Query: The Impact of Comments and Recommendation System on Online Shopper Buying Behaviour

Relevant passages:
Friends and neighbors on the Web <=SEP=> The Internet has become a rich and large repository of information about us as individuals. Anything from the links and text on a user's homepage to the mailing lists the user subscribes to are reflections of social interactions a user has in the real world. In this paper we devise techniques to mine this information in order to predict relationships between individuals. Further we show that some pieces of information are better indicators of social connections than others, and that these indicators vary between user populations and provide a glimpse into the social lives of individuals in different communities. Our techniques provide potential applications in automatically inferring real-world connections and discovering, labeling, and characterizing communities.

Consumer Decision Making in Online Shopping Environments: The Effects of Interactive Decision Aids <=SEP=> Despite the explosive growth of electronic commerce and the rapidly increasing number of consumers who use interactive media (such as the World Wide Web) for prepurchase information search and online shopping, very little is known about how consumers make purchase decisions in such settings. A unique characteristic of online shopping environments is that they allow vendors to create retail interfaces with highly interactive features. One desirable form of interactivity from a consumer perspective is the implementation of sophisticated tools to assist shoppers in their purchase decisions by customizing the electronic shopping environment to their individual preferences. The availability of such tools, which we refer to as interactive decision aids for consumers, may lead to a transformation of the way in which shoppers search for product information and make purchase decisions. The primary objective of this paper is to investigate the nature of the effects that interactive decision aids may have on consumer decision making in online shopping environments. While making purchase decisions, consumers are often unable to evaluate all available alternatives in great depth and, thus, tend to use two-stage processes to reach their decisions. At the first stage, consumers typically screen a large set of available products and identify a subset of the most promising alternatives. Subsequently, they evaluate the latter in more depth, perform relative comparisons across products on important attributes, and make a purchase decision. Given the different tasks to be performed in such a two-stage process, interactive tools that provide support to consumers in the following respects are particularly valuable: (1) the initial screening of available products to determine which ones are worth considering further, and (2) the in-depth comparison of selected products before making the actual purchase decision. This paper examines the effects of two decision aids, each designed to assist consumers in performing one of the above tasks, on purchase decision making in an online store. The first interactive tool, a recommendation agent (RA), allows consumers to more efficiently screen the (potentially very large) set of alternatives available in an online shopping environment. Based on self-explicated information about a consumer’s own utility function (attribute importance weights and minimum acceptable attribute levels), the RA generates a personalized list of recommended alternatives. The second decision aid, a comparison matrix (CM), is designed to help consumers make in-depth comparisons among selected alternatives. The CM allows consumers to organize attribute information about multiple products in an alternatives attributes matrix and to have alternatives sorted by any attribute. Based on theoretical and empirical work in marketing, judgment and decision making, psychology, and decision support systems, we develop a set of hypotheses pertaining to the effects of these two decision aids on various aspects of consumer decision making. In particular, we focus on how use of the RA and CM affects consumers’ search for product information, the size and quality of their consideration sets, and the quality of their purchase decisions in an online shopping environment. A controlled experiment using a simulated online store was conducted to test the hypotheses. The results indicate that both interactive decision aids have a substantial impact on consumer decision making. As predicted, use of the RA reduces consumers’ search effort for product information, decreases the size but increases the quality of their consideration sets, and improves the quality of their purchase decisions. Use of the CM also leads to a decrease in the size but an increase in the quality of consumers’ consideration sets, and has a favorable effect on some indicators of decision quality. In sum, our findings suggest that interactive tools designed to assist consumers in the initial screening of available alternatives and to facilitate in-depth comparisons among selected alternatives in an online shopping environment may have strong favorable effects on both the quality and the efficiency of purchase decisions—shoppers can make much better decisions while expending substantially less effort. This suggests that interactive decision aids have the potential to drastically transform the way in which consumers search for product information and make purchase decisions. (Decision Making; Online Shopping; Electronic Commerce; Decision Aids; Recommendation Agents; Consumer Behavior; Information Search; Consideration Sets; Information Processing)

Efficient identification of Web communities <=SEP=> "# %$& $' & )(*$' $+ , .-/ 0 %1 !23 3 45$768 3 + 91: 1 3 ;< * ="> 1 $? )(> 0 % +@ 3 A % B C @ ="> 1 $ D*EA ="> 91 $F %(:$' G H % = @ I . % A"> * J 3 K 2 L K . 3 = & ,M 3 ON . QP 3 @ R S(T1G % 9 ! %1 4>U) 7 1 V W$' 1 ! 3$? X> %$' . Y %( 45 , 7 ="> 1 $ U% % :$' L 4Y $' L$Z $V %( ! 232 @[4 . 7 + % @[ ="> 91 $ D \ (] K $' . A 91G . 723 17 ) 0 1G 723$7 = Y M . X C . ) A )X X 1 ,M @ & , ^ 3 & ="> 91 $' 3XA"K A % _% K 3 _= ^_%1G %X B 3 @ ="K ` ! 1G 72 7 23 3 45$< ^ *-5 1' %2 $' 3 4Y 5 Dba! c 9(T@ (T 3-/ $'$7 %(< * %X X 1 .M 3 & ) 3 A %23_ )1 3 d L$c $Z '1G ) . 7 R$' -/ 1G )2* 1G 72*1 $' 23 $C ) C L K (T e " $ UF % %1 3 $ U : "A1 3 _%$ U ) A ) 1723 3 4 %X> 23 _% L $f , c )1 ^ $' (] 2g" 7 ) %$' 32 A . , _ )1 3h . iD \ X X 23 3 . ) 3 % $7 %(< % 1c )X X 1 % G A 3 2L F(T %@ $' . A 91G . 723 1 $F % C$' . ,1 9 B _ 3 $ U> % & , L ^X> %X 2j , 3 )( X> )1' G %2g . ) _% %1 3 $ Uk ) L X 1 ,-/ . 2 1 3 _ D Categories and Subject Descriptors l`D m5D n o pCq r q sSq t u vQq wSq x u yzu wir9{[|i}^ , G %" %$' \ X X 2L 3 . , 3 $I~  %[F=] ]5  l D 5D ^o  wg  %yzq r.T>wzir  ,q x u`q w?A&u5r )Tu5 q j{|  (T %1 & ) 3 % 5 . )1 G % f 9 '1 L )2 ~  5  99]K%^].  %9+%8 % ¡   99T5 5¢ D m5D m o pATt.£ ,u r u vQq r ¤Su yzq r.T£/t {| ¢ 1G %X =a! %1' K~ >98¥W % ¦^§ ' / ̈  9 

Reducing Buyer Search Costs : Implications for Electronic Marketplaces <=SEP=> Information systems can serve as intermediaries between the buyers and the sellers in a market, creating an “electronic marketplace” that lowers the buyers' cost to acquire information about seller prices and product offerings. As a result, electronic marketplaces reduce the inefficiencies caused by buyer search costs, in the process reducing the ability of sellers to extract monopolistic profits while increasing the ability of markets to optimally allocate productive resources. This article models the role of buyer search costs in markets with differentiated product offerings. The impact of reducing these search costs is analyzed in the context of an electronic marketplace, and the allocational efficiencies such a reduction can bring to a differentiated market are formalized. The resulting implications for the incentives of buyers, sellers and independent intermediaries to invest in electronic marketplaces are explored. Finally, the possibility to separate price information from product attribute information is introduced, and the implications of designing markets promoting competition along each of these dimensions are discussed. Copyright © 1997 by The Institute of Management Sciences

Learning Probabilistic Models of Link Structure <=SEP=> Most real-world data is heterogeneous and richly interconnected. Examples include the Web, hypertext, bibliometric data and social networks. In contrast, most statistical learning methods work with “flat” data representations, forcing us to convert our data into a form that loses much of the link structure. The recently introduced framework of probabilistic relational models(PRMs) embraces the object-relational nature of structured data by capturing probabilistic interactions between attributes of related entities. In this paper, we extend this framework by modeling interactions between the attributes and the link structure itself. An advantage of our approach is a unified generative model for both content and relational structure. We propose two mechanisms for representing a probabilistic distribution over link structures: reference uncertaintyandexistence uncertainty . We describe the appropriate conditions for using each model and present learning algorithms for each. We present experimental results showing that the learned models can be used to predict link structure and, moreover, the observed link structure can be used to provide better predictions for the attributes in the model.

A 2.1 MHz Crystal Oscillator Time Base with a Current Consumption under 500 nA <=SEP=> A micro-power circuit is encapsulated with a 2.1 MHz ZT-cut quartz in a vacuum package. The oscillator core has 2 complementary active MOSFETS and amplitude stabilization. New coupling and biasing circuits, and dynamic frequency dividers allow to achieve ±2 ppm frequency stability down to 1.8 V with a current under 0.5 ¿A.

Partial Image Encryption based on Block wise Shuffling using Arnold Map <=SEP=> To prevent image from unauthorized access, Encryption techniques of digital images play a very important role . The Fully image encryption techniques is very complex in nature and takes a lot of computational time. But, certain applications do not require total encryption; it requires a part of the multimedia data to be transparent to all users, like Pay-TV or Payable Internet Imaging Albums which involves encrypting the most meaningful parts of an image. In this paper we focus on a partial image encryption technique based on block wise shuffling with the help of Arnold map . Firstly image is divided into blocks then Blocks within the image are permuted by using Arnold map and final permuted block are combined that yields partial encrypted images . This process is repeated for different block size. The PSNR and NPCR obtained by our technique shows that the proposed technique gives better result than the existing techniques.

Solid lipid nanoparticles: production, characterization and applications. <=SEP=> Solid lipid nanoparticles (SLN) have attracted increasing attention during recent years. This paper presents an overview about the selection of the ingredients, different ways of SLN production and SLN applications. Aspects of SLN stability and possibilities of SLN stabilization by lyophilization and spray drying are discussed. Special attention is paid to the relation between drug incorporation and the complexity of SLN dispersions, which includes the presence of alternative colloidal structures (liposomes, micelles, drug nanosuspensions, mixed micelles, liquid crystals) and the physical state of the lipid (supercooled melts, different lipid modifications). Appropriate analytical methods are needed for the characterization of SLN. The use of several analytical techniques is a necessity. Alternative structures and dynamic phenomena on the molecular level have to be considered. Aspects of SLN administration and the in vivo fate of the carrier are discussed.

School-based interventions for aggressive and disruptive behavior: update of a meta-analysis. <=SEP=> BACKGROUND
Research about the effectiveness of school-based psychosocial prevention programs for reducing aggressive and disruptive behavior was synthesized using meta-analysis. This work updated previous work by the authors and further investigated which program and student characteristics were associated with the most positive outcomes.


METHODS
Two hundred forty-nine experimental and quasi-experimental studies of school-based programs with outcomes representing aggressive and/or disruptive behavior were obtained. Effect sizes and study characteristics were coded from these studies and analyzed.


RESULTS
Positive overall intervention effects were found on aggressive and disruptive behavior and other relevant outcomes. The most common and most effective approaches were universal programs and targeted programs for selected/indicated children. The mean effect sizes for these types of programs represent a decrease in aggressive/disruptive behavior that is likely to be of practical significance to schools. Multicomponent comprehensive programs did not show significant effects and those for special schools or classrooms were marginal. Different treatment modalities (e.g., behavioral, cognitive, social skills) produced largely similar effects. Effects were larger for better-implemented programs and those involving students at higher risk for aggressive behavior.


CONCLUSIONS
Schools seeking prevention programs may choose from a range of effective programs with some confidence that whatever they pick will be effective. Without the researcher involvement that characterizes the great majority of programs in this meta-analysis, schools might be well-advised to give priority to those that will be easiest to implement well in their settings.

Management patterns: SDN-enabled network resilience management <=SEP=> Software-defined networking provides abstractions and a flexible architecture for the easy configuration of network devices, based on the decoupling of the data and control planes. This separation has the potential to considerably simplify the implementation of resilience functionality (e.g., traffic classification, anomaly detection, traffic shaping) in future networks. Although software-defined networking in general, and OpenFlow as its primary realisation, provide such abstractions, support is still needed for orchestrating a collection of OpenFlow-enabled services that must cooperate to implement network-wide resilience. In this paper, we describe a resilience management framework that can be readily applied to this problem. An important part of the framework are policy-controlled management patterns that describe how to orchestrate individual resilience services, implemented as OpenFlow applications.

The Dark Side(-Channel) of Mobile Devices: A Survey on Network Traffic Analysis <=SEP=> In recent years, mobile devices (e.g., smartphones and tablets) have met an increasing commercial success and have become a fundamental element of the everyday life for billions of people all around the world. Mobile devices are used not only for traditional communication activities (e.g., voice calls and messages) but also for more advanced tasks made possible by an enormous amount of multi-purpose applications (e.g., finance, gaming, and shopping). As a result, those devices generate a significant network traffic (a consistent part of the overall Internet traffic). For this reason, the research community has been investigating security and privacy issues that are related to the network traffic generated by mobile devices, which could be analyzed to obtain information useful for a variety of goals (ranging from fine-grained user profiling to device security and network optimization). In this paper, we review the works that contributed to the state of the art of network traffic analysis targeting mobile devices. In particular, we present a systematic classification of the works in the literature according to three criteria: 1) the goal of the analysis; 2) the point where the network traffic is captured; and 3) the targeted mobile platforms. In this survey, we consider points of capturing such as Wi-Fi access points, software simulation, and inside real mobile devices or emulators. For the surveyed works, we review and compare analysis techniques, validation methods, and achieved results. We also discuss possible countermeasures, challenges, and possible directions for future research on mobile traffic analysis and other emerging domains (e.g., Internet of Things). We believe our survey will be a reference work for researchers and practitioners in this research field.

Proportions of Hand Segments <=SEP=> Such investigation methods as thermography or isotope scanning allow visualizing inflammation, neoplastic and some other pathological processes, and their location in the body. But the knowledge of regional topography is necessary for exact localization of the process. Such external anatomical landmarks of the hand as bone prominences and creases help to localize its joints and other structures during the clinical examination. On X-ray pictures, MRIs and CT images or during ultrasonography, anatomical structures are well visualized and their localization is not a problem. However, thermography and isotope scanning allow us to see only the shape and outlines of the anatomical region. So the identification of anatomical structures, particularly hand joints, on thermograms and bone scans is often difficult.

Removing rolling shutter wobble <=SEP=> We present an algorithm to remove wobble artifacts from a video captured with a rolling shutter camera undergoing large accelerations or jitter. We show how estimating the rapid motion of the camera can be posed as a temporal super-resolution problem. The low-frequency measurements are the motions of pixels from one frame to the next. These measurements are modeled as temporal integrals of the underlying high-frequency jitter of the camera. The estimated high-frequency motion of the camera is then used to re-render the sequence as though all the pixels in each frame were imaged at the same time. We also present an auto-calibration algorithm that can estimate the time between the capture of subsequent rows in the camera.

A Multi-ESD-Path Low-Noise Amplifier With a 4.3-A TLP Current Level in 65-nm CMOS <=SEP=> This paper studies the electrostatic discharge (ESD)-protected RF low-noise amplifiers (LNAs) in 65-nm CMOS technology. Three different ESD designs, including double-diode, modified silicon-controlled rectifier (SCR), and modified-SCR with double-diode configurations, are employed to realize ESD-protected LNAs at 5.8 GHz. By using the modified-SCR in conjunction with double-diode, a 5.8-GHz LNA with multiple ESD current paths demonstrates a 4.3-A transmission line pulse (TLP) failure level, corresponding to a ~ 6.5-kV Human-Body-Mode (HBM) ESD protection level. Under a supply voltage of 1.2 V and a drain current of 6.5 mA, the proposed ESD-protected LNA demonstrates a noise figure of 2.57 dB with an associated power gain of 16.7 dB. The input third-order intercept point (IIP3) is - 11 dBm, the input and output return losses are greater than 15.9 and 20 dB, respectively.

High-performance carbon nanotube field-effect transistor with tunable polarities <=SEP=> State-of-the-art carbon nanotube field-effect transistors (CNFETs) behave as Schottky-barrier-modulated transistors. It is known that vertical scaling of the gate oxide significantly improves the performance of these devices. However, decreasing the oxide thickness also results in pronounced ambipolar transistor characteristics and increased drain leakage currents. Using a novel device concept, we have fabricated high-performance enhancement-mode CNFETs exhibiting n- or p-type unipolar behavior, tunable by electrostatic and/or chemical doping, with excellent OFF-state performance and a steep subthreshold swing (S=63 mV/dec). The device design allows for aggressive oxide thickness and gate-length scaling while maintaining the desired device characteristics.

Reduced Number of Hypocretin Neurons in Human Narcolepsy <=SEP=> Murine and canine narcolepsy can be caused by mutations of the hypocretin (Hcrt) (orexin) precursor or Hcrt receptor genes. In contrast to these animal models, most human narcolepsy is not familial, is discordant in identical twins, and has not been linked to mutations of the Hcrt system. Thus, the cause of human narcolepsy remains unknown. Here we show that human narcoleptics have an 85%-95% reduction in the number of Hcrt neurons. Melanin-concentrating hormone (MCH) neurons, which are intermixed with Hcrt cells in the normal brain, are not reduced in number, indicating that cell loss is relatively specific for Hcrt neurons. The presence of gliosis in the hypocretin cell region is consistent with a degenerative process being the cause of the Hcrt cell loss in narcolepsy.

Enhancing Reliability of Workflow Execution Using Task Replication and Spot Instances <=SEP=> Cloud environments offer low-cost computing resources as a subscription-based service. These resources are elastically scalable and dynamically provisioned. Furthermore, cloud providers have also pioneered new pricing models like spot instances that are cost-effective. As a result, scientific workflows are increasingly adopting cloud computing. However, spot instances are terminated when the market price exceeds the users bid price. Likewise, cloud is not a utopian environment. Failures are inevitable in such large complex distributed systems. It is also well studied that cloud resources experience fluctuations in the delivered performance. These challenges make fault tolerance an important criterion in workflow scheduling. This article presents an adaptive, just-in-time scheduling algorithm for scientific workflows. This algorithm judiciously uses both spot and on-demand instances to reduce cost and provide fault tolerance. The proposed scheduling algorithm also consolidates resources to further minimize execution time and cost. Extensive simulations show that the proposed heuristics are fault tolerant and are effective, especially under short deadlines, providing robust schedules with minimal makespan and cost.

Automatic Tongue Diagnosis Using a Smart Phone <=SEP=> An automatic tongue diagnosis framework is proposed to analyzing tongue images taken by smart phones. Different from conventional tongue diagnosis systems, our input tongue images are usually in low resolution and taken under unknown lighting conditions. Consequently, existing tongue diagnosis methods cannot be directly applied to give accurate results. We propose a lighting condition estimation method based on the SVM classifier to predict the color correction matrix according to color difference of images taken with and without flashlight. We also modify the state of the art work of fur and fissure detection and successfully improve the detection accuracy by taking hue information into consideration and adding a de-noising step.

Visual scanning of faces in autism. <=SEP=> The visual scanpaths of five high-functioning adult autistic males and five adult male controls were recorded using an infrared corneal reflection technique as they viewed photographs of human faces. Analyses of the scanpath data revealed marked differences in the scanpaths of the two groups. The autistic participants viewed nonfeature areas of the faces significantly more often and core feature areas of the faces (i.e., eyes, nose, and mouth) significantly less often than did control participants. Across both groups of participants, scanpaths generally did not differ as a function of the instructions given to the participants (i.e., "Please look at the faces in any manner you wish." vs. "Please identify the emotions portrayed in these faces."). Autistic participants showed a deficit in emotion recognition, but this effect was driven primarily by deficits in the recognition of fear. Collectively, these results indicate disorganized processing of face stimuli in autistic individuals and suggest a mechanism that may subserve the social information processing deficits that characterize autism spectrum disorders.

Adaptive block truncation filter for MVC depth image enhancement <=SEP=> In Multiview Video plus Depth (MVD) format, virtual views are generated from decoded texture videos with decoded depth images through Depth Image based Rendering (DIBR). 3DV-ATM is a reference model for H.264/AVC based Multiview Video Coding (MVC) and aims at achieving high coding efficiency for 3D video in MVD format. Depth images are first downsampled then coded by 3DV-ATM. However, sharp object boundary characteristic of depth images does not well match with the transform coding of 3DV-ATM. Depth boundaries are often blurred with ringing artifacts in the decoded depth images that result in noticeable artifacts in synthesized views. This paper presents a low complexity adaptive block truncation filter to recover the sharp object boundaries of depth images using adaptive block repositioning and expansion for increasing the depth values refinement accuracy. This new approach is very efficient and can avoid false depth boundary refinement when block boundaries lie around the depth edge regions and ensure sufficient information within the processing block for depth layers classification. Experimental results show that sharp depth edges can be recovered using the proposed filter and boundary artifacts in the synthesized views can be removed. The proposed method can provide improvement up to 3.25dB in the depth map enhancement and bitrate reduction of 3.06% in the synthesized views.

A novel topology design approach using an integrated deep learning network architecture <=SEP=> Topology design optimization offers tremendous oppo rtunity in design and manufacturing freedoms by designing and producing a part from the ground-up without a meaningful initial design as required by conventional shape design opt imization approaches. Ideally, with adequate problem statements, to formulate and solve the topo logy design problem using a standard topology optimization process, such as SIMP (Simpli fied Isotropic Material with Penalization) is possible. In reality, an estimated over thousands o f design iterations is often required for just a few design variables, the conventional optimization approach is in general impractical or computationally unachievable for real world applica tions significantly diluting the development of the topology optimization technology. There is, therefore, a need for a different approach that will be able to optimize the initial design topolog y effectively and rapidly. Therefore, this work presents a new topology design procedure to generat e optimal structures using an integrated Generative Adversarial Networks (GANs) and convolut i nal neural network architecture. The discriminator in the GANs as well as the convolutio nal network are initially trained through the dataset of 3024 true optimized planner structure im ages generated from a conventional topology design approach (SIMP). The convolutional network m aps the optimized structure to the volume fraction, penalty and radius of the smoothening fil ter. Once the GAN is trained, the generator produced a large number of new unseen structures sa ti fying the design requirements. The corresponding input variables of these new structur es can be evaluated using the trained convolutional network. The structures generated by the GANs are also minutely post-processed to aberrations. Validation of the results is made b y generating the images with same conditions using existing topology optimization algorithms. T his paper presents a proof of concept which 1 Corresponding Author, email: shen.1@osu.edu

Networks and the Epidemiology of Infectious Disease <=SEP=> The science of networks has revolutionised research into the dynamics of interacting elements. It could be argued that epidemiology in particular has embraced the potential of network theory more than any other discipline. Here we review the growing body of research concerning the spread of infectious diseases on networks, focusing on the interplay between network theory and epidemiology. The review is split into four main sections, which examine: the types of network relevant to epidemiology; the multitude of ways these networks can be characterised; the statistical methods that can be applied to infer the epidemiological parameters on a realised network; and finally simulation and analytical methods to determine epidemic dynamics on a given network. Given the breadth of areas covered and the ever-expanding number of publications, a comprehensive review of all work is impossible. Instead, we provide a personalised overview into the areas of network epidemiology that have seen the greatest progress in recent years or have the greatest potential to provide novel insights. As such, considerable importance is placed on analytical approaches and statistical methods which are both rapidly expanding fields. Throughout this review we restrict our attention to epidemiological issues.

Exploiting Visibility Information in Surface Reconstruction to Preserve Weakly Supported Surfaces <=SEP=> We present a novel method for 3D surface reconstruction from an input cloud of 3D points augmented with visibility information. We observe that it is possible to reconstruct surfaces that do not contain input points. Instead of modeling the surface from input points, we model free space from visibility information of the input points. The complement of the modeled free space is considered full space. The surface occurs at interface between the free and the full space. We show that under certain conditions a part of the full space surrounded by the free space must contain a real object also when the real object does not contain any input points; that is, an occluder reveals itself through occlusion. Our key contribution is the proposal of a new interface classifier that can also detect the occluder interface just from the visibility of input points. We use the interface classifier to modify the state-of-the-art surface reconstruction method so that it gains the ability to reconstruct weakly supported surfaces. We evaluate proposed method on datasets augmented with different levels of noise, undersampling, and amount of outliers. We show that the proposed method outperforms other methods in accuracy and ability to reconstruct weakly supported surfaces.

Modeling Multibody Dynamic Systems With Uncertainties . Part II : Numerical Applications <=SEP=> This study applies generalized polynomial chaos theory to model complex nonlinear multibody dynamic systems operating in the presence of parametric and external uncertainty. Theoretical and computational aspects of this methodology are discussed in the companion paper “Modeling Multibody Dynamic Systems With Uncertainties. Part I: Theoretical and Computational Aspects”. In this paper we illustrate the methodology on selected test cases. The combined effects of parametric and forcing uncertainties are studied for a quarter car model. The uncertainty distributions in the system response in both time and frequency domains are validated against Monte-Carlo simulations. Results indicate that polynomial chaos is more efficient than Monte Carlo and more accurate than statistical linearization. The results of the direct collocation approach are similar to the ones obtained with the Galerkin approach. A stochastic terrain model is constructed using a truncated Karhunen-Loeve expansion. The application of polynomial chaos to differential-algebraic systems is illustrated using the constrained pendulum problem. Limitations of the polynomial chaos approach are studied on two different test problems, one with multiple attractor points, and the second with a chaotic evolution and a nonlinear attractor set. The overall conclusion is that, despite its limitations, generalized polynomial chaos is a powerful approach for the simulation of multibody dynamic systems with uncertainties.

Zero-Knowledge Proof of Decryption for FHE Ciphertexts <=SEP=> Zero-knowledge proofs of knowledge and fully-homomorphic encryption are two areas that have seen considerable advances in recent years, and these two techniques are used in conjunction in the context of verifiable decryption. Existing solutions for verifiable decryption are aimed at the batch setting, however there are many applications in which there will only be one ciphertext that requires a proof of decryption. The purpose of this paper is to provide a zero-knowledge proof of correct decryption on an FHE ciphertext, which for instance could hold the result of a cryptographic election. We give two main contributions. Firstly, we present a bootstrapping-like protocol to switch from one FHE scheme to another. The first scheme has efficient homomorphic capabilities; the second admits a simple zero-knowledge protocol. To illustrate this, we use the Brakerski et al. (ITCS, 2012) scheme for the former, and Gentry’s original scheme (STOC, 2009) for the latter. Secondly, we present a simple one-shot zero-knowledge protocol for verifiable decryption using Gentry’s original FHE scheme.

Monophyly of Primary Photosynthetic Eukaryotes: Green Plants, Red Algae, and Glaucophytes <=SEP=> Between 1 and 1.5 billion years ago, eukaryotic organisms acquired the ability to convert light into chemical energy through endosymbiosis with a Cyanobacterium (e.g.,). This event gave rise to "primary" plastids, which are present in green plants, red algae, and glaucophytes ("Plantae" sensu Cavalier-Smith). The widely accepted view that primary plastids arose only once implies two predictions: (1) all plastids form a monophyletic group, as do (2) primary photosynthetic eukaryotes. Nonetheless, unequivocal support for both predictions is lacking (e.g.,). In this report, we present two phylogenomic analyses, with 50 genes from 16 plastid and 15 cyanobacterial genomes and with 143 nuclear genes from 34 eukaryotic species, respectively. The nuclear dataset includes new sequences from glaucophytes, the less-studied group of primary photosynthetic eukaryotes. We find significant support for both predictions. Taken together, our analyses provide the first strong support for a single endosymbiotic event that gave rise to primary photosynthetic eukaryotes, the Plantae. Because our dataset does not cover the entire eukaryotic diversity (but only four of six major groups in), further testing of the monophyly of Plantae should include representatives from eukaryotic lineages for which currently insufficient sequence information is available.

Role of social media in online travel information search <=SEP=> Social media are playing an increasingly important role as information sources for travelers. The goal of this study is to investigate the extent to which social media appear in search engine results in the context of travel-related searches. The study employed a research design that simulates a traveler’s use of a search engine for travel planning by using a set of pre-defined keywords in combination with nine U.S. tourist destination names. The analysis of the search results showed that social media constitute a substantial part of the search results, indicating that search engines likely direct travelers to social media sites. This study confirms the growing importance of social media in the online tourism domain. It also provides evidence for challenges faced by traditional providers of travel-related information. Implications for tourism marketers in terms of online marketing strategies are discussed. 2009 Elsevier Ltd. All rights reserved.

Answering Top-k Queries with Multi-Dimensional Selections: The Ranking Cube Approach <=SEP=> Observed in many real applications, a top-k query often consists of two components to reflect a user's preference: a selection condition and a ranking function. A user may not only propose ad hoc ranking functions, but also use different interesting subsets of the data. In many cases, a user may want to have a thorough study of the data by initiating a multi-dimensional analysis of the top-k query results. Previous work on top-k query processing mainly focuses on optimizing data access according to the ranking function only. The problem of efficient answering top-k queries with multi-dimensional selections has not been well addressed yet.This paper proposes a new computational model, called ranking cube, for efficient answering top-k queries with multi-dimensional selections. We define a rank-aware measure for the cube, capturing our goal of responding to multi-dimensional ranking analysis. Based on the ranking cube, an efficient query algorithm is developed which progressively retrieves data blocks until the top-k results are found. The curse of dimensionality is a well-known challenge for the data cube and we cope with this difficulty by introducing a new technique of ranking fragments. Our experiments on Microsoft's SQL Server 2005 show that our proposed approaches have significant improvement over the previous methods.

Dual-Band and Wideband Dual-Polarized Cylindrical Dielectric Resonator Antennas <=SEP=> This letter investigates dual-band and wideband designs of dual-polarized cylindrical dielectric resonator antennas (DRAs). These designs make use of the fundamental HEM111 mode and higher-order HEM113 mode of the DRA. The strip- and slot-fed excitation methods are used for Ports 1 and 2 of the antennas, respectively. For demonstration, a dual-band dual-polarized DRA for DCS (1.71-1.88 GHz) and WLAN (2.4-2.48 GHz) bands and a wideband version that covers the 2.4-GHz WLAN band were designed. The S-parameters, radiation patterns, antenna gains, antenna efficiencies, and envelope correlations of the two designs are studied. It was found that the dual-band and wideband designs have port isolations of higher than 36 and 37 dB, respectively. Good agreement between the measured and simulated results is observed.

Feature based image retrieval system using Zernike moments and Daubechies Wavelet Transform <=SEP=> In image processing research field, image retrieval is extensively used in various application. Increasing need of the image retrieval, it is quiet most exciting research field. In image retrieval system, features are the most significant process used for indexing, retrieving and classifying the images. For computer systems, automatic indexing, storing and retrieving larger image collections effectively are a critical task. Nowadays several retrieval systems were implemented to overcome these issues but still there is a lack of speed and accuracy during image retrieval process. First, address the various issues on performance degradation of image retrieval then analyze and compare the methods and results in previous work. Second, discover the effective approach to be used to increase the accuracy of retrieval system significantly. This work provides a framework based on low level features extraction using Daubechies Wavelet Transform (DWT) and Zernike moments. Based on that features images are retrieved by using the distance measure. Zernike moments constitute a powerful shape descriptor due to its strength and narrative capability. Experimental results shows that our scheme provides significant improvement on retrieval accuracy compared to existing system based on the combination of both the color and edge features by using Discrete Wavelet Transform. In this paper, wang's image dataset is used for experiments.