Cache memories are the major application of high-speed SRAMs, and they are frequently installed in high performance logic VLSIs including microprocessors. This paper presents a 4-way set-associative, SOI cache-tag memory. To obtain higher operating speed with less power dissipation, we devised an I/O-separated memory cell with a dual-rail wordline, which is used to transmit complementary selection signals. The address decoding delay was shortened using CMOS dual-rail logic. To enhance the maximum operating frequency, bitline's recovery operations after writing data were eliminated using a memory array configuration without half-selected cells. Moreover, conventional, sensitive but slow differential amplifiers were successfully removed from the data I/O circuitry with a hierarchical bitline scheme. As regards the stored data management, we devised a new hardware-oriented LRU-data replacement algorithm on the basis of 6-bit directed graph. With the experimental results obtained with a test chip fabricated with a 0.25-µm CMOS/SIMOX process, the core of the cache-tag memory with a 1024-set configuration can achieve a 1.5-ns address access time under typical conditions of a 2-V power supply and 25°C. The power dissipation during standby was less than 14 µW, and that at the 500-MHz operation was 13-83 mW, depending on the bit-stream data pattern.

Analysis of security governed by dynamics of power systems, which we refer to as dynamic security analysis, is a primary but challenging task because of its hybrid nature, that is, nonlinear continuous-time dynamics integrated with discrete switchings. In this paper, we formulate this analysis problem as checking the reachability of a mathematical model representing dynamic performances of a target power system. We then propose a computational approach to the analysis based on the so-called RRT (Rapidly-exploring Random Tree) algorithm. This algorithm searches for a feasible trajectory connecting an initial state possibly at a lower security level and a target set with a desirable higher security level. One advantage of the proposed approach is that it derives a concrete control strategy to guarantee the desirable security level if the feasible trajectory is found. The performance and effectiveness of the proposed approach are demonstrated by applying it to two running examples on power system studies: single machine-infinite system and two-area system for frequency control problem.

Purchase behavior prediction is one of the most important issues for the precision marketing of e-commerce companies. This Letter presents our solution to the purchase behavior prediction problem in E-commerce, specifically the task of Big Data Contest of China Computer Federation in 2014. The goal of this task is to predict which users will have the purchase behavior based on users' historical data. The traditional methods of recommendation encounter two crucial problems in this scenario. First, this task just predicts which users will have the purchase behavior, rather than which items should be recommended to which users. Second, the large-scale dataset poses a big challenge for building the empirical model. Feature engineering and Factorization Model shed some light on these problems. We propose to use Factorization Machines model based on the multiple classes and high dimensions of feature engineering. Experimental results on a real-world dataset demonstrate the advantages of our proposed method.

In recent years, much work has been devoted to developing protocols and architectures for supporting the growing trend of data-oriented services. One drawback of many of these proposals is the need to upgrade or replace all the routers in order for the new systems to work. Among the few systems that allow for gradual deployment is the recently-proposed Breadcrumbs technique for distributed coordination among caches in a cache network. Breadcrumbs uses information collected locally at each cache during past downloads to support in-network guiding of current requests to desired content. Specifically, during content download a series of short-term pointers, called breadcrumbs, is set up along the download path. Future requests for this content are initially routed towards the server which holds (a copy of) this content. However, if this route leads the request to a Breadcrumbs-supporting router, this router re-directs the request in the direction of the latest downloaded, using the aforementioned pointers. Thus, content requests are initially forwarded by a location ID (e.g., IP address), but encountering a breadcrumb entry can cause a shift over to content-based routing. This property enables the Breadcrumbs system to be deployed gradually, since it only enhances the existing location-based routing mechanism (i.e. IP-based routing). In this paper we evaluate the performance of a network where Breadcrumbs is only partially deployed. Our simulation results show Breadcrumbs performs poorly when sparsely deployed. However, if an overlay of Breadcrumbs-supporting routers is set-up, system performance is greatly improved. We believe that the reduced load on servers achieved with even a limited deployment of Breadcrumbs-supporting routers, combined with the flexibility of being able to deploy the system gradually, should motivate further investigation and eventual deployment of Breadcrumbs. In the paper, we also evaluate more coarse level than router level, i.e. ISP-level Breadcrumbs deployment issues. Our evaluation results show that Higher-layer first deployment approach obtains great improvement caused by Breadcrumbs redirections because of traffic aggregation in higher layer ISP.

Although many 3D structures have been solved for proteins to date, functions of some proteins remain unknown. To predict protein functions, comparison of local structures of proteins with pre-defined model structures, whose functions have been elucidated, is widely performed. For the comparison, the root mean square deviation (RMSD) has been used as a conventional index. In this work, adaptive deviation was incorporated, along with Bregmann Divergence Regularized Machine, in order to detect analogous local structures with such model structures more effectively than the conventional index.

Remote Access Trojans (RAT) is a spyware which can steal the confidential information from a target organization. The detection of RATs becomes more and more difficult because of targeted attacks, since the victim usually cannot realize that he/she is being attacked. After RAT's intrusion, the attacker can monitor and control the victim's PC remotely, to wait for an opportunity to steal the confidential information. As this situation, the main issue we face now is how to prevent confidential information being leaked back to the attacker. Although there are many existing approaches about RAT detection, there still remain two challenges: to detect RAT sessions as early as possible, and to distinguish them from the normal applications with a high accuracy. In this paper, we propose a novel approach to detect RAT sessions by their network behavior during the early stage of communication. The early stage is defined as a short period of time at communication's beginning; it also can be seen as the preparation period of the communication. We extract network behavior features from this period, to differentiate RAT sessions and normal sessions. For the implementation and evaluation, we use machine learning techniques with 5 algorithms and K-Fold cross-validation. As the results, our approach could detect RAT sessions in the communication's early stage with the accuracy over 96% together with the FNR of 10% by Random Forest algorithm.

To investigate electrostatic discharge (ESD) immunity testing for wearable electronic devices, the worst scenario i.e., an ESD event occurs when the body-mounted device approaches a grounded conductor is focused in this paper. Discharge currents caused by air discharges from a charged human through a hand-held metal bar or through a semi-sphere metal attached to the head, arm or waist in lieu of actual wearable devices are measured. As a result, it is found that at a human charge voltage of 1kV, the peak current from the semi-sphere metal is large in order of the attachment of the waist (15.4A), arm (12.8A) and head (12.2A), whereas the peak current (10.0A) from the hand-held metal bar is the smallest. It is also found that the discharge currents through the semi-sphere metals decrease to zero at around 50ns regardless of the attachment positions, although the current through the hand-held metal bar continues to flow at over 90ns. These discharge currents are further characterized by the discharge resistance, the charge storage capacitance and the discharge time constant newly derived from the waveform energy, which are validated from the body impedance measured through the hand-held and body-mounted metals. The above finding suggests that ESD immunity test methods for wearable devices require test specifications entirely different from the conventional ESD immunity testing.

Postcopy live migration is a promising alternative of virtual machine (VM) migration, which transfers memory pages after switching the execution host of a VM. It allows a shorter and more deterministic migration time than precopy migration. There is, however, a possibility that postcopy migration would degrade VM performance just after switching the execution host. In this paper, we propose a performance improvement technique of postcopy migration, extending the para-virtualized page fault mechanism of a virtual machine monitor. When the guest operating system accesses a not-yet-transferred memory page, our proposed mechanism allows the guest kernel to defer the execution of the current process until the page data is transferred. In parallel with the page transfer, the guest kernel can yield VCPU to other active processes. We implemented the proposed technique in our postcopy migration mechanism for Qemu/KVM. Through experiments, we confirmed that our technique successfully alleviated performance degradation of postcopy migration for web server and database benchmarks.

Big data analysis and a data storing applications require a huge volume of storage and a high I/O performance. Applications can achieve high level of performance and cost efficiency by exploiting the high I/O performance of direct attached storages (DAS) such as internal HDDs. With the size of stored data ever increasing, it will be difficult to replace servers since internal HDDs contain huge amounts of data. Generally, the data is copied via Ethernet when transferring the data from the internal HDDs to the new server. However, the amount of data will continue to rapidly increase, and thus, it will be hard to make these types of transfers through the Ethernet since it will take a long time. A storage area network such as iSCSI can be used to avoid this problem because the data can be shared with the servers. However, this decreases the level of performance and increases the costs. Improving the flexibility without incurring I/O performance degradation is required in order to improve the DAS architecture. In response to this issue, we propose FlexDAS, which improves the flexibility of direct attached storage by using a disk area network (DAN) without degradation the I/O performance. A resource manager connects or disconnects the computation nodes to the HDDs via the FlexDAS switch, which supports the SAS or SATA protocols. This function enables for the servers to be replaced in a short period of time. We developed a prototype FlexDAS switch and quantitatively evaluated the architecture. Results show that the FlexDAS switch can disconnect and connect the HDD to the server in just 1.16 seconds. We also confirmed that the FlexDAS improves the performance of the data intensive applications by up to 2.84 times compared with the iSCSI.

The researches on model-based testing mainly focus on the models with single component, such as FSM and EFSM. For the network protocols which have multiple components communicating with messages, CFSM is a widely accepted solution. But in some network protocols, parallel and data-shared components maybe exist in the same network entity. It is infeasible to precisely specify such protocol by existing models. In this paper we present a new model, Parallel Parameterized Extended Finite State Machine (PaP-EFSM). A protocol system can be modeled with a group of PaP-EFSMs. The PaP-EFSMs work in parallel and they can read external variables form each other. We present a 2-stage test generation approach for our new models. Firstly, we generate test sequences for internal variables of each machine. They may be non-executable due to external variables. Secondly, we process the external variables. We make the sequences for internal variables executable and generate more test sequences for external variables. For validation, we apply this method to the conformance testing of real-life protocols. The devices from different vendors are tested and implementation faults are exposed.

Automatic performance tuning of a practical application could be time-consuming and sometimes infeasible, because it often needs to evaluate the performances of a large number of code variants to find the best one. In this paper, hence, a light-weight rollback mechanism is proposed to evaluate each of code variants at a low cost. In the proposed mechanism, once one code variant of a target code block is executed, the execution state is rolled back to the previous state of not yet executing the block so as to repeatedly execute only the block to find the best code variant. It also has a feature of terminating a code variant whose execution time is longer than the shortest execution time so far. As a result, it can prevent executing the whole application many times and thus reduces the timing overhead of an auto-tuning process required for finding the best code variant.

Gentle AdaBoost is widely used in object detection and pattern recognition due to its efficiency and stability. To focus on instances with small margins, Gentle AdaBoost assigns larger weights to these instances during the training. However, misclassification of small-margin instances can still occur, which will cause the weights of these instances to become larger and larger. Eventually, several large-weight instances might dominate the whole data distribution, encouraging Gentle AdaBoost to choose weak hypotheses that fit only these instances in the late training phase. This phenomenon, known as “classifier distortion”, degrades the generalization error and can easily lead to overfitting since the deviation of all selected weak hypotheses is increased by the late-selected ones. To solve this problem, we propose a new variant which we call “Penalized AdaBoost”. In each iteration, our approach not only penalizes the misclassification of instances with small margins but also restrains the weight increase for instances with minimal margins. Our method performs better than Gentle AdaBoost because it avoids the “classifier distortion” effectively. Experiments show that our method achieves far lower generalization errors and a similar training speed compared with Gentle AdaBoost.

In this paper, we propose a novel Autonomous Decentralized Control (ADC) scheme for indirectly controlling a system performance variable of large-scale and wide-area networks. In a large-scale and wide-area network, since it is impractical for any one node to gather full information of the entire network, network control must be realized by inter-node collaboration using information local to each node. Several critical network problems (e.g., resource allocation) are often formulated by a system performance variable that is an amount to quantify system state. We solve such problems by designing an autonomous node action that indirectly controls, via the Markov Chain Monte Carlo method, the probability distribution of a system performance variable by using only local information. Analyses based on statistical mechanics confirm the effectiveness of the proposed node action. Moreover, the proposal is used to implement traffic-aware virtual machine placement control with load balancing in a data center network. Simulations confirm that it can control the system performance variable and is robust against system fluctuations. A comparison against a centralized control scheme verifies the superiority of the proposal.

In this paper, we introduce a distributed power allocation strategy for random access, that has the capabilities of multipacket reception (MPR) and successive interference cancellation (SIC). The proposed random access scheme is suitable for machine-to-machine (M2M) communication application in fifth-generation (5G) cellular networks. A previous study optimized the probability distribution for discrete transmission power levels, with implicit limitations on the successful decoding of at most two packets from a single collision. We formulate the optimization problem for the general case, where a base station can decode multiple packets from a single collision, and this depends only on the signal-to-interference-plus-noise ratio (SINR). We also propose a feasible suboptimal iterative per-level optimization process; we do this by introducing relationships among the different discrete power levels. Compared with the conventional power allocation scheme with MPR and SIC, our method significantly improves the system throughput; this is confirmed by computer simulations.

Sparse Matrix-Vector Multiplication (SpMxV) is widely used in many high-performance computing applications, including information retrieval, medical imaging, and economic modeling. To eliminate the overhead of zero padding in SpMxV, prior works have focused on partitioning a sparse matrix into row vectors sets (RVS's) or sub-matrices. However, performance was still degraded due to the sparsity pattern of a sparse matrix. In this letter, we propose a heuristics, called recursive merging, which uses a greedy approach to recursively merge those row vectors of nonzeros in a matrix into the RVS's, such that each set included is ensured a local optimal solution. For ten uneven benchmark matrices from the University of Florida Sparse Matrix Collection, our proposed partitioning algorithm is always identified as the method with the highest mean density (over 96%), but with the lowest average relative difference (below 0.07%) over computing powers.

Machine learning is used in various fields and demand for implementations is increasing. Within machine learning, a Random Forest is a multi-class classifier with high-performance classification, achieved using bagging and feature selection, and is capable of high-speed training and classification. However, as a type of ensemble learning, Random Forest determines classifications using the majority of multiple trees; so many decision trees must be built. Performance increases with the number of decision trees, requiring memory, and decreases if the number of decision trees is decreased. Because of this, the algorithm is not well suited to implementation on small-scale hardware as an embedded system. As such, we have proposed Boosted Random Forest, which introduces a boosting algorithm into the Random Forest learning method to produce high-performance decision trees that are smaller. When evaluated using databases from the UCI Machine learning Repository, Boosted Random Forest achieved performance as good or better than ordinary Random Forest, while able to reduce memory use by 47%. Thus, it is suitable for implementing Random Forests on embedded hardware with limited memory.

A salient feature extraction algorithm is proposed to improve the recognition rate of the speech emotion. Firstly, the spectrogram of the emotional speech is calculated. Secondly, imitating the selective attention mechanism, the color, direction and brightness map of the spectrogram is computed. Each map is normalized and down-sampled to form the low resolution feature matrix. Then, each feature matrix is converted to the row vector and the principal component analysis (PCA) is used to reduce features redundancy to make the subsequent classification algorithm more practical. Finally, the speech emotion is classified with the support vector machine. Compared with the tradition features, the improved recognition rate reaches 15%.

Many previous studies on image quality assessment of 3D still images or video clips have been conducted. In particular, it is important to know the region in which assessors are interested or on which they focus in images or video clips, as represented by the ROI (Region of Interest). For multi-view 3D images, it is obvious that there are a number of viewpoints; however, it is not clear whether assessors focus on objects or background regions. It is also not clear on what assessors focus depending on whether the background region is colored or gray scale. Furthermore, while case studies on coded degradation in 2D or binocular stereoscopic videos have been conducted, no such case studies on multi-view 3D videos exist, and therefore, no results are available for coded degradation according to the object or background region in multi-view 3D images. In addition, in the case where the background region is gray scale or not, it was not revealed that there were affection for gaze point environment of assessors and subjective image quality. In this study, we conducted experiments on the subjective evaluation of the assessor in the case of coded degradation by JPEG coding of the background or object or both in 3D CG images using an eight viewpoint parallax barrier method. Then, we analyzed the results statistically and classified the evaluation scores using an SVM.

Considering worse-case channel uncertainties, we investigate the robust energy efficient (EE) beamforming design problem in a K-user multiple-input-single-output (MISO) interference channel. Our objective is to maximize the worse-case sum EE under individual transmit power constraints. In general, this fractional programming problem is NP-hard for the optimal solution. To obtain an insight into the problem, we first transform the original problem into its lower bound problem with max-min and fractional form by exploiting the relationship between the user rate and the minimum mean square error (MMSE) and using the min-max inequality. To make it tractable, we transform the problem of fractional form into a subtractive form by using the Dinkelbach transformation, and then propose an iterative algorithm using Lagrangian duality, which leads to the locally optimal solution. Simulation results demonstrate that our proposed robust EE beamforming scheme outperforms the conventional algorithm.

We demonstrated generation of arbitrarily patterned optical pulse trains and frequency tunable terahertz (THz) pulses by spectral synthesis of optical combs generated by a Mach-Zehnder-modulator-based flat comb generator (MZ-FCG). In our approach, THz pulses were generated by photomixing of a multi-tone signal, which is elongated pulse train, and a single-tone signal. Both signals were extracted from a comb signal by using optical tunable bandpass filters. In the case of optical pulse train generation, the MZ-FCG generated comb signals with 10 GHz-spacing and 330 GHz-width, which was converted to a 2.85 ps-width pulse train by chirp compensation using a single-mode fiber. By combining the MZ-FCG with a pulse picker composed of a 40 Gbps intensity modulator, divided pulse trains and arbitrarily bit sequences were successfully generated. The single-mode light was extracted by an optical bandpass filter and the band-controlled pulse train was extracted by an optical bandpass filter. By photomixing them, a THz pulse was successfully generated. In the case of THz pulse generation, by photomixing a single-tone and a multi-tone signals extracted by tunable bandpass filters, THz pulses with a center frequency of 300 GHz was successfully generated. Furthermore, frequency tunability of the center frequency was also demonstrated.