Flash memory based solid state drives (SSDs) have emerged as a new alternative to replace magnetic disks due to their high performance and low power consumption. However, random writes on SSDs are much slower than SSD reads. Therefore, traditional index structures, which are designed based on the symmetrical I/O property of magnetic disks, cannot completely exert the high performance of SSDs. In this paper, we propose an SSD-optimized linear hashing index called Self-Adaptive Linear Hashing (SALhashing) to reduce small random-writes to SSDs that are caused by index operations. The contributions of our work are manifold. First, we propose to organize the buckets of a linear hashing index into groups and sets to facilitate coarse-grained writes and adaptivity to access patterns. A group consisting of a fixed number of buckets is proposed to transform small random writes to buckets into coarsegrained writes and in turn improve write performance of the index. A set consists of a number of groups, and we propose to employ different split strategies for each set. With this mechanism, SAL-hashing is able to adapt to the changes of access patterns. Second, we attach a log region to each set, and amortize the cost of reads and writes by committing updates to the log region in batch. Third, in order to reduce search cost, each log region is equipped with Bloom filters to index update logs. We devise a cost-based online algorithm to adaptively merge the log region with the corresponding set when the set becomes search-intensive. Fourth, we propose a new technique called virtual split to optimize the search performance of SAL-hashing. Finally, we propose a new scheme for the management of the log buffer. We conduct extensive experiments on real SSDs. The results suggest that our proposal is self-adaptive according to the change of access patterns, and outperforms several competitors under various workloads.

Hadoop, especially Hadoop 2.0, has been a dominant framework for real-time big data processing. However, Hadoop is not optimized for energy efficiency. Aiming to solve this problem, in this paper, we propose a new framework to improve the energy efficiency of Hadoop 2.0. We focus on the resource manager in Hadoop 2.0, namely YARN, and propose energy-efficient task scheduling mechanisms on YARN. Particularly, we focus on CPU-intensive streaming jobs and classify streaming jobs into two types, namely batch streaming jobs (i.e., a set of jobs are submitted simultaneously) and online streaming jobs (i.e., jobs are continuously submitted one by one). We devise different energy-efficient task scheduling algorithms for each kind of streaming jobs. Specially, we first propose to abstractly model performance and energy consumption by considering the characteristics of tasks as well as the computational resources in YARN. Based on this model, we study the energy efficiency of streaming tasks which consist of the performance model and energy consumption model of task. We propose two key principles for improving energy efficiency: 1) CPU usage aware task allocation, partitions tasks to NMs based on the task characteristic in term of CPU usage; and 2) resource efficient task allocation, reduce idle resource. Then, we propose a D-based binning algorithm for the batch task scheduling and K-based binning algorithm for the online task scheduling that can adapt to continuously arriving tasks. We conduct extensive experiments on a real Hadoop 2.0 cluster and use two kinds of workloads to evaluate the performance and energy efficiency of our proposal. Compared with Storm (the streaming data processing tool in Hadoop 2.0) and other approaches including TAPA and DVFS-MR, our proposal is more energy efficient. The batch task scheduling algorithm reduces up to 10 percent of energy consumption and keeps comparable performance. In addition, the online task scheduling algorithm reduces up to 7 percent over the existing algorithms.

With the development of networked radars and wireless communication technologies, electromagnetic data is becoming a new type of big data.Compared with other kinds of big data, such as Internet big data, financial big data, and healthcare big data, electromagnetic big data has some special properties.For example, they usually contain rich and varying labels that describe the features of electromagnetic space.However, existing big-data benchmark tools cannot support the generation and test of electromagnetic big data.In this paper, we aim at providing a simulation-based testbed for electromagnetic big data.The testbed, named STEM, can simulate real-world electromagnetic big data.It supports generating real-time electromagnetic data streams with varying labels.In addition, it is reusable, reconfigurable, and flexible for users to generate workloads for different scenarios.After a brief introduction on the architecture of STEM, we present the implemental details of STEM.Then, we present a case study as well as performance evaluation to demonstrate the usability and flexibility of STEM.

Elements of a person's biometrics are typically stable over the duration of a lifetime, and thus, it is highly important to protect biometric data while supporting recognition (it is also called secure biometric recognition). However, the biometric data that are derived from a person usually vary slightly due to a variety of reasons, such as distortion during picture capture, and it is difficult to use traditional techniques, such as classical encryption algorithms, in secure biometric recognition. The negative database (NDB) is a new technique for privacy preservation. Reversing the NDB has been demonstrated to be an NP-hard problem, and several algorithms for generating hard-to-reverse NDBs have been proposed. In this paper, first, we propose negative iris recognition, which is a novel secure iris recognition scheme that is based on the NDB. We show that negative iris recognition supports several important strategies in iris recognition, e.g., shifting and masking. Next, we analyze the security and efficiency of negative iris recognition. Experimental results show that negative iris recognition is an effective and secure iris recognition scheme. Specifically, negative iris recognition can achieve a highly promising recognition performance (i.e., GAR = 98.94% at FAR = 0.01%, EER = 0.60%) on the typical database CASIA-IrisV3-Interval.

Dynamic constrained optimization problems (DCOPs) are difficult to solve because both objective function and constraints can vary with time. Although DCOPs have drawn attention in recent years, little work has been performed to solve DCOPs with multiple dynamic feasible regions from the perspective of locating and tracking multiple feasible regions in parallel. Moreover, few benchmarks have been proposed to simulate the dynamics of multiple disconnected feasible regions. In this paper, first, the idea of tracking multiple feasible regions, originally proposed by Nguyen and Yao, is enhanced by specifically adopting multiple subpopulations. To this end, the dynamic species-based particle swam optimization (DSPSO), a representative multipopulation algorithm, is adopted. Second, an ensemble of locating and tracking feasible regions strategies is proposed to handle different types of dynamics in constraints. Third, two benchmarks are designed to simulate the DCOPs with dynamic constraints. The first benchmark, including two variants of G24 (called G24v and G24w), could control the size of feasible regions. The second benchmark, named moving feasible regions benchmark (MFRB), is highly configurable. The global optimum of MFRB is calculated mathematically for experimental comparisons. Experimental results on G24, G24v, G24w, and MFRB show that the DSPSO with the ensemble of strategies performs significantly better than the original DSPSO and other typical algorithms.

change memory (PCM) as a newly developed storage medium has many attractive properties such as non-volatility, byte addressability, high density and low energy consumption. Thus, PCM can be used to build non-volatile main memory databases. However, PCM's long write latency and high write energy bring challenges to PCM-based memory systems. In this paper, we propose an improvement over the B+-tree for PCM. Particularly, we consider the read/write tendency of leaf nodes. For write-intensive leaf nodes, we use an overflow-node technique to reduce PCM writes, while for read-intensive ones, we adjust the tree structure to remove overflow nodes to improve read performance. Our experimental results suggest that our proposal outperforms the traditional B+-tree and the overflow B+-tree.

Flash memory based solid state drives (SSDs) have emerged as a new alternative to replace magnetic disks due to their high performance and low power consumption. However, random writes on SSDs are much slower than SSD reads. Therefore, traditional index structures, which are designed based on the symmetrical I/O property of magnetic disks, cannot completely exert the high performance of SSDs. In this paper, we propose an SSD-optimized linear hashing index called Self-Adaptive Linear Hashing (SAL-Hashing) to reduce small random writes to SSDs that are caused by index operations. The contributions of our work are manifold. First, we propose to organize buckets into groups and sets to facilitate coarse-grained writes and lazy-split so as to avoid intermediate writes on the hash structure. A group consists of a fixed number of buckets and a set consists of a number of groups. Second, we attach a log region to each set, and amortize the cost of reads and writes by committing updates to the log region in batch. Third, in order to reduce search cost, each log region is equipped with Bloom filters to index update logs. We devise a cost-based online algorithm to adaptively merge the log region with the corresponding set when the set becomes search-intensive. Finally, in order to exploit the internal package-level parallelisms of SSDs, we apply coarse-grained writes for merging or split operations to achieve a high bandwidth. Our experimental results suggest that our proposal is self-adaptive according to the change of access patterns, and outperforms several competitors under various workloads on two commodity SSDs.

Current research of emotion recognition from electroencephalogram (EEG) signals rarely considers common patterns embodied in multiple subjects and individual patterns for each subject simultaneously. Therefore, in this paper, we propose a novel emotion recognition approach using subjects or subject groups as privileged information, which is only available during training. First, five frequency features are extracted from each channel of the EEG signals, and features are selected by statistical tests. Then, we propose two three-node Bayesian networks to capture the joint probability distribution function of emotion labels, EEG features, and subjects or subject groups during training. Through the learned joint probability distribution, the Bayesian networks model both common and individual emotion patterns simultaneously. During testing, emotion labels can be estimated from EEG features only by marginalized over the privileged information, i.e. subjects or subject groups. Experimental results on three benchmark databases, i.e. the MAHNOB-HCI database, the DEAP database and the USTC-ERVS database, demonstrate that our approach incorporating subjects and clusters achieves better emotion recognition performance than training a classifier for each subject, as well as training a classifier without subject information on the whole dataset.

The rapid development of Internet of Things (IoT) enables people to track objects by deploying multiple sensors, e.g., to track people in indoor spaces using RFID sensors. Multi-sensor object-tracking data are usually produced as records, which are thereby organized into small files and written to servers. However, the small-size property and high arriving-rate of multi-sensor object-tracking data will result in poor I/O performance of file systems such as HDFS. In this paper, we propose the first read/write-optimized solution for storing multi-sensor object-tracking data on HDFS. In particular, we exploit a distributed caching mechanism and a parallel file-merging policy to improve the I/O performance of HDFS. With our design, object-tracking data are first cached by a Distributed Memory File System (DMFS) on top of HDFS. These data are further merged into large files, which are then flushed to HDFS in parallel. We demonstrate that this mechanism is able to improve the write throughput of HDFS and outperforms existing centralized-cache-based approaches. In addition, in order to improve the search performance of object queries over multi-sensor object-tracking data, we propose a Sensor-Dependence Graph (SDG) to model sensor dependence and further present an SDG-based algorithm to efficiently cluster sensors. The object-tracking data from the sensors in the same cluster are merged into the same large files, which can reduce file scans during query processing and therefore improve search performance. We conduct extensive experiments to evaluate the performance of our proposal. The results suggest the efficiency of our proposal with respect to disk-write throughput, memory-write throughput, search performance, and sensor clustering.

Both multipopulation and memory are widely used approaches in the field of evolutionary dynamic optimization. It would be interesting to examine the effect of the combinations of multipopulation algorithms (MPAs) and memory schemes. However, since most of the existing memory schemes are proposed with single population algorithms, straightforwardly applying them to MPAs may cause problems. By addressing the possible problems, a new memory scheme is proposed for MPAs in this paper. In the experiments, several existing memory schemes and the newly proposed scheme are combined with a MPA, i.e. the Species-based Particle Swarm Optimizer (SPSO), and these combinations are tested on cyclic and acyclic problems. The experimental results indicate that 1) straightforwardly using the existing memory schemes sometimes degrades the performance of SPSO even on cyclic problems; 2) the newly proposed memory scheme is very competitive.

In this paper, we propose multiple facial action unit recognition by modeling their relations from both features and target labels. First, a multi-task feature learning method is adopted to divide action unit recognition tasks into several groups, and then learn the shared features for each group. Second, a Bayesian network is used to model the co-existent and mutual-exclusive semantic relations among action units from the target labels of facial images. After that, the learned Bayesian network employs the recognition results of the multi-task learning, and realizes multiple facial action recognition by probabilistic inference. Experiments on the extended Cohn-Kanade database and the Denver Intensity of Spontaneous Facial Actions database demonstrate the effectiveness of our approach.

Phase change memory (PCM) has become one of the most promising storage media particularly for memory systems, due to its byte addressability, high access speed, and low energy consumption. In addition, hybrid memory systems involving both PCM and DRAM can utilize the merits of both media and overcome some typical drawbacks of PCM such as high write latency and limited lifecycle. In this paper, we present a novel page replacement algorithm called APP-LRU (Access-Pattern-prediction-based LRU) for PCM/DRAM-based hybrid memory systems. APP-LRU aims to reduce writes to PCM while maintaining stable time performance. Particularly, we detect read/write intensity for each page in the memory, and put read-intensive pages into PCM while placing write-intensive pages in DRAM. We conduct trace-driven experiments on six synthetic traces and one real OLTP trace. The results show that our proposal is able to reduce up to 5 times of migrations more than its competitors.

Indoor moving objects management has been a research focus in recent years. In order to get the trajectory data of indoor moving objects, people have to deploy a lot of positioning equipment, such as RFID readers and tags, which takes lots of money, time, and other costs. In addition, it is a very complex and costly process to construct different environment settings for various indoor applications. Aiming to provide experimental trajectory data for various indoor operations and mining algorithms, in this paper we present a flexible tool to generate trajectories for indoor moving objects, which is named IndoorSTG (Indoor Spatiotemporal Trajectory Generator). IndoorSTG can simulate different indoor environments using various elements including rooms, doors, corridors, stairs, elevators, and virtual positioning devices such as RFID or Bluetooth readers. Meanwhile, it can generate semantic-based trajectories for indoor moving objects in a specific indoor space. After an overview of the general features of IndoorSTG, we discuss the architecture and implementation of IndoorSTG. And finally, a case study of IndoorSTG's demonstration is presented.

Most Web pages contain temporal information, which can be utilized by search engines to improve searching performance for users. However, traditional search engines have little support in processing temporal-textual Web queries. Aiming at solving this problem, in this paper we present and implement a prototype system for time-sensitive queries, which is called TASE (Time-Aware Search Engine). TASE extracts both the explicit and implicit temporal expressions for each Web page, and calculates the relevant score between the Web page and each temporal expression, and then re-rank search results based on the temporal-textual relevance between Web pages and the queries. It is demonstrated that TASE can improve the effectiveness of temporal-textual Web queries.

In content-based image retrieval (CBIR), color is the most intuitive image features and it is widely used. But The current color feature can describe the semantics of the whole image effectively, but does not reflect characteristics of the color salience objects in an image. For the purpose of giving prominence to the color characteristics of salience objects, this paper proposes a new color feature descriptor based on color visual perception model. By analyzing the human color visual perception process, the color perception model is proposed at first. This model integrates the intensity, the color contrast and self saliency, sparsity and centricity saliency to describe human color visual perception of the image. Then, the new color feature descriptor is calculated by weighting the significant bit-plane histograms with color perception map. Finally, similarity measure is presented for the new color feature. Experiment results show that the proposed color feature is more accurate and efficient in retrieving images with user-interested color objects. Compared with the other three retrieval methods, the proposed technique improves the retrieval accuracy effectively.

One-dimensional threshold segmentation algorithm has poor adaptability and poor noise immunity, while two-dimensional threshold segmentation has high computational complexity. To solve these problems, we propose a novel threshold segmentation algorithm for image segmentation. The whole method contains three steps: constructing one-dimensional histogram by using each pixel's gray value and its surrounding area's mean gray value, by integrating three classic segmentation algorithms: minimum error, maximum entropy and Otsu, we construct a new threshold selection algorithm, making use of the threshold to instruct segmentation. The experimental results show that our algorithm has strong adaptability, stable and robust noise immunity when comparing to one-dimensional threshold segmentation algorithm and a wider range of noise adaptability, lower computation complexity when comparing to two-dimensional Otsu algorithm.

Ship detection is one of the most important applications of target recognition based on optical remote sensing images. In this paper, we propose an uncertain ship target extraction algorithm based on dynamic fusion model of multi-feature and variance feature of optical remote sensing image. We choose several geometrical features, such as length, wide, rectangular ratio, tightness ratio and so on, using SVM to train and predict the uncertain ship targets extracted by our algorithm automatically. Experiments show that our algorithm is very robust, and the recognition rate of our algorithm can reach or even better than 95%, with the false alarm rate is kept at 3%.

Flash disks have become a popular alternative for magnetic disks, due to their fast I/O speed and other features such as small-size, shock-resistance, energy-efficient and non-volatile. However, flash disks also have characteristics of out-of-place update and asymmetric I/O latencies for read, write, and erase operations, which introduce new challenges into the indexing mechanism for flash disks. Traditional index structures do not take the flash I/O characteristics into account and, therefore, will cause poor performance. To address this problem, we present a new hybrid index structure for flash disks in this paper, which is called HashTree. HashTree aims at getting better update performance while keeping relatively high search efficiency. The HashTree uses a hash-based index to split the indexed records into several buckets, and then we develop a new tree structure named FTree to organize the records in each bucket. Compared with the previous tree-based indexes, our policy can reduce the costs of maintaining the hierarchical tree structure. We also introduce a tuning mechanism into the HashTree so that we can obtain appropriate trade-off between search performance and update performance. We conducted an experiment on a commercial SSD to evaluate the performance of HashTree. Compared with its competitors such as BFTL and FD-tree, our experimental results show that HashTree performs best in terms of both update performance and overall performance.

In recent years, many research works indicate that human's visual attention is very helpful in some research areas that are related to computer vision, such as object recognition, scene understanding and object-based image/video retrieval or annotation. This paper presents a visual attention model for natural scenes based on a dynamic feature combination strategy. The model can be divided into three parts, which are feature extraction, dynamic feature combination and salient objects detection. First, the saliency features of color, information entropy and salient boundary are extracted from an original colored image. After that, two different evaluation measurements are proposed for two different categories of feature maps defined in this dynamic combination strategy, which measures the contribution of each feature map to saliency and carries out a dynamic weighting of individual feature maps. Finally, salient objects are located from an integrated saliency map and a computational method is given to simulate the location shift of the real human visual attention. Experimental results show that this model is effective and robust for saliency detection in natural scenes, also similar to the real human visual attention mechanism.

Polymorphic gates are special kinds of logic gates that can exhibit different functions under the control of environmental parameters, such as light, temperature, and VDD. These polymorphic gates can be used to build polymorphic circuits that perform different functions under different environments. Because polymorphic gates are different from traditional logic gates, the existent completeness theory for the traditional logic gate set is not suitable for the polymorphic gate set. So far, only the definition of the complete polymorphic gate set is given. There is no approach to judging whether a given polymorphic gate set is complete. The contributions of this article include three aspects. First, the impact of logic-1 and logic-0 on the completeness of the polymorphic gate set is discussed. Second, the theory and two related algorithms for judging the completeness of polymorphic gate sets with two modes are given. Finally, the theory and related algorithms for complete polymorphic gate sets with more than two modes are proposed.

In this paper, we present a flexible simulation environment for the performance evaluation of flash-aware algorithms, which is called Flash-DBSim. The main purpose of Flash-DBSim is to provide a configurable virtual flash disk for upper systems, such as file system and DBMS, so that the algorithms in those systems can be easily evaluated on different types of flash disks. Moreover, it also offers a prototyping environment for those algorithms inside flash disk, e.g. the algorithms for garbage collection or wear-leveling. After an overview of the general features of Flash-DBSim, we discuss the architecture of Flash-DBSim. And finally, a case study of Flash-DBSim's demonstration is presented.

In this paper, an eXtended Stepwise Dimension Reduction approach (XSDR) to evolutionary design of relatively large combinational logic circuits is proposed. In our previous work, a Stepwise Dimension Reduction approach (SDR) is introduced. The SDR divides a circuit into several layers. The layers are evolved one after another. However, some layers are difficult to be evolved. The XSDR improves the SDR by decomposing the original truth table of a layer to two truth tables. The new truth tables after decomposing are easy to be evolved. The proposed method has been tested with multipliers and the circuits taken from the Microelectronics Center of North Carolina (MCNC) benchmark library. The experimental results demonstrate that the XSDR extensively improves the performance of the SDR in terms of the number of fitness evaluations and the computational time.

Salient objects detection in time sequenced images has a very important role in many applications such as surveillance systems, tracking and recognition systems, scene analysis and so on. This paper presents a novel approach for salient objects detection in time sequenced images. The approach in this paper is based on a visual saliency model which is proposed for analysis in time sequenced images. The model in this paper is based on a bottom-up visual saliency model which is presented by Itti in 1998. Multi different features are introduced to describe salient objects globally in time sequenced images. And they are combined into a single saliency map. Salient objects in time sequenced images can be detected by the final saliency map. The detection algorithm is unsupervised and fast. The results of the experiments indicate that our approach is effective and very robust to noise, blur, contrast level and brightness level.

In this paper, we present a CASE tool supporting conceptual modeling for moving objects database applications, which is called STXER (spatio-temporal extended entity-relational model). The main purpose of STXER is to support the database design for moving objects applications on typical object-relational DBMS. The STXER tool enhances the traditional ER model with moving characteristics. It can express rich spatiotemporal semantics for moving objects applications. After an overview of the general features of STXER, we discuss the architecture of STXER. And finally, a case study of STXER's demonstration is presented.