Patarin proposed a crytographic trapdoor called Hidden Field Equation (HFE), a trapdoor based on the Multivariate Quadratic (MQ) and the Isomorphism of Polynomials (IP) problems. The MQ problem was proved by Patarin et al.'s to be NP-complete. Although the basic HFE has been proved to be vulnerable to attacks, its variants obtained by some modifications have been proved to be stronger against attacks. The Quartz digital signature scheme based on the HFEv- trapdoor (a variant of HFE) with particular choices of parameters, has been shown to be stronger against algebraic attacks to recover the private key. Furthermore, it generates reasonably short signatures. However, Joux et al. proved (based on the Birthday Paradox Attack) that Quartz is malleable in the sense that, if an adversary gets a valid pair of message and signature, a valid signature to another related message is obtainable with 250 computations and 250 queries to the signing oracle. Currently, the recommended minimum security level is 2112. Our signature scheme is also based on Quartz but we achieve a 2112 security level against Joux et al.'s attack. It is also more efficient in signature verification and vector initializations. Furthermore, we implemented both the original and our improved Quartz signature and run empirical comparisons.

In this paper, a novel slow-wave half mode substrate integrated waveguide (SW-HMSIW) structure is presented and experimentally demonstrated, and some interesting slow-wave propagation effects are obtained. The SW-HMSIW enables the cutoff frequency reduction and phase velocity to decrease without sacrificing its performance at the same lateral dimension, which equivalently reduces the lateral dimension and longitudinal size at the same frequency. Specifically, with the different loading microstrip width, a cutoff frequency reduction of 16%, 25%, 30% is achieved compared to the conventional HMSIW at the same lateral dimension. Both lateral and longitudinal size reductions significantly extend the operating range of SIW structures to low frequency region.

Light-field image processing has been widely employed in many areas, from mobile devices to manufacturing applications. The fundamental process to extract the usable information requires significant computation with high-resolution raw image data. A graphics processing unit (GPU) is used to exploit the data parallelism as in general image processing applications. However, the sparse memory access pattern of the applications reduced the performance of GPU devices for both systematic and algorithmic reasons. Thus, we propose an optimization technique which redesigns the memory access pattern of the applications to alleviate the memory bottleneck of rendering application and to increase the data reusability for depth extraction application. We evaluated our optimized implementations with the state-of-the-art algorithm implementations on several GPUs where all implementations were optimally configured for each specific device. Our proposed optimization increased the performance of rendering application on GTX-780 GPU by 30% and depth extraction application on GTX-780 and GTX-980 GPUs by 82% and 18%, respectively, compared with the original implementations.

In this paper, a discrete particle swarm optimization method is proposed to solve the multi-objective task assignment problem in distributed environment. The objectives of optimization include the makespan for task execution and the budget caused by resource occupation. A two-stage approach is designed as follows. In the first stage, several artificial particles are added into the initialized swarm to guide the search direction. In the second stage, we redefine the operators of the discrete PSO to implement addition, subtraction and multiplication. Besides, a fuzzy-cost-based elite selection is used to improve the computational efficiency. Evaluation shows that the proposed algorithm achieves Pareto improvement in comparison to the state-of-the-art algorithms.

Inter-person occlusion handling is a critical issue in the field of tracking, and it has been extensively researched. Several state-of-the-art methods have been proposed, such as focusing on the appearance of the targets or utilizing knowledge of the scene. In contrast with the approaches proposed in the literature, we propose to address this issue using a social interaction model, which allows us to explore spatio-temporal information pertaining to the targets involved in the occlusion situation. Our experimental results show promising results compared with those obtained using other methods.

This paper proposes a new multi-value sequence generated by utilizing primitive element, trace, and power residue symbol over odd characteristic finite field. In detail, let p and k be an odd prime number as the characteristic and a prime factor of p-1, respectively. Our proposal generates k-value sequence T={ti | ti=fk(Tr(ωi)+A)}, where ω is a primitive element in the extension field $F{p}{m}$, Tr(⋅) is the trace function that maps $F{p}{m} ightarrow {p}$, A is a non-zero scalar in the prime field ${p}$, and fk(⋅) is a certain mapping function based on k-th power residue symbol. Thus, the proposed sequence has four parameters as p, m, k, and A. Then, this paper theoretically shows its period, autocorrelation, and cross-correlation. In addition, this paper discusses its linear complexity based on experimental results. Then, these features of the proposed sequence are observed with some examples.

Sound source localization is an essential technique in many applications, e.g., speech enhancement, speech capturing and human-robot interaction. However, the performance of traditional methods degrades in noisy or reverberant environments, and it is sensitive to the spatial location of sound source. To solve these problems, we propose a sound source localization framework based on bi-direction interaural matching filter (IMF) and decision weighting fusion. Firstly, bi-directional IMF is put forward to describe the difference between binaural signals in forward and backward directions, respectively. Then, a hybrid interaural matching filter (HIMF), which is obtained by the bi-direction IMF through decision weighting fusion, is used to alleviate the affection of sound locations on sound source localization. Finally, the cosine similarity between the HIMFs computed from the binaural audio and transfer functions is employed to measure the probability of the source location. Constructing the similarity for all the spatial directions as a matrix, we can determine the source location by Maximum A Posteriori (MAP) estimation. Compared with several state-of-the-art methods, experimental results indicate that HIMF is more robust in noisy environments.

To reduce the server load and communication costs of machine-to-machine (M2M) systems, sensor data are aggregated in M2M gateways. Aggregation logic is typically programmed in the C language and embedded into the firmware. However, developing aggregation programs is difficult for M2M service providers because it requires gateway-specific knowledge and consideration of resource issues, especially RAM usage. In addition, modification of aggregation logic requires the application of firmware updates, which are risky. We propose a rule-based sensor data aggregation system, called the complex sensor data aggregator (CSDA), for M2M gateways. The functions comprising the data aggregation process are subdivided into the categories of filtering, statistical calculation, and concatenation. The proposed CSDA supports this aggregation process in three steps: the input, periodic data processing, and output steps. The behaviors of these steps are configured by an XML-based rule. The rule is stored in the data area of flash ROM and is updatable through the Internet without the need for a firmware update. In addition, in order to keep within the memory limit specified by the M2M gateway's manufacturer, the number of threads and the size of the working memory are static after startup, and the size of the working memory can be adjusted by configuring the sampling setting of a buffer for sensor data input. The proposed system is evaluated in an M2M gateway experimental environment. Results show that developing CSDA configurations is much easier than using C because the configuration decreases by 10%. In addition, the performance evaluation demonstrates the proposed system's ability to operate on M2M gateways.

Wireless Local Area Networks (WLANs) based on the IEEE 802.11 standard have been increasingly used. Access Points (APs) are being established in various public places, such as railway stations and airports, as well as private residences. Moreover, the rate of public WLAN services continues to increase. Throughput prediction of an AP in a multi-rate environment, i.e., predicting the amount of receipt data (including retransmission packets at an AP), is an important issue for wireless network design. Moreover, it is important to solve AP placement and selection problems. To realize the throughput prediction, we have proposed an AP throughput prediction method that considers terminal distribution. We compared the predicted throughput of the proposed method with a method that uses linear order computation and confirmed the performance of the proposed method, not by a network simulator but by the numerical computation. However, it is necessary to consider the impact of CSMA/CA in the MAC layer, because throughput is greatly influenced by frame collision. In this paper, we derive an effective transmission rate considering CSMA/CA and frame collision. We then compare the throughput obtained using the network simulator NS2 with a prediction value calculated by the proposed method. Simulation results show that the maximum relative error of the proposed method is approximately 6% and 15% for UDP and TCP, respectively, while that is approximately 17% and 21% in existing method.

This paper provides a mobile agent based distributed variational Bayesian (MABDVB) algorithm for density estimation in sensor networks. It has been assumed that sensor measurements can be statistically modeled by a common Gaussian mixture model. In the proposed algorithm, mobile agents move through the routes of the network and compute the local sufficient statistics using local measurements. Afterwards, the global sufficient statistics will be updated using these local sufficient statistics. This procedure will be repeated until convergence is reached. Consequently, using this global sufficient statistics the parameters of the density function will be approximated. Convergence of the proposed method will be also analytically studied, and it will be shown that the estimated parameters will eventually converge to their true values. Finally, the proposed algorithm will be applied to one-dimensional and two dimensional data sets to show its promising performance.

Petri Net (PN) is a frequently-used model for deadlock detection. Among various detection methods on PN, reachability analysis is the most accurate one since it never produces any false positive or false negative. Although suffering from the well-known state space explosion problem, reachability analysis is appropriate for small- and medium-scale programs. In order to mitigate the explosion problem several kinds of techniques have been proposed aiming at accelerating the reachability analysis, such as net reduction and abstraction. However, these techniques are for general PN and do not take the particularity of application into consideration, so their optimization potential is not adequately developed. In this paper, the feature of mutual exclusion-based program is considered, therefore several strategies are proposed to accelerate the reachability analysis. Among these strategies a customized net reduction rule aims at reducing the scale of PN, two marking compression methods and two pruning methods can reduce the volume of reachability graph. Reachability analysis on PN can only report one deadlock on each path. However, the reported deadlock may be a false alarm in which situation real deadlocks may be hidden. To improve the detection efficiency, we proposed a deadlock recovery algorithm so that more deadlocks can be detected in a shorter time. To validate the efficiency of these methods, a prototype is implemented and applied to SPLASH2 benchmarks. The experimental results show that these methods accelerate the reachability analysis for mutual exclusion-based deadlock detection significantly.

Many distributed systems use a replication mechanism for reliability and availability. On the other hand, application developers have to consider minimum consistency requirement for each application. Therefore, a replication protocol that supports multiple consistency models is required. Multi-Consistency Data Replication (McRep) is a proxy-based replication protocol and can support multiple consistency models. However, McRep has a potential problem in that a replicator relaying all request and reply messages between clients and replicas can be a performance bottleneck and a Single-Point-of-Failure (SPoF). In this paper, we introduce the multi-consistency support mechanism of McRep to a combined state-machine and deferred-update replication protocol to eliminate the performance bottleneck and SPoF. The state-machine and deferred-update protocols are well-established approaches for fault-tolerant data management systems. But each method can ensure only a specific consistency model. Thus, we adaptively select a replication method from the two replication bases. In our protocol, the functionality of the McRep's replicator is realized by clients and replicas. Each replica has new roles in serialization of all transactions and managing all views of the database, and each client has a new role in managing status of its transactions. We have implemented and evaluated the proposed protocol and compared to McRep. The evaluation results show that the proposed protocol achieved comparable throughput of transactions to McRep. Especially the proposed protocol improved the throughput up to 16% at a read-heavy workload in One-Copy. Finally, we demonstrated the proposed failover mechanism. As a result, a failure of a leader replica did not affect continuity of the entire replication system unlike McRep.

Web search queries are usually vague, ambiguous, or tend to have multiple intents. Users have different search intents while issuing the same query. Understanding the intents through mining subtopics underlying a query has gained much interest in recent years. Query suggestions provided by search engines hold some intents of the original query, however, suggested queries are often noisy and contain a group of alternative queries with similar meaning. Therefore, identifying the subtopics covering possible intents behind a query is a formidable task. Moreover, both the query and subtopics are short in length, it is challenging to estimate the similarity between a pair of short texts and rank them accordingly. In this paper, we propose a method for mining and ranking subtopics where we introduce multiple semantic and content-aware features, a bipartite graph-based ranking (BGR) method, and a similarity function for short texts. Given a query, we aggregate the suggested queries from search engines as candidate subtopics and estimate the relevance of them with the given query based on word embedding and content-aware features by modeling a bipartite graph. To estimate the similarity between two short texts, we propose a Jensen-Shannon divergence based similarity function through the probability distributions of the terms in the top retrieved documents from a search engine. A diversified ranked list of subtopics covering possible intents of a query is assembled by balancing the relevance and novelty. We experimented and evaluated our method on the NTCIR-10 INTENT-2 and NTCIR-12 IMINE-2 subtopic mining test collections. Our proposed method outperforms the baselines, known related methods, and the official participants of the INTENT-2 and IMINE-2 competitions.

This paper proposes a Bayesian approach to image recognition based on separable lattice hidden Markov models (SL-HMMs). The geometric variations of the object to be recognized, e.g., size, location, and rotation, are an essential problem in image recognition. SL-HMMs, which have been proposed to reduce the effect of geometric variations, can perform elastic matching both horizontally and vertically. This makes it possible to model not only invariances to the size and location of the object but also nonlinear warping in both dimensions. The maximum likelihood (ML) method has been used in training SL-HMMs. However, in some image recognition tasks, it is difficult to acquire sufficient training data, and the ML method suffers from the over-fitting problem when there is insufficient training data. This study aims to accurately estimate SL-HMMs using the maximum a posteriori (MAP) and variational Bayesian (VB) methods. The MAP and VB methods can utilize prior distributions representing useful prior information, and the VB method is expected to obtain high generalization ability by marginalization of model parameters. Furthermore, to overcome the local maximum problem in the MAP and VB methods, the deterministic annealing expectation maximization algorithm is applied for training SL-HMMs. Face recognition experiments performed on the XM2VTS database indicated that the proposed method offers significantly improved image recognition performance. Additionally, comparative experiment results showed that the proposed method was more robust to geometric variations than convolutional neural networks.

Many data-intensive applications need large memory to boost system performance. The expansion of DRAM is restricted by its high power consumption and price per bit. Flash as an existing technology of Non-Volatile Memory (NVM) can make up for the drawbacks of DRAM. In this paper, we propose a hybrid main memory architecture named SSDRAM that expands RAM with flash-based SSD. SSDRAM implements a runtime library to provide several transparent interfaces for applications. Unlike using SSD as system swap device which manages data at a page level, SSDRAM works at an application object granularity to boost the efficiency of accessing data on SSD. It provides a flexible memory partition and multi-mapping strategy to manage the physical memory by micro-pages. Experimental results with a number of data-intensive workloads show that SSDRAM can provide up to 3.3 times performance improvement over SSD-swap.

Since digital ICs are often designed and fabricated by third parties at any phases today, we must eliminate risks that malicious attackers may implement Hardware Trojans (HTs) on them. In particular, they can easily insert HTs during design phase. This paper proposes an HT rank which is a new quantitative analysis criterion against HTs at gate-level netlists. We have carefully analyzed all the gate-level netlists in Trust-HUB benchmark suite and found out several Trojan net features in them. Then we design the three types of Trojan points: feature point, count point, and location point. By assigning these points to every net and summing up them, we have the maximum Trojan point in a gate-level netlist. This point gives our HT rank. The HT rank can be calculated just by net features and we do not perform any logic simulation nor random test. When all the gate-level netlists in Trust-HUB, ISCAS85, ISCAS89 and ITC99 benchmark suites as well as several OpenCores designs, HT-free and HT-inserted AES netlists are ranked by our HT rank, we can completely distinguish HT-inserted ones (which HT rank is ten or more) from HT-free ones (which HT rank is nine or less). The HT rank is the world-first quantitative criterion which distinguishes HT-inserted netlists from HT-free ones in all the gate-level netlists in Trust-HUB, ISCAS85, ISCAS89, and ITC99.

In this paper, we propose a novel decomposition method to segment multiple object regions simultaneously in cluttered videos. This method formulates object regions segmentation as a labeling problem in which we assign object IDs to the superpixels in a sequence of video frames so that the unary color matching cost is low, the assignment induces compact segments, and the superpixel labeling is consistent through time. Multi-object segmentation in a video is a combinatorial problem. We propose a binary linear formulation. Since the integer linear programming is hard to solve directly, we relax it and further decompose the relaxation into a sequence of much simpler max-flow problems. The proposed method is guaranteed to converge in a finite number of steps to the global optimum of the relaxation. It also has a high chance to obtain all integer solution and therefore achieves the global optimum. The rounding of the relaxation result gives an N-approximation solution, where N is the number of objects. Comparing to directly solving the integer program, the novel decomposition method speeds up the computation by orders of magnitude. Our experiments show that the proposed method is robust against object pose variation, occlusion and is more accurate than the competing methods while at the same time maintains the efficiency.

This letter investigates transmit optimization in multi-user multi-input multi-output (MIMO) wiretap channels. In particular, we address the transmit covariance optimization for an artificial-noise (AN)-aided secrecy rate maximization (SRM) when subject to individual harvested energy and average transmit power. Owing to the inefficiency of the conventional interior-point solvers in handling our formulated SRM problem, a custom-designed algorithm based on penalty function (PF) and projected gradient (PG) is proposed, which results in semi-closed form solutions. The proposed algorithm achieves about two orders of magnitude reduction of running time with nearly the same performance comparing to the existing interior-point solvers. In addition, the proposed algorithm can be extended to other power-limited transmit design problems. Simulation results demonstrate the excellent performance and high efficiency of the algorithm.

We propose subquadratic space complexity multipliers for any finite field $mathbb{F}_{q^n}$ over the base field $mathbb{F}_q$ using the Dickson basis, where q is a prime power. It is shown that a field multiplication in $mathbb{F}_{q^n}$ based on the Dickson basis results in computations of Toeplitz matrix vector products (TMVPs). Therefore, an efficient computation of a TMVP yields an efficient multiplier. In order to derive efficient $mathbb{F}_{q^n}$ multipliers, we develop computational schemes for a TMVP over $mathbb{F}_{q}$. As a result, the $mathbb{F}_{2^n}$ multipliers, as special cases of the proposed $mathbb{F}_{q^n}$ multipliers, have lower time complexities as well as space complexities compared with existing results. For example, in the case that n is a power of 3, the proposed $mathbb{F}_{2^n}$ multiplier for an irreducible Dickson trinomial has about 14% reduced space complexity and lower time complexity compared with the best known results.

Recently, cloud systems have started to be utilized for services which analyze user's data in the field of computer vision. In these services, keypoints are extracted from images or videos, and the data is identified by machine learning with a large database in the cloud. To reduce the number of keypoints which are sent to the cloud, Keypoints of Interest (KOI) extraction has been proposed. However, since its computational complexity is large, hardware implementation is required for real-time processing. Moreover, the hardware resource must be low because it is embedded in devices of users. This paper proposes a hardware-friendly KOI algorithm with low amount of computations and its real-time hardware implementation based on dual threshold keypoint detection by gradient histogram and parallelization of connectivity of adjacent keypoint-utilizing register counters. The algorithm utilizes dual-histogram based detection and keypoint-matching based calculation of motion information and dense-clustering based keypoint smoothing. The hardware architecture is composed of a detection module utilizing descriptor, and grid-region-parallelization based density clustering. Finally, the evaluation results of hardware implementation show that the implemented hardware achieves Full-HD (1920x1080)-60 fps spatio-temporal keypoint extraction. Further, it is 47 times faster than low complexity keypoint extraction on software and 12 times faster than spatio-temporal keypoint extraction on software, and the hardware resources are almost the same as SIFT hardware implementation, maintaining accuracy.