Accurate visual correspondence is the foundation of many computer vision based applications. Since existing image matching algorithms generate mismatches inevitably, a reliable mismatch-removal algorithm is highly desired to remove mismatches and preserve true matches. This paper proposes a hierarchical progressive trust (HPT) model to solve this problem. The HPT model first adopts a “trust the most trustworthy ones” strategy to select anchor inliers in its bottom layer, and then progressively propagates the trust from bottom layer to other layers in a bottom-up way: 1) bottom layer verifies anchor inliers with the guidance of local features; 2) middle layers progressively estimate local transformations and perform local verifications; 3) top layer estimates a global transformation with an anchor-inliers-guided expectation maximization (EM) algorithm and performs global verifications. Experimental results show that the proposed HPT model achieves higher performance than state-of-the-art mismatch-removal methods under both rigid transformations and non-rigid deformations.

In this paper, we present a hybrid message logging protocol consisting of three modules for two-level hierarchical and distributed architectures to address the drawbacks of sender-based message logging. The first module reduces the number of in-group control messages and, the rest, the number of inter-group control messages while localizing recovery. In addition, it can distribute the load of logging and keeping inter-group messages to group members as evenly as possible. The simulation results show the proposed protocol considerably outperforms the traditional protocol in terms of message logging overhead and scalability.

Topic modeling as a well-known method is widely applied for not only text data mining but also multimedia data analysis such as video data analysis. However, existing models cannot adequately handle time dependency and multimodal data modeling for video data that generally contain image information and speech information. In this paper, we therefore propose a novel topic model, sequential symmetric correspondence hierarchical Dirichlet processes (Seq-Sym-cHDP) extended from sequential conditionally independent hierarchical Dirichlet processes (Seq-CI-HDP) and sequential correspondence hierarchical Dirichlet processes (Seq-cHDP), to improve the multimodal data modeling mechanism via controlling the pivot assignments with a latent variable. An inference scheme for Seq-Sym-cHDP based on a posterior representation sampler is also developed in this work. We finally demonstrate that our model outperforms other baseline models via experiments.

Tracking-by-detection methods consider tracking task as a continuous detection problem applied over video frames. Modern tracking-by-detection trackers have online learning ability; the update stage is essential because it determines how to modify the classifier inherent in a tracker. However, most trackers search for the target within a fixed region centered at the previous object position; thus, they lack spatiotemporal consistency. This becomes a problem when the tracker detects an incorrect object during short-term occlusion. In addition, the scale of the bounding box that contains the target object is usually assumed not to change. This assumption is unrealistic for long-term tracking, where the scale of the target varies as the distance between the target and the camera changes. The accumulation of errors resulting from these shortcomings results in the drift problem, i.e. drifting away from the target object. To resolve this problem, we present a drift-free, online learning-based tracking-by-detection method using a single static camera. We improve the latent structured support vector machine (SVM) tracker by designing a more robust tracker update step by incorporating two Kalman filter modules: the first is used to predict an adaptive search region in consideration of the object motion; the second is used to adjust the scale of the bounding box by accounting for the background model. We propose a hierarchical search strategy that combines Bhattacharyya coefficient similarity analysis and Kalman predictors. This strategy facilitates overcoming occlusion and increases tracking efficiency. We evaluate this work using publicly available videos thoroughly. Experimental results show that the proposed method outperforms the state-of-the-art trackers.

In this paper, we study a control problem of a concurrent discrete event system, where several subsystems are partially synchronized via shared events, under local and global constraints described by linear temporal logic formulas. We propose a hierarchical control architecture consisting of local supervisors and a coordinator. While the supervisors ensure the local requirements, the coordinator decides which shared events to be disabled so as to satisfy the global specification. First, we construct Rabin games to obtain local supervisors. Next, we reduce them based on shared transitions. Finally, we construct a global Rabin game from the reduced supervisors and a deterministic Rabin automaton that accepts every run satisfying the global specification. By solving it, we obtain a coordinator that disables shared events to guarantee the global requirement. Moreover, the concurrent system controlled by the coordinator and the local supervisors is deadlock-free.

Nonnegative Matrix Factorization (NMF) with sparseness and smoothness constraints has attracted increasing attention. When these properties are considered, NMF is usually formulated as an optimization problem in which a linear combination of an approximation error term and some regularization terms must be minimized under the constraint that the factor matrices are nonnegative. In this paper, we focus our attention on the error measure based on the Euclidean distance and propose a new iterative method for solving those optimization problems. The proposed method is based on the Hierarchical Alternating Least Squares (HALS) algorithm developed by Cichocki et al. We first present an example to show that the original HALS algorithm can increase the objective value. We then propose a new algorithm called the Gauss-Seidel HALS algorithm that decreases the objective value monotonically. We also prove that it has the global convergence property in the sense of Zangwill. We finally verify the effectiveness of the proposed algorithm through numerical experiments using synthetic and real data.

This paper presents a hierarchical-masked image filtering method for privacy-protection. Cameras are widely used for various applications, e.g., crime surveillance, environment monitoring, and marketing. However, invasion of privacy has become a serious social problem, especially regarding the use of surveillance cameras. Many surveillance cameras point at many people; thus, a large amount of our private information of our daily activities are under surveillance. However, several surveillance cameras currently on the market and related research often have a complicated or institutional masking privacy-protection functionality. To overcome this problem, a Hierarchical-Masked image Filtering (HMF) method is proposed, which has unmaskable (mask reversal) capability and is applicable to current surveillance camera systems for privacy-information protection and can satisfy privacy-protection related requirements. This method has five main features: unmasking of the original image from only the masked image and a cipher key, hierarchical-mask level control using parameters for the length of a pseudorandom number, robustness against malicious attackers, fast processing on an embedded processor, and applicability of mask operation to current surveillance camera systems. Previous studies have difficulty in providing these features. To evaluate HMF on actual equipment, an HMF-based prototype system is developed that mainly consists of a USB web camera, ultra-compact single board computer, and notebook PC. Through experiments, it is confirmed that the proposed method achieves mask level control and is robust against attacks. The increase in processing time of the HMF-based prototype system compared with a conventional non-masking system is only about 1.4%. This paper also reports on the comparison of the proposed method with conventional privacy protection methods and favorable responses of people toward the HMF-based prototype system both domestically and abroad. Therefore, the proposed HMF method can be applied to embedded systems such as those equipped with surveillance cameras for protecting privacy.

Delay Tolerant Networks (DTN) protocols based on the store-and-carry principle offer useful functions such as forwarding, utility value, social networks, and network coding. Although many DTN protocol proposals have been offered, work continues to improve performance. In order to implement DTN functions, each protocol introduces multiple parameters; their performance is largely dependent on how the parameter values are set. In this paper, we focus on improving spray and wait (S&W) by proposing a communication protocol named a Spray and AHP-GRA-based Forwarding (S&AGF) and Spray and Fuzzy based Forwarding (S&FF) scheme for DTN. The proposed protocols include a new forwarding scheme intended to extend network lifetime as well as maintain acceptable delivery ratio by addressing a deficiency in existing schemes that do not take energy into consideration. We choose the most suitable relay node by taking the energy, mobility, measured parameters of nodes into account. The simulation-based comparison demonstrates that the proposed S&AGF and S&FF schemes show better balanced performance level in terms of both delivery ratio and network lifetime than original S&W and its variants.

Restoration is an important area in improving the visual quality, and lays the foundation for accurate object detection or terrain classification in image analysis. In this paper, we introduce Beta process priors into hierarchical sparse Bayesian learning for recovering underlying degraded hyperspectral images (HSI), including suppressing the various noises and inferring the missing data. The proposed method decomposes the HSI into the weighted summation of the dictionary elements, Gaussian noise term and sparse noise term. With these, the latent information and the noise characteristics of HSI can be well learned and represented. Solved by Gibbs sampler, the underlying dictionary and the noise can be efficiently predicted with no tuning of any parameters. The performance of the proposed method is compared with state-of-the-art ones and validated on two hyperspectral datasets, which are contaminated with the Gaussian noises, impulse noises, stripes and dead pixel lines, or with a large number of data missing uniformly at random. The visual and quantitative results demonstrate the superiority of the proposed method.

We previously proposed an unsupervised model using the inclusion-exclusion principle to compute sentence information content. Though it can achieve desirable experimental results in sentence semantic similarity, the computational complexity is more than O(2n). In this paper, we propose an efficient method to calculate sentence information content, which employs the thinking of the difference set in hierarchical network. Impressively, experimental results show that the computational complexity decreases to O(n). We prove the algorithm in the form of theorems. Performance analysis and experiments are also provided.

Video data mining based on topic models as an emerging technique recently has become a very popular research topic. In this paper, we present a novel topic model named sequential correspondence hierarchical Dirichlet processes (Seq-cHDP) to learn the hidden structure within video data. The Seq-cHDP model can be deemed as an extended hierarchical Dirichlet processes (HDP) model containing two important features: one is the time-dependency mechanism that connects neighboring video frames on the basis of a time dependent Markovian assumption, and the other is the correspondence mechanism that provides a solution for dealing with the multimodal data such as the mixture of visual words and speech words extracted from video files. A cascaded Gibbs sampling method is applied for implementing the inference task of Seq-cHDP. We present a comprehensive evaluation for Seq-cHDP through experimentation and finally demonstrate that Seq-cHDP outperforms other baseline models.

The self-organizing nature of ad hoc networks is a good aspect in that terminals are not dependent on any infrastructure, that is, networks can be formed with decentralized and autonomous manner according to communication demand. However, this characteristic might affect the performance in terms of stability, reliability and so forth. Moreover, ad hoc networks face a scalability problem, which arise when the number of terminals in a network increases or a physical network domain expands, due to the network capacity limitation caused by the decentralized and the autonomous manner. Regarding this problem, some hierarchical and cluster-based routings have been proposed to effectively manage the networks. In this paper, we apply the concept of hierarchical routing and clustering to opportunistic routing, which can forward packets without using any pre-established path to achieve a path diversity gain with greater reachability. The simulation results show that the proposed method can achieve 11% higher reliability with a reasonable end-to-end delay in dense environments and 30% higher in large-scale networks.

Analyzing the schedulability of hierarchical real-time systems is difficult because of the systems' complex behavior. It gets more complicated when shared resources or dependencies among tasks are included. This paper introduces a framework based on UPPAAL that can analyze the schedulability of hierarchical real-time systems.

User-based and item-based collaborative filtering (CF) are two of the most important and popular techniques in recommender systems. Although they are widely used, there are still some limitations, such as not being well adapted to the sparsity of data sets, failure to consider the hierarchical structure of the items, and changes in users' interests when calculating the similarity of items. To overcome these shortcomings, we propose an evolutionary approach based on hierarchical structure for dynamic recommendation system named Hierarchical Temporal Collaborative Filtering (HTCF). The main contribution of the paper is displayed in the following two aspects. One is the exploration of hierarchical structure between items to improve similarity, and the other is the improvement of the prediction accuracy by utilizing a time weight function. A unique feature of our method is that it selects neighbors mainly based on hierarchical structure between items, which is more reliable than co-rated items utilized in traditional CF. To the best of our knowledge, there is little previous work on researching CF algorithm by combining object implicit or latent object-structure relations. The experimental results show that our method outperforms several current recommendation algorithms on recommendation accuracy (in terms of MAE).

Hierarchical scheduling for multiple resources is partially responsible for the performance achievements in large scale datacenters. However, the latest scheduling technique, Hierarchy Dominant Resource Fairness (H-DRF)[1], has some shortcomings in heterogeneous environments, such as starving certain jobs or unfair resource allocation. This is because a heterogeneous environment brings new challenges. In this paper, we propose a novel scheduling algorithm called Dominant Fairness Fairness (DFF). DFF tries to keep resource allocation fair, avoid job starvation, and improve system resource utilization. We implement DFF in the YARN system, a most commonly used scheduler for large scale clusters. The experimental results show that our proposed algorithm leads to higher resource utilization and better throughput than H-DRF.

Hierarchical multi-tenancy, which enables tenants to be divided into subtenants, is a flexible and scalable architecture for representing subsets of users and application resources in the real world. However, the resource isolation and sharing relations for tenants with hierarchies are more complicated than those between tenants in the flat Multi-Tenancy Architecture. In this paper, a hierarchical tenant-based access control model based on Administrative Role-Based Access Control in Software-as-a-Service is proposed. Autonomous Areas and AA-tree are used to describe the autonomy and hierarchy of tenants, including their isolation and sharing relationships. AA is also used as an autonomous unit to create and deploy the access permissions for tenants. Autonomous decentralized authorization and authentication schemes for hierarchical multi-tenancy are given out to help different level tenants to customize efficient authority and authorization in large-scale SaaS systems.

Nonnegative component representation (NCR) is a mid-level representation based on nonnegative matrix factorization (NMF). Recently, it has attached much attention and achieved encouraging result for action recognition. In this paper, we propose a novel hierarchical dictionary learning strategy (HDLS) for NMF to improve the performance of NCR. Considering the variability of action classes, HDLS clusters the similar classes into groups and forms a two-layer hierarchical class model. The groups in the first layer are disjoint, while in the second layer, the classes in each group are correlated. HDLS takes account of the differences between two layers and proposes to use different dictionary learning methods for this two layers, including the discriminant class-specific NMF for the first layer and the discriminant joint dictionary NMF for the second layer. The proposed approach is extensively tested on three public datasets and the experimental results demonstrate the effectiveness and superiority of NCR with HDLS for large-scale action recognition.

With the development of silicon-based Nano-photonics, Optical Network on Chip (ONoC) is, due to its high bandwidth and low latency, becoming an important choice for future multi-core networks. As a key ONoC technology, the arbitration scheme should provide differential arbitration service with high throughput and low latency for various types and priorities of traffic in CMPs. In this work, we propose a fast hierarchical arbitration scheme based on multi-level priority QoS. First, given multi-priority data buffer queue, arbiters provide differential transmissions with fair service for all nodes and guarantee the max-transmit-delay and min-communication-bandwidth for all queues. Second, arbiter adopts the transmit bound resource reservation scheme to reserve time slots for all nodes fairly, thereby achieving a throughput of 100%. Third, we propose fast arbitration with a layout of fast optical arbitration channels (FOACs) to reduce the arbitration period, thereby reducing packet transmitting delay. Simulation results show that with our hierarchical arbitration scheme, all nodes are allocated almost equal service access probability under various traffic patterns; thus, the min-communication-bandwidth and max-transmit-delay is guaranteed to be 5% and 80 cycles, respectively, under the overload demands. This scheme improves throughput by 17% compared to FeatherWeight under a self-similar traffic pattern and decreases arbitration delay by 15% compare to 2-pass arbitration, incurring a total power overhead of 5%.

In this paper, we construct ideal and probabilistic secret sharing schemes for some multipartite access structures, including the General Hierarchical Access Structure and Compartmented Access Structures. We devise an ideal scheme which implements the general hierarchical access structure. For the compartmented access structures, we consider three special access structures. We propose ideal and probabilistic schemes for these three compartmented access structures by bivariate interpolation.

Computer networks require sophisticated control mechanisms to realize fair resource allocation among users in conjunction with efficient resource usage. To successfully realize fair resource allocation in a network, someone should control the behavior of each user by considering fairness. To provide efficient resource utilization, someone should control the behavior of all users by considering efficiency. To realize both control goals with different granularities at the same time, a hierarchical network control mechanism that combines microscopic control (i.e., fairness control) and macroscopic control (i.e., efficiency control) is required. In previous works, Aida proposed the concept of chaos-based hierarchical network control. Next, as an application of the chaos-based concept, Aida designed a fundamental framework of hierarchical transmission rate control based on the chaos of coupled relaxation oscillators. To clarify the realization of the chaos-based concept, one should specify the chaos-based hierarchical transmission rate control in enough detail to work in an actual network, and confirm that it works as intended. In this study, we implement the chaos-based hierarchical transmission rate control in a popular network simulator, ns-2, and confirm its operation through our experimentation. Results verify that the chaos-based concept can be successfully realized in TCP/IP networks.