A hash-based index for processing frequent updates and continuous location-based range queries

Knowledge and Information Systems - Wide range of location-based services and sensors in GIS have to manage moving objects that change their position with respect to time. These applications...     

Efficient editing of solid models by exploiting structural and spatial locality

Computer aided design systems based on solid modellers must provide fast visual feedback to users when objects are edited. This implies that boundary representations must be updated rapidly, because displays typically are generated in current-generation modellers from face, edge and vertex data.This paper describes algorithms for updating a boundary representation when an object's constructive solid geometry (CSG) representation is edited. The algorithms exploit the structural (representational) locality inherent in most object modifications by taking advantage of previously computed boundary representations for (sub-) objects that are not affected by the editing operations. They also exploit spatial locality by re-computing boundaries only within the spatial region where changes can occur. The algorithms are efficient, and are guaranteed to produce valid solids because they are based on CSG.     

Object tracking by exploiting adaptive region-wise linear subspace representations and adaptive templates in an iterative particle filter

Aiming at tracking visual objects under harsh conditions, such as partial occlusions, illumination changes, and appearance variations, this paper proposes an iterative particle filter incorporated with an adaptive region-wise linear subspace (RWLS) representation of objects. The iterative particle filter employs a coarse-to-fine scheme to decisively generate particles that convey better hypothetic estimates of tracking parameters. As a result, a higher tracking accuracy can be achieved by aggregating the good hypothetic estimates from particles. Accompanying with the iterative particle filter, the RWLS representation is a special design to tackle the partial occlusion problem which often causes tracking failure. Moreover, the RWLS representation is made adaptive by exploiting an efficient incremental updating mechanism. This incremental updating mechanism can adapt the RWLS to gradual changes in object appearances and illumination conditions. Additionally, we also propose the adaptive mechanism to continuously adjust the object templates so that the varying appearances of tracked objects can be well handled. Experimental results demonstrate that the proposed approach achieves better performance than other related prior arts.     

Block compressed sampling of image signals by saliency based adaptive partitioning

Multimedia Tools and Applications - In recent years, block compressed sampling (BCS) has emerged as a considerable attractive sampling technology for image acquisition. However, the general BCS...     

Parallelizing skyline queries over uncertain data streams with sliding window partitioning and grid index

Skyline query processing over uncertain data streams has attracted considerable attention in database community recently, due to its importance in helping users make intelligent decisions over complex data in many real applications. Although lots of recent efforts have been conducted to the skyline computation over data streams in a centralized environment typically with one processor, they cannot be well adapted to the skyline queries over complex uncertain streaming data, due to the computational complexity of the query and the limited processing capability. Furthermore, none of the existing studies on parallel skyline computation can effectively address the skyline query problem over uncertain data streams, as they are all developed to address the problem of parallel skyline queries over static certain data sets. In this paper, we formally define the parallel query problem over uncertain data streams with the sliding window streaming model. Particularly, for the first time, we propose an effective framework, named distributed parallel framework to address the problem based on the sliding window partitioning. Furthermore, we propose an efficient approach (parallel streaming skyline) to further optimize the parallel skyline computation with an optimized streaming item mapping strategy and the grid index. Extensive experiments with real deployment over synthetic and real data are conducted to demonstrate the effectiveness and efficiency of the proposed techniques.     

Stochastic distributed data stream partitioning using task locality: design,implementation, and optimization

The Journal of Supercomputing - Distributed stream processing engines (DSPEs) provide stream partitioning methods for distributing messages to tasks deployed in the distributed environment for...     

Design and analysis of a demand adaptive and locality aware streaming media server cluster

The wide availability of broadband networking technologies such as cable modems and DSL coupled with the growing popularity of the Internet has led to a dramatic increase in the availability and the use of online streaming media. With the “last mile” network bandwidth no longer a constraint, the bottleneck for video streaming has been pushed closer to the server. Streaming high-quality audio and video to a myriad of clients imposes significant resource demands on the server. In this work, we propose a demand adaptive and locality aware (DALA) clustered media server architecture that can dynamically allocate resources to adapt to changing demand and also maximize the number of clients serviced by the server cluster. Moreover, our design exploits temporal locality among requests by dispatching newly arriving requests to servers that are already servicing prior requests for those objects, thereby extracting the benefits of locality. We explore the efficacy of the DALA clustered architecture through both performance models and simulations. Evaluation of the models and simulation results show that DALA exhibits significant performance gains when compared to static schemes. Furthermore, our simulation results show that DALA is highly adaptive, and has a low system overhead. Our results demonstrate that DALA is a simple, yet effective approach for designing clustered media servers.     

Yoda,an adaptive soft classification model: content-based similarity queries and beyond

Abstract. Providing a customized result set based upon a user preference is the ultimate objective of many content-based image retrieval systems. There are two main challenges in meeting this objective: First, there is a gap between the physical characteristics of digital images and the semantic meaning of the images. Secondly, different people may have different perceptions on the same set of images. To address both these challenges, we propose a model, named Yoda, that conceptualizes content-based querying as the task of soft classifying images into classes. These classes can overlap, and their members are different for different users. The “soft” classification is hence performed for each and every image feature, including both physical and semantic features. Subsequently, each image will be ranked based on the weighted aggregation of its classification memberships. The weights are user-dependent, and hence different users would obtain different result sets for the same query. Yoda employs a fuzzy-logic based aggregation function for ranking images. We show that, in addition to some performance benefits, fuzzy aggregation is less sensitive to noise and can support disjunctive queries as compared to weighted-average aggregation used by other content-based image retrieval systems. Finally, since Yoda heavily relies on user-dependent weights (i.e., user profiles) for the aggregation task, we utilize the users' relevance feedback to improve the profiles using genetic algorithms (GA). Our learning mechanism requires fewer user interactions, and results in a faster convergence to the user's preferences as compared to other learning techniques. Correspondence to: Y.-S. Chen (E-mail: yishinc@usc.edu) This research has been funded in part by NSF grants EEC-9529152 (IMSC ERC) and IIS-0082826, NIH-NLM R01-LM07061, DARPA and USAF under agreement nr. F30602-99-1-0524, and unrestricted cash gifts from NCR, Microsoft, and Okawa Foundation.     

利用自适应相位分区和边缘位置约束的直线提取新方法

由于利用边缘的梯度相位和位置提取直线容易受到噪声干扰,从而导致直线断裂。为了增强直线提取算法的抗噪性能,首先通过定量分析直线边缘梯度相位和位置分布的统计特性,建立了边缘连接的自适应相位分区和位置约束准则;然后基于两类新准则,提出了一种直线提取新方法。该方法首先采用Canny算子检测边缘;然后初步连接短直线,并估计边缘连接的约束准则参数;最后通过“相位准则连接”与“位置准则连接”模式并行交互执行来提取直线。实验结果表明,该方法提取的直线完整、断裂少、准确度高。     

An adaptive reversible data hiding scheme based on prediction error histogram shifting by exploiting signed-digit representation

A prediction error histogram shifting (PEHS)-based reversible data hiding scheme is proposed in this paper. A novel representation for the secret stream, called signed-digit representation, is proposed to improve the image quality. The secret binary stream is first converted into a signed-digit stream, which results in a high occurrence of ‘0’. Meanwhile, a block-wise-based prediction is performed on the original image to generate prediction errors, which lead to a sharp prediction error histogram. Then, the converted signed-digit stream is embedded into the prediction errors according to the improved histogram shifting (HS)-based scheme with multiple selected peak points, resulting in an adaptive embedding capacity. The experimental results validate that the proposed scheme outperforms state-of-the-art schemes in terms of embedding capacity while maintaining a good image quality.

     

Dual adaptive alignment and partitioning network for visible and infrared cross-modality person re-identification

Visible and infrared person re-identification (VI-ReID) describes the task of matching the images of a person, captured by visible-light and infrared cameras; this is a particular challenge in night time surveillance applications. Existing cross-modality recognition studies have been conducted mainly with a focus on learning the global and shareable feature representation of pedestrians to handle cross-modality discrepancies. However, the global features of pedestrian images cannot solve the unaligned image pairs efficiently, particularly when encountering the human appearance or posture misalignment caused by inaccurate pedestrian detection boxes. To mitigate the impact of these problems, we propose an end-to-end dual alignment and partitioning network to simultaneously learn global and local modal invariant features of pedestrians. First, we use two adaptive spatial transform modules to align the visible and infrared input images. Subsequently, the aligned image is divided horizontally, and the features of each local block are extracted. Then, we fuse these local features with global features. To alleviate the differences between heterogeneous modals and learn the common feature representation of heterogeneous modals, we map the features of heterogeneous modes into the same feature embedding space. Finally, we use the combination of identity loss and weighted regularized TriHard loss to improve the recognition accuracy. Extensive experimental results on two cross-modality datasets, RegDB and SYSU-MM01, demonstrate the superiority of the proposed method over other existing state-of-the-art methods.

     

自适应分块的医学图像混沌加解密算法

针对现有基于混沌的医学图像加密算法未考虑图像纹理特征的不足,提出了一种自适应分块的医学图像混沌加解密算法。首先利用2D Sine Logistic混沌系统生成两个具有良好混沌特性的安全序列;然后将图像分成固定尺寸的图像块,并计算图像块的最大像素差和方差,根据设定的阈值将图像块划分成纹理平滑块和纹理复杂块;最后利用混沌序列1对平滑块进行密文反馈加密,利用混沌序列2对复杂块进行明文反馈加密,得到加密后的图像。算法有效地考虑了图像块的纹理特性,优化了混沌加密算法,提高了医学图像的加解密效率。实验仿真结果表明,提出的算法具有高的安全性和加解密效率,相比现有方法加解密速度提高1倍左右。算法适用于大数据量的医学图像实时加解密。     

Partitioned encryption and achieving simultaneity by partitioning

We present a method called Partitioned Encryption whose main property is its simplicity. It is an extension of Probabilistic Public-Key Encryption, which can be used in designing cryptographic protocols and can be applied to distributed problem solving. We also give a modification of Secret Sharing called Partitioned Secret Sharing. We demonstrate the power of Partitioned Encryption: combining it with the partitioning of the user set gives a solution scheme for ‘Verifiable Secret Sharing’ and ‘Simultaneous Broadcast in the Presence of Faults’, which are important primitives of fault-tolerant distributed computing introduced by Chor, Goldwasser, Micali and Awerbuch (1985). The scheme is fully polynomial, simple, and efficient in terms of communication rounds. The basic partitioning methods are suggested as general tools for distributed computing, which are easy to implement and analyze.     

Advancing content-based image retrieval by exploiting image color and region features

In this paper, we propose a novel system that strives to achieve advanced content-based image retrieval using seamless combination of two complementary approaches: on the one hand, we propose a new color-clustering method to better capture color properties of the original images; on the other hand, expecting that image regions acquired from the original images inevitably contain many errors, we make use of the available erroneous, ill-segmented image regions to accomplish the object-region-based image retrieval. We also propose an effective image-indexing scheme to facilitate fast and efficient image matching and retrieval. The carefully designed experimental evaluation shows that our proposed image retrieval system surpasses other methods under comparison in terms of not only quantitative measures, but also image retrieval capabilities.     

Facial action unit recognition by exploiting their dynamic and semantic relationships

A system that could automatically analyze the facial actions in real time has applications in a wide range of different fields. However, developing such a system is always challenging due to the richness, ambiguity, and the dynamic nature of facial actions. Although a number of research groups attempt to recognize facial action units (AUs) by either improving facial feature extraction techniques, or the AU classification techniques, these methods often recognize AUs or certain AU combinations individually and statically, ignoring the semantic relationships among AUs and the dynamics of AUs. Hence, these approaches cannot always recognize AUs reliably, robustly, and consistently.In this paper, we propose a novel approach that systematically accounts for the relationships among AUs and their temporal evolutions for AU recognition. Specifically, we use a dynamic Bayesian network (DBN) to model the relationships among different AUs. The DBN provides a coherent and unified hierarchical probabilistic framework to represent probabilistic relationships among various AUs and to account for the temporal changes in facial action development. Within our system, robust computer vision techniques are used to obtain AU measurements. And such AU measurements are then applied as evidence to the DBN for inferring various AUs. The experiments show that the integration of AU relationships and AU dynamics with AU measurements yields significant improvement of AU recognition, especially for spontaneous facial expressions and under more realistic environment including illumination variation, face pose variation, and occlusion.     

基于语法和语义结合的源代码精确搜索方法

针对在编写软件、复用源代码的过程中仅依靠关键词无法精准搜索到适用源代码的问题,提出一种将语法和语义结合的源代码精准搜索方法。首先依据源代码语法语义的客观和唯一性,增加语法结构和"输入/输出"语义作为用户录入请求的一部分,并规范了具体的请求格式;然后在此基础上分别设计源代码语法匹配算法、"输入/输出"语义匹配算法、关键词兼容匹配,以及源代码搜索结果可信度计算算法;最后综合上述算法实现对源代码的精准搜索。测试结果表明：与单纯的关键词搜索相比,提出的方法对搜索的平均排序倒数（MRR）有超过62%的提升,有助于实现源代码的精准搜索。     

Optimization problem and efficient partitioning algorithm for transitions to finer-scale models in adaptive resolution simulation of articulated biopolymers

Adaptive coarse graining of highly complex biomolecular systems is conducted by either imposing constraints on the system model to generate a coarser model, or releasing certain constraints from it to create a higher fidelity one. This paper addresses some of the challenges of transitions to finer-scale models. It is shown that the kinetic energy associated with ignored modes of motion during the coarse graining process must be estimated and put back into the simulation. As such, unlike real mechanical systems, transitioning back to a finer biomolecular model may result in an infinite number of solutions. Herein, an optimization-based approach subject to the satisfaction of impulse–momentum balance and desired kinetic energy is presented to arrive at a physically meaningful state of the system immediately after the transition. In order to reduce the cost of formulating and solving this optimization problem, generalized velocities of the system are partitioned into two categories: independent joint velocities associated with the released joints, and dependent joint velocities associated with the rest of the joints. This paper further develops a divide-and-conquer algorithm to efficiently perform this partitioning process and express dependent velocities in terms of independent ones. The presented method is highly parallelizable and avoids the construction of the mass and Jacobian matrices of the entire system, resulting in a significant improvement in computational efficiency.     

融合显/隐式反馈的社会化协同排序推荐算法

传统的基于评分预测的社会化协同过滤推荐算法存在预测值与真实排序不匹配的固有缺陷,而基于排序预测的社会化协同排序推荐算法更符合真实的应用场景。然而,现有的大多数基于排序预测的社会化协同排序推荐算法要么仅仅关注显式反馈数据,要么仅仅关注隐式反馈数据,没有充分挖掘这些数据的价值。为充分挖掘用户的社交网络和推荐对象的显/隐式评分信息,同时克服基于评分预测的社会化协同过滤推荐算法存在的固有缺陷,在xCLiMF模型和TrustSVD模型基础上,提出一种新的融合显/隐式反馈的社会化协同排序推荐算法SPR_SVD++。该算法同时挖掘用户评分矩阵和社交网络矩阵中的显/隐式信息,并优化排序学习的评价指标预期倒数排名（ERR）。在真实数据集上的实验结果表明,采用归一化折损累计增益（NDCG）和ERR作为评价指标,SPR_SVD++算法均优于最新的TrustSVD、MERR_SVD++和SVD++算法。可见SPR_SVD++算法性能好、可扩展性强,在互联网信息推荐领域有很好的应用前景。     

Reducing cache and TLB power by exploiting memory region and privilege level semantics

The L1 cache in today’s high-performance processors accesses all ways of a selected set in parallel. This constitutes a major source of energy inefficiency: at most one of the N fetched blocks can be useful in an N-way set-associative cache. The other N-1 cachelines will all be tag mismatches and subsequently discarded.We propose to eliminate unnecessary associative fetches by exploiting certain software semantics in cache design, thus reducing dynamic power consumption. Specifically, we use memory region information to eliminate unnecessary fetches in the data cache, and ring level information to optimize fetches in the instruction cache. We present a design that is performance-neutral, transparent to applications, and incurs a space overhead of mere 0.41% of the L1 cache.We show significantly reduced cache lookups with benchmarks including SPEC CPU, SPECjbb, SPECjAppServer, PARSEC, and Apache. For example, for SPEC CPU 2006, the proposed mechanism helps to reduce cache block fetches from the data and instruction caches by an average of 29% and 53% respectively, resulting in power savings of 17% and 35% in the caches, compared to the aggressively clock-gated baselines.