FreeRec: An Anonymous and Distributed Personalization Architecture Abstract. We present and evaluate FreeRec, an anonymous decentralized peer-to-peer architecture, designed to bring personalization while protecting the privacy of its users. FreeRec's decentralized approach makes it independent of any entity wishing to collect personal data about users. At the same time, its onion-routing-like gossip-based overlay protocols effectively hide the association between users and their interest profiles without affecting the quality of personalization. The core of FreeRec consists of three layers of overlay protocols: the bottom layer, rps, consists of a standard random peer sampling protocol ensuring connectivity; the middle layer, PRPS, introduces anonymity by hiding users behind anonymous proxy chains, providing mutual anonymity; finally, the top clustering layer identifies for each anonymous user, a set of anonymous nearest neighbors. We demonstrate the effectiveness of FreeRec by building a decentralized and anonymous content dissemination system. Our evaluation by simulation and through extensive PlanetLab experiments show that FreeRec effectively decouples users from their profiles without hampering the quality of personalized content delivery. The Web 2.0 has transformed the way users interact with the Internet. Users are no longer pure consumers, but they now generate a large portion of the available content. As a result, personalized services have become a requirement for most online applications. While personalization and social applications greatly enhance user experience, they amplify the Internet's inherent privacy risks and concerns. For instance, personalization in a social application can reveal potentially embarrassing information directly to friends, family, and colleagues. In addition, users publishing controversial or prohibited information on social platforms can easily be identified and located through their IP addresses.. The reason for the privacy risks associated with personalized services lies in their inevitable dependence on personal data. As another example, consider one of the most common forms of personalized services: recommendation. A common technology providing this service is user-based Collaborative Filtering (CF) [25] . This paradigm leverages interest similarities to identify correlations between the preferences of different users. While users are not generally aware of who else shares their own interests, their centralized implementation requires service providers to store accurate information about the interests of users. This clashes with the need to protect personal data.. Anonymity services provide an attractive way to overcome the privacy issues associated with personalized services. They hide the real identity (i.e. IP address) of a user through pseudonym (e.g. IP address of another node). Several such solutions are available on the Internet [1] and offer users the possibility to navigate anonymously behind a proxy. However, the use of a single proxy is vulnerable to adversaries that can observe traffic going in and out of the proxy. Distributed solutions, such as Tor [11] provide better guarantees. Nonetheless, they do not eradicate the concentration of personal data within the servers of a single provider. Decentralized personalization based on the P2P paradigm [5, 8] addresses the issue of concentrated data while providing naturally scalability. Yet, they remain vulnerable to the presence of malicious users.. Clearly anonymity alone does not protect users privacy, nor does decentralization alone. In this work, we seek to address these issues by combining the benefits of decentralized personalization and anonymity. The result is FreeRec, an anonymous and distributed personalization architecture. Our solution implements a distributed user-based (CF) scheme through an anonymous and interestbased topology and uses the resulting overlay to recommend items to users. Unlike existing decentralized personalization platforms, FreeRec protects the interest profiles stored at every node by means of anonymous exchanges with other peers. This makes FreeRec a generic personalization architecture that can be leveraged to build a number of distributed applications that may benefit from recommendation services.. FreeRec builds anonymous chains of nodes by relying on three layers of gossip protocols providing mutual anonymity. A standard random-peer-sampling protocol provides nodes with the members of their anonymous chains. A second private peer-sampling protocol uses these chains to provide each node with an anonymous sample of the network. A top clustering layer implements a decentralized collaborative-filtering overlay by creating decentralized clusters of anonymous profiles. This layered architecture makes FreeRec self organizing and capable to adapt to the arrival and departure of nodes and to changes in the interests of users. We evaluate FreeRec using both simulation and a real deployment on PlanetLab. Our results on a news-personalization use case show that users are able to effectively receive and publish content even in presence of path failure with reasonable overhead.. The rest of the paper is organized as follows: Section2 presents our system models and Section3 details our solution. Experiment setup is presented in Section 4 while FreeRec is extensively evaluated in Section 5. Related works are introduced in Section 6 and Section 7 concludes our paper. We presented FreeRec, a decentralized architecture for building anonymous personalized services. FreeRec equips nodes with bidirectional onion-routing-like proxy chains that allow nodes to exchange their interest profiles without ever revealing their identities. FreeRec's core consists of three layers of gossip protocols. The bottom one is a standard random-peer-sampling protocol that provides nodes with the necessary information to build their proxy chains. The middle layer, the prps, constitutes the main contribution of this work and is an augmented RPS protocol: it builds and maintains proxy chains and uses them to provide each node with a continuously changing anonymous sample of the network. The top layer completes the picture by providing each node with a cluster of anonymous interest profiles that most closely resemble its own. We extensively evaluated FreeRec in several ways: through simulations, with a planetlab deployment, and through a probabilistic analysis. Our results show its effectiveness in combining privacy with effective recommendations. We carried out an extensive evaluation of FreeRec by simulation and by deploying its implementation on PlanetLab. We present the results by analysing the overhead, the impact of proxy changes, the latency, and the probability for a proxy chain to be compromized by a set of colluding nodes. While personalization greatly enhances user experience, it raises privacy risks and concerns. Several collaborative-filtering approaches [21, 22] have tried to preserve the privacy of sensitive data address by applying randomized masking and distortion techniques to user profiles. However, [13, 17] show that privacy-sensitive information can be separated from such random distortion. To overcome this limitation, [7] uses noise that is not random but depends on the interest of users. This limits the amount of information exchanged between users to coarse-grained user profiles that only reveal the least sensitive information. Other decentralized approaches such as [9, 10, 20] exploit homomorphic encryption in a P2P environment. [14] , in turn, addresses privacy by trust where participants exchange information and profile only across trusted content producer/consumer pairs. [2] proposes a differentially private protocol to measure the similarity between profiles. While differential privacy provides a strong notion of privacy, [19] highlights its important trade-off between privacy and accuracy.. A number of authors have proposed to address privacy by means of anonymity. Some, like [4] achieve receiver anonymity using group communication primitives like broadcasting, multicasting, and flooding. Others [11] focus on sender anonymity and relay messages from a node along a single anonymous path formed by nodes within the infrastructure.. Onion routing belongs to the latter group. It uses chains of router nodes that pack messages into onions: recursively encrypted data structures that contain the necessary routing information at each layer. When receiving an onion, a router removes a layer by decrypting it with its private key. At this point, it discovers either that it is the destination of the message, or the identity of the next router in the onion's forwarding path. . Tor [11] uses this model but cannot be readily integrated with decentralized personalization services.. Some authors have already suggested the integration of gossip and anonymous services. The work in [24] uses gossip protocols to improve the robustness of trust-based overlays to provide privacy-preserving data dissemination. More precisely, it creates and maintains additional anonymous links on top of an existing social overlay. Similarly, [23] relies on gossip protocols to supports confidential communications and private group membership. This solution leverages existing multi-hops paths to circumvent network limitations such as NAT and firewalls to form anonymous channel. Neither however combines anonymity with personalization. Gossple [5] does this to some extent and builds a fully decentralized anonymous collaborative network. Its gossip-on-behalf protocol hides the association between a user and her profile. Yet, in Gossple, a proxy controls some of the node's data structures. This is a significant disadvantage if the proxy wishes to censor specific information. In FreeRec, on the other hand, a proxy can at most drop messages randomly as it has no way to access their content.. Other works on gossip-based protocols have focused on tolerating byzantine faults such as BAR gossip [18] , the secure peer sampling [16] , Brahms [6] or PuppetCast [3] . In this work, we do not consider that nodes can act as active adversary by operating maliciously in the protocol. In case of malicious nodes cheating in the protocol, FreeRec could leverage one of these solutions to tolerate byzantine nodes. Finally, some authors [30, 29] have suggested to address churn by replacing each onion router with a group of nodes. Such a technique could easily be integrated with our solution. We evaluated FreeRec in the context of a news-personalization use case. We combine FreeRec with a gossip-based dissemination protocol to recommend news items to a population of users. A user interest profile contains the news items she received and liked. When a user generates an item or expresses a positive opinion on a received item, she forwards it to her neighborhood in FreeRec's anonymous interest-based topology. Gossip frequency in all protocols is set to one per simulation cycle and of one every 2s in PlanetLab.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O


Surface-Preserving Robust Watermarking of 3-D Shapes This paper describes a new statistical approach for watermarking mesh representations of 3-D graphical objects. A robust digital watermarking method has to mitigate among the requirements of watermark invisibility, robustness, embedding capacity and key security. The proposed method employs a mesh propagation distance metric procedure called the fast marching method (FMM), which defines regions of equal geodesic distance width calculated with respect to a reference location on the mesh. Each of these regions is used for embedding a single bit. The embedding is performed by changing the normalized distribution of local geodesic distances from within each region. Two different embedding methods are used by changing the mean or the variance of geodesic distance distributions. Geodesic distances are slightly modified statistically by displacing the vertices in their existing triangle planes. The vertex displacements, performed according to the FMM, ensure a minimal surface distortion while embedding the watermark code. Robustness to a variety of attacks is shown according to experimental results. This paper proposes a new statistical 3-D watermarking methodology based on embeddings into geodesic distance distributions calculated from the object surface using the FMM. The proposed method has the following pre-watermarking stages: object alignment, defining a source location and the 3-D graphical object surface split into strips. The vertices from each strip are used for embedding a single bit. Two different statistical methods are proposed for watermark embedding by changing the distribution mean and variance, respectively, of geodesic distance distributions from each strip. Vertices are changed along the graphical object surface, using the Vertex Placement Scheme. The study from this paper shows that the proposed methodology ensures a minimal distortion in graphical objects following watermarking. The proposed method requires only the knowledge of the watermark code length in the detection stage, has low computational demands and results in watermarks which are robust to various mesh attacks except for object cropping which would change the object center. Overall, the proposed methodology provides a very good tradeoff among the requirements of watermarking: surface preservation, robustness to attacks, bit capacity embedding and watermark security. Surface preservation following 3-D watermarking is required in a large variety of applications, including for CAD objects, medical visualization, 3-D graphics for patent registration, or in virtual markets for graphical objects.

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Ordered domain algebras Abstract. We give a finite axiomatisation to representable ordered domain algebras and show that finite algebras are representable on finite bases. Domain algebras provide an elegant, one-sorted formalism for automated reasoning about program and system verification, see [DS08a, DS08b] and [HM11] for details and further motivation. The algebraic behaviour of domain algebras have been investigated, e.g. in [DJS09a, DJS09b] . Their primary models are algebras of relations, viz. representable domain algebras. P. Jipsen and G. Struth raised the question whether the class R(;, dom) of representable domain algebras of the minimal signature (;, dom) is finitely axiomatisable. To formulate the question precisely, let us recall the definition of representable domain algebras R(;, dom). Definition 1.1. The class R(;, dom) is defined as the isomorphs of A = (A, ;, dom) where A ⊆ ℘(U × U ) for some base set U and x ; y = {(u, v) ∈ U × U : (u, w) ∈ x and (w, v) ∈ y for some w ∈ U } dom(x) = {(u, u) ∈ U × U : (u, v) ∈ x for some v ∈ U } for every x, y ∈ A.. The signature (;, dom) can be expanded to larger signatures τ by including other operations. For instance, we can define. and also include the bottom element 0 (interpreted as the empty set ∅) and the ordering ≤ (interpreted as the subset relation ⊆) to yield representable algebraic structures. The corresponding representation classes R(τ ) for larger signatures τ are defined analogously to the definition of R(;, dom).. It turned out that the answer to the above problem is negative.. ] Let τ be a similarity type such that (;, dom) ⊆ τ ⊆ (;, dom, ran, 0, 1 ). The class R(τ ) of representable τ -algebras is not finitely axiomatisable in first-order logic.. Note that the above theorem does not apply to signatures where the ordering ≤ is present. In fact, D.A. Bredikhin proved [Bre77] that the class R(;, dom, ran, , ≤) of representable algebraic structures is finitely axiomatisable. Our aim is to provide an alternative, and slightly more general, proof that R(;, dom, ran, , 0, 1 , ≤) is finitely axiomatisable. The advantage of our proof is that it uses a Cayley-type representation of abstract algebraic structures that also shows finite representability, i.e. that finite elements of R(;, dom, ran, , 0, 1 , ≤) can be represented on finite bases. In passing we note that if composition is not definable in τ , then R(τ ) has the finite representation property, but it can be shown that every signature containing (·, ;, 1 ) or (·, ;, ) (where · is interpreted as intersection) fails to have the finite representation property.

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Histogram of Template for Pedestrian Detection n this paper, we propose a novel feature named histogram of template (HOT) for human detection in still images. For every pixel of an image, various templates are defined, each of which contains the pixel itself and two of its neighboring pixels. If the texture and gradient values of the three pixels satisfy a pre-defined formula, the central pixel is regarded to meet the corresponding template for this formula. Histograms of pixels meeting various templates are calculated for a set of formulas, and combined to be the feature for detection. Compared to the other features, the proposed feature takes texture as well as the gradient information into consideration. Besides, it reflects the relationship between 3 pixels, instead of focusing on only one. Experiments for human detection are performed on INRIA dataset, which shows the proposed HOT feature is more discriminative than histogram of orientated gradient (HOG) feature, under the same training method. Human detection technique is widely used in many applications ranging from image analysis, smart cars, and visual surveillance to behavioral analysis. In recent years, lots of research work has been focused on this field. But human detection is still a challenging task because of many difficulties. Most natural humans have large variations, such as the appearance, the pose and so on. Difference in clothes brings further challenge because some features such as skin color in the face detection can't be used in this application. Besides, complex backgrounds, illumination, occlusions and different scales must be considered in the detection. A robust detector must be independent for all these variations.. The gradient information is efficient for the object detection. A lot of human descriptors contain the gradient information more or less [1] [2] [3] [4] [5] [6] . Histogram of orientated gradient (HOG) [1] and covariance matrix [3] are excellent descriptors using the gradient information. HOG is a graylevel image feature formed by a set of normalized gradient histogram. In [1] [2] , HOG feature is compared with many other features, such as Harr-like feature, Wavelet feature and so on, and it gets the best performance. Covariance matrix integrates coordinates and intensity derivatives into a matrix. They represent the gradient information well, and can get a good result on INRIA human dataset. But only using the gradient information may be not enough to detect humans from complex backgrounds or images in low resolution.. The texture information also has some discriminative abilities in the human detection. Some research work has been done on the feature of local binary pattern (LBP) [7] and Gabor filter [8] . Gabor filter and LBP are widely used in texture classification and face recognition. They represent the intensity information well. But only using these features is not enough to get the good result. The original definition of LBP is not suitable for the human detection. It must be combined with other features such as Laplacian EigenMap (LEM) in [8] . In [7] , two variants of LBP: Semantic-LBP and Fourier-LBP are proposed. The modified definition of LBP makes it suitable for the human detection.. Besides the feature extraction, the training method is also very important for the human detection. They are two key components for the pattern classification problem. The features extracted from a large number of training samples are used to train a classifier. Support vector machine (SVM) [9] and various boosting methods [10] are efficient to train a classifier in practical applications. The SVM has some advantages. It is easy to train and the global optimum is guaranteed. The variance caused by the suboptimal training is avoided for the fair comparison. The boosting method combined with the cascade strategy is widely using in real-time applications. The boosting method aims at producing an accurate combined classifier from a sequence of weak classifiers, which are fitted to iteratively reweighted versions of the data. The cascade strategy saves detection time and makes it possible to detect object real time.. So there are two research directions for the human detection: finding more discriminative local features [1, 3] , and developing more efficient training methods [11] .. The main contribution of this paper is along the first direction. It focuses on building a more powerful local feature for the human detection. A new feature, histogram of template, is proposed. It extracts the texture information as well as the gradient information, and makes the two different types of information homologous. Compared with features using the gradient information, such as the HOG feature, the proposed feature shows more discriminative ability. Besides, this feature can encode the relationship of three pixels in one template. Compared with features that deal with each pixel independently, HOT feature can get higher detection rate. Last, the HOT feature has some properties of local binary pattern, such as illumination-invariance. So the normalization is not so important for the detection result. This property can be used to reduce computation complexity in some circumstance. A new feature for human detection is proposed in this paper. A histogram of pixels meeting different templates is used as a feature for the human detection. It integrates the texture information and the gradient information together, and shows more discriminative ability than only gradient based feature, even if the length of feature is shorter. Other advantage is that HOT feature is illumination-invariant, so the normalization is not the necessary step for detection, which is very useful for some hardware accelerators that only support integer calculation. In our experiment, the size of the sub window is fixed. It is expected that the variable window size and the boosting method will further improve the performance of the HOT feature. The computation of the HOT feature is parallel, so it is easy for hardware acceleration. Besides, integral image can also be used for computation. These factors make it possible for real-time application. Human detection algorithms now can be separated into three groups.. The first group of methods is based on local features [1-4, 6-7, 11-13] . They extract some features from sub regions of images in the training dataset, to train a classifier by support vector machine (SVM) or boosting methods, such as Adaboost or Logitboost. For a new image, they extract the same features and send them to the classifier which will give a classification result. In [14] , a local receptive fields (LRF) feature is extracted using multilayer perceptrons by means of their hidden layer. In [13] , Haar wavelet is used as human descriptor. SVM in [9] is used to train the classifier. [1] uses the HOG feature as descriptor for the human detection, and [2] is developed from this one. It integrates the cascade-of-rejecter approach, and uses the Adaboost method in [11] to choose best sub window in each stage. In [11] Haar-like feature is used to detect humans. It uses the integral image to speed up the detection process. Cascade rejection method is proposed to make real-time human detection possible. In [3] the covariance matrix feature is used as human descriptor, to represent the coordinates, and the gradient information of humans. Covariance matrix can be formulated as connect Riemannian manifold. Each matrix can be treated as a point in Riemannian manifold, and can be mapped into a vector space. An edge let descriptor is used in [12] for the human detection. Different from just combining the orientations in horizontal and vertical direction in [4] , it combines the orientations in edge let defined direction, which makes it more efficient for the human detection. This group of methods has a good performance, and if enlarge the training dataset, the detection rate can be improved.. The second group of methods is based on local appearance, and [15] [16] [17] [18] are based on this. They detect the interesting points in the training images and use the patches around the interest points to construct a codebook. When given a new image, they first find the similar patches in codebook and all patches vote for the positions of humans.. The third group is based on chamfer matching. They use human templates to find the most marching regions in the edge map of an input image. [19] [20] are based on this method. In [19] a direct template matching approach for the global shape-based human detection is proposed, and [20] is developed from this but uses some hierarchical templates to reduce the detection time and solve the occlusion problem to some extent. These methods may not give a good result when there are too many edge clusters in the edge map.. Our method belongs to the first group. It uses HOT feature to extract the texture information and the gradient information for the human detection. Two types of information are made homologous to increase the discriminative abilities of the proposed feature. The covariance matrix feature in [3] gets higher detection rate than HOG feature in [1] . But the training method is different. [3] uses the logitboost method and the size of sub windows is variable, but the SVM training method and the fixed sub window strategy are used in [1] . So it is hard to say that whether the covariance matrix feature is more discriminative or the training method is better. The HOT feature is compared with the HOG feature using the same training method, for the fair comparison.

B I I O O O O O O O O O O O O B I I O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Size Distributions of Cadmium Sulfide Nanoparticles Obtained from Templating Methods Cadmium sulfide (CdS) nanoparticles were obtained by soft templating methods using either an already established revered micelle route or a new procedure based on gel electrophoresis. The UVVis absorption or the photoluminescence excitation spectra were fitted using the CdS electronic structure available in the literature together with a size distribution. The obtained results indicate that the amount of sodium dodecyl sulphate as a component of the agarose gel formulation has a profound effect on the resulting nanoparticle population. Nanotechnology is a field of very active investigation. There is a wide scope of technological applications ranging from photocatalysis and sensors to fluorescence imaging.. In this work, cadmium sulfide (CdS) nanoparticles have been prepared by templating methods using surfactants and gel electrophoresis. The surfactant templating protocol was based on that of Pileni, 1 using AOT [sodium bis (2-ethylhexyl) sulfosuccinate] waterin-oil microemulsions. In the gel electrophoresis procedure, cadmium and sulfide ions migrate under an electric field in opposite directions until they meet and react within the gel pores. This reaction is thus controlled by diffusion through the gel pores. The application of reaction-diffusion processes in nanofabrication involving gels has recently been reviewed. 2 The size dependence of CdS electronic states was obtained by a tight binding approximation. 3 Using these theoretical results, in conjunction with a size distribution and a Mie formalism for the scatter/absorption of nanoparticles of a given size, we were able to fit the experimental absorption and excitation spectra of CdS nanoparticles, either in AOT reversed micelles or in dried gels. In the case of AOT templating, the resulting particles should be spherical and the calculated average sizes can be compared to those obtained using empirical relations between first absorption peak and nanoparticle size proposed by Yu and colleagues. 4 In the gel electrophoresis templating experiments, excess concentration of one of the ions and the presence of sodium dodecyl sulphate (SDS) surfactant was found to influence the size distribution of the nanoparticles and the corresponding photoluminescence spectra.

O O O O O O O O B O O O B I O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O B O


A Method of Parallelizing Consensuses for Accelerating Byzantine Fault Tolerance We propose a new method that accelerates asynchronous Byzantine Fault Tolerant (BFT) protocols designed on the principle of state machine replication. State machine replication protocols ensure consistency among replicas by applying operations in the same order to all of them. A naive way to determine the application order of the operations is to repeatedly execute the BFT consensus to determine the next executed operation, but this may introduce inefficiency caused by waiting for the completion of the previous execution of the consensus protocol. To reduce this inefficiency, our method allows parallel execution of the consensuses while keeping consistency of the consensus results at the replicas. In this paper, we also prove the correctness of our method and experimentally compare it with the existing method in terms of latency and throughput. The evaluation results show that our method makes a BFT protocol three or four times faster than the existing one when some machines or message transmissions are delayed. Byzantine failures, where faulty machines behave any way without restriction, are the most malicious failures. Such failures can model any kind of malfunction caused by hardware faults, infection by a virus, intrusion of crackers and so on. In the services provided on open networks like the Internet, these failures cause serious damage, and thus robust fault tolerance against them is strongly demanded.. One of the most robust approaches for implementing Byzantine fault tolerant services is state machine replication [1] , where a server is modeled as a state machine and replicated on different host machines. The behavior of a state machine is determined by its current state and the set of received messages. In the state machine replication of a server, server replicas are arranged and execute the same tasks to tolerate Byzantine faults. Requests may arrive in different order at different replicas. To maintain consistency Manuscript received July 1, 2013. † The authors are with the Graduate School of Information Science and Technology, Osaka University, Suita-shi, 565-0871 Japan.. † † The author is with NTT Communication Science Laboratories, NTT Corporation, Kyoto-fu, 619-0237 Japan.. † † † The author is with the Dept. of Computer Science and Engineering, Toyohashi University of Technology, Toyohashi-shi, 441-8580 Japan.. a) E-mail: junya-n@ist.osaka-u.ac.jp b) E-mail: araragi.tadashi@lab.ntt.co.jp c) E-mail: masuzawa@ist.osaka-u.ac.jp d) E-mail: masuyama@tut.jp DOI: 10.1587/transinf.E97.D.53. among the replicas, they communicate with each other and agree on the order of processing the received requests. By processing the requests in an order common to all the replicas, non-faulty server replicas behave identically. Even if a minority of replicas malfunction and return wrong or forged results, clients receive the same and correct results from a majority of replicas (or non-faulty ones) and can ignore such wrong or forged results from the faulty replicas. Here, we assume that the clients are non-faulty and multicast identical requests to all the server replicas. Thus, a main technical issue of state machine replication is to develop a Byzantine consensus protocol for achieving the above agreement in the presence of Byzantine faults. This paper targets Byzantine fault tolerance for huge distributed systems working on open networks like the Internet. Such systems are generally asynchronous. That is, we cannot guarantee that messages are received in expected time intervals after being sent. As is well known, no deterministic Byzantine consensus protocol exists in asynchronous systems [2] . There are two main approaches for circumventing that impossibility. One is based on randomization [3] - [7] and the other is based on a rotating coordinator [8] - [11] . Our acceleration method is based on the randomization approach, which is less efficient but more robust than the coordinator approach and is suitable for open networks.. In the randomization approach, randomized actions are introduced to avoid critical damage from attackers. However, the approach is likely to be inefficient, since a number of rounds must be repeated until the correct replicas reach agreement. To improve efficiency, a request set agreement is employed rather than an agreement on a sequential number (the order to be processed) of each request. Once agreement on a request set is achieved, the requests in the set are processed in a predefined order (e.g., the order of the IDs of the clients submitting requests) among them. This request set agreement is repeated sequentially and all requests are arranged in a common order. However, if some replicas work very slowly or some requests reach very late, a request set agreement may take a long time. It seriously delays the next invocation of the consensus protocol. This paper presents a method of solving this problem by parallelizing the request set agreements.. Next, we explain more details of the randomization approach and the involved problem. Many randomized protocols based on request set agreement have been already proCopyright c 2014 The Institute of Electronics, Information and Communication Engineers posed [3] - [5] . The consensus protocol is invoked periodically with a given time interval measured by a local clock of each replica. When an execution of the protocol is finished by agreeing on a request set, the requests in the set are arranged in a predefined order. By this series of arrangements, all the requests are arranged in a common order among the replicas. At each invocation of the consensus protocol, each replica proposes a set of the requests that were received so far but not included in the previous agreement results. Of course, these proposals can be different among the replicas because of the delay of the request arrival or the machine behavior. However, the set agreement protocol guarantees that all non-faulty replicas agree on a subset of the union of the request sets proposed by non-faulty replicas.. The length of the local time interval between invocations of the set agreement affects the efficiency, but it is difficult to decide a suitable one. If it is short, the number of invocations of the consensus protocol will increase. If it is long, requests have to wait long for the invocation of the agreement protocol, and the agreement may take a long time because the size of the proposal grows. When an execution of the consensus protocol does not terminate within the local time interval, a big delay might occur. In this case, the invocation of the consensus protocol is kept waiting until the termination of the previous consensus, even if the local time interval passes to prevent inconsistency of the total order of requests among the replicas. Such blocking of the invocation makes the following invocations of agreement postponed. As a result, the number of unprocessed requests grows and the efficiency of the replication method is reduced. When request arrivals or machine behaviors are delayed, the validity check becomes very time consuming in the agreement, and the termination is easily delayed over the local time interval. Here, the validity check is a process in the agreement for excluding forged requests. In this paper, we proposed a method to accelerate state machine replication for Byzantine fault tolerance by parallelizing the executions of request set consensus and adding an extra multi-valued consensus for deciding the processing order of agreed sets. We also show the correctness of the protocol for parallelizing agreements. Parallelism has a strong advantage in spite of requiring an additional agreement, especially when some replicas work slowly or some requests are delivered late. We showed this property by an experimental evaluation. In this evaluation, our parallelizing method accelerates the latency of replication three or four times more than the existing sequential method in delayed situations. Clement et al. experimentally compared the performances in such delay situations among representative protocols based on rotating coordinator approach [16] . They showed that Castro-Liskov protocol [8] , which is known to be practically very fast in the normal situation, degrades the performance in the delayed situations. For randomization approach on which this paper focuses, Moniz et al. evaluated RITAS in the WAN environment where communication speeds between processes are not uniform [3] . However, the evaluation models were not so detailed as the one of this paper. To the best of our knowledge, for randomization approach, this is the first paper that evaluated in such detail the effect caused by the delay of message delivery and slow behavior of replicas. As stated above, there are two main approaches for replications based on Byzantine agreement in asynchronous distributed systems: randomization [3] - [5] and a rotating coordinator [8] - [11] .. In the rotating coordinator approach, a special replica (a rotating coordinator) determines a sequence number (the processing order) for each received request and announces it to all the other replicas. Therefore, all the replicas can process the requests in the same order and maintain consistency.. If the coordinator is faulty, its role is taken over by another replica. From the impossibility result of FLP [2] , this approach needs some assumptions on synchrony (weak synchrony) to guarantee termination. On the other hand, the randomization approach guarantees termination with probability 1 and needs no additional assumption. It is more robust but less efficient.. Among the protocols in the coordinator approach, the Castro-Liskov protocol [8] achieves very high performance and is considered a practical replication method. Under the above assumption, it terminates in a few rounds and exe-cutions of the consensus protocol are executed in parallel. Although the original Castro-Liskov protocol executes the consensus protocol for each request, it is not hard to modify the protocol to allow each process to propose a request set like the randomization approach. However, parallel execution of the agreements for request sets in the coordinate approach is essentially different from that in the randomization approach. Actually, the modification of the CastroLiskov protocol reduces the number of agreement executions and consequently improves efficiency in ordinary situations. However, it worsens when requests or replicas are delayed. Because of the delay, a coordinator is suspected to be faulty and coordinator alternation often happens. At each alternation, a heavy load procedure must be done to maintain this protocol's integrity.. For the existing protocols in the randomization approach, to the best of our knowledge, our parallelization proposal is the first.

O O O O O O O B I I O O O O O O O O O O O O O O O O O O B I I B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O B O O O O O B O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B I I O B I I O O O O O O O B O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


SYNTHETIC OVERSAMPLING OF INSTANCES USING CLUSTERING Imbalanced data sets in the class distribution is common to many real world applications. As many classifiers tend to degrade their performance over the minority class, several approaches have been proposed to deal with this problem. In this paper, we propose two new cluster-based oversampling methods, SOI-C and SOI-CJ. The proposed methods create clusters from the minority class instances and generate synthetic instances inside those clusters. In contrast with other oversampling methods, the proposed approaches avoid creating new instances in majority class regions. They are more robust to noisy examples (the number of new instances generated per cluster is proportional to the cluster's size). The clusters are automatically generated. Our new methods do not need tuning parameters, and they can deal both with numerical and nominal attributes. The two methods were tested with twenty artificial datasets and twenty three datasets from the UCI Machine Learning repository. For our experiments, we used six classifiers and results were evaluated with recall, precision, F-measure, and AUC measures, which are more suitable for class imbalanced datasets. We performed ANOVA and paired t-tests to show that the proposed methods are competitive and in many cases significantly better than the rest of the oversampling methods used during the comparison. In many real-world classification problems, there is a strong imbalance in the class distribution. This means that most of the instances belong to some classes and only a few of them belong to other classes. In such cases, users are usually more interested in the accuracy results from the minority classes. However, some classifiers perform poorly in them. Trying to improve the classification performance of the minority class is important. This is relevant to many real-world applications such as fraud detection, 10 medical diagnosis of rare diseases, 3 unreliable communications for clients, 9 images classification, 18 detection of intestinal contractions from endoscopy video, 22 author's identification, 19 and many more. Sampling techniques apply oversampling algorithms on the minority class and/or subsampling techniques on the majority class. In both cases, it is possible to apply random or "intelligent" sampling techniques. This paper focuses on intelligent oversampling techniques. The simplest oversampling method is known as random oversampling (ROS), which randomly duplicates instances of the minority class. However, this method might strengthen decision regions of the learner (making them smaller), and overfit the minority class.. In this paper we compare our methods based on Synthetic Oversampling of Instances (SOI) against ROS, SMOTE, and Borderline-Smote. We also show that our two proposed algorithms are significantly better than the others under different testing conditions. The rest of this paper is organized as follows: Section 2 describes representative research related to working with imbalanced data, including the algorithms that we use in the comparison with the proposed algorithms. Section 3 describes the proposed algorithms. Section 4 provides a description of the experiments performed to validate the proposed algorithms over different datasets, with different classification algorithms, and several performance measures used for imbalanced datasets. In Section 5 the main results of our experiments are presented. In Section 6 we present a discussion of the findings of our results. Finally, Section 7 describes the main conclusions and suggests future research directions. Imbalanced datasets are common to many real-world applications. However, many classifiers tend to perform poorly on the minority class, which is often the class of interest. Several approaches have been previously proposed to improve the performance of algorithms in the minority class. These approaches, however, tend to perform similarly or worse than random oversampling, generate examples in majority class regions, and have problems with noisy examples. This paper presents two new cluster-based oversampling methods. The idea is to form clusters with the minority class instances and oversample within each cluster avoiding some of the problems of previous approaches. In the experiments performed, the results from the proposed algorithms are, on average, half of the time significantly better than the rest of the oversampling methods. There are several research directions that are worth exploring. In particular, we would like to distinguish with some confidence noisy instances from truly minority class instances. The identification of singleton clusters can be a first step in this direction. We would like to test similar ideas for undersampling algorithms based Although we have only presented complete tables for one classifier, in this case for J48 from Weka, similar results are obtained for the rest of the classifiers. As expected, some classifiers tend to have better performance than others but they are not consistently better in all measures or for all datasets. The complete tables for all the algorithms can be consulted in Ref. 17 .. As it can be seen from Tables 10 to 13, our cluster-based algorithms perform better than the other oversampling methods in most datasets and for most of the performance metrics, except for AUC for which they have very similar results. Our algorithms are not always better, but in general when they win, their difference Synthetic Oversampling of Instances Using Clustering with respect to the other algorithms is statistically significant, while when they lose, their difference is not significant. On average, half of the time our algorithms produce significantly better results than the rest of the oversampling methods. It is interesting to note that the proposed cluster-based method that generates new instances using a jittering process is a very clear winner over the rest of the algorithms, SOI-CJ even outperforms SOI-C. One possible explanation for this behavior could be that the merging process for clusters allows for the introduction of a large number of instances of the majority class. Consequently, the method that interpolates between elements in the clusters for oversampling (SOI-C) may still be generating instances in a majority class region, while the jittering approach generates instances between jittered instances. SOI-C, however, still outperforms ROS, SMOTE, and Borderline-SMOTE.. The proposed algorithms scale poorly with the number of attributes and size of the data since they have to evaluate the distances between all the data and all the minority class examples. In the performed tests, their running times, however, were equivalent to those of Borderline-SMOTE, although they were larger than ROS and SMOTE. Faster running times can be achieved, for instance, with better data structures to find similar examples faster (e.g., kd-trees) or with parallel computing architectures, such as CUDA. We leave the implementation of faster versions of our algorithms as future work. We present the results of our experiments for the J48 algorithm in Tables 2 to 9 . It is important to mention that although in this paper we only show results for the J48 algorithm, similar tables were produced for all the datasets, for each classifier, and for each performance metric. All these tables are reported in Ref. 17 , in which we show one table for each combination of performance metric, kind of data, and classifier. In these tables, the first column corresponds to the dataset name. The second, to the percentage of oversampling performed over the minority class. The third column shows the original performance of the algorithm (without oversampling). The rest of the columns show the name of the oversampling technique used (ROS, SMOTE, borderSM1, borderSM2, SOI-C, and SOI-CJ) for each of the datasets of the corresponding row. A "**" in the first column means that the ANOVA test found that the results are in general, statistically significant and they are not a consequence of randomness introduced by the oversampling methods. In the rest of the columns, a "*" means a statistically significant difference with a confidence level of 95% with SOI-C and a "+" means a statistically significant difference with SOI-CJ. The best results are shown in bold face. As can be seen from these tables, all the tested algorithms win in some datasets and lose in others. Similar results are obtained when we used other classifiers. In order to present our results in a clearer way we also made a summary of the results obtained according to each performance metric. For the twenty artificial datasets with six classifiers, there are one hundred and twenty results for each performance metric to show the performance of the six oversampling methods (four in the state of the art, SOI-C, and SOI-CJ). In the case of the twenty three UCI datasets, there are one hundred and thirty eight results. In Tables 10 to 13 we only show a summary of those results in which the ANOVA or pair-t tests showed statistical significance. The results of these tables are presented in pairs N r /N c , where N r denotes the number of times that the oversampling method of that row is better than the oversampling method of that column, and N c denotes the times that the oversampling method of that column is better than the oversampling method of that row. For example, in Table 10 the number of times that SOI-C is significantly better/worse than the other algorithms in the artificial datasets is: 56/1 for ROS, meaning that SOI-C is significantly better (with 95% of confidence level) than ROS fifty six times and significantly worse only once, 63/7 for SMOTE, 75/3 for borderSM1, 70/4 for borderSM2 and 2/19 for SOI-CJ. This means that although the number of overall wins and ties for SOI-C is roughly equivalent to that of the other algorithms, when compared with each algorithm, its superiority is clearly shown as it has more results that are significantly better than the other algorithms. Working with a small number of instances of one class does not allow a learning algorithm to generalize for such a class, consequently causing overfitting of the model. A widely used solution to avoid this problem is based on sampling techniques. As it was mentioned in the introduction section, in this research we focus on oversampling techniques.. A popular method that avoids replicating instances (among the intelligent oversampling methods) is called SMOTE (Synthetic Minority Oversampling Technique). 6 The main idea of SMOTE is to create new minority (positive) class instances by interpolating pairs of close minority class instances (see Figure 1 left). The pairs of close instances used to create each synthetic example are taken by Synthetic Oversampling of Instances Using Clustering Fig. 1 . The synthetic generation of minority class instances is normally performed by interpolating between a pair of minority class instances (left). However, sometimes new instances can be created inside a majority class region (right) when the chosen pair is separated by majority class instances.. randomly selecting a minority class instance and one of its k Nearest Neighbors (k normally takes a value of five). This technique is described in detail in Ref. 6 . SMOTE, however, may have problems with (i) isolated instances as it may generate instances in the majority class region by interpolating between two distant minority instances (see Figure 1 right), (ii) when there is overlapping between classes, possibly due to noise, and (iii) the value of k needs to be determined for each domain, which may require a computationally expensive trial-and-error process.. Recently, Han, Wang, and Mao proposed a modification of SMOTE, called borderline-SMOTE, where new instances are created only at the border of the minority class decision region.. 12 This method improves SMOTE by generating new instances only from the instances located at the borderline of the minority class. This method has two variants: Borderline-Smote1 (borderSM1), in which the instances are generated only with instances of the positive class, and Borderline-Smote2 (borderSM2), which interpolates between one positive and one negative class instance to generate a new one. Borderline-Smote, however, may have problems when there is a non-well defined border between instances of both classes, as it does not generate instances in such cases.. In Ref. 5, the authors introduced the Cluster-SMOTE algorithm. Their idea is that identifying even an approximation of the minority class regions would enhance the performance on global classification. Cluster-SMOTE simply creates clusters from the minority class examples with the k-means algorithm and then applies SMOTE to each cluster. In Cluster-SMOTE the parameter k, used as seed to find the clusters, is defined by the user. Results presented in Ref. 5 showed that, although there was not a clear winner, Cluster-SMOTE was preferred over SMOTE when evaluating with convex hull analysis over the ROC graph. If the selected k is smaller. than the actual number of clusters of the minority class, Cluster-SMOTE can still produce instances within majority class regions. The problem with Cluster-SMOTE is that the user has to know (or find) the right number of clusters (parameter k), and this is a really difficult task. 16 In fact, this problem is known as "the fundamental problem of cluster validity". 25 In our algorithms, the goal is to release the user from this task.. A recent method presented in Ref. 15 shows how, by adding noise to the new instances, the class imbalance problem can be reduced. They created an over and undersampling technique with noise to work with boosting algorithms. Their algorithm, called JOUS-Boost, combines over/undersampling with jittering. The way in which new examples are created (to avoid only replicating instances) consists on the addition of a small amount of random noise or jittering to the original values of the attributes of the instances. Uniform noise (−vσ j , vσ j ) is added according to the standard deviation of the attributes (denoted as σ), and a parameter (v) that is tuned to optimize performance. The value of v depends on the data set characteristics. This algorithm is designed to work with AdaBoost. It considers only numerical attributes, and the amount of jittering depends on the standard deviation of the attributes in the whole dataset. Our algorithms work with any classical classifier, with both numerical and nominal attributes. The amount of jittering, in the case of SOI-CJ, depends on the standard deviation of each attribute in the whole dataset and in each cluster, producing a more conservative strategy. Finally, they do not depend on any tuning parameter.. In this paper, we propose two new oversampling methods called SOI-C and SOI-CJ. In contrast with Cluster-SMOTE, our methods automatically generate clusters, so they do not depend on the definition of a k parameter. They also avoid the generation of instances in a majority class region. The number of new generated instances is proportional to the size of the clusters, so they are more robust to noise as very little oversampling will be produced around isolated (possibly noisy) examples. Finally, our algorithms deal with both numerical and nominal attributes.. It has recently been shown that ROS may achieve better performance than SMOTE and Borderline-Smote. 21 In our experimental setting, we worked with problems of two classes and generated as many synthetic instances as needed to balance the classes (for all the oversampling methods). In the case of datasets with more than two classes, we selected the minority class and merged the rest of the classes into a single majority class.. We tested our methods with forty three datasets. Twenty three were obtained from the UCI Machine Learning Repository 3 and twenty were artificially constructed. The datasets from UCI show different imbalance ratios, number of at-tributes, and have continuous and categorical attributes. The artificial datasets were generated to test the performance of the algorithms with a variety of controlled conditions.. For the artificial datasets, we considered different conditions as explained by Japkowicz et al. 13 We generated ten datasets with two continuous attributes (continuous); five datasets with two nominal attributes (nominal); and five datasets with one continuous and one nominal attribute (mixed). The ten continuous datasets were generated as follows: each instance belongs to a bi-dimensional domain in which each attribute is in the [0, 1] range, and each instance is associated with one of two classes. The input range for each dimension is divided in 2n regular intervals, where n is a complexity parameter that takes values from 1 to 5. With this configuration, there are 2(2n) areas in the bi-dimensional domain to create instances where adjacent areas have opposite output classes. The instances in the 2(2n) areas are randomly generated. We consider an imbalance ratio of 1:10 for five datasets and 1:20 for the other five datasets. In order to create the five nominal datasets, we considered two nominal attributes with four possible values each. The values for each attribute of an instance were randomly selected. We considered an imbalance ratio of 1:10 for the five nominal datasets. For the creation of the five combined datasets, we used the same technique used in the continuous datasets for the first attribute, and the same technique used in the nominal datasets for the second attribute. The characteristics of all datasets are shown in Table 1 .. Although accuracy is typically used to evaluate the performance of machine learning algorithms, this is not appropriate when the data classes are imbalanced. This is because accuracy averages the performance of all the classes, hiding the accuracy on the minority class. The evaluation metrics used for imbalanced domains are typically obtained from the confusion matrix. The results of our experiments were obtained using recall, precision, F-measure, and AUC (Area under the ROC curve) of the minority class. These measures have shown to give a better estimate of the result for the minority class classification.. We decided to use well-known and commonly-used learning algorithms to evaluate our oversampling methods that have also been tested on class imbalanced problems. The following algorithms from WEKA 24 were used in the tests: J48 (the Weka implementation of C4.5), Naive Bayes, IBK with k=3, PART, Multilayer perceptron, and AdaBoostM1 with J48.. We performed an ANOVA test to verify if the use of our oversampling technique produced a significant improvement in the performance of the algorithms. Additionally, we performed paired t-tests between our proposed algorithms and the rest of the oversampling algorithms with 95% of confidence.. We performed a ten-fold cross validation experiment ten times in which we created synthetic instances only for the training set, and averaged the results. This process is repeated for all the oversampling methods (ROS, SMOTE, borderSM1, borderSM2, SOI-C, and SOI-CJ) and all the performance metrics (Recall, Precision, F-measure, and AUC) over all the datasets.

O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O 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O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O 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Peripheral Circadian Oscillators Circadian rhythms are ~24-hour cycles of physiology and behavior that are synchronized to environmental cycles, such as the light-dark cycle. During the 20th century, most research focused on establishing the fundamental properties of circadian rhythms and discovering circadian pacemakers that were believed to reside in the nervous system of animals. During this time, studies that suggested the existence of circadian oscillators in peripheral organs in mammals were largely dismissed. The discovery of a single-locus circadian pacemaker in the nervous system of several animals affirmed the single-oscillator model of the circadian system. However, the discovery of the genes that constituted the molecular timekeeping system provided the tools for demonstrating the existence of bona fide circadian oscillators in nearly every peripheral tissue in animals, including rodents, in the late 1990s and early 2000s. These studies led to our current understanding that the circadian system in animals is a hierarchical multi-oscillatory network, composed of master pacemaker(s) in the brain and oscillators in peripheral organs. Further studies showed that altering the temporal relationship between these oscillators by simulating jet-lag and metabolic challenges in rodents caused adverse physiological outcomes. Herein we review the studies that led to our current understanding of the function and pathology of the hierarchical multi-oscillator circadian system. Circadian rhythms, which are 24-hour cycles of behavior and physiology (e.g. sleep/wake and core body temperature cycles), are ubiquitous and observed in most organisms. Before the 1960s, scientists established the three fundamental properties of circadian rhythms. First, the circadian rhythm must be self-sustained and free-run with a period of ~24 hours (i.e. the 24h rhythm persists in constant conditions). Second, the circadian rhythm should entrain to (be synchronized by) environmental cycles, such as the light-dark cycle. Third, the circadian rhythm should maintain a ~24h period across a physiological range of temperatures (i.e. it is temperature-compensated). After these formal properties were established, the next goal was to discover the locus of the self-sustained circadian oscillator.. During the 1960s and 1970s, scientists searched for the location of the circadian pacemaker in multicellular organisms. At this time, it was believed that a pacemaker(s) located in the brain or in a neuroendocrine gland drives overt circadian rhythms in behavior and physiology. The approaches used were to lesion, transplant, and culture candidate pacemaker tissues. For an organ to qualify as the circadian pacemaker, it had to adhere to the fundamental circadian properties. Lesioning should abolish the overt circadian rhythms and transplantation of that tissue should restore the circadian rhythms. Moreover, the tissue must express a circadian rhythm in vitro in constant conditions. Using these methods, the locus of the circadian pacemaker was identified in the central nervous system of several animals-the optic lobe in cockroaches (lesion [1] , transplant [2] , in vitro [3] ), the pineal gland in house sparrows (lesion [4] , transplant [5, 6] ) and the suprachiasmatic nucleus (SCN †) of the hypothalamus in rodents (lesion [7, 8] , in vivo isolation [9] , transplant [10] [11] [12] [13] , in vitro [14] [15] [16] ). It was later shown that the pacemakers in non-mammalian vertebrate species are distributed in a circadian axis and include the retina, SCN, and pineal gland. The dominant pacemaker in the axis varies in each species [17] [18] [19] .. Although the single-pacemaker model of the circadian system was the prevailing view in the 20 th century, scientists were also making discoveries suggesting that circadian oscillators could be located in peripheral tissues. This hierarchical multi-oscillator view of the circadian system was not widely accepted. More than 40 years after the initial discoveries that implicated peripheral tissues as circadian clocks, it is now dogma in the field of chronobiology that the circadian system is a hierarchical multi-oscillator network of circadian clocks. Herein we review the studies that led to our current understanding of the hierarchical multi-oscillator circadian system.

B B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B O O O O O O O O O O O O O O O O O O O O O B O O B O O O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O B O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O B O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O B O O O O O O O B O O O O O O O O O O O O O O O B O O B O O O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O B O


The construction of viewpoint aspect: the imperfective revisited Abstract. This paper argues for a constructionist approach for Aspect by exploring the idea that viewpoint aspect does not exert any altering force on the situation aspect properties of predicates. The proposal is developed by analyzing the point of view where conflicts between situation and viewpoint aspect have been argued to appear in previous literature, namely, the imperfective. The paper focuses on the syntax and semantics of the imperfective, which has been attributed a coercer role as a de-telicizer and de-stativizer in the progressive reading, and as a de-eventizizer in the so-called ability (or attitudinal) and habitual readings. This paper proposes that this is not necessary and provides a unified semantics for the imperfective preserving the properties of eventualities throughout the derivation. The article defends that the semantics of viewpoint aspect is encoded in functional heads containing interval-ordering predicates and quantifiers. This richer structure allows us to analyze aspectual forms with in principle contradictory content such as perfective and progressive, which sheds light onto other issues such as the understanding of non-culminating accomplishments. The proposed syntax is argued to have a corresponding explicit morphology in languages such as Spanish and a non-differentiating one in languages such as English, while the syntax-semantics underlying both of these languages is argued to be the same. " Although viewpoint aspect has been argued to be independent from the other aspectual realm known as situation aspect (also called lexical or inner aspect) by many authors (Smith 1991 , Bertinetto 2000 , Borik & Reinhart 2004 , Bohnemeyer & Swift 2004 , others have defended that viewpoint aspect properties follow from the semantic type of the verb (Herweg 1991) and can affect and modify situation aspect properties, working as a coercive mechanism (Dowty 1979 , Kamp & Rohrer 1983 Bertinetto 1994 , de Swart 1998 . In this work I analyze viewpoint aspect and address its relation with situation aspect properties. I argue that viewpoint aspect is construed in the syntax via a set of functional projections containing interval-ordering predicates (Zagona 1990 , Stowell 1993 , 2007 Klein 1994 Klein , 2009 Demirdache & Uribe-Etxebarria 2000 , 2007 and quantifiers over occasions (Verkuyl 1999) and that these functional projections exert no altering force on the properties of situation aspect of the predicates. In the spirit of proposals such as the one by Borer (2005, to appear), the perspective taken here is that if a certain combination occurs in the syntax an interpretation will be given to it. In this paper I have argued that the contribution of the viewpoint aspect heads is "constructive" and does not modify the event description by mutating the properties of predicates. I have revisited classical issues where the semantics of situation aspect properties have been understood to be at odds with viewpoint aspect properties and, in essence, I have argued that predicates keep their properties throughout. This does not amount to saying that any combination is possible (e.g. any state with the progressive); this means that when a combination is given in the syntax, viewpoint aspect heads do not alter the nature of the predicate. I have argued that different imperfective readings (e.g. progressive, attitudinal, habitual) emerge from a specific syntax-semantics based on intervalordering predicates and quantifiers and I have discussed the relations between such syntactic structures and their morphological forms. I have also shown that the progressive cannot be associated with the imperfective only and must be deconstructed. I have suggested that a syntax-semantics corresponding to perfective progressives underlie Spanish Perfective accomplishments with nonculminating readings, which situates the account of lack of completion within the realm of viewpoint aspect. Correspondingly, culminating readings are proposed to be a by-product of an implicature process based on assuming that the amount of information provided is the maximum (Grice 1975) . Regarding the correlations between the syntactic-semantic representation and the morphology I have argued that they vary across languages; the fact that only one form is shown morphologically (e.g. English past) does not mean that the language does not have different semantic components represented in the syntax.. The proposal that homo/heterogeneity is a property that depends on syntactic structure and that it does not correlate with given aspectual forms casts doubt on the correlation very often assumed between (a)telicity and (im)perfectivity. This has consequences for the analysis of different issues that at the moment mostly rely on this correlation, such as the acquisition of viewpoint aspect forms. First and second language acquisition studies often show that the relationship between viewpoint and situation aspect gives inconclusive results (see Salaberry 2002, 2008 and references therein, among others) . If the analysis explored in this paper is on the right track, the alleged rationale for the route of the acquisition of the imperfective/perfective contrast, based on situation aspectviewpoint correlations, should be taken cum grano salis and revisited.

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The multiscenario lot size problem with concave costs AbstractThe dynamic single-facility single-item lot size problem is addressed. The finite planning horizon is divided into 9 several time periods. Although the total demand is assumed to be a fixed value, the distribution of this demand among 10 the different periods is unknown. Therefore, for each period the demand can be chosen from a discrete set of values. For 11 this reason, all the combinations of the demand vector yield a set of different scenarios. Moreover, we assume that the 12 production/reorder and holding cost vectors can vary from one scenario to another. For each scenario, we consider as 13 the objective function the sum of the production/reorder and the holding costs. The problem consists of determining all 14 the Pareto-optimal or non-dominated production plans with respect to all scenarios. We propose a solution method 15 based on a multiobjective branch and bound approach. Depending on whether shortages are considered or not, dif-16 ferent upper bound sets are provided. Computational results on several randomly generated problems are reported. Since the late 1950s, special attention has been paid to the dynamic lot sizing problems. The interest lies 21 in the fact that these models fit a great number of real world problems. Wagner and Whitin [24], and in-22 dependently, Manne [9] pioneered this field. They assumed a multiperiod planning horizon with known 23 demand, and proposed a procedure which is based on both the dynamic programming approach and the 24 zero inventory order (ZIO) property. This property states that, among all those optimal plans, there exists 25 at least one, in which for each period, the product between the stock level and the production/reorder must 26 be equal to zero. This cost-minimizing production/reorder schedule has interesting qualitative features. The 27 extension to backlogging was studied by Zangwill [25,27] and Manne and Veinott [10]. Also, Veinott [20] 28 introduced the case with convex costs. 29Unlike the original dynamic lot size problem [24], where the demands through the whole horizon are 30 known, in this paper we consider that the demand vector is unknown rather than the total demand, which is

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