Query: A survey on expert finding techniques

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A Unified Probabilistic Framework for Name Disambiguation in Digital Library <=SEP=> Despite years of research, the name ambiguity problem remains largely unresolved. Outstanding issues include how to capture all information for name disambiguation in a unified approach, and how to determine the number of people K in the disambiguation process. In this paper, we formalize the problem in a unified probabilistic framework, which incorporates both attributes and relationships. Specifically, we define a disambiguation objective function for the problem and propose a two-step parameter estimation algorithm. We also investigate a dynamic approach for estimating the number of people K. Experiments show that our proposed framework significantly outperforms four baseline methods of using clustering algorithms and two other previous methods. Experiments also indicate that the number K automatically found by our method is close to the actual number.

Broad expertise retrieval in sparse data environments <=SEP=> Expertise retrieval has been largely unexplored on data other than the W3C collection. At the same time, many intranets of universities and other knowledge-intensive organisations offer examples of relatively small but clean multilingual expertise data, covering broad ranges of expertise areas. We first present two main expertise retrieval tasks, along with a set of baseline approaches based on generative language modeling, aimed at finding expertise relations between topics and people. For our experimental evaluation, we introduce (and release) a new test set based on a crawl of a university site. Using this test set, we conduct two series of experiments. The first is aimed at determining the effectiveness of baseline expertise retrieval methods applied to the new test set. The second is aimed at assessing refined models that exploit characteristic features of the new test set, such as the organizational structure of the university, and the hierarchical structure of the topics in the test set. Expertise retrieval models are shown to be robust with respect to environments smaller than the W3C collection, and current techniques appear to be generalizable to other settings.

Relevance-Based Language Models <=SEP=> We explore the relation between classical probabilistic models of information retrieval and the emerging language modeling approaches. It has long been recognized that the primary obstacle to effective performance of classical models is the need to estimate a relevance model: probabilities of words in the relevant class. We propose a novel technique for estimating these probabilities using the query alone. We demonstrate that our technique can produce highly accurate relevance models, addressing important notions of synonymy and polysemy. Our experiments show relevance models outperforming baseline language modeling systems on TREC retrieval and TDT tracking tasks. The main contribution of this work is an effective formal method for estimating a relevance model with no training data.

Voting for candidates: adapting data fusion techniques for an expert search task <=SEP=> In an expert search task, the users' need is to identify people who have relevant expertise to a topic of interest. An expert search system predicts and ranks the expertise of a set of candidate persons with respect to the users' query. In this paper, we propose a novel approach for predicting and ranking candidate expertise with respect to a query. We see the problem of ranking experts as a voting problem, which we model by adapting eleven data fusion techniques.We investigate the effectiveness of the voting approach and the associated data fusion techniques across a range of document weighting models, in the context of the TREC 2005 Enterprise track. The evaluation results show that the voting paradigm is very effective, without using any collection specific heuristics. Moreover, we show that improving the quality of the underlying document representation can significantly improve the retrieval performance of the data fusion techniques on an expert search task. In particular, we demonstrate that applying field-based weighting models improves the ranking of candidates. Finally, we demonstrate that the relative performance of the adapted data fusion techniques for the proposed approach is stable regardless of the used weighting models.

Finding Groups in Data: An Introduction to Cluster Analysis <=SEP=> Description: The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. "Cluster analysis is the increasingly important and practical subject of finding groupings in data. The authors set out to write a book for the user who does not necessarily have an extensive background in mathematics. They succeed very well." —Mathematical Reviews "Finding Groups in Data [is] a clear, readable, and interesting presentation of a small number of clustering methods. In addition, the book introduced some interesting innovations of applied value to clustering literature." —Journal of Classification "This is a very good, easy-to-read, and practical book. It has many nice features and is highly recommended for students and practitioners in various fields of study." —Technometrics An introduction to the practical application of cluster analysis, this text presents a selection of methods that together can deal with most applications. These methods are chosen for their robustness, consistency, and general applicability. This book discusses various types of data, including interval-scaled and binary variables as well as similarity data, and explains how these can be transformed prior to clustering.

A wirelessly controlled implantable LED system for deep brain optogenetic stimulation <=SEP=> In recent years optogenetics has rapidly become an essential technique in neuroscience. Its temporal and spatial specificity, combined with efficacy in manipulating neuronal activity, are especially useful in studying the behavior of awake behaving animals. Conventional optogenetics, however, requires the use of lasers and optic fibers, which can place considerable restrictions on behavior. Here we combined a wirelessly controlled interface and small implantable light-emitting diode (LED) that allows flexible and precise placement of light source to illuminate any brain area. We tested this wireless LED system in vivo, in transgenic mice expressing channelrhodopsin-2 in striatonigral neurons expressing D1-like dopamine receptors. In all mice tested, we were able to elicit movements reliably. The frequency of twitches induced by high power stimulation is proportional to the frequency of stimulation. At lower power, contraversive turning was observed. Moreover, the implanted LED remains effective over 50 days after surgery, demonstrating the long-term stability of the light source. Our results show that the wireless LED system can be used to manipulate neural activity chronically in behaving mice without impeding natural movements.

Optimal detection of fetal chromosomal abnormalities by massively parallel DNA sequencing of cell-free fetal DNA from maternal blood. <=SEP=> BACKGROUND
Massively parallel DNA sequencing of cell-free fetal DNA from maternal blood can detect fetal chromosomal abnormalities. Although existing algorithms focus on the detection of fetal trisomy 21 (T21), these same algorithms have difficulty detecting trisomy 18 (T18).


METHODS
Blood samples were collected from 1014 patients at 13 US clinic locations before they underwent an invasive prenatal procedure. All samples were processed to plasma, and the DNA extracted from 119 samples underwent massively parallel DNA sequencing. Fifty-three sequenced samples came from women with an abnormal fetal karyotype. To minimize the intra- and interrun sequencing variation, we developed an optimized algorithm by using normalized chromosome values (NCVs) from the sequencing data on a training set of 71 samples with 26 abnormal karyotypes. The classification process was then evaluated on an independent test set of 48 samples with 27 abnormal karyotypes.


RESULTS
Mapped sites for chromosomes of interest in the sequencing data from the training set were normalized individually by calculating the ratio of the number of sites on the specified chromosome to the number of sites observed on an optimized normalizing chromosome (or chromosome set). Threshold values for trisomy or sex chromosome classification were then established for all chromosomes of interest, and a classification schema was defined. Sequencing of the independent test set led to 100% correct classification of T21 (13 of 13) and T18 (8 of 8) samples. Other chromosomal abnormalities were also identified.


CONCLUSION
Massively parallel sequencing is capable of detecting multiple fetal chromosomal abnormalities from maternal plasma when an optimized algorithm is used.

Practical Byzantine Fault Tolerance <=SEP=> Our growing reliance on online services accessible on the Internet demands highly-available systems that provide correct service without interruptions. Byzantine faults such as software bugs, operator mistakes, and malicious attacks are the major cause of service interruptions. This thesis describes a new replication algorithm, BFT, that can be used to build highly-available systems that tolerate Byzantine faults. It shows, for the first time, how to build Byzantine-fault-tolerant systems that can be used in practice to implement real services because they do not rely on unrealistic assumptions and they perform well. BFT works in asynchronous environments like the Internet, it incorporates mechanisms to defend against Byzantine-faulty clients, and it recovers replicas proactively. The recovery mechanism allows the algorithm to tolerate any number of faults over the lifetime of the system provided fewer than 1/3 of the replicas become faulty within a small window of vulnerability. The window may increase under a denial-of-service attack but the algorithm can detect and respond to such attacks and it can also detect when the state of a replica is corrupted by an attacker. BFT has been implemented as a generic program library with a simple interface. The BFT library provides a complete solution to the problem of building real services that tolerate Byzantine faults. We used the library to implement the first Byzantine-fault-tolerant NFS file system, BFS. The BFT library and BFS perform well because the library incorporates several important optimizations. The most important optimization is the use of symmetric cryptography to authenticate messages. Public-key cryptography, which was the major bottleneck in previous systems, is used only to exchange the symmetric keys. The performance results show that BFS performs 2% faster to 24% slower than production implementations of the NFS protocol that are not replicated. Therefore, we believe that the BFT library can be used to build practical systems that tolerate Byzantine faults. Thesis Supervisor: Barbara H. Liskov Title: Ford Professor of Engineering

Data-driven Personas: Constructing Archetypal Users with Clickstreams and User Telemetry <=SEP=> User Experience (UX) research teams following a user centered design approach harness personas to better understand a user's workflow by examining that user's behavior, goals, needs, wants, and frustrations. To create target personas these researchers rely on workflow data from surveys, self-reports, interviews, and user observation. However, this data not directly related to user behavior, weakly reflects a user's actual workflow in the product, is costly to collect, is limited to a few hundred responses, and is outdated as soon as a persona's workflows evolve. To address these limitations we present a quantitative bottom-up data-driven approach to create personas. First, we directly incorporate user behavior via clicks gathered automatically from telemetry data related to the actual product use in the field; since the data collection is automatic it is also cost effective. Next, we aggregate 3.5 million clicks from 2400 users into 39,000 clickstreams and then structure them into 10 workflows via hierarchical clustering; we thus base our personas on a large data sample. Finally, we use mixed models, a statistical approach that incorporates these clustered workflows to create five representative personas; updating our mixed model ensures that these personas remain current. We also validated these personas with our product's user behavior experts to ensure that workflows and the persona goals represent actual product use.

Fast and Scalable Learning of Neuro-Symbolic Representations of Biomedical Knowledge <=SEP=> In this work we address the problem of fast and scalable learning of neuro-symbolic representations for general biological knowledge. Based on a recently published comprehensive biological knowledge graph (Alshahrani, 2017) that was used for demonstrating neurosymbolic representation learning, we show how to train fast (under 1 minute) log-linear neural embeddings of the entities. We utilize these representations as inputs for machine learning classifiers to enable important tasks such as biological link prediction. Classifiers are trained by concatenating learned entity embeddings to represent entity relations, and training classifiers on the concatenated embeddings to discern true relations from automatically generated negative examples. Our simple embedding methodology greatly improves on classification error compared to previously published state-of-the-art results, yielding a maximum increase of +0.28 F-measure and +0.22 ROC AUC scores for the most difficult biological link prediction problem. Finally, our embedding approach is orders of magnitude faster to train (≤ 1 minute vs. hours), much more economical in terms of embedding dimensions (d = 50 vs. d = 512), and naturally encodes the directionality of the asymmetric biological relations, that can be controlled by the order with which we concatenate the embeddings.

A paradigmatic and methodological examination of knowledge management research: 2000 to 2004 <=SEP=> Knowledge management (KM) research has been evolving for more than a decade, yet little is known about KM theoretical perspectives, research paradigms, and research methods. This paper explores KM research in influential journals for the period 2000–2004. A total of 160 KM articles in ten top-tier information systems and management journals are analyzed. Articles that may serve as useful exemplars of KM research from positivist, interpretivist, and critical pluralist paradigms are selected. We find that KM research in information systems journals differs from that in management journals, but neither makes a balanced use of positivist and non-positivist research approaches. © 2007 Elsevier B.V. All rights reserved.

A fast and memory-efficient Discrete Focal Stack Transform for plenoptic sensors <=SEP=> Article history: Available online 23 December 2014

How to Choose the Best Pivot Language for Automatic Translation of Low-Resource Languages <=SEP=> Recent research on multilingual statistical machine translation focuses on the usage of pivot languages in order to overcome language resource limitations for certain language pairs. Due to the richness of available language resources, English is, in general, the pivot language of choice. However, factors like language relatedness can also effect the choice of the pivot language for a given language pair, especially for Asian languages, where language resources are currently quite limited. In this article, we provide new insights into what factors make a pivot language effective and investigate the impact of these factors on the overall pivot translation performance for translation between 22 Indo-European and Asian languages. Experimental results using state-of-the-art statistical machine translation techniques revealed that the translation quality of 54.8% of the language pairs improved when a non-English pivot language was chosen. Moreover, 81.0% of system performance variations can be explained by a combination of factors such as language family, vocabulary, sentence length, language perplexity, translation model entropy, reordering, monotonicity, and engine performance.

Sistemas de reconocimiento basados en la imagen facial <=SEP=> ─ This article summarizes the most important research projects developed in thearea of recognitions y stemsusing facial images. It presents the description of the principal lines of study about people identification systems using face recognition. Furthermore, it contains a synthesis of recent mathematical techniques for pattern sextraction in these systems.

An integrated approach to characterize genetic interaction networks in yeast metabolism <=SEP=> Although experimental and theoretical efforts have been applied to globally map genetic interactions, we still do not understand how gene-gene interactions arise from the operation of biomolecular networks. To bridge the gap between empirical and computational studies, we i, quantitatively measured genetic interactions between ∼185,000 metabolic gene pairs in Saccharomyces cerevisiae, ii, superposed the data on a detailed systems biology model of metabolism and iii, introduced a machine-learning method to reconcile empirical interaction data with model predictions. We systematically investigated the relative impacts of functional modularity and metabolic flux coupling on the distribution of negative and positive genetic interactions. We also provide a mechanistic explanation for the link between the degree of genetic interaction, pleiotropy and gene dispensability. Last, we show the feasibility of automated metabolic model refinement by correcting misannotations in NAD biosynthesis and confirming them by in vivo experiments.

Sequence of central nervous system myelination in human infancy. II. Patterns of myelination in autopsied infants. <=SEP=> The timing and synchronization of postnatal myelination in the human central nervous system (CNS) are complex. We found eight time-related patterns of CNS myelination during the first two postnatal years in autopsied infants. The intensity of myelination was graded in 162 infants with diverse diseases on an ordinal scale of degrees 0-4. The Ayer method for maximum likelihood estimates for censored data was utilized to generate curves of the temporal changes in the percent of infants with degrees 0 through 4 of myelin in 62 white matter sites. These sites fall into eight subgroups determined by the presence or absence of microscopic myelin (degree 1) at birth and the median age at which mature myelin (degree 3) is reached. There is variability in the timing of myelination within and across axonal systems, and early onset of myelination is not always followed by early myelin maturation. We reexamined general rules governing the timing of myelination proposed by previous investigators, and found that those rules are neither complete nor inviolate, and that there is a complex interplay among them. This study specifies distinct periods of maturation in which myelinating pathways are potentially vulnerable to insult during the first two postnatal years.

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DALEX: explainers for complex predictive models <=SEP=> Predictive modeling is invaded by elastic, yet complex methods such as neural networks or ensembles (model stacking, boosting or bagging). Such methods are usually described by a large number of parameters or hyper parameters a price that one needs to pay for elasticity. The very number of parameters makes models hard to understand. This paper describes a consistent collection of explainers for predictive models, a.k.a. black boxes. Each explainer is a technique for exploration of a black box model. Presented approaches are model-agnostic, what means that they extract useful information from any predictive method despite its internal structure. Each explainer is linked with a specific aspect of a model. Some are useful in decomposing predictions, some serve better in understanding performance, while others are useful in understanding importance and conditional responses of a particular variable. Every explainer presented in this paper works for a single model or for a collection of models. In the latter case, models can be compared against each other. Such comparison helps to find strengths and weaknesses of different approaches and gives additional possibilities for model validation. Presented explainers are implemented in the DALEX package for R. They are based on a uniform standardized grammar of model exploration which may be easily extended. The current implementation supports the most popular frameworks for classification and regression.

Visualizing Semantic Table Annotations with TableMiner+ <=SEP=> This paper describes an extension of the TableMiner system, an open source Semantic Table Interpretation system that annotates Web tables using Linked Data in an effective and efficient approach. It adds a graphical user interface to TableMiner, to facilitate the visualization and correction of automatically generated annotations. This makes TableMiner an ideal tool for the semi-automatic creation of high-quality semantic annotations on tabular data, which facilitates the publication of Linked Data on the Web.

Adaptive sequential Monte Carlo by means of mixture of experts <=SEP=> Selecting appropriately the proposal kernel of particle filters is an issue of significant importance, since a bad choice may lead to deterioration of the particle sample and, consequently, waste of computational power. In this paper we introduce a novel algorithm approximating adaptively the so-called optimal proposal kernel by a mixture of integrated curved exponential distributions with logistic weights. This family of distributions, referred to as mixtures of experts, is broad enough to be used in the presence of multi-modality or strongly skewed distributions. The mixtures are fitted, via Monte Carlo EM or online-EM methods, to the optimal kernel through minimization of the Kullback-Leibler divergence between the auxiliary target and instrumental distributions of the particle filter. At each iteration of the particle filter, the algorithm is required to solve only a single optimization problem for the whole particle sample, as opposed to existing methods solving one problem per particle. In addition, we illustrate in a simulation study how the method can be successfully applied to optimal filtering in nonlinear state-space models.

Preservation of nostril morphology in nasal base reduction <=SEP=> Asian patients often desire reduction of the base and alar lobules of the Asian mesorrhine nose. Sill excision is commonly used, but may result in an angular or notched nostril rim. We developed an internal method of alar base reduction involving triangle flaps for sill resection. This method avoids alar rim notching and teardrop deformity. Cinching sutures and double-layer closure avoid tension on the wound. We categorized the results in 50 patients (4 men, 46 women) who underwent surgery between November 2012 and August 2015 and who could be followed up for more than 3 months. The mean age of the subjects was 26.3 years and the mean follow-up period was 8.9 months. Forty patients underwent base reduction with the internal method, while ten with alar flare were treated with additional external resection. The mean reduction of the nostril sill width was 4.8 mm for both methods. In the subjects receiving flare resection, the mean reduction of the lateral alar width was 4.4 mm. There was no notching at the suture site. Complications included a short scar running obliquely under the sill in 13 patients and a trap door deformity in one patient. Nasal base reduction is widely performed, but subject to outcomes with abnormal nostril contour. We used triangle flaps to narrow the sill, and cinching sutures to prevent tension on the wound. Our methods prevent nostril notching and/or teardrop deformity. Scarring can occur, but can be reduced using cinching sutures for wound relaxation. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.

A localization and tracking scheme for target gangs based on big data of Wi-Fi locations <=SEP=> The modeling and analysis of target gangs’ usual haunts plays a very important role in law enforcement and supervision. Existing localization and tracking schemes usually need to deploy a large number of monitoring devices or continue to move with the target, which lead to high cost. In this paper, a localization and tracking scheme based on big data of Wi-Fi locations is proposed. Firstly, the characteristic of the smart mobile device that continuously broadcasts probe request frames is used to obtain its MAC address and Wi-Fi connection history. Secondly, the service set identifier (SSID) in the Wi-Fi connection history of smart mobile devices held by the target gangs are queried from the Wi-Fi location database, and the target gangs’ usual haunts are gained by statistical analysis. Lastly, monitoring devices are deployed in these places, and most of the target gangs’ activity pattern are known with only a small number of monitoring devices. The results of the related experimental tests demonstrate the feasibility of the proposed scheme.

DO INVESTMENT-CASH FLOW SENSITIVITIES PROVIDE USEFUL MEASURES OF FINANCING CONSTRAINTS?* <=SEP=> No. This paper investigates the relationship between financing constraints and investment-cash flow sensitivities by analyzing the firms identified by Fazzari, Hubbard, and Petersen as having unusually high investment-cash flow sensitivities. We find that firms that appear less financially constrained exhibit significantly greater sensitivities than firms that appear more financially constrained. We find this pattern for the entire sample period, subperiods, and individual years. These results (and simple theoretical arguments) suggest that higher sensitivities cannot be interpreted as evidence that firms are more financially constrained. These findings call into question the interpretation of most previous research that uses this methodology.

The Role of Gender Stereotypes in Perceptions of Entrepreneurs and Intentions to Become an Entrepreneur <=SEP=> In this study we examine the role of socially constructed gender stereotypes in entrepreneurship and their influence on men and women’s entrepreneurial intentions. Data on characteristics of males, females, and entrepreneurs were collected from young adults in three countries. As hypothesized, entrepreneurs were perceived to have predominantly masculine characteristics. Additional results revealed that although both men and women perceive entrepreneurs to have characteristics similar to those of males (masculine genderrole stereotype), only women also perceived entrepreneurs and females as having similar characteristics (feminine gender-role stereotype). Further, though men and women did not differ in their entrepreneurial intentions, those who perceived themselves as more similar to males (high on male gender identification) had higher entrepreneurial intentions than those who saw themselves as less similar to males (low male gender identification). No such difference was found for people who saw themselves as more or less similar to females (female gender identification). The results were consistent across the three countries. Practical implications and directions for future research are discussed.

Diced cartilage grafts in rhinoplasty surgery: current techniques and applications. <=SEP=> BACKGROUND
The author has used diced cartilage grafts in nasal surgery for more than 30 years. However, the number of cases and the variety of techniques have increased dramatically over the past 6 years.


METHODS
The author uses three methods of diced cartilage in rhinoplasty surgery: diced cartilage, diced cartilage wrapped in fascia (with fascia then sewn closed), and diced cartilage covered with fascia (with the recipient area covered with fascia after graft placement). The constructs are highly varied to fit the specific defect. Pieces of diced cartilage without any fascia can be placed in pockets in the peripyriform area, radix, or alongside structural rib grafts.


RESULTS
Over a 2-year period, the author treated 546 rhinoplasty cases in which 79 patients (14 percent) had a total of 91 diced cartilage grafts. There were 34 primary and 45 secondary operations involving the radix (n = 11), half-length grafts (n = 14), full-length dorsum (n = 43), peripyriform (n = 16), infralobule (n = 4), and lateral wall (n = 3). All charts were reviewed for the 256 rhinoplasties performed in 2006 of which 30 patients had 35 diced cartilage grafts. With a median follow-up of 19 months (range, 13 to 25 months), two patients had had revisions unrelated to their diced cartilage grafts. The three most common technical problems were overcorrection, visibility, and junctional step-offs.


CONCLUSIONS
Diced cartilage grafts are a valuable addition to rhinoplasty surgery. They are highly flexible and useful throughout the nose. Their use simplifies one of the greatest challenges in all of rhinoplasty--dorsal augmentation. Complications have been relatively minor and their correction relatively simple.

STRADS: a distributed framework for scheduled model parallel machine learning <=SEP=> Machine learning (ML) algorithms are commonly applied to big data, using distributed systems that partition the data across machines and allow each machine to read and update all ML model parameters --- a strategy known as data parallelism. An alternative and complimentary strategy, model parallelism, partitions the model parameters for non-shared parallel access and updates, and may periodically repartition the parameters to facilitate communication. Model parallelism is motivated by two challenges that data-parallelism does not usually address: (1) parameters may be dependent, thus naive concurrent updates can introduce errors that slow convergence or even cause algorithm failure; (2) model parameters converge at different rates, thus a small subset of parameters can bottleneck ML algorithm completion. We propose scheduled model parallelism (SchMP), a programming approach that improves ML algorithm convergence speed by efficiently scheduling parameter updates, taking into account parameter dependencies and uneven convergence. To support SchMP at scale, we develop a distributed framework STRADS which optimizes the throughput of SchMP programs, and benchmark four common ML applications written as SchMP programs: LDA topic modeling, matrix factorization, sparse least-squares (Lasso) regression and sparse logistic regression. By improving ML progress per iteration through SchMP programming whilst improving iteration throughput through STRADS we show that SchMP programs running on STRADS outperform non-model-parallel ML implementations: for example, SchMP LDA and SchMP Lasso respectively achieve 10x and 5x faster convergence than recent, well-established baselines.

One-shot learning for fine-grained relation extraction via convolutional siamese neural network <=SEP=> Extracting fine-grained relations between entities of interest is of great importance to information extraction and large-scale knowledge graph construction. Conventional approaches on relation extraction require an existing knowledge graph to start with or sufficient observed samples from each relation type in the training process. However, such resources are not always available, and fine-grained manual labeling is extremely time-consuming and requires extensive expertise for specific domains such as healthcare and bioinformatics. Additionally, the distribution of fine-grained relations is often highly imbalanced in practice. We tackle this label scarcity and distribution imbalance issue from a one-shot classification perspective via a convolutional siamese neural network which extracts discriminative semantic-aware features to verify the relations between a pair of input samples. The proposed siamese network effectively extracts uncommon relations with only limited observed samples on the tasks of 1-shot and few-shot classification, demonstrating significant benefits to domain-specific information extraction in practical applications.

Image Stylization using Depth Information for Hatching and Engraving effects by <=SEP=> In this thesis inspired by artists such as Gustave Doré and copperplate engravers, we present a new approach for producing a sketch-type hatching effect as well as engraving art seen in the US dollar bills using depth maps as input. The approach uses the acquired depth to generate pen-and-ink type of strokes that exaggerate surface characteristics of faces and objects. The background on this area is presented with an emphasis on line drawing, pen-and-ink illustrations, engraving art and other artistic styles. The patterns generated are composed of parallel lines that change their directionality smoothly while avoiding intersections between them. The style communicates tone and surface characteristics using curve deformation, thickness, density, and spacing while at the same time adding crosshatching effects. This is achieved with our algorithmic approach which uses the data to perform operations that deforms patterns. The components of our methodology and algorithms are detailed, as well as the equations that govern these effects. This work also presents an array of different results by varying parameters or by rendering variations.

Evaluating Personalization and Persuasion in E-Commerce <=SEP=> The use of personalization and persuasion has been shown to optimize customers’ shopping experience in e-commerce. This study aims to identify the personalization methods and persuasive principles that make an ecommerce company successful. Using Amazon as a case study, we evaluated the personalization methods implemented using an existing process framework. We also applied the PSD model to Amazon to evaluate the persuasive principles it uses. Our results show that all the principles of the PSD model were implemented in Amazon. This study can serve as a guide to e-commerce businesses and software developers for building or improving existing e-commerce platforms.

DBpedia: A Multilingual Cross-domain Knowledge Base <=SEP=> The DBpedia project extracts structured information from Wikipedia editions in 97 different languages and combines this information into a large multi-lingual knowledge base covering many specific domains and general world knowledge. The knowledge base contains textual descriptions (titles and abstracts) of concepts in up to 97 languages. It also contains structured knowledge that has been extracted from the infobox systems of Wikipedias in 15 different languages and is mapped onto a single consistent ontology by a community effort. The knowledge base can be queried using a structured query language and all its data sets are freely available for download. In this paper, we describe the general DBpedia knowledge base and extended data sets that specifically aim at supporting computational linguistics tasks. These task include Entity Linking, Word Sense Disambiguation, Question Answering, Slot Filling and Relationship Extraction. These use cases are outlined, pointing at added value that the structured data of DBpedia provides.