Data-clustering task can be considered as the most important unsupervised learning algorithms. For about all clustering algorithms, finding the Nearest Neighbors of a point within a certain radius${r}$(NN-${r}$), is a critical task. For a high-dimensional dataset, this task becomes too time consuming. This article proposes a simple dimensionality reduction (DR) technique. For point${p}$in${d}$-dimensional space, it produces point$p^{\prime }$in$d^{\prime }$-dimensional space, where$d^{\prime } < < {d}$. In addition, for any pair of points${p}$and${q}$, and their maps$p^{\prime }$and$q^{\prime }$in the target space, it is proved that$|p, q| > |p^{\prime }, q^{\prime }|$is preserved, where$|,|$used to denote the Euclidean distance between a pair of points. This property can speed up finding NN-${r}$. For a certain radius${r}$, and a pair of points${p}$and${q}$, whenever$|p^{\prime }, q^{\prime }| > r$, then${q}$can not be in NN-${r}$of${p}$. Using this trick, the task of finding the NN-${r}$is speeded up. Then, as a case study, it is applied to accelerate the${k}$-means, one of the most famous unsupervised learning algorithms, where it can automatically determine the$d^{\prime }$. The proposed NN-${r}$method and the accelerated${k}$-means are compared with recent state-of-the-arts, and both yield favorable results.

Verifiable secret sharing (VSS) is one of the basic problems in the theory of distributed cryptography and has an important role in secure multiparty computation. In this case, it is tried to share a confidential data as secret, between multiple nodes in a distributed system, in the presence of an active adversary that can destroy some nodes, such that the secret can be reconstructed with the participation of certain size of honest nodes. A dynamic adversary can change its corrupted nodes among the protocol. So far, there is not a formal definition and there are no protocols of dynamic adversaries in VSS context. Also, another important question is, would there exist a protocol to share a secret with a static adversary with at most 1 broadcast round? In this paper, we provide a formal definition of the dynamic adversary. The simulation results prove the efficiency of the proposed protocol in terms of the runtime, the memory usage, and the number of message exchanges. We show that the change period of the dynamic adversary could not happen in less than 4 rounds in order to have a perfectly secure VSS, and then we establish a protocol to deal with this type of adversary. Also, we prove that the lower bound of broadcast complexity for the static adversary is (2,0)-broadcast rounds.

Conflict is one of the important factors affecting user satisfaction and trust in smart environments, yet conflict modeling in mixed initiative smart environments has not been sufficiently explored. Most of the existing literature on conflict in smart homes are centered on conflicts between users. Although research has shown that about 75% of conflicts are between users and system [1], only a few studies have considered user-system conflicts in smart homes. The aim of this article is to empirically propose both a definition and a run-time detection method for conflicts between users and smart home systems. Our empirical study is based on conflict sample scenarios collected from 163 users. Using clustering on these scenarios, we form an empirical definition of user-system conflict in smart homes. We also propose two functions that characterize each class of the collected scenarios, and we detect conflicts from this characterization. Our conflict detection model could help users achieve a more satisfactory experience in smart homes. Moreover, the model can offer benefits for system developers to design and deploy more reliable smart homes.

This paper presents a novel algorithm to estimate spatio-temporal noise variance in videos. The algorithm uses mutual information from the spatial and temporal noise statistics to detect homogeneous blocks in a video frame and estimate the noise. The experimental results show the accuracy and robustness of the algorithm over videos with low to high spatial and temporal complexities, and varying levels of noise power and noise correlations.

Assessment of architectural evolution is a challenge and plays a significant role in system evolution management. Although the evolution rules of the software architecture are defined by some expert engineers or architects, there is no guarantee that applying them will end to the desired change. So, having a reliable assessment technique promotes the overall accuracy and quality of the evolution process. Compliance checking with expert-defined rules is a well-known assessment approach that can be applied in architectural evolution. In this paper, an approach is proposed for compliance checking of evolution processes. To this end, evolution paths and processes are modeled by Reo coordination language and compliance checking is automatically performed by model checking of the Reo circuits. To demonstrate the applicability of our approach, we show how it can be applied to a real-world architecture evolution problem.

Assessing the perceptual quality of videos is critical for monitoring and optimizing video processing pipelines. In this paper, we focus on predicting the perceptual quality of videos distorted by noise. Existing video quality metrics are tuned for "white", i.e., spatially uncorrelated noise. However, white noise is very rare in real videos. Based on our analysis of the noise correlation patterns in a broad and comprehensive video set, we build a video database that simulates the commonly encountered noise characteristics. Using the database, we develop a perceptual quality assessment algorithm that explicitly incorporates the noise correlations. Experimental results show that, for videos with spatially correlated noises, the proposed algorithm presents high accuracy in predicting perceptual qualities.

This paper introduces a novel system for automatic detection and height estimation of buildings with polygonal shape roofs in singular satellite images. The system is capable of detecting multiple flat polygonal buildings with no angular constraints or shape priors. The proposed approach employs image primitives such as lines, and line intersections, and examines their relationships with each other using a graph-based search to establish a set of rooftop hypotheses. The height (mean height from rooftop edges to the ground) of each rooftop hypothesis is estimated using shadows and acquisition geometry. The potential ambiguities in identification of shadows in an image and the uncertainty in identifying true shadows of a building have motivated for a fuzzy logic-based approach that estimates buildings heights according to the strength of shadows and the overlap between identified shadows in the image and expected shadows according to the building profile. To reduce the time complexity of the implemented system, a maximum number of eight sides for polygonal rooftops is assumed. Promising experimental results verify the effectiveness of the presented system with overall mean shape accuracy of 94% and mean height error of 0.53 m on QuickBird satellite (0.6 m/pixel) imageries.

This paper presents an automatic point matching algorithm for establishing accurate match correspondences in two or more images. The proposed algorithm utilizes a group of feature points to explore their geometrical relationship in a graph arrangement. The algorithm starts with a set of matches (including outliers) between the two images. A set of nondirectional graphs is then generated for each feature and its K nearest matches (chosen from the initial set). Using the angular distances between edges that connect a feature point to its K nearest neighbors in the graph, the algorithm finds a graph in the second image that is similar to the first graph. In the case of a graph including outliers, the algorithm removes such outliers (one by one, according to their strength) from the graph and re-evaluates the angles until the two graphs are matched or discarded. This is a simple intuitive and robust algorithm that is inspired by a previous work. Experimental results demonstrate the superior performance of this algorithm under various conditions, such as rigid and nonrigid transformations, ambiguity due to partial occlusions or match correspondence multiplicity, scale, and larger view variation.

Reo is an exogenous coordination language for component connectors extending data flow networks with synchronization and context-dependent behavior. The first proposed formalism to capture the operational semantics of Reo is called constraint automaton. In this paper, we propose another operational model of Reo based on Büchi automata in which port synchronization is modeled by records labeling the transitions, whereas context dependencies are stored in the states. It is shown that constraint automata can be recast into our proposed Büchi automata of records. Also, we provide a composition operator which models the joining of two connectors and show that it can be obtained by using two standard operators: alphabet extension and automata product. Our semantics has the advantage over previous models in that it is based on standard automata theory, so that existing theories and tools can be easily reused. Moreover, it is the first formal model addressing all of Reo’s features: synchronization, mutual exclusion, hiding, and context-dependency.

This paper presents a novel height estimation method for buildings with complex rooftop contours in the presence of partial occlusions or interference by neighboring buildings in monocular satellite/aerial images. The proposed method employs fuzzy rules to estimate the most accurate heights even for buildings with partial occlusions using projected shadows of each building. The system utilizes the genetic algorithm for finding the best solution in a search space that is not limited to the traditional incremental height steps. The proposed method is independent of the acquisition method and can be used for both satellite and aerial imageries. Experimental results verify the effectiveness of the proposed method with mean height error of 27 cm for satellite and 15 cm for aerial images.

This paper introduces a novel automatic building detection method for aerial images. The proposed method incorporates a hierarchical multilayer feature based image segmentation technique using color. A number of geometrical/regional attributes are defined to identify potential regions in multiple layers of segmented images. A tree-based mechanism is utilized to inspect segmented regions using their spatial relationships with each other and their regional/geometrical characteristics. This process allows the creation of a set of candidate regions that are validated as rooftops based on the overlap between existing and predicted shadows of each region according to the image acquisition information. Experimental results show an overall shape accuracy and completeness of 96%.

Constraint automata as acceptors of timed data streams are the semantic models of component connectors specified in the language of Reo. In this paper, we investigate the augmentation of the theory of constraint automata by live- ness and fairness requirements. We define the notion of liveness for constraint automata as a set of infinite runs and show that our definitions of weak and strong fairness requirements, as we expect, satisfy the liveness requirements. It is shown that live or fair constraint automata can be composed using extended versions of the join operator for ordinary constraint automata such that, the resulted automaton itself be live or fair, respectively. Also, we present a linear temporal logic interpreted over infinite strings of transitions of constraint automata as a specification language for their properties. This temporal logic can be used as the specification language in the field of model checking. We show that our defined fairness conditions can be expressed not only by sets of computations but also by temporal formulas in the proposed linear temporal logic of steps.

In this paper, a novel algorithm for foreground detection and shadow removal is presented. The proposed method employs a region-based approach by processing two foregrounds resulted from gradient-and color-based background subtraction methods. The performance of the system is compared against conventional approaches for five indoor and outdoor video sequences. Experimental results confirm that the detection rate exceeds 90%, and the robustness is greatly improved.

Reo is an exogenous coordination language for component connectors extending data flow networks with synchronization and context-dependent behavior. We propose an operational model of Reo based on Buchi automata in which port synchronization is modeled by records labeling the transitions, whereas context dependencies are stored in the states. We provide a composition operator which models the joining of two connectors, and show that it can be obtained by using two standard operators: alphabet extension and automata product. Our semantics has the advantage over previous models in that it is based on standard automata theory, so that existing theories and tools can be easily reused. Moreover, it is the first formal model addressing all of Reo's features: synchronization, mutual exclusion, hiding, and context-ependency.

A component-based computing system consists of two main parts: a set of components and a coordination subsystem. Reo is an exogenous coordination language for compositional construction of the coordination subsystem. Constraint automaton has been defined as the operational semantics of Reo. The main goal of this paper is to prepare a model checking method for verifying linear time temporal properties of component-based systems whose coordinating subsystems are modeled by Reo and components are modeled by labeled transition systems. For this purpose, we introduce modified definitions of constraint automata and their composition operators by which, every constraint automaton can be considered as a labeled transition system and each labeled transition system can be translated into a constraint automaton. We show that failure-based equivalences CFFD and NDFD are congruences with respect to the composition operators of constraint automata. Also we present a method for compositional model checking of component-based systems using these equivalences for reducing the sizes of constraint automata models.

Reo is an exogenous coordination language for compositional construction of the coordinating subsystems of component-based softwares. Constraint automaton has been proposed as the operational semantics of Reo networks. The main goal of this work is to prepare a model checking based verification environment for component-based systems, whose component connectors are modeled by Reo networks and constraint automata. We use the methods of compositional reduction and abstraction in model checking of component-based systems and their component connectors modeled by Reo.