methodology and experiment validation to determine the position and orientation of object storage will be presented. The system consists of object storage, object shelves, RFID reader, and RFID tags. RFID tags are attached to the object storage. Meanwhile, RFID reader is at fixed position and detects tags’ data to obtain the coordinates of stored object. The information represent the coordinate of stored object is located as well as how the object is oriented. The orientation provides useful information to the intelligent storage system which the user may know whether the object is placed in upside down position or otherwise. [ABSTRACT FROM AUTHOR]

The exploitation of internal parallelism over hundreds of NAND flash memories is becoming a key design issue in high-speed solid state disks (SSDs). In this study, we simulate a cycle-accurate SSD platform with diverse parallel data access methods and 24 page allocation strategies, which are geared toward exploiting both system-level parallelism and flash-level parallelism, using a variety of design parameters. Our extensive experimental analysis reveals that 1) the previously proposed channel striping-based page allocation strategy is not the best from a performance perspective, 2) as opposed to the common belief that system-level and flash-level concurrency mechanisms are largely orthogonal, the system-level parallel data access methods employed interferes with flash-level parallelism, 3) when most of the current currency controls and page allocation strategies are implemented, the SSD internal resources are significantly underutilized, and 4) while the performance of all the page allocation strategies on read-intensive workloads (reads $>$ 99 percent) is improved by employing a high frequency flash interface, the performance enhancements are significantly limited. Finally, we present several optimization points to extract the maximum internal parallelism by offering comprehensive evaluations with controllable and easy-to-understand micro-benchmarks. [ABSTRACT FROM PUBLISHER]

Function evaluation is an important arithmetic computation in many signal processing applications, such as special function units in modern graphics processing units (GPUs). Hardware implementations of function evaluation usually consists of lookup tables (LUT) and some simple arithmetic units of multipliers and/or adders. LUT usually takes a significant portion of total area cost, especially when function evaluators are allowed to compute several different arithmetic functions with shared arithmetic units where evaluation of each function needs separate LUT. In this paper, we focus on the category of table-addition (TA) function evaluators that are composed of two types of LUT, table of initial values (TI) and table of offset values (TO), followed by a multi-operand adder. It has been shown that multipartite table method (MP) has significant improvement over prior similar designs such as symmetric bipartite table methods (SBTM) and symmetric table addition methods (STAM) for applications with low-to-medium precision requirements. This paper presents an extension of MP, called hierarchical multipartite (HMP), which further reduces total table size by applying several levels of table decompositions. Furthermore, we perform the bit-width optimization by jointly considering the impacts of all error sources during the search of best table decompositions, leading to more efficient hardware design. Besides, a new lossless decomposition of TI is presented, resulting in additional saving of table size without incurring any extra errors. Experimental results show that the proposed design can efficiently reduce the total area cost in ASIC and FPGA implementations. [ABSTRACT FROM PUBLISHER]

Diverse realistic workloads are urgently needed by SaaS providers and researchers to study the cloud environment. Workload model is an important way to specify and produce workloads. However, existing workload models are usually simplified. They cannot comprehensively describe the variations of the behaviors in user-level, application-level and service level in real environment. Moreover, existing workload models are aimed to only specific applications. They fail to support multi-users, multi-applications and multi-service-units in one workload model. To solve the problem, a hierarchical workload modeling approach is proposed in this paper. The approach constructs a cloud workload model on three layers: User, application, service-units. At each layer two key time-varying variations are captured; amount and composition. Then the workload model is the superposition of the three layers. The approach provides a unified model for different applications and various user behaviors. The experiments give the hierarchical workload models of three examples, which applications are Bag-of-Tasks, MapReduce and self-contained service-units. The experimental results show the flexibility of the novel workload modeling. Finally, architecture of a workload generator based on the model is given. [ABSTRACT FROM PUBLISHER]

Scene understanding is a popular research direction. In this area, many attempts focus on the problem of naming objects in the complex natural scene, and visual semantic integration model (VSIM) is the representative. This model consists of two parts: semantic level and visual level. In the first level, it uses a four-level pachinko allocation model (PAM) to capture the semantics behind images. However, this four-level PAM is inflexible and lacks of considerations of common subtopics that represent the background semantics. To address these problems, we use hierarchical PAM (hPAM) to replace PAM. Since hPAM is flexible, we investigate two variations of hPAM to boost VSIM in this paper. We derive the Gibbs sampler to learn the proposed models. Empirical results validate that our works can obtain better performance than the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

The NAND flash memory has been widely used in computer and consumer electronic devices. A multilevel-cell (MLC) NAND flash memory allows two or more bits to be stored per cell, improving the cell density and, hence, reducing the cost. The lower price of the MLC NAND has made it appearing in both consumer product and datacenter markets. In MLC NAND, the page read latency varies according to the page type. For read-dominated workloads, placing read-intensive data into pages with longer read latencies may result in degraded read performance. In this brief, an online method called FastRead is proposed to improve the read performance of MLC NAND-based storage. The FastRead method dynamically identifies read-intensive data and migrates them to pages with shorter read latency to improve the read performance. According to the performance results, FastRead can reduce the read response time by up to 22% (17% on average) for read-dominated workloads on a Triple-level cell NAND. In addition, FastRead does not have noticeable performance impact for write-dominated workloads, and the overhead is insignificant. [ABSTRACT FROM PUBLISHER]

Recently, part-based model has drawn much attention in visual tracking for its promising results in handling occlusion and deformation. However how to divide the target into parts and how to model the relationships between parts are still open problems. In this paper, we propose a robust tracker based on multi-level target representation and hierarchical tree structural constraint. The multi-level target representation models the target at three different levels: the bounding box (top) level, the superpixel (middle) level and the keypoint (bottom) level. The relationships between parts at all levels are modeled by the proposed hierarchical tree which includes intra-layer and inter-layer structural constraints. The positions of all the parts are optimized jointly in a unified objective function taking into account both the appearance similarity and the hierarchical tree structural constraint. The appearance model and the hierarchical tree structure are updated online to adapt to the changes of the target in both appearance and structure. Extensive experiments on various challenging video sequences demonstrate that the proposed method outperforms the state-of-the-art trackers significantly. [ABSTRACT FROM AUTHOR]

Sudoku, of order n , is a combinatorial puzzle having partially filled n 2 × n 2 grid consisting of sub-grids of n × n dimension. In this paper, a new membrane algorithm, namely MA_PSO_M, is presented. It uses the modified rules of Particle Swarm Optimization coupled with a carefully designed mutation operator within the framework of cell-like P-systems. Another significant contribution of this paper is the novel way in which the search space for solving the Sudoku problem is defined. Initially, the proposed algorithm is used to solve Sudoku puzzles of order 3 available in literature. On the basis of experiments performed on sample Sudoku puzzles of ‘easy’ and ‘medium’ difficulty levels it is concluded that the proposed membrane algorithm, MA_PSO_M, is very efficient and reliable. For the ‘hard’ and ‘evil’ difficultly levels, too the algorithm performs very well after incorporating an additional deterministic phase. The performance of the algorithm is further enhanced with an increased population size in a very small computational time. To further demonstrate efficiency of algorithm it is applied to Sudoku puzzles of order 4. The obtained results prove that the proposed membrane algorithm clearly dominates any of the PSO based membrane algorithm existing in the literature. [ABSTRACT FROM AUTHOR]

Locating and analysing the location of new stores or outlets is one of the common issues facing retailers and franchisers. This is due to assure that new opening stores are at their strategic location to attract the highest possible number of customers. Spatial information is used to manage, maintain and analyse these store locations. However, since the business of franchising and chain stores in urban areas runs within high rise multi-level buildings, a three-dimensional (3D) method is prominently required in order to locate and identify the surrounding information such as at which level of the franchise unit will be located or is the franchise unit located is at the best level for visibility purposes. One of the common used analyses used for retrieving the surrounding information is Nearest Neighbour (NN) analysis. It uses a point location and identifies the surrounding neighbours. However, with the immense number of urban datasets, the retrieval and analysis of nearest neighbour information and their efficiency will become more complex and crucial. In this paper, we present a technique to retrieve nearest neighbour information in 3D space using a clustered hierarchical tree structure. Based on our findings, the proposed approach substantially showed an improvement of response time analysis compared to existing approaches of spatial access methods in databases. The query performance was tested using a dataset consisting of 500,000 point locations building and franchising unit. The results are presented in this paper. Another advantage of this structure is that it also offers a minimal overlap and coverage among nodes which can reduce repetitive data entry. [ABSTRACT FROM AUTHOR]

Peer-to-peer technologies have proven their strength in large-scale resource sharing and data transfer. Such systems, however, still need to address a variety of issues, including efficient routing, security, quality of service, incentives, and reputation. Recent research started leveraging social information to develop new and effective techniques to improve the performance of peer-to-peer systems. However, using social information is a double-edged sword, which can bring benefits as well as new challenges. This survey presents and classifies the types of social information that have been used so far in the design of peer-to-peer systems, how the social fabric has been used to facilitate transactions in the system, and some challenges caused by using social information. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the Casacore Table Data System (CTDS) used in the casacore and CASA libraries, and methods to parallelize it. CTDS provides a storage manager plugin mechanism for third-party developers to design and implement their own CTDS storage managers. Having this in mind, we looked into various storage backend techniques that can possibly enable parallel I/O for CTDS by implementing new storage managers. After carrying on benchmarks showing the excellent parallel I/O throughput of the Adaptive IO System (ADIOS), we implemented an ADIOS based parallel CTDS storage manager. We then applied the CASA MSTransform frequency split task to verify the ADIOS Storage Manager. We also ran a series of performance tests to examine the I/O throughput in a massively parallel scenario. [ABSTRACT FROM AUTHOR]

An operational consistency model for real-time dynamic traffic assignment (DTA) applications seeks to correct the time-dependent path assignment within a rolling horizon scheme. This study extends an existing consistency framework to develop a hierarchy for the time-dependent path set based upon their relative importance to ensuring consistency. Using the analytic hierarchy process, the eigenvalue associated with a path is identified as the parameter which enables the rank ordering of paths. The ability to identify a subset of dominant paths relative to enhancing consistency enhances the computational viability of the consistency framework for real-time implementation and has significant practical implications. Additionally, it provides insights on the complex dynamics that are inherent to the operational consistency problem. [ABSTRACT FROM AUTHOR]

In power system control unicontrol with single storage units or centralized control with multiple storage units to meet different level targets is challenging. Considering the charge and discharge characteristics of storage devices, this paper proposes a hierarchical configuration structure of a battery and supercapacitor mixed storage scenario, and develops a convenient control method for accessing various DC loads and can central manage mass batteries in one place. Aiming at the optimal management of large scale battery storage, the paper proposes a three-layer battery hierarchical control structure and the control objects and control circuits are discussed. Simulation studies are used to verify the control effect of the hierarchical storage system and the results show that the strategy can effectively decrease photovoltaic output fluctuation. [ABSTRACT FROM AUTHOR]

Verifying the integrity of outsourced data is an important issue in cloud storage. Recently, many integrity verify protocols have been proposed that can support the public verifiability, data dynamics, and privacy against verifiers but involve a third-party auditor's participation. In this work, we propose a novel identity-based storage management and integrity verify protocol for secure outsourcing in multi-cloud. From theoretical analysis and a lot of simulation experiments, we show our proposed protocol is secure and efficient, without using any trusted organizer. Furthermore, the protocol enjoys many important security attributes including prevention of various attacks, user anonymity, local password verification, and so on. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Background: A living cell has a complex, hierarchically organized signaling system that encodes and assimilates diverse environmental and intracellular signals, and it further transmits signals that control cellular responses, including a tightly controlled transcriptional program. An important and yet challenging task in systems biology is to reconstruct cellular signaling system in a data-driven manner. In this study, we investigate the utility of deep hierarchical neural networks in learning and representing the hierarchical organization of yeast transcriptomic machinery. Results: We have designed a sparse autoencoder model consisting of a layer of observed variables and four layers of hidden variables. We applied the model to over a thousand of yeast microarrays to learn the encoding system of yeast transcriptomic machinery. After model selection, we evaluated whether the trained models captured biologically sensible information. We show that the latent variables in the first hidden layer correctly captured the signals of yeast transcription factors (TFs), obtaining a close to one-to-one mapping between latent variables and TFs. We further show that genes regulated by latent variables at higher hidden layers are often involved in a common biological process, and the hierarchical relationships between latent variables conform to existing knowledge. Finally, we show that information captured by the latent variables provide more abstract and concise representations of each microarray, enabling the identification of better separated clusters in comparison to gene-based representation. Conclusions: Contemporary deep hierarchical latent variable models, such as the autoencoder, can be used to partially recover the organization of transcriptomic machinery. [ABSTRACT FROM AUTHOR]

State-of-the-art science gateways can be connected to several distributed computing infrastructures (DCIs) and are able to run jobs and workflows simultaneously in all those DCIs. Flexibility of accessing diverse data storages from these workflows and assisting end users to manage these storages are however the missing features in current gateway implementations, in which these problems often prove to be a barrier of exploiting the power of distributed computing by user communities having no or little IT competence. This paper addresses these issues by integrating a data bridging service called Data Avenue into WS-PGRADE/gUSE portal framework. Data Avenue offers tools for the end users to easily manage their data residing on various storage resources, and also, jobs become capable of accessing different storages regardless of the particular distributed computing infrastructure where the job is currently being run. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

A proxy re-encryption (PRE) allows a data owner to delegate the decryption rights of some encrypted data stored on the cloud without revealing the data to an honest-but-curious cloud service provider (i.e. the PRE proxy). Furthermore, the data owner can offload most of the computational operations to the cloud service provider and hence, usingPREfor encrypted cloud data sharing can be very effective even for data owners using limited resource devices (e.g. mobile devices). However, PRE schemes only enables data owners to delegate the decryption rights of all their encrypted data. A more practical notion is a conditional PRE (CPRE) that allows us to specify under what condition the decryption of an encrypted data can be delegated, for example, only sharing all the encrypted files under a directory called 'public'. In this paper, we provide an affirmative result on the long-standing question of building a full chosen-ciphertext attacks (CCA)-secure CPRE system in the standard model and for the first time, we show that a class of Hierarchical Identity-Based Encryption (HIBE) schemes can be transferred to building a CCA-secure CPRE in the standard model.We also list out some concrete HIBE schemes which fall into this class, e.g., Lewko-Waters HIBE. All existing CCA-securePRE schemes in the standard model are not conditional while all existing CPRE schemes are either not CCA secure or not in the standard model. By instantiating our generic HIBE-based transformation, we show that an efficient and concrete CPRE scheme which is both CCA secure in the standard model and conditional can be built. [ABSTRACT FROM AUTHOR]

Under the technique of Hierarchical Storage Management (HSM), this paper discusses optimal times of data transfers for enormous data within a server system. First of all, we propose three classifications in storage, i.e., Solid State Drive (SSD), Serial Attached SCSI (SAS) HDD, and Serial ATA (SATA) HDD, which are used for frequent accessed, inactive, and long-term preserved data blocks. To save the total time for data scans and data transfers, we secondly formulate two models in which the times when data transfers should be made: 1) Number N of data transfers from SSD to SAS; 2) Time T of data transfers from SAS to SATA. We suppose that data scans occur at non-homogeneous and homogeneous Poisson processes, and optimize respective models in number N and time T. Finally, two cases of numerical examples are given to illustrate the proposed models. [ABSTRACT FROM AUTHOR]

Hierarchical multilabel classification is a complex classification problem where an instance can be assigned to more than one class simultaneously, and these classes are hierarchically organized with superclasses and subclasses, that is, an instance can be classified as belonging to more than one path in the hierarchical structure. This article experimentally analyses the behavior of different decision tree-based hierarchical multilabel classification methods based on the local and global classification approaches. The approaches are compared using distinct hierarchy-based and distance-based evaluation measures, when they are applied to a variation of real multilabel and hierarchical datasets' characteristics. Also, the different evaluation measures investigated are compared according to their degrees of consistency, discriminancy, and indifferency. As a result of the experimental analysis, we recommend the use of the global classification approach and suggest the use of the Hierarchical Precision and Hierarchical Recall evaluation measures. [ABSTRACT FROM AUTHOR]

We propose the complete cubic network structure to extend the existing class of hierarchical cubic networks, and establish a general connectivity result which states that the surviving graph of a complete cubic network, when a linear number of vertices are removed, consists of a large (connected) component and a number of smaller components which altogether contain a limited number of vertices. As applications, we characterize several fault-tolerance properties for the complete cubic network, including its restricted connectivity, i.e., the size of a minimum vertex cut such that the degree of every vertex in the surviving graph has a guaranteed lower bound; its cyclic vertex-connectivity, i.e., the size of a minimum vertex cut such that at least two components in the surviving graph contain a cycle; its component connectivity, i.e., the size of a minimum vertex cut whose removal leads to a certain number of components in its surviving graph; and its conditional diagnosability, i.e., the maximum number of faulty vertices that can be detected via a self-diagnostic process, in terms of the common Comparison Diagnosis model. [ABSTRACT FROM AUTHOR]