QUBLE: towards blending interactive visual subgraph search queries on large networks

In a previous paper, we laid out the vision of a novel graph query processing paradigm where instead of processing a visual query graph after its construction, it interleaves visual query formulation and processing by exploiting the latency offered by the gui to filter irrelevant matches and prefetch partial query results [8]. Our recent attempts at implementing this vision [8, 9] show significant improvement in system response time (srt) for subgraph queries. However, these efforts are designed specifically for graph databases containing a large collection of small or medium-sized graphs. In this paper, we propose a novel algorithm called quble (QUery Blender for Large nEtworks) to realize this visual subgraph querying paradigm on very large networks (e.g., protein interaction networks, social networks). First, it decomposes a large network into a set of graphlets and supergraphlets using a minimum cut-based graph partitioning technique. Next, it mines approximate frequent and small infrequent fragments (sifs) from them and identifies their occurrences in these graphlets and supergraphlets. Then, the indexing framework of [9] is enhanced so that the mined fragments can be exploited to index graphlets for efficient blending of visual subgraph query formulation and query processing. Extensive experiments on large networks demonstrate effectiveness of quble.     

A Learning to Rank framework applied to text-image retrieval

We present a framework based on a Learning to Rank setting for a text-image retrieval task. In Information Retrieval, the goal is to compute the similarity between a document and an user query. In the context of text-image retrieval where several similarities exist, human intervention is often needed to decide on the way to combine them. On the other hand, with the Learning to Rank approach the combination of the similarities is done automatically. Learning to Rank is a paradigm where the learnt objective function is able to produce a ranked list of images when a user query is given. These score functions are generally a combination of similarities between a document and a query. In the past, Learning to Rank algorithms were successfully applied to text retrieval where they outperformed baselines such as BM25 or TFIDF. This inspired us to apply our state-of-the-art algorithm, called OWPC (Usunier et al. 2009), to the text-image retrieval task. At this time, no benchmarks are available, therefore we present a framework for building one. The empirical validation of this algorithm is done on the dataset constructed through comparison of typical text-image retrieval similarities. In both cases, visual only and text and visual, our algorithm performs better than a simple baseline.     

基于分类模型的查询扩展方法

查询扩展作为查询优化的重要组成部分,对改善信息检索系统的性能起到了至关重要的作用.传统的伪相关反馈查询扩展方法虽然在一定程度上提高了检索性能,但选择的扩展词中会包含一部分与原查询不相关的词语,这对检索性能的提升产生了不利影响.提出了一种基于分类模型的查询扩展方法,该算法综合候选扩展词的统计信息和多种特征,采用朴素贝叶斯分类模型对初次得到的候选扩展词进行再次分类选择,进一步去除与查询词相关性小的扩展词.在TREC 2013数据集上的实验结果表明,提出的查询扩展方法能够有效提高用户查询的查准率和查全率.     

Testing for Structural Breaks at Unknown Time: A Steeplechase

This paper analyzes the role of common data problems when identifying structural breaks in small samples. Most notably, we survey small sample properties of the most commonly applied endogenous break tests developed by Brown et al. (J R Stat Soc B 37:149–163, 1975) and Zeileis (Stat Pap 45(1):123–131, 2004), Nyblom (J Am Stat Assoc 84(405):223–230, 1989) and Hansen (J Policy Model 14(4):517–533, 1992), and Andrews et al. (J Econ 70(1):9–38, 1996). Power and size properties are derived using Monte Carlo simulations. We find that the Nyblom test is on par with the commonly used F type tests in a small sample in terms of power. While the Nyblom test’s power decreases if the structural break occurs close to the margin of the sample, it proves far more robust to nonnormal distributions of the error term that are found to matter strongly in small samples although being irrelevant asymptotically for all tests that are analyzed in this paper.     

A New Nonlocal H 1 Model for Image Denoising

Following ideas of Kindermann et al. (Multiscale Model. Simul. 4(4):1091–1115, 2005) and Gilboa and Osher (Multiscale Model. Simul. 7:1005–1028, 2008) we introduce new nonlocal operators to interpret the nonlocal means filter (NLM) as a regularization of the corresponding Dirichlet functional. Then we use these nonlocal operators to propose a new nonlocal H ¹ model, which is (slightly) different from the nonlocal H ¹ model of Gilboa and Osher (Multiscale Model. Simul. 6(2):595–630, 2007; Proc. SPIE 6498:64980U, 2007). The key point is that both the fidelity and the smoothing term are derived from the same geometric principle. We compare this model with the nonlocal H ¹ model of Gilboa and Osher and the nonlocal means filter, both theoretically and in computer experiments. The experiments show that this new nonlocal H ¹ model also provides good results in image denoising and closer to the nonlocal means filter than the H ¹ model of Gilboa and Osher. This means that the new nonlocal operators yield a better interpretation of the nonlocal means filter than the nonlocal operators given in Gilboa and Osher (Multiscale Model. Simul. 7:1005–1028, 2008).     

A human factors experimental comparison of SQL and QBE

SQL and QBE are compared in the same operating environment, and the effects of query language type and other variables on user performance and satisfaction are studied. The experimental design combined a factorial design and a counterbalanced design in an effort to compare SQL and QBE. The results indicated that query language type affects user performance in paper and pencil testing, with QBE users having higher scores than SQL users. In contrast, in online testing, query language type had no effect on user performance. In addition, under certain conditions, query complexity had a significant effect on user performance and user satisfaction was influenced by query language type. Moreover, order of exposure impacted user performance on the basis of interaction with query language type, query complexity, and programming experience     

On the Power of Non-adaptive Learning Graphs

We introduce a notion of the quantum query complexity of a certificate structure. This is a formalization of a well-known observation that many quantum query algorithms only require the knowledge of the position of possible certificates in the input string, not the precise values therein. Next, we derive a dual formulation of the complexity of a non-adaptive learning graph and use it to show that non-adaptive learning graphs are tight for all certificate structures. By this, we mean that there exists a function possessing the certificate structure such that a learning graph gives an optimal quantum query algorithm for it. For a special case of certificate structures generated by certificates of bounded size, we construct a relatively general class of functions having this property. The construction is based on orthogonal arrays and generalizes the quantum query lower bound for the k-sum problem derived recently by Belovs and ?palek (Proceeding of 4th ACM ITCS, 323–328, 2013). Finally, we use these results to show that the learning graph for the triangle problem by Lee et al. (Proceeding of 24th ACM-SIAM SODA, 1486–1502, 2013) is almost optimal in the above settings. This also gives a quantum query lower bound for the triangle sum problem.     

A new spatial IP assignment method for IP-based wireless sensor networks

Wireless sensor networks (WSNs), one of the commercial wireless mesh networks (WMNs), are envisioned to provide an effective solution for sensor-based AmI (Ambient Intelligence) systems and applications. To enable the communications between AmI sensor networks and the most popular TCP/IP networks seamlessly, the best solution model is to run TCP/IP directly on WSNs (Mulligan et al. 2009; Hui and Culler 2008; Han and Mam 2007; Kim et al. 2007; Xiaohua et al. 2004; Dunkels et al. 2004; Dunkels et al. 2004; Dunkels 2001; Dunkels et al. 2004). In this case, an IP assignment method is required to assign each sensor node a unique IP address. SIPA (Dunkels et al. 2004) is the best known IP assignment method that uses spatial relations and locations of sensor nodes to assign their IP addresses. It has been applied in Contiki (Dunkels et al. 2004), a famous WSN operating system, to support the 6LowPAN protocol. In Chang et al. (2009), we proposed the SLIPA (Scan-Line IP Assignment) algorithm to improve the assignment success rate (ASR) obtained by SIPA. SLIPA can achieve a good ASR when sensor nodes are uniformly distributed. However, if sensor nodes are deployed by other distributions, the improvements would be limited. This paper proposes a new spatial IP assignment method, called SLIPA-Q (SLIPA with equal-quantity partition), to improve SLIPA. Experiments show that, by testing the proposed method 1,000 times with 1,000 randomly deployed sensor nodes, the average ASR obtained by SLIPA-Q is over two times of that obtained by SLIPA. Under the same 88% ASR, the average numbers of sensor nodes those can be successfully assigned by SLIPA-Q, SLIPA, and SIPA are 950, 850, and 135, respectively. Comparing to previous spatial IP assignment methods, SLIPA-Q can achieve dramatic improvements in ASR for assigning IP addresses to a large set of sensor nodes.     

云计算下支持语义的可搜索加密方法研究

传统的云计算下的可搜索加密算法没有对查询关键词进行语义扩展,导致了用户查询意图与返回结果存在语义偏差,并且对检索结果的相关度排序不够合理,无法满足用户对智能搜索的需求。对此,提出了一种支持语义的可搜索加密方法。该方法利用本体知识库实现了用户查询的语义拓展,并通过语义相似度来控制扩展词的个数,防止因拓展词过多影响检索的精确度。同时,该方法利用文档向量、查询向量分块技术构造出对应的标记向量,以过滤无关文档,并在查询-文档的相似度得分中引入了语义相似度、关键词位置加权评分及关键词-文档相关度等影响因子,实现了检索结果的有效排序。实验结果表明,该方法在提高检索效率的基础上显著改善了检索结果的排序效果,提高了用户满意度。     

A linear system form solution to compute the local space average color

In this document, we present an alternative to the method introduced by Ebner (Pattern Recognit 60–67, 2003; J Parallel Distrib Comput 64(1):79–88, 2004; Color constancy using local color shifts, pp 276–287, 2004; Color Constancy, 2007; Mach Vis Appl 20(5):283–301, 2009) for computing the local space average color. We show that when the problem is framed as a linear system and the resulting series is solved, there is a solution based on LU decomposition that reduces the computing time by at least an order of magnitude.     

Reverse engineering database queries from examples: State-of-the-art,challenges, and research opportunities

With the popularization of data access and usage, an increasing number of users without expert knowledge of databases is required to perform data interactions. Often, these users face the challenges of writing and reformulating database queries, which consume a considerable amount of time and frequently yield unsatisfactory results. To facilitate this human–database interaction, researchers have investigated the Query By Example (QBE) paradigm in which database queries are (semi) automatically discovered from data examples given by users. This paradigm allows non-database experts to formulate queries without relying on complex query languages. In this context, this work aims to present a systematic review of the recent developments, open challenges, and research opportunities of the QBE reported in the literature. This work also describes strategies employed to leverage efficient example acquisition and query reverse engineering. The obtained results show that recent research developments have focused on enhancing the expressiveness of produced queries, minimizing user interaction, and enabling efficient query learning in the context of data retrieval, exploration, integration, and analytics. Our findings indicate that future research should concentrate efforts to provide innovative solutions to the challenges of improving controllability and transparency, considering diverse user preferences in the processes of learning personalized queries, ensuring data quality, and improving the support of additional SQL features and operators.     

Multi-agent based middleware for protecting privacy in IPTV content recommender services

This work presents our efforts to design an agent based middleware that enables the end-users to use IPTV content recommender services without revealing their sensitive preference data to the service provider or any third party involved in this process. The proposed middleware (called AMPR) preserves users’ privacy when using the recommender service and permits private sharing of data among different users in the network. The proposed solution relies on a distributed multi-agent architecture involving local agents running on the end-user set up box to implement a two stage concealment process based on user role in order to conceal the local preference data of end-users when they decide to participate in recommendation process. Moreover, AMPR allows the end-users to use P3P policies exchange language (APPEL) for specifying their privacy preferences for the data extracted from their profiles, while the recommender service uses platform for privacy preferences (P3P) policies for specifying their data usage practices. AMPR executes the first stage locally at the end user side but the second stage is done at remote nodes that can be donated by multiple non-colluding end users that we will call super-peers Elmisery and Botvich (2011a, b, c); or third parties mash-up service Elmisery A, Botvich (2011a, b). Participants submit their locally obfuscated profiles anonymously to their local super-peer who collect and mix these preference data from multiple participants. The super-peer invokes AMPR to perform global perturbation process on the aggregated preference data to ensure a complete concealment of user’s profiles. Then, it anonymously submits these aggregated profiles to a third party content recommender service to generate referrals without breaching participants’ privacy. In this paper, we also provide an IPTV network scenario and experimentation results. Our results and analysis shows that our two-stage concealment process not only protect the users’ privacy, but also can maintain the recommendation accuracy     

Comments and further improvements on “pth moment stability of stochastic neural networks with Markov volatilities”

In a very recent paper, Peng and Liu (Neural Comput Appl 20:543–547, 2011) investigated the pth moment stability of the stochastic Grossberg–Hopfield neural networks with Markov volatilities by Mao et al. (Bernoulli 6:73–90, 2000, Theorem 4.1). We should point out that Mao et al. (Bernoulli 6:73–90, 2000, Theorem 4.1) investigated the pth moment exponentially stable for a class of stochastic dynamical systems with constant delay; however, this theorem cannot apply to the case of variable time delays. It is also worthy to emphasize that Peng and Liu (Neural Comput Appl 20:543–547, 2011) discussed by Mao et al. (Bernoulli 6:73–90, 2000, Theorem 4.1) the pth moment exponentially stable for the Grossberg–Hopfield neural networks with variable delays, and therefore, there are some gaps between Peng and Liu (Neural Comput Appl 20:543–547, 2011, Theorem 1) and Mao et al. (Bernoulli 6:73–90, 2000, Theorem 4.1). In this paper, we fill up this gap. Moreover, a numerical example is also provided to demonstrate the effectiveness and applicability of the theoretical results.     

Event retrieval in video archives using rough set theory and partially supervised learning

This paper develops a query-by-example method for retrieving shots of an event (event shots) using example shots provided by a user. The following three problems are mainly addressed. Firstly, event shots cannot be retrieved using a single model as they contain significantly different features due to varied camera techniques, settings and so forth. This is overcome by using rough set theory to extract multiple classification rules with each rule specialized to retrieve a portion of event shots. Secondly, since a user can only provide a small number of example shots, the amount of event shots retrieved by extracted rules is inevitably limited. We thus incorporate bagging and the random subspace method. Classifiers characterize significantly different event shots depending on example shots and feature dimensions. However, this can result in the potential retrieval of many unnecessary shots. Rough set theory is used to combine classifiers into rules which provide greater retrieval accuracy. Lastly, counter example shots, which are a necessity for rough set theory, are not provided by the user. Hence, a partially supervised learning method is used to collect these from shots other than example shots. Counter example shots, which are as similar to example shots as possible, are collected because they are useful for characterizing the boundary between event shots and the remaining shots. The proposed method is tested on TRECVID 2009 video data.     

pth Moment Exponential Stability of Stochastic Recurrent Neural Networks with Markovian Switching

This paper investigates the problem of the pth moment exponential stability for a class of stochastic recurrent neural networks with Markovian jump parameters. With the help of Lyapunov function, stochastic analysis technique, generalized Halanay inequality and Hardy inequality, some novel sufficient conditions on the pth moment exponential stability of the considered system are derived. The results obtained in this paper are completely new and complement and improve some of the previously known results (Liao and Mao, Stoch Anal Appl, 14:165–185, 1996; Wan and Sun, Phys Lett A, 343:306–318, 2005; Hu et al., Chao Solitions Fractals, 27:1006–1010, 2006; Sun and Cao, Nonlinear Anal Real, 8:1171–1185, 2007; Huang et al., Inf Sci, 178:2194–2203, 2008; Wang et al., Phys Lett A, 356:346–352, 2006; Peng and Liu, Neural Comput Appl, 20:543–547, 2011). Moreover, a numerical example is also provided to demonstrate the effectiveness and applicability of the theoretical results.     

Some types of filters in residuated lattices

In this paper, inspired by some types of $BL$ -algebra filters (deductive systems) introduced in Haveshki et al. (Soft Comput 10:657–664, 2006), Kondo and Dudek (Soft Comput 12:419–423, 2008) and Turunen (Arch Math Log 40:467–473, 2001), we defined residuated lattice versions of them and study them in connection with Van Gasse et al. (Inf Sci 180(16):3006–3020, 2010), Lianzhen and Kaitai (Inf Sci 177:5725–5738, 2007), Zhu and Xu (Inf Sci 180:3614–3632, 2010). Also we consider some relations between these filters and quotient residuated lattice that are constructed via these filters.     

基于粗糙集的个性化Web搜索系统

本文提出了一种基于粗糙集理论的个性化web搜索系统。用户偏好文件中对关键字进行分组以表示用户兴趣类别。利用粗糙集理论处理自然语言的内在含糊性，根据用户偏好文件对查询条件进行扩展。搜索组件使用扩展后的查询条件搜索相关信息。为了进一步排除不相关信息，排序组件计算查询条件和搜索结果之间的相似程度，根据计算值对搜索结果进行排序。与传统搜索引擎进行了比较，实验结果表明，该系统有效地提高了搜索结果的精度，满足了用户的个性化需求。     

A Splitting Algorithm for Image Segmentation on Manifolds Represented by the Grid Based Particle Method

We propose a numerical approach to solve variational problems on manifolds represented by the grid based particle method (GBPM) recently developed in Leung et al. (J. Comput. Phys. 230(7):2540–2561, 2011), Leung and Zhao (J. Comput. Phys. 228:7706–7728, 2009a, J. Comput. Phys. 228:2993–3024, 2009b, Commun. Comput. Phys. 8:758–796, 2010). In particular, we propose a splitting algorithm for image segmentation on manifolds represented by unconnected sampling particles. To develop a fast minimization algorithm, we propose a new splitting method by generalizing the augmented Lagrangian method. To efficiently implement the resulting method, we incorporate with the local polynomial approximations of the manifold in the GBPM. The resulting method is flexible for segmentation on various manifolds including closed or open or even surfaces which are not orientable.     

Comparison Between Reduced Basis and Stochastic Collocation Methods for Elliptic Problems

The stochastic collocation method (Babu?ka et al. in SIAM J Numer Anal 45(3):1005–1034, 2007; Nobile et al. in SIAM J Numer Anal 46(5):2411–2442, 2008a; SIAM J Numer Anal 46(5):2309–2345, 2008b; Xiu and Hesthaven in SIAM J Sci Comput 27(3):1118–1139, 2005) has recently been applied to stochastic problems that can be transformed into parametric systems. Meanwhile, the reduced basis method (Maday et al. in Comptes Rendus Mathematique 335(3):289–294, 2002; Patera and Rozza in Reduced basis approximation and a posteriori error estimation for parametrized partial differential equations Version 1.0. Copyright MIT, http://augustine.mit.edu, 2007; Rozza et al. in Arch Comput Methods Eng 15(3):229–275, 2008), primarily developed for solving parametric systems, has been recently used to deal with stochastic problems (Boyaval et al. in Comput Methods Appl Mech Eng 198(41–44):3187–3206, 2009; Arch Comput Methods Eng 17:435–454, 2010). In this work, we aim at comparing the performance of the two methods when applied to the solution of linear stochastic elliptic problems. Two important comparison criteria are considered: (1), convergence results of the approximation error; (2), computational costs for both offline construction and online evaluation. Numerical experiments are performed for problems from low dimensions $O(1)$ to moderate dimensions $O(10)$ and to high dimensions $O(100)$ . The main result stemming from our comparison is that the reduced basis method converges better in theory and faster in practice than the stochastic collocation method for smooth problems, and is more suitable for large scale and high dimensional stochastic problems when considering computational costs.