Assessing Probabilistic Timing Constraints on System Performance

We propose an algorithm for assessing probabilistic timing constraints for systems including components with uncertain delays. We make a case for designing systems based on a probabilistic relaxation of such constraints, as this has the potential for resulting in lower silicon area and/or power consumption. We consider a concrete example, an MPEG decoder, for which we discuss modeling and assessment of probabilistic throughput constraints.     

Wavelet-based image denoising using a Markov random field a priorimodel

This paper describes a new method for the suppression of noise in images via the wavelet transform. The method relies on two measures. The first is a classic measure of smoothness of the image and is based on an approximation of the local Holder exponent via the wavelet coefficients. The second, novel measure takes into account geometrical constraints, which are generally valid for natural images. The smoothness measure and the constraints are combined in a Bayesian probabilistic formulation, and are implemented as a Markov random field (MRF) image model. The manipulation of the wavelet coefficients is consequently based on the obtained probabilities. A comparison of quantitative and qualitative results for test images demonstrates the improved noise suppression performance with respect to previous wavelet-based image denoising methods.     

基于定性多用户偏好的Web服务选择

随着Web服务数量逐渐增多,出现了许多服务提供者提供功能相同或相似服务的情况.如何在一些功能相似或相同的服务集合中,根据用户对服务质量的需求选出更符合用户要求的服务,是当前研究人员普遍关心的课题.本文提出一种定性的服务选择方法,用来解决涉及多个用户的Web服务选择问题.各用户对服务质量的需求被表示为偏好.提出的算法将能够综合考虑各个用户的偏好,使选出的服务尽可能满足大部分用户的偏好要求,并给出了一个实例说明该算法的应用.实验结果表明了算法的有效性.     

The JOURNEY active network model

Faster processors are quickly enabling a new class of computationally intensive applications that actively transform information flows. Performing such computation at the terminal end is contrary to current trends toward low-power terminal devices. Moreover, scalability and efficiency concerns are also encouraging service providers to outsource computation when operating under loaded conditions. To address the problem of deploying such applications, we introduce the JOURNEY network model, which provides computation as an integrated network service. Contrary to other distributed computing models, JOURNEY does not attempt to guarantee that a given computational job will indeed be completed. Instead, the JOURNEY model trades off such hard guarantees in favor of architectural simplicity, and hence scalability. In order for the JOURNEY model to be applicable to real-time multimedia flows, we introduce the notion of soft quality-of-service (QoS) that provides a probabilistic bound on the unprocessed packet rate (UPR). Based on this notion, we describe a packet-processing admission control algorithm that additionally takes into consideration a flow's real-time constraints. We also propose a computing router architecture that is based on cluster technology. This architecture can track technology advances in both routing and computing independently. We further present a motivating multimedia application that employs a semantically driven video transcoding service within the JOURNEY framework we implemented, and describe our experience along with performance measurements     

Design-Space Exploration for Block-Processing Based Temporal Partitioning of Run-Time Reconfigurable Systems

The reconfiguration capability of modern FPGA devices can be utilized to execute an application by partitioning it into multiple segments such that each segment is executed one after the other on the device. This division of an application into multiple reconfigurable segments is called temporal partitioning. We present an automated temporal partitioning technique for acyclic behavior level task graphs. To be effective, any behavior-level partitioning method should ensure that each temporal partition meets the underlying resource constraints. For this, a knowledge of the implementation cost of each task on the hardware should be known. Since multiple implementations of a task that differ in area and delay are possible, we perform design-space exploration to choose the best implementation of a task from among the available implementations.To overcome the high reconfiguration overhead of the current day FPGA devices, we propose integration of the temporal partitioning and design space exploration methodology with block-processing. Block-processing is used to process multiple blocks of data on each temporal partition so as to amortize the reconfiguration time. We focus on applications that can be represented as task graphs that have to be executed many times over a large set of input data. We have integrated block-processing in the temporal partitioning framework so that it also influences the design point selection for each task. However, this does not exclude usage of our system for designs for which block-processing is not possible. For both block-processing and non block-processing designs our algorithm selects the best possible design point to minimize the execution time of the design.We present an ILP-based methodology for the integrated temporal partitioning, design space exploration and block-processing technique that is solved to optimality for small sized design problems and in an iterative constraint satisfaction approach for large sized design problems. We demonstrate with extensive experimental results for the Discrete Cosine Transform (DCT) and random graphs the validity of our approach.     

Multiscale spatial temporal attention graph convolution network for skeleton-based anomaly behavior detection

Anomaly behavior detection plays a significant role in emergencies such as robbery. Although a lot of works have been proposed to deal with this problem, the performance in real applications is still relatively low. Here, to detect abnormal human behavior in videos, we propose a multiscale spatial temporal attention graph convolution network (MSTA-GCN) to capture and cluster the features of the human skeleton. First, based on the human skeleton graph, a multiscale spatial temporal attention graph convolution block (MSTA-GCB) is built which contains multiscale graphs in temporal and spatial dimensions. MSTA-GCB can simulate the motion relations of human body components at different scales where each scale corresponds to different granularity of annotation levels on the human skeleton. Then, static, globally-learned and attention-based adjacency matrices in the graph convolution module are proposed to capture hierarchical representation. Finally, extensive experiments are carried out on the ShanghaiTech Campus and CUHK Avenue datasets, the final results of the frame-level AUC/EER are 0.759/0.311 and 0.876/0.192, respectively. Moreover, the frame-level AUC is 0.768 for the human-related ShanghaiTech subset. These results show that our MSTA-GCN outperforms most of methods in video anomaly detection and we have obtained a new state-of-the-art performance in skeleton-based anomaly behavior detection.     

概率时态认知逻辑模型检测中三值抽象技术的研究

 为缓解概率时态认知逻辑模型检测中的状态空间爆炸问题,提出了概率时态认知逻辑的三值抽象技术.具体研究内容包括:定义抽象模型及模型上概率时态认知逻辑的三值语义,依据状态空间等价划分建立初始抽象模型,并证明抽象技术对概率时态认知逻辑的满足性保持关系;提出概率时态认知逻辑模型检测算法;依据初始模型检测的结果,给出利用最小证据和最小反例引导的抽象系统的求精过程.最后通过Dining Cryptographer协议说明了抽象技术的应用,及其在约简系统状态空间方面的效果.     

Analyzing Product-Form Stochastic Networks Via Factor Graphs and the Sum-Product Algorithm

A large number of stochastic networks including loss networks and certain queueing networks have product-form steady-state probabilities. However, for most practical networks, evaluating the system performance is a difficult task due to the presence of a normalization constant. We propose a new framework based on probabilistic graphical models to tackle this task. Specifically, we use factor graphs to model the stationary distribution of a network. For networks with arbitrary topology, we can apply efficient message-passing algorithms like the sum-product algorithm to compute the exact or approximate marginal distributions of all state variables and related performance measures such as blocking probabilities. Through extensive numerical experiments, we show that the sum-product algorithm returns very accurate blocking probabilities and greatly outperforms the reduced load approximation for loss networks with a variety of topologies. The factor graph model also provides a promising approach for analyzing product-form queueing networks.     

Maximum error modeling for fault-tolerant computation using maximum a posteriori (MAP) hypothesis

The application of current generation computing machines in safety-centric applications like implantable biomedical chips and automobile safety has immensely increased the need for reviewing the worst-case error behavior of computing devices for fault-tolerant computation. In this work, we propose an exact probabilistic error model that can compute the maximum error over all possible input space in a circuit-specific manner and can handle various types of structural dependencies in the circuit. We also provide the worst-case input vector, which has the highest probability to generate an erroneous output, for any given logic circuit. We also present a study of circuit-specific error bounds for fault-tolerant computation in heterogeneous circuits using the maximum error computed for each circuit. We model the error estimation problem as a maximum a posteriori (MAP) estimate [28] and [29], over the joint error probability function of the entire circuit, calculated efficiently through an intelligent search of the entire input space using probabilistic traversal of a binary Join tree using Shenoy-Shafer algorithm [20] and [21]. We demonstrate this model using MCNC and ISCAS benchmark circuits and validate it using an equivalent HSpice model. Both results yield the same worst-case input vectors and the highest percentage difference of our error model over HSpice is just 1.23%. We observe that the maximum error probabilities are significantly larger than the average error probabilities, and provides a much tighter error bounds for fault-tolerant computation. We also find that the error estimates depend on the specific circuit structure and the maximum error probabilities are sensitive to the individual gate failure probabilities.     

基于生成图的工作流多过程动态 时序一致性验证方法

 提出了基于生成图的多过程动态时序一致性验证方法.首先从多过程的时间工作流网构建生成图,以图形化方式表达实例可能经过的路径和时间信息.在动态检测时,依据已经完成活动对生成图进行部分更新,再利用图中节点相关信息进行时间约束的验证.该方法可以解决资源约束情况下多过程时序一致性动态验证问题,而且能定位模型中出问题的路径,指导用户进行工作流时序异常处理或优化工作流模型;另一方面,生成图可供多个时序约束进行验证使用,具有较好的可重用性.     

Root modulus constraints in autoregressive model estimation

The stability of autoregressive (AR) models is an important issue in many applications such as spectral estimation, simulation of EEG, and synthesis of speech. There are methods for AR parameter estimation that guarantee the stability of the model, that is, all roots of the characteristic polynomial of the model have moduli less than unity. However, in some situations, such as EEG simulation, the models that exhibit roots with almost unit moduli are difficult to use. In this paper we propose a method for estimating AR models that guarantees hyperstability, that is, the moduli of the roots are less than or equal to some arbitrary positive number. The method is based on an iterative minimization scheme in which the associated nonlinear constraints are linearized sequentially.     

基于时序约束分解的QoS感知的Web服务组合

基于时序约束的QoS感知的Web服务组合(TC-QSC)问题是在考虑时序约束的基础上寻找满足QoS约束或效用最大化的Web服务组合问题,受到了越来越多的关注.本文提出了一种时序约束分解方法,把施加于整个或部分工作流的时序约束分解为施加于每个活动的局部时序约束,从而将TC-QSC问题转换为一般的QoS感知的Web服务组合(QSC)问题,并通过过滤不满足局部时序约束的候选服务,一定程度上减小原问题的规模.这种时序约束分解过程主要依赖于工作流及其涉及的活动,而与各活动的候选服务关联不大,复杂度较低.实验测试了该方法的效果与时间开销,验证了其对于局部优选算法的必要性.     

基于角色的时态对象存取控制模型

以基于角色的存取控制模型RBAC₃为基础,提出一种新的时态对象存取控制模型TRBAC,讨论了模型的构成要素,体系结构,时态多重继承机制和存取控制方法.它支持时态用户、时态角色和时态客体及其层次结构,具有动态存取控制功能,在时态数据库和工作流系统等领域会得到广泛应用.     

Bayesian regularization and nonnegative deconvolution for room impulse response estimation

This paper proposes Bayesian Regularization And Nonnegative Deconvolution (BRAND) for accurately and robustly estimating acoustic room impulse responses for applications such as time-delay estimation and echo cancellation. Similar to conventional deconvolution methods, BRAND estimates the coefficients of convolutive finite-impulse-response (FIR) filters using least-square optimization. However, BRAND exploits the nonnegative, sparse structure of acoustic room impulse responses with nonnegativity constraints and L/sub 1/-norm sparsity regularization on the filter coefficients. The optimization problem is modeled within the context of a probabilistic Bayesian framework, and expectation-maximization (EM) is used to derive efficient update rules for estimating the optimal regularization parameters. BRAND is demonstrated on two representative examples, subsample time-delay estimation in reverberant environments and acoustic echo cancellation. The results presented in this paper show the advantages of BRAND in high temporal resolution and robustness to ambient noise compared with other conventional techniques.     

Automatic Generation of System Level Assertions from Transaction Level Models

We automatically generate assertions from Transaction Level Model (TLM) simulation traces. The generated assertions express design specifications in the form of linear temporal logic with quantitative temporal constraints [4]. We first generate the assertions without regard to the quantitative time constraints. They are mined in the form of frequent patterns in the simulation traces. We mine simulation traces using episode mining to identify frequent episodes comprising function calls and events. We then annotate the episodes with real time parameters to express quantitative time constraints among the function calls or events in the episode. When mining such TLM assertions, we employ symbolic execution to generalize the parameters and return values of function calls in the traces to help the mining engine generate high quality assertions. We have constructed a realistic AXI-based interconnection network platform that we demonstrate experimental results on. We show that our technique efficiently generates high quality performance and functional assertions on the AXI-based platform as well as a transaction level AMBA-based DMA controller. We demonstrate that episode mining is more scalable and able to generate a more compact set of high quality TLM assertions than previous efforts using sequential pattern mining. The number of generated assertions using episode mining can be reduced by up to 228 times, and the time interval between two events/function calls in each assertion is smaller than 50 time units.     

Multiple instance deep learning for weakly-supervised visual object tracking

Intelligently tracking objects with varied shapes, color, lighting conditions, and backgrounds is an extremely useful application in many HCI applications, such as human body motion capture, hand gesture recognition, and virtual reality (VR) games. However, accurately tracking different objects under uncontrolled environments is a tough challenge due to the possibly dynamic object parts, varied lighting conditions, and sophisticated backgrounds. In this work, we propose a novel semantically-aware object tracking framework, wherein the key is weakly-supervised learning paradigm that optimally transfers the video-level semantic tags into various regions. More specifically, give a set of training video clips, each of which is associated with multiple video-level semantic tags, we first propose a weakly-supervised learning algorithm to transfer the semantic tags into various video regions. The key is a MIL (Zhong et al., 2020) [1]-based manifold embedding algorithm that maps the entire video regions into a semantic space, wherein the video-level semantic tags are well encoded. Afterward, for each video region, we use the semantic feature combined with the appearance feature as its representation. We designed a multi-view learning algorithm to optimally fuse the above two types of features. Based on the fused feature, we learn a probabilistic Gaussian mixture model to predict the target probability of each candidate window, where the window with the maximal probability is output as the tracking result. Comprehensive comparative results on a challenging pedestrian tracking task as well as the human hand gesture recognition have demonstrated the effectiveness of our method. Moreover, visualized tracking results have shown that non-rigid objects with moderate occlusions can be well localized by our method.     

Probabilistic response‐time analysis for real‐time systems in body area sensor networks

Advances in real‐time system and wireless communication have led to the deployment of body area sensor networks (BASNs) for effective real‐time healthcare applications. Real‐time systems in BASNs tend increasingly to be probabilistic and mixed critical to meet stringent requirements on space, weight, and power consumption. Response‐time analysis is an important and challenging task for BASNs to provide some critical services. In this paper, we propose a request‐based compositional probabilistic response‐time analysis framework for probabilistic real‐time task models with fixed‐priority preemptive scheduling in BASNs. In this method, each probabilistic real‐time task is abstracted as a probabilistic request function. Rough response‐time distribution is computed first based on the cumulative request distribution and then exact response‐time distribution is obtained by refinement based on the request increase distribution. Our strategy can effectively improve performance by reducing repetitive computational overhead for the probabilistic response‐time analysis of all tasks in the system. Our evaluation demonstrates that our proposed method significantly outperforms the existing probabilistic response‐time analysis algorithm in terms of analysis duration. Copyright © 2015 John Wiley & Sons, Ltd.     

Distributed probabilistic routing for sensor network lifetime optimization

A probabilistic and distributed routing approach for multi-hop sensor network lifetime optimization is presented in this paper. In particular, each sensor self-adjusts their routing probabilities locally to their forwarders based on its neighborhood knowledge, while aiming at optimizing the overall network lifetime (defined as the elapsed time before the first node runs out of energy). The theoretical feasibility and a practical routing algorithm are presented. Specifically, a sufficient distributed condition regarding the neighborhood state for distributed probabilistic routing to achieve the optimal network lifetime is presented theoretically. Based on it, a distributed adaptive probabilistic routing (DAPR) algorithm, which considered both the transmission scheduling and the routing probability evolvement is developed. We prove quantitatively that DAPR could lead the routing probabilities of the distributed sensors to converge to an optimal state which optimizes the network lifetime. Further, when network dynamics happen, such as topology changes, DAPR can adjust the routing probabilities quickly to converge to a new state for optimizing the remained network lifetime. We presented the convergence speed of DAPR. Extensive simulations verified its convergence and near-optimal properties. The results also showed its quick adaptation to both the network topology and data rate dynamics.     

概率实时时态认知逻辑模型检测中抽象技术的研究

概率实时时态认知逻辑PTACTLK模型检测面临着与传统模型检测同样的挑战,即状态空间爆炸问题.抽象是缓解状态空间爆炸问题的最为有效的方法之一.为了缓解概率实时时态认知逻辑模型检测中的状态空间爆炸问题,我们给出了一种抽象技术:对于PTACTLK中的实时部分PTACTL,采用抽象离散时钟赋值,把概率实时解释系统的无限状态空间转化成有限形式;对于PTACTLK中的认知算子K,给出了抽象状态关于智体认知等价的定义.定义了概率实时解释系统的抽象模型,给出了抽象模型上概率实时时态认知逻辑的语义,并证明了由抽象技术演绎得到的抽象模型是原始模型的上近似.最后通过一个通信协议来说明抽象技术的有效性.     

Design, analysis, and evaluation of concurrent checking sortingnetworks

In this brief, we propose two new concurrent error-detection (CED) schemes for a class of sorting networks, e.g., odd-even transposition, bitonic, and perfect shuffle sorting networks. A probabilistic method is developed to analyze the fault coverage, and the hardware overhead is evaluated. We first propose a CED scheme by which all errors caused by single faults in a concurrent checking sorting network can be detected. This scheme is the first one available to use significantly less hardware overhead than duplication without compromising throughput. From this scheme, we develop another fault detection scheme which sharply reduces the hardware overhead (using an additional 10%~30% hardware) but still achieves virtually 1001 fault coverage