Collective activity recognition plays an important role in high-level video analysis. Most current feature representations look at contextual information extracted from the behaviour of nearby people. Every person needs to be detected and his pose should be estimated. After extracting the feature, hierarchical graphical models are always employed to model the spatio-temporal patterns of individuals and their interactions, and so can not avoid complex preprocessing and inference operations. To overcome these drawbacks, we present a new feature representation method, called attribute-based spatio-temporal (AST) descriptor. First, two types of information, spatio-temporal (ST) features and attribute features, are exploited. Attribute-based features are manually specified. An attribute classifier is trained to model the relationship between the ST features and attribute-based features, according to which the attribute features are refreshed. Then, the ST features, attribute features and the relationship between the attributes are combined to form the AST descriptor. An objective classifier can be specified on the AST descriptor and the weight parameters of the classifier are used for recognition. Experiments on standard collective activity benchmark sets show the effectiveness of the proposed descriptor.

In our previous work [12], [13], we introduced generalized feed-forward shift registers (GF2SR, for short) to apply them to secure and testable scan design, where we considered the security problem from the viewpoint of the complexity of identifying the structure of GF2SRs. Although the proposed scan design is secure in the sense that the structure of a GF2SR cannot be identified only from the primary input/output relation, it may not be secure if part of the contents of the circuit leak out. In this paper, we introduce a more secure concept called strong security such that no internal state of strongly secure circuits leaks out, and present how to design such strongly secure GF2SRs.

Prediction of seasonal infectious disease spread is traditionally dealt with as a function of time. Typical methods are time series analysis such as ARIMA (autoregressive, integrated, and moving average) or ANN (artificial neural networks). However, if we regard the time series data as the matrix form, e.g., consisting of yearly magnitude in row and weekly trend in column, we may expect to use a different method (matrix approach) to predict the disease spread when seasonality is dominant. The MD (matrix decomposition) method is the one method which is used in recommendation systems. The other is the IRT (item response theory) used in ability evaluation systems. In this paper, we apply these two methods to predict the disease spread in the case of infectious gastroenteritis caused by norovirus in Japan, and compare the results obtained by using two conventional methods in forecasting, ARIMA and ANN. We have found that the matrix approach is simple and useful in prediction for the seasonal infectious disease spread.

A connected-(r,s)-out-of-(m,n): F system is a kind of the connected-X-out-of-(m,n): F system defined by Boehme et al. [2]. A connected-(r,s)-out-of-(m,n): F system consists of m×n components arranged in (m,n)-matrix. This system fails if and only if there exists a grid of size r×s in which all components are failed. When m=r, this system can be regarded as a consecutive-s-out-of-n: F system, and then the optimal arrangement of this system satisfies theorem which stated by Malon [9] in the case of s=2. In this study, we proposed a new algorithm for obtaining optimal arrangement of the connected-(r,2)-out-of-(m,n): F system based on the above mentioned idea. We performed numerical experiments in order to compare the proposed algorithm with the algorithm of enumeration method, and calculated the order of the computation time of these two algorithms. The numerical experiments showed that the proposed algorithm was more efficiently than the algorithm of enumeration method.

We discuss software reliability assessment considering multiple changes of software fault-detection phenomenon. The testing-time when the characteristic of the software failure-occurrence or fault-detection phenomenon changes notably in the testing-phase of a software development process is called change-point. It is known that the occurrence of the change-point influences the accuracy for the software reliability assessment based on a software reliability growth models, which are mainly divided into software failure-occurrence time and fault counting models. This paper discusses software reliability growth modeling frameworks considering with the effect of the multiple change-point occurrence on the software reliability growth process in software failure-occurrence time and fault counting modeling. And we show numerical illustrations for the software reliability analyses based on our models by using actual data.

A new modeling approach for the non-homogeneous Poisson processes (NHPPs) based software reliability modeling is proposed to describe the stochastic behavior of software fault-detection processes, of which the failure rate is not monotonic. The fundamental idea is to apply the Marshall-Olkin distribution to the software fault-detection time distribution. The applicability of Marshall-Olkin distribution in software reliability modeling is studied. The data fitting abilities of the proposed NHPP-based software reliability model is compared with the existing typical ones through real software project data analysis.

In this study, we investigate whether copula modeling contributes to the improvement of reliability evaluation in a cascading failure-occurrence environment. In particular, as a basic problem, we focus on a 2-unit parallel system whose units may fail dependently each other. As a result, the reliability assessment of the system by using the maximal copula provides more accurate evaluation than the traditional Weibull analysis, if the degree of dependency between two units are high. We show this result by using several simulation studies.

Neural networks are widely used in various fields due to their superior learning abilities. This paper proposes a hardware winner-take-all neural network (WTANN) that employs a new winner-take-all (WTA) circuit with phase-modulated pulse signals and digital phase-locked loops (DPLLs). The system uses DPLL as a computing element, so all input values are expressed by phases of rectangular signals. The proposed WTA circuit employs a simple winner search circuit. The proposed WTANN architecture is described by very high speed integrated circuit (VHSIC) hardware description language (VHDL), and its feasibility was tested and verified through simulations and experiments. Conventional WTA takes a global winner search approach, in which vector distances are collected from all neurons and compared. In contrast, the WTA in the proposed system is carried out locally by a distributed winner search circuit among neurons. Therefore, no global communication channels with a wide bandwidth between the winner search module and each neuron are required. Furthermore, the proposed WTANN can easily extend the system scale, merely by increasing the number of neurons. The circuit size and speed were then evaluated by applying the VHDL description to a logic synthesis tool and experiments using a field programmable gate array (FPGA). Vector classifications with WTANN using two kinds of data sets, Iris and Wine, were carried out in VHDL simulations. The results revealed that the proposed WTANN achieved valid learning.

In this paper, we consider a distributed power control scheme that can maximize overall capacity of an interference-limited wireless system in which the same radio resource is spatially reused among different transmitter-receiver pairs. This power control scheme employs a gradient-descent method in each transmitter, which adapts its own transmit power to co-channel interference dynamically to maximize the total weighted sum rate (WSR) of the system over a given interval. The key contribution in this paper is to propose a common feedback channel, over which a backward physical signal is accumulated for computing the gradient of the transmit power in each transmitter, thereby significantly reducing signaling overhead for the distributed power control. We show that the proposed power control scheme can achieve almost 95% of its theoretical upper WSR bound, while outperforming the non-power-controlled system by roughly 63% on average.

Heterogeneous networks (HetNet) with different radio access technologies have been deployed to support a range of communication services. To manage these HetNets efficiently, some interworking solutions such as MIH (media independent handover), ANQP (access network query protocol) or ANDSF (access network discovery and selection function) have been studied. Recently, the millimeter-wave (mm-wave) based HetNet has been explored to provide multi-gigabits-per-second data rates over short distances in the 60GHz frequency band for 5G wireless networks. WiGig (Wireless Gigabit Alliance) is one of the available radio access technologies using mm-wave. However, the conventional interworking solutions are not sufficient for the implementation of LTE (Long Term Evolution)/WiGig HetNets. Since the coverage area of WiGig is very small due to the high propagation loss of the mm-wave band signal, it is difficult for UEs to perform cell discovery and handover if using conventional LTE/WLAN (wireless local area networks) interworking solutions, which cannot support specific techniques of WiGig well, such as beamforming and new media access methods. To solve these problems and find solutions for LTE/WiGig interworking, RAN (radio access network)-level tightly coupled interworking architecture will be a promising solution. As a RAN-level tightly coupled interworking solution, this paper proposes to design a LTE/WiGig protocol adaptor above the protocol stacks of WiGig to process and transfer control signaling and user data traffic. The proposed extended control plane can assist UEs to discover and access mm-wave BSs successfully and support LTE macro cells to jointly control the radio resources of both LTE and WiGig, so as to improve spectrum efficiency. The effectiveness of the proposal is evaluated. Simulation results show that LTE/WiGig HetNets with the proposed interworking solution can decrease inter-cell handover and improve user throughput significantly. Moreover, the downlink backhaul throughput and energy efficiency of mm-wave HetNets are evaluated and compared with that of 3.5GHz LTE HetNets. Results indicate that 60GHz mm-wave HetNets have better energy efficiency but with much heavier backhaul overhead.

Multi-agent based simulation has been widely used in behavior finance, and several single-processed simulation platforms with Agent-Based Modeling (ABM) have been proposed. However, traditional simulations of stock markets on single processed computers are limited by the computing capability since financial researchers need larger and larger number of agents and more and more rounds to evolve agents' intelligence and get more efficient data. This paper introduces a distributed multi-agent simulation platform, named PSSPAM, for stock market simulation focusing on large scale of parallel agents, communication system and simulation scheduling. A logical architecture for distributed artificial stock market simulation is proposed, containing four loosely coupled modules: agent module, market module, communication system and user interface. With the customizable trading strategies inside, agents are deployed to multiple computing nodes. Agents exchange messages with each other and with the market based on a customizable network topology through a uniform communication system. With a large number of agent threads, the round scheduling strategy is used during the simulation, and a worker pool is applied in the market module. Financial researchers can design their own financial models and run the simulation through the user interface, without caring about the complexity of parallelization and related problems. Two groups of experiments are conducted, one with internal communication between agents and the other without communication between agents, to verify PSSPAM to be compatible with the data from Euronext-NYSE. And the platform shows fair scalability and performance under different parallelism configurations.

Sharing data might bring the risk of disclosing the sensitive knowledge in it. Usually, the data owner may choose to sanitize data by modifying some items in it to hide sensitive knowledge prior to sharing. This paper focuses on protecting sensitive knowledge in the form of frequent itemsets by data sanitization. The sanitization process may result in side effects, i.e., the data distortion and the damage to the non-sensitive frequent itemsets. How to minimize these side effects is a challenging problem faced by the research community. Actually, there is a trade-off when trying to minimize both side effects simultaneously. In view of this, we propose a data sanitization method based on evolutionary multi-objective optimization (EMO). This method can hide specified sensitive itemsets completely while minimizing the accompanying side effects. Experiments on real datasets show that the proposed approach is very effective in performing the hiding task with fewer damage to the original data and non-sensitive knowledge.

Dynamic analysis is frail and insufficient to find hidden paths in environment-intensive program. By analyzing a broad spectrum of different concolic testing systems, we conclude that a number of them cannot handle programs that interact with the environment or require a complete working model. This paper addresses this problem by automatically identifying and modifying outputs of the data input interface function(DIIF). The approach is based on fine-grained taint analysis for detecting and updating the data that interacts with the environment to generate a new set of inputs to execute hidden paths. Moreover, we developed a prototype and conducted extensive experiments using a set of complex and environmentally intensive programs. Finally, the result demonstrates that our approach could identify the DIIF precisely and discover hidden path obviously.

In this paper, we study on an availability analysis for a multibase system with lateral resupply of spare items between bases. We construct a basic model that a spare item of a base is transported for operation to another base without spare upon occurrence of failure, and simultaneously, the base that supplies the spare item receives the failed item of the other base for repair. We propose an approximation method to obtain the availability of the system and show the accuracy of the solution through numerical experiments. Also, two modified models are constructed to show the efficiency of the basic model. The two models modify the assumption on the lateral resupply of spare items between bases in the basic model. We numerically illustrate that the basic model can increase the availability of the system compared with the two modified models through Monte Carlo simulation.

We present a power-saving method for large-scale storage systems of cloud data sharing services, particularly those providing media (video and photograph) sharing services. The idea behind our method is to periodically rearrange stored data in a disk array, so that the workload is skewed toward a small subset of disks, while other disks can be sent to standby mode. This idea is borrowed from the Popular Data Concentration (PDC) technique, but to avoid an increase in response time caused by the accesses to disks in standby mode, we introduce a function that predicts future access frequencies of the uploaded files. This function uses the correlation of potential future accesses with the combination of elapsed time after upload and the total number of accesses in the past. We obtain this function in statistical analysis of the real access patterns of 50,000 randomly selected publicly available photographs on Flickr over 7,000 hours (around 10 months). Moreover, to adapt to a constant massive influx of data, we propose a mechanism that effectively packs the continuously uploaded data into the disk array in a storage system based on the PDC. To evaluate the effectiveness of our method, we measured the performance in simulations and a prototype implementation. We observed that our method consumed 12.2% less energy than the static configuration (in which all disks are in active mode). At the same time, our method maintained a preferred response time, with 0.23% of the total accesses involving disks in standby mode.

Most of the current voice conversion methods are conducted based on parallel speech, which is not easily obtained in practice. In this letter, a novel iterative speaker model alignment (ISMA) method is proposed to address this problem. First, the source and target speaker models are each trained from the background model by adopting maximum a posteriori (MAP) algorithm. Then, a novel ISMA method is presented for alignment and transformation of spectral features. Finally, the proposed ISMA approach is further combined with a Gaussian mixture model (GMM) to improve the conversion performance. A series of objective and subjective experiments are carried out on CMU ARCTIC dataset, and the results demonstrate that the proposed method significantly outperforms the state-of-the-art approach.

We consider the exploration problem with a single agent in an undirected graph. The problem requires the agent starting from an arbitrary node to explore all the nodes and edges in the graph and return to the starting node. Our goal is to minimize both the number of agent moves and the memory size of the agent, which dominate the amount of communication during the exploration. We focus on the local memory called the whiteboard of each node. There are several exploration algorithms which are very fast (i.e. the exploration is completed within a small number of agent moves such as 2m and m+3n) and do not use whiteboards. These algorithms, however, require large agent memory because the agent must keep the entire information in its memory to explore a graph. We achieve the above goal by reducing the agent memory size of such algorithms with using whiteboards. Specifically, we present two algorithms with no agent memory based on the traditional depth-first traversal and two algorithms with O(n) and O(nlog n) space of agent memory respectively based on the fastest algorithms in the literature by Panaite and Pelc [J. Alg., Vol.33 No.2, 1999].

Methods of window matching to estimate 3D points are the most serious factors affecting the accuracy, robustness, and computational cost of Multi-View Stereo (MVS) algorithms. Most existing MVS algorithms employ window matching based on Normalized Cross-Correlation (NCC) to estimate the depth of a 3D point. NCC-based window matching estimates the displacement between matching windows with sub-pixel accuracy by linear/cubic interpolation, which does not represent accurate sub-pixel values of matching windows. This paper proposes a technique of window matching that is very accurate using Phase-Only Correlation (POC) with geometric correction for MVS. The accurate sub-pixel displacement between two matching windows can be estimated by fitting the analytical correlation peak model of the POC function. The proposed method also corrects the geometric transformations of matching windows by taking into consideration the 3D shape of a target object. The use of the proposed geometric correction approach makes it possible to achieve accurate 3D reconstruction from multi-view images even for images with large transformations. The proposed method demonstrates more accurate 3D reconstruction from multi-view images than the conventional methods in a set of experiments.

In this paper we discuss the system failure probability of a k-out-of-n system considering common-cause failures. The conventional implicit technique is first introduced. Then the failure probabilities are formulated when the independence between common-cause failure events is assumed. We also provide algorithms to enumerate all the cut sets and the minimal cut sets, and to calculate the system failure probability. These methods are extendable to the case of systems with non-identical components. We verify the effectiveness of our method by comparison with the exact solution obtained by numerical calculation.

Combining heterogeneous wireless networks that cross licensed and unlicensed spectra is a promising way of supporting the surge in mobile traffic. The unlicensed band is mostly used by wireless LAN (WLAN) nodes which employ carrier sense multiple access/collision avoidance (CSMA/CA). Since the number of WLAN devices and their traffic are increasing, the wireless resource of the unlicensed band is expected be more depleted in 2020s. In such a wireless environment, the throughput could be extremely low and unstable due to the hidden terminal problem and exposed terminal problem despite of the large resources of the allocated frequency band and high peak PHY rate. In this paper, we propose user equipment (UE) centric access in the unlicensed band, with support by licensed band access in the mobile network. The proposed access enables robust downlink transmission from the access point (AP) to the UEs by mitigating the hidden terminal problem. The licensed spectrum access passes information on the user data waiting at the AP to the UEs and triggers UE reception opportunity (RXOP) acquisition. Furthermore, the adaptive use of UE centric downlink access is presented by using the channel utilization measured at the AP. Computer simulations confirm that licensed access assistance enhances the robustness of the unlicensed band access against the hidden terminal problem.