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"abstract": "Trust is required in a file sharing peer-to-peer system to achieve better cooperation among peers. In reputation-based peer-to-peer systems, reputation is used to build trust among peers. In these systems, highly reputable peers will usually be selected to upload requested files, decreasing significantly malicious uploads in the system. However, these peers need to be motivated to upload files by increasing the benefits that they receive from the system. In addition, it is necessary to motivate free riders to contribute to the system by sharing files. Malicious peers must be forced to contribute positively by uploading authentic files instead of malicious ones. In this paper, the contribution behavior of the peer is used as a guideline for service differentiation. The new concept of availability is introduced for partially-decentralized peer-to-peer systems. Both availability and involvement of the peer are used to assess its contribution behavior. Simulation results confirm the ability of the proposed scheme to effectively identify both free riders and malicious peers and reduce the level of service provided to them. Simulation results also confirm that based on rational behavior, peers are motivated to increase their contribution to receive services. Moreover, using our scheme, peers must continuously participate, reducing significantly the so-called milking phenomenon.",
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"content": "Trust is required in a file sharing peer-to-peer system to achieve better cooperation among peers. In reputation-based peer-to-peer systems, reputation is used to build trust among peers. In these systems, highly reputable peers will usually be selected to upload requested files, decreasing significantly malicious uploads in the system. However, these peers need to be motivated to upload files by increasing the benefits that they receive from the system. In addition, it is necessary to motivate free riders to contribute to the system by sharing files. Malicious peers must be forced to contribute positively by uploading authentic files instead of malicious ones. In this paper, the contribution behavior of the peer is used as a guideline for service differentiation. The new concept of availability is introduced for partially-decentralized peer-to-peer systems. Both availability and involvement of the peer are used to assess its contribution behavior. Simulation results confirm the ability of the proposed scheme to effectively identify both free riders and malicious peers and reduce the level of service provided to them. Simulation results also confirm that based on rational behavior, peers are motivated to increase their contribution to receive services. Moreover, using our scheme, peers must continuously participate, reducing significantly the so-called milking phenomenon.",
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"normalizedAbstract": "Trust is required in a file sharing peer-to-peer system to achieve better cooperation among peers. In reputation-based peer-to-peer systems, reputation is used to build trust among peers. In these systems, highly reputable peers will usually be selected to upload requested files, decreasing significantly malicious uploads in the system. However, these peers need to be motivated to upload files by increasing the benefits that they receive from the system. In addition, it is necessary to motivate free riders to contribute to the system by sharing files. Malicious peers must be forced to contribute positively by uploading authentic files instead of malicious ones. In this paper, the contribution behavior of the peer is used as a guideline for service differentiation. The new concept of availability is introduced for partially-decentralized peer-to-peer systems. Both availability and involvement of the peer are used to assess its contribution behavior. Simulation results confirm the ability of the proposed scheme to effectively identify both free riders and malicious peers and reduce the level of service provided to them. Simulation results also confirm that based on rational behavior, peers are motivated to increase their contribution to receive services. Moreover, using our scheme, peers must continuously participate, reducing significantly the so-called milking phenomenon.",
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"title": "Domain-Aware Universal Style Transfer",
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"abstract": "Style transfer aims to reproduce content images with the styles from reference images. Existing universal style transfer methods successfully deliver arbitrary styles to original images either in an artistic or a photo-realistic way. However, the range of \"arbitrary style\" defined by existing works is bounded in the particular domain due to their structural limitation. Specifically, the degrees of content preservation and stylization are established according to a predefined target domain. As a result, both photo-realistic and artistic models have difficulty in performing the desired style transfer for the other domain. To overcome this limitation, we propose a unified architecture, Domain-aware Style Transfer Networks (DSTN) that transfer not only the style but also the property of domain (i.e., domainness) from a given reference image. To this end, we design a novel domainness indicator that captures the domainness value from the texture and structural features of reference images. Moreover, we introduce a unified framework with domainaware skip connection to adaptively transfer the stroke and palette to the input contents guided by the domainness indicator. Our extensive experiments validate that our model produces better qualitative results and outperforms previous methods in terms of proxy metrics on both artistic and photo-realistic stylizations. All codes and pre-trained weights are available at Kibeom-Hong/Domain-Aware-Style-Transfer.",
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"abstract": "Most existing style transfer methods follow the assumption that styles can be represented with global statistics (e.g., Gram matrices or covariance matrices), and thus address the problem by forcing the output and style images to have similar global statistics. An alternative is the assumption of local style patterns, where algorithms are designed to swap similar local features of content and style images. However, the limitation of these existing methods is that they neglect the semantic structure of the content image which may lead to corrupted content structure in the output. In this paper, we make a new assumption that image features from the same semantic region form a manifold and an image with multiple semantic regions follows a multi-manifold distribution. Based on this assumption, the style transfer problem is formulated as aligning two multi-manifold distributions and a Manifold Alignment based Style Transfer (MAST) framework is proposed. The proposed frame-work allows semantically similar regions between the output and the style image share similar style patterns. Moreover, the proposed manifold alignment method is flexible to allow user editing or using semantic segmentation maps as guidance for style transfer. To allow the method to be applicable to photorealistic style transfer, we propose a new adaptive weight skip connection network structure to preserve the content details. Extensive experiments verify the effectiveness of the proposed framework for both artistic and photorealistic style transfer. Code is available at https://github.com/NJUHuoJing/MAST.",
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"title": "Saliency-Guided Image Style Transfer",
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"abstract": "In this paper, we propose an automatic saliency-guide style transfer method, which deploys style transfer to the salient object in an image. Our method can generate a new artwork form of image, which aggregates the characteristics of real-world images and artistic images. First, the existing style transfer method is utilized to combine the content of an arbitrary real-world image with the appearance of a well-known artwork, and to produce the stylized image with high perceptual quality. Then, the saliency map of real-world image is used to fuse the real-world image and the stylized image. In this way, the fusion image not only contains the style of artwork but also highlights the salient object. Finally, the saliency-based refinement is employed to improve the quality of object boundaries, and to enable the stylized object blends into the surroundings naturally. Experimental results demonstrate the effectiveness of the proposed saliency-guided image style transfer method.",
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"content": "In this paper, we propose an automatic saliency-guide style transfer method, which deploys style transfer to the salient object in an image. Our method can generate a new artwork form of image, which aggregates the characteristics of real-world images and artistic images. First, the existing style transfer method is utilized to combine the content of an arbitrary real-world image with the appearance of a well-known artwork, and to produce the stylized image with high perceptual quality. Then, the saliency map of real-world image is used to fuse the real-world image and the stylized image. In this way, the fusion image not only contains the style of artwork but also highlights the salient object. Finally, the saliency-based refinement is employed to improve the quality of object boundaries, and to enable the stylized object blends into the surroundings naturally. Experimental results demonstrate the effectiveness of the proposed saliency-guided image style transfer method.",
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"affiliation": "Shanghai University",
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"abstract": "Predicting model which refers to mechanical properties with material composition and techniques can be founded to reduce test times, increase efficiency and realize the optimization of process. This paper proposes to apply the Support Vector Machine to set up the nonlinear mapping from influence factors of material performances to mechanical properties. Taking the wheat straw-reinforced composite for instance, the prediction model based on support vector machine has been built. Besides, the model is used to optimize process parameters of injection molding and find the range of best parameters. The simulation result shows the founded model has preferable learning and generalization capabilities, which performs effectively in predicting mechanical properties. Therefore it is feasible to optimize process parameters and the technology is worthy to be applied and spread in the research of material performance.",
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"title": "RF MEMS resonators: Material properties extraction",
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"abstract": "Analytical formula for the resonance frequencies of circular, square, and hexagonal MEMS resonators are extracted based on the analytical model for extensional vibrations of MEMS resonators. Material properties for the MEMS resonators are then extracted based on the analytical formula. Experimental measurement is done to extract the material properties of single crystal silicon MEMS resonators to verify the extraction method.",
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"affiliation": "American University in Cairo, Cairo, Egypt",
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"abstract": "Sentiment analysis of the text data available on the web either in the form of blogs or at social media sites such as Twitter, Facebook, and LinkedIn, offers information through which to assess people's perspective of products and services that are of interest to them. Consumers routinely scour the Internet to assess other user's reviews for a product or a service before making their own decision. Likewise, these same data can potentially provide businesses a snapshot in time of the users' response to their products/services and even the trends over time. This information gained through sentiment analysis can then be used by businesses to make decisions about improving their products and services, and gain an increased edge over their competitors. In this paper, we illustrate the potential of sentiment analysis of Twitter data to gauge users' response to popular smart phone brands and their underlying operating systems. Specifically, our objective is to investigate whether the tweets available on the web are sufficient to gain useful insight about the performance of popular smart phone brands, their battery life, screen quality, and on the perceived performance of the phones operating systems. Our results show that although the Twitter data does provide some information about users' sentiments to the popular smart phone brands and their underlying operating systems, the amount of data available for different brands varies significantly. This limitation makes the comprehensive analysis of users' response somewhat more challenging for some brands compared to others and consequently makes the comparison between brands almost impossible.",
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"content": "Sentiment analysis of the text data available on the web either in the form of blogs or at social media sites such as Twitter, Facebook, and LinkedIn, offers information through which to assess people's perspective of products and services that are of interest to them. Consumers routinely scour the Internet to assess other user's reviews for a product or a service before making their own decision. Likewise, these same data can potentially provide businesses a snapshot in time of the users' response to their products/services and even the trends over time. This information gained through sentiment analysis can then be used by businesses to make decisions about improving their products and services, and gain an increased edge over their competitors. In this paper, we illustrate the potential of sentiment analysis of Twitter data to gauge users' response to popular smart phone brands and their underlying operating systems. Specifically, our objective is to investigate whether the tweets available on the web are sufficient to gain useful insight about the performance of popular smart phone brands, their battery life, screen quality, and on the perceived performance of the phones operating systems. Our results show that although the Twitter data does provide some information about users' sentiments to the popular smart phone brands and their underlying operating systems, the amount of data available for different brands varies significantly. This limitation makes the comprehensive analysis of users' response somewhat more challenging for some brands compared to others and consequently makes the comparison between brands almost impossible.",
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"abstract": "Permission mechanisms serve as the main measure to protect users privacy and security in Android applications. Modern smartphone operating systems (Android 6.0 and later versions) prompt users to regulate permissions using ask-on-first-use policy. Much research has been done to dynamically regulate permissions depending on user preferences and contexts in modern operation systems. However, all these techniques have limitations-they heavily rely on users' current or historical decisions on granting permissions, ignoring the fact that users are not experts on privacy protection, i.e., whether a permission shall be granted. In this work, we propose a system to automatically recommend runtime-permission to users. The main idea behind is that the application descriptions reflecting functional information can be used to analyze whether a permission is needed by the application. In more details, using description mining, we extract multiple topics and build a topic-permission mapper. Given an application as input, we first decide which topics it belongs to and then recommend the permissions according to the topic-permission mapper. As the output, besides binary recommendation of \"allow\" or \"deny\" recommendations, we provide explanations for the recommendations to uncover the reason for users. We implemented our approach in a tool- AutoPer, and evaluated the approach using 28,850 Android applications from Google Play. The experiments show that our approach achieves a fairly good performance with an accuracy of 81.0%, which demonstrates the effectiveness of AutoPer for permission recommendation.",
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"affiliation": "Nankai University",
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"affiliation": "National University of Singapore",
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"abstract": "This paper proposes an image recommendation system for automatic report generation on economic topics. For a given specific headline query on daily economic events, the proposed system collects candidate images through a public search engine and choose the most appropriate one for summary report on the event. The proposed system is composed of two deep learning-based modules of different modalities: image filtering module and text matching module. In the image filtering module, an image classifier network is adopted to filter out non-photo images such as graph and tables. In the text matching module, a sentence embedding network is adopted to get text query vector and image caption vector and calculate their matching scores. By analyzing image and text information together, the proposed system can recommend suitable images both visually and semantically. Through computational experiments using a number of recent economic topics, we confirm that recommended images of the proposed system are more appropriate than that of conventional search engine.",
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"abstract": "Knowledge about the biological variability of anatomical objects is essential for statistical shape analysis and discrimination between healthy and pathological structures. This paper describes ongoing research on a novel approach that incorporates variability of a training set into the generation of a characteristic 3D shape model. The proposed shape representation is a hybrid of a fine-scale global boundary description and a coarse-scale local medial description. The hybrid overcomes inherent limitations of pure medial based or pure boundary based descriptions. The medial description composed of a net of medial primitives (M-rep) with fixed graph properties is derived from the shape space spanned by the major deformation eigenmodes of a boundary description based on spherical harmonic descriptors (SPHARM). The topology of the M-rep is determined by studying pruned 3D Voronoi skeletons in the given shape space. Its SPHARM descriptors and an individually deformed M-rep model characterize shapes. The hybrid shape description gives an implicit correspondence on the boundary and on the medial manifold, thus enabling a more powerful statistical analysis.",
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"abstract": "In this paper an interactive real time 3D segmentation method is presented. Not as the usual slice by slice segmentation in 2D, we perform volumetric segmentation on the 3D image directly, by the extention of fast marching algorithm to 3D. The key steps that set seed points and construct velocity in the fast marching method are put forward. Then 3D segmentation results are show in various anatomical organ (lung, trachea), and the whole segmentation procedure takes only several minutes.",
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"content": "In this paper an interactive real time 3D segmentation method is presented. Not as the usual slice by slice segmentation in 2D, we perform volumetric segmentation on the 3D image directly, by the extention of fast marching algorithm to 3D. The key steps that set seed points and construct velocity in the fast marching method are put forward. Then 3D segmentation results are show in various anatomical organ (lung, trachea), and the whole segmentation procedure takes only several minutes.",
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"affiliation": "Northwestern Polytechnical University, China",
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"title": "Application of Pixel Intensity Based Medical Image Segmentation Using NSGA II Based Opti MUSIG Activation Function",
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"abstract": "Medical image segmentation is a challenging task for analyzing the magnetic resonance (MRI) images. These type of images contain missing or diffuse organ/tissue boundaries due to poor image contrast. Medical image segmentation can be addressed effectively by genetic algorithms (GAs). In this article, an application of pixel intensity based medical image segmentation is presented by the non-dominated sorting genetic algorithm-II (NSGA II) based optimized MUSIG (Opti MUSIG) activation function with a multilayer self organizing neural network (MLSONN) architecture. This method is compared with the process of medical image segmentation by the MUSIG activation function with the MLSONN architecture. Both the methods are applied on two real life MRI images. The comparison shows that NSGA II based Opti MUSIG activation function performs better medical image segmentation than the MUSIG activation function based method.",
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"content": "Medical image segmentation is a challenging task for analyzing the magnetic resonance (MRI) images. These type of images contain missing or diffuse organ/tissue boundaries due to poor image contrast. Medical image segmentation can be addressed effectively by genetic algorithms (GAs). In this article, an application of pixel intensity based medical image segmentation is presented by the non-dominated sorting genetic algorithm-II (NSGA II) based optimized MUSIG (Opti MUSIG) activation function with a multilayer self organizing neural network (MLSONN) architecture. This method is compared with the process of medical image segmentation by the MUSIG activation function with the MLSONN architecture. Both the methods are applied on two real life MRI images. The comparison shows that NSGA II based Opti MUSIG activation function performs better medical image segmentation than the MUSIG activation function based method.",
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"abstract": "[Context] The market for software targeting children, both for education and entertainment, is growing. Existing work, mainly from HCI, has considered the effectiveness of elicitation techniques for eliciting requirements from children as part of a design process. [Objective] However, we are lacking work which compares requirements elicitation techniques when used with children. [Methods] This study compares five elicitation techniques, taking into consideration the effectiveness and efficiency of each technique. Techniques were used with a total of 54 children aged 8-13, eliciting requirements for a museum flight simulator. We compare techniques by looking at the number and type of requirements discovered, perceived participant satisfaction, resources required, perceived usefulness, and requirements coverage of domain specific categories. [Conclusions] We observed notable differences between the techniques, including the effectiveness of observations and relative ineffectiveness of questionnaires. We present a set of guidelines to aid industry in eliciting requirements for child-friendly software.",
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"abstract": "Accurate measurements of the physiological parameters like branching angles, branch lengths and diameters of bronchial tree structures help in addressing the diagnostic questions related to obstructive lung disease. To facilitate these measurements, 3D bronchial trees are reduced to a straight line central axis tree. We designed a two pass algorithm to compute the central axis tree of bronchial tree structures. In the first pass, the topological branching tree structure T is obtained by using a top-down region growing algorithm on the tree volume. In the second pass, T is used to region grow bottom-up from the leaves, in order to obtain accurate centroid points that lie along the axes of the branches. Using these centroid points at each bifurcation, the branch point and the three direction vectors along the branches are computed, by solving a non-linear optimization problem. By connecting the computed branch points at each bifurcation with straight lines, we obtain the central axis tree on which we make the measurements. We also ran our algorithm on 3D tree model (cylindrical branches) that simulate bronchial trees and the computed central axis compared favorably with the ground truth central axis in terms of the measured physiological parameters.",
"abstracts": [
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"content": "Accurate measurements of the physiological parameters like branching angles, branch lengths and diameters of bronchial tree structures help in addressing the diagnostic questions related to obstructive lung disease. To facilitate these measurements, 3D bronchial trees are reduced to a straight line central axis tree. We designed a two pass algorithm to compute the central axis tree of bronchial tree structures. In the first pass, the topological branching tree structure T is obtained by using a top-down region growing algorithm on the tree volume. In the second pass, T is used to region grow bottom-up from the leaves, in order to obtain accurate centroid points that lie along the axes of the branches. Using these centroid points at each bifurcation, the branch point and the three direction vectors along the branches are computed, by solving a non-linear optimization problem. By connecting the computed branch points at each bifurcation with straight lines, we obtain the central axis tree on which we make the measurements. We also ran our algorithm on 3D tree model (cylindrical branches) that simulate bronchial trees and the computed central axis compared favorably with the ground truth central axis in terms of the measured physiological parameters.",
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"abstract": "We study the expressive power of logical interpretations on the class of scattered trees, namely those with countably many infinite branches. Scattered trees can be thought of as the tree analogue of scattered linear orders. Every scattered tree has an ordinal rank that reflects the structure of its infinite branches. We prove, roughly, that trees and orders of large rank cannot be interpreted in scattered trees of small rank. We consider a quite general notion of interpretation: each element of the interpreted structure is represented by a set of tuples of subsets of the interpreting tree. Our trees are countable, not necessarily finitely branching, and may have finitely many unary predicates as labellings. We also show how to replace injective set-interpretations in (not necessarily scattered) trees by âfinitary' set-interpretations.",
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"abstract": "Indoor positioning is one of the difficult problems in current navigation systems. There is an increasing demand for detecting the locations of objects and humans inside closed environments in various fields including surveillance, robotics, and entertainment. Recent works focus on indoor navigation systems using different technologies including Wireless Local Area Network (WLAN), Radio Frequency Identification (RFID), Inertial Measurement Unit (IMU), and Simultaneous Localization and Mapping (SLAM). Research reveals using these technologies alone is inefficient in terms of accuracy and cost. To address this issue, we propose a marker-based Augmented Reality (AR) indoor navigation system with integrated SLAM and IMU. We use Unity's AR Foundation Framework for highly accurate results with minimum hardware requirements.",
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"affiliation": "Bilkent University,Department of Computer Engineering,Ankara,Turkey",
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"abstract": "One of the critical issues in search engines is the size of search indexes: as the number of documents handled by an engine increases, the search must preserve its efficiency, despite the growth of indexing structures. A widely agreed solution to this problem is the adoption of smaller, or pruned, indexes that allow increasing the retrieval speed while keeping the search quality as high as possible. This paper extends the notion of pruned index to semantic search systems based on conceptual vector space models and proposes a new self-similarity metric for index pruning. A conceptual vector space model represents documents as vectors ina n-dimensional space where each dimension corresponds to anontology concept. The pruning algorithm proposed in this paper acts on the basis of document self-similarity, preserving only the most significant components of a document conceptual vector. Unlike many already proposed algorithms, the self-similarity metric is only based on local information and does not require to recompute the whole pruned index when new documents are added, i.e., it can be used on-line, possibly combined with other off-line pruning policies. The proposed metric is tested against two benchmark sets respectively related to Siderurgy (250 documents annotated with respect to the e-Class ontology) and Disability (2500 documents annotated with respect to the Passepartout ontology). Results show that the compression ratio achieved by this technique is satisfying (50%), while ranking similarity with results coming from non-pruned indexes remains sufficiently high (80%), thus preserving the quality of provided results.",
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"content": "One of the critical issues in search engines is the size of search indexes: as the number of documents handled by an engine increases, the search must preserve its efficiency, despite the growth of indexing structures. A widely agreed solution to this problem is the adoption of smaller, or pruned, indexes that allow increasing the retrieval speed while keeping the search quality as high as possible. This paper extends the notion of pruned index to semantic search systems based on conceptual vector space models and proposes a new self-similarity metric for index pruning. A conceptual vector space model represents documents as vectors ina n-dimensional space where each dimension corresponds to anontology concept. The pruning algorithm proposed in this paper acts on the basis of document self-similarity, preserving only the most significant components of a document conceptual vector. Unlike many already proposed algorithms, the self-similarity metric is only based on local information and does not require to recompute the whole pruned index when new documents are added, i.e., it can be used on-line, possibly combined with other off-line pruning policies. The proposed metric is tested against two benchmark sets respectively related to Siderurgy (250 documents annotated with respect to the e-Class ontology) and Disability (2500 documents annotated with respect to the Passepartout ontology). Results show that the compression ratio achieved by this technique is satisfying (50%), while ranking similarity with results coming from non-pruned indexes remains sufficiently high (80%), thus preserving the quality of provided results.",
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"abstract": "There is a huge increase of interest for time series methods and techniques. Virtually every piece of information collected from human, natural, and biological processes is susceptible to changes over time, and the study of how these changes occur is a central issue in fully understanding such processes. Among all time series mining tasks, classification is likely to be the most prominent one. In time series classification there is a significant body of empirical research that indicates that k-nearest neighbor rule in the time domain is very effective. However, certain time series features are not easily identified in this domain and a change in representation may reveal some significant and unknown features. In this work, we propose the use of recurrence plots as representation domain for time series classification. Our approach measures the similarity between recurrence plots using Campana-Keogh (CK-1) distance, a Kolmogorov complexity-based distance that uses video compression algorithms to estimate image similarity. We show that recurrence plots allied to CK-1 distance lead to significant improvements in accuracy rates compared to Euclidean distance and Dynamic Time Warping in several data sets. Although recurrence plots cannot provide the best accuracy rates for all data sets, we demonstrate that we can predict ahead of time that our method will outperform the time representation with Euclidean and Dynamic Time Warping distances.",
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"abstract": "Quantification of the similarity between data sequences is important in different database and data mining tasks such as indexing, retrieving, clustering and classification, many similarity metrics (Euclidean, DTW, among others) operate directly in the raw representation of the sequences, but this implies when the sequences are compared, not take into account information about the collective behavior of the data that forms a sequence and the hidden relations between such data, such information can be important for classification of sequences based on their structures and their relations with the dynamics that such structures can represent (e.g. stationary, random, complex). We propose a computational technique for similarity analysis and classification of recurrence plot patterns: RecurrenceVs. The results show that the proposed technique is able to classify data sequences by similarity families based on the recurrence plot patterns, which preserve the information about the structure and dynamics represented by the data sequences.",
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"id": "trans/td/2012/02/ttd2012020271",
"title": "Delegation-Based I/O Mechanism for High Performance Computing Systems",
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"abstract": "The complexity of parallel I/O systems imposes significant challenge in managing and utilizing the available system resources to meet application performance, portability and usability goals. We believe that a parallel I/O system that automatically selects efficient I/O plans for user applications is a solution to this problem. In this paper, we present such an automatic performance optimization approach for scientific applications performing collective I/O requests on multidimensional arrays. The approach is based on a high level description of the target workload and execution environment characteristics, and applies genetic algorithms to select high quality I/O plans. We have validated this approach in the Panda parallel I/O library. Our performance evaluations on the IBM SP show that this approach can select high quality I/O plans under a variety of system conditions with a low overhead, and the genetic algorithm-selected I/O plans are in general better than the default plans used in Panda.",
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