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A jigsaw puzzle is a recreational activity that involves assembling a certain number of pieces into a combined and well-fitting unit without creating gaps between adjacent pieces. Two-dimensional puzzles are divided into two main categories, the "apictorial" in which the only information available is the shape of the pieces and the "pictorial" which may take into account not only the shape of the pieces, but also their content. Jigsaw puzzles are considered as one of the most popular category of puzzles. The majority of them are accompanied by a guiding image and there is only one "counterpart" for each side of each piece (pictorial jigsaw puzzles), although some more difficult variants have blank pieces, the so-called apictorial jigsaw puzzles. In this paper, we will examine the open problem of solving pictorial and apictorial jigsaw puzzles, and their various applications, such as the reconstruction of two-dimensional fragmented objects, the restoration of fragmented wall-paintings and the repair of shredded documents. We will also present an evaluation of the state-of-the- art jigsaw puzzle reassembly techniques in pictorial and apictorial puzzles. [ABSTRACT FROM AUTHOR]

The scientific impact and rankings of individual persons, institutions, or nations are very important for their international recognition and prestige. At the same time, they are also vital for the governments, businesses and trusts which must decide scientific priorities and funding. In this paper we show that the ranking of a country changes significantly when we take into consideration its scientific personnel that has migrated abroad (i.e., brain-drain). Given that immigrant scientists are considered recoverable assets who can always return home, we believe that governments, businesses and trusts should anticipate immigrant scholars when deciding their scientific priorities, optimizing their research allocations, re-orienting their research support, or augmenting research productivity. [ABSTRACT FROM AUTHOR]

This paper studies the problem of unsupervised detection of geometrically similar fragments (segments) in curves, in the context of boundary matching. The goal is to determine all pairs of sub-curves that are geometrically similar, under local scale invariance. In particular, we aim to locate the existence of a similar section (independent of length and/or orientation) in the second curve, to a section of the first curve, as indicated by the user. The proposed approach is based on a suitable distance matrix of the two given curves. Additionally, a suitable objective function is proposed to capture the trade-off between the similarity of the common sub-sequences and their lengths. The goal of the algorithm is to minimize this objective function via an efficient graph-based approach that capitalizes on Dynamic Time Warping to compare the two subcurves. We apply the proposed technique in the context of geometric matching of coastline pairs. This application is crucial for investigating the forcing factors related to the coastline evolution. The proposed method was successfully applied to global coastline data, yielding a bipartite graph with analytical point-to-point correspondences. [ABSTRACT FROM AUTHOR]

In the era of internet access, recommender systems try to alleviate the difficulty that consumers face while trying to find items (e.g., services, products, or information) that better match their needs. To do so, a recommender system selects and proposes (possibly unknown) items that may be of interest to some candidate consumer, by predicting her/his preference for this item. Given the diversity of needs between consumers and the enormous variety of items to be recommended, a large set of approaches have been proposed by the research community. This paper provides a review of the approaches proposed in the entire research area of collaborative filtering recommend systems. To facilitate understanding, we provide a categorization of each approach based on the tools and techniques employed, which results to the main contribution of this paper, a collaborative filtering recommender systems taxonomy. This way, the reader acquires a quick and complete understanding of this research area. Finally, we provide a comparison of collaborative filtering recommender systems according to their ability to efficiently handle well-known drawbacks. [ABSTRACT FROM AUTHOR]

Recommender systems try to provide users with accurate personalized suggestions for items based on an analysis of previous user decisions and the decisions made by other users. These systems suffer from profile injection attacks, where malicious profiles are generated in order to promote or demote a particular item introducing abnormal ratings. The problem of automatic detection of such malicious profiles has been recently addressed by a great number of authors in the literature using supervised and unsupervised approaches. In this paper, we propose a framework to identify anomalous rating profiles, where each attacker (outlier) hurriedly creates profiles that inject into the system an unspecified combination of random ratings and specific ratings, without any prior knowledge of the existing ratings. This attack is a superset of the two different attacks (Uniform and Delta) proposed in Harper et al. (ACM Trans Interact Intell Syst 5(4):19, 2016) making the attack model more realistic and its detection more challenging. The proposed detection method is based on several attributes related to the unpredictable behavior of the outliers in a validation set, on the user-item rating matrix, on the similarity between users and on the filler items. In this work, we propose a new attribute (RIS) to capture the randomness in item selection of the abnormal profiles. In this work, three different systems are proposed: (1) a probabilistic framework that estimates the probability of a user to be an outlier by combining several features in a completely unsupervised way. (2) An unsupervised clustering system based on the k-means algorithm that automatically spots the spurious profiles. (3) A supervised framework that uses a random forest classifier for cases where labeling sample data is available. Experimental results on the MovieLens and the Small Netflix datasets demonstrate the high performance of the proposed methods as well as the discrimination accuracy of the proposed features. [ABSTRACT FROM AUTHOR]

In this paper, we propose an unsupervised point clustering framework. The goal is to cluster N given points into K clusters, so that similarities between objects in the same group are high while the similarities between objects in different groups are low. The point similarity is defined by a voting measure that takes into account the point distances. Using the voting formulation, the problem of clustering is reduced to the maximization of the sum of votes between the points of the same cluster. We have shown that the resulting clustering based on voting maximization has advantages concerning the cluster's compactness, working well for clusters of different densities and/or sizes. In addition, the proposed scheme is able to detect outliers. Experimental results and comparisons to existing methods on real and synthetic datasets demonstrate the high performance and robustness of the proposed scheme. [ABSTRACT FROM AUTHOR]

We present a model-based, top-down solution to the problem of tracking the 3D position, orientation and full articulation of the human body from markerless visual observations obtained by two synchronized RGBD cameras. Inspired by recent advances to the problem of model-based hand tracking Oikonomidis et al. (Efficient Model-based 3D Tracking of Hand Articulations using Kinect, ), we treat human body tracking as an optimization problem that is solved using stochastic optimization techniques. We show that the proposed approach outperforms in accuracy state of the art methods that rely on a single RGBD camera. Thus, for applications that require increased accuracy and can afford the extra-complexity introduced by the second sensor, the proposed approach constitutes a viable solution to the problem of markerless human motion tracking. Our findings are supported by an extensive quantitative evaluation of the method that has been performed on a publicly available data set that is annotated with ground truth. [ABSTRACT FROM AUTHOR]

A fundamental problem in networking and computing is community detection. Various applications like finding web communities, uncovering the structure of social networks, or even analyzing a graph's structure to uncover Internet attacks are just some of the applications for which community detection is important. In this paper, we propose an algorithm that finds the entire community structure of a network, represented by an undirected, unweighted graph, based on local interactions between neighboring nodes and on an unsupervised centralized clustering algorithm. The novelty of the proposed approach is the fact that the algorithm is based on the use of network coordinates computed by a distributed algorithm. Experimental results and comparisons with the Lancichinetti et al. method (Phys. Rev. E 80(5 Pt 2), 056117, ; New J. Phys. 11(3), 033015, ) are presented for a variety of benchmark graphs with known community structure, derived by varying a number of graph parameters. Emphasis is given on benchmark graphs with significant variations in the size of their communities. Further experimental results are presented for two real dataset graphs, namely the Enron, and the Epinions graphs, from SNAP, the Stanford Large Network Dataset Collection. The experimental results demonstrate the high performance of our algorithm in terms of accuracy to detect communities, and its computational efficiency. [ABSTRACT FROM AUTHOR]

We present new mathematical and geological models to assist civil protection authorities in the mitigation of potential oil spill accidents in the Eastern Mediterranean Sea. Oil spill simulations for 19 existing offshore wells were carried out based on novel and high resolution bathymetric, meteorological, oceanographic, and geomorphological data. The simulations show a trend for east and northeast movement of oil spills into the Levantine Basin, affecting the coastal areas of Israel, Lebanon and Syria. Oil slicks will reach the coast in 1 to 20 days, driven by the action of the winds, currents and waves. By applying a qualitative analysis, seabed morphology is for the first time related to the direction of the oil slick expansion, as it is able to alter the movement of sea currents. Specifically, the direction of the major axis of the oil spills, in most of the cases examined, is oriented according to the prevailing azimuth of bathymetric features. This work suggests that oil spills in the Eastern Mediterranean Sea should be mitigated in the very few hours after their onset, and before wind and currents disperse them. We explain that protocols should be prioritized between neighboring countries to mitigate any oil spills. [ABSTRACT FROM AUTHOR]