Query: Cross-domain Feature Selection for Language Identification

Relevant passages:
A Comparison of Event Models for Naive Bayes Text Classification <=SEP=> Recent approaches to text classification have used two different first-order probabilistic models for classification, both of which make the naive Bayes assumption. Some use a multi-variate Bernoulli model, that is, a Bayesian Network with no dependencies between words and binary word features (e.g. Larkey and Croft 1996; Koller and Sahami 1997). Others use a multinomial model, that is, a uni-gram language model with integer word counts (e.g. Lewis and Gale 1994; Mitchell 1997). This paper aims to clarify the confusion by describing the differences and details of these two models, and by empirically comparing their classification performance on five text corpora. We find that the multi-variate Bernoulli performs well with small vocabulary sizes, but that the multinomial performs usually performs even better at larger vocabulary sizes—providing on average a 27% reduction in error over the multi-variate Bernoulli model at any vocabulary size.

An Extensive Empirical Study of Feature Selection Metrics for Text Classification <=SEP=> Machine learning for text classification is the cor nerstone of document categorization, news filtering, document routing, and personalization. In text domains, effective feature selection is essential to make the learning task efficient and m ore accurate. This paper presents an empirical comparison of twelve feature selection methods (e.g . Information Gain) evaluated on a benchmark of 229 text classification problem instances that w ere gathered from Reuters, TREC, OHSUMED, etc. The results are analyzed from multiple goal p ers ectives—accuracy, F-measure, precision, and recall—since each is appropriate in different si tuat ons. The results reveal that a new feature selection me tric we call ‘Bi-Normal Separation’ (BNS), outperformed the others by a substantial margin in most situations. This margin widened in tasks with high class skew, which is rampant in text clas sification problems and is particularly challenging for induction algorithms. A new evaluation methodology is offered that focus es on the needs of the data mining practitioner faced with a single dataset who seeks to choose one (or a pair of) metrics that are most likely to yield the best performance. From this perspect iv , BNS was the top single choice for all goals except precision, for which Information Gain yielded the best result most often. This analysis also revealed, for example, that Informati on Gain and Chi-Squared have correlated failures, and so they work poorly together. When c hoosing optimal pairs of metrics for each of the four performance goals, BNS is consistently a membe r of the pair—e.g., for greatest recall, the pair BNS + F1-measure yielded the best performance on the greatest number of tasks by a considerable margin.

Language Identification in the Limit <=SEP=> Language learnabi l i ty has been inves t igated . This refers to the following s i tua t ion: A class of possible languages is specified, t oge the r wi th a method of present ing informat ion to the learner about an unknown language, which is to be chosen from the class. The quest ion is now asked, " I s the informat ion sufficient to determine which of the possible languages is the unknown language?" M a n y definitions of l ea rnab i l i ty are possible, bu t only the following is considered here: Time is quant ized and has a finite s t a r t ing time. At each t ime the learner receives a un i t of in format ion and is to make a guess as to the i den t i t y of the unknown language on the basis of the informat ion received so far. This process continues forever. The class of languages will be considered lea~nabIe with respect to the specified me thod of in format ion p resen ta t ion if there is an a lgor i thm t h a t the learner can use to make his guesses, the a lgor i thm hav ing the following p roper ty : Given any language of the class, there is some finite t ime af te r which the guesses will all be the same and they will be correct . In this pre l iminary invest igat ion, a language is t aken to be a set of s tr ings on some finite a lphabet . The a lphabe t is the same for all languages of tile class. Several var ia t ions of each of the following two basic methods of informat ion presenta t ion are inves t iga ted: A text for a language generates the s t r ings of the language in any order such t h a t every s t r ing of the language occurs a t least once. An informant for a language tells whe the r a s t r ing is in the language, and chooses the s t r ings in some order such t h a t every s t r ing occurs at least once. I t was found t h a t the class of context-sensi t ive languages is learnable from an informant, but that not even the class of regular languages is learnable from a text.

Introduction to Information Retrieval: Index <=SEP=> 

Domain Adaptation for Statistical Classifiers <=SEP=> The most basic assumption used in statistical learning theory is that training data and test data are drawn from the same underlying distribution. Unfortunately, in many applications, the “in-domain” test data is drawn from a distribution that is related, but not identical, to the “out-of-domain” distribution of the training data. We consider the common case in which labeled out-of-domain data is plentiful, but labeled in-domain data is scarce. We introduce a statistical formulation of this problem in terms of a simple mixture model and present an instantiation of this framework to maximum entropy classifiers and their linear chain counterparts. We present efficient inference algorithms for this special case based on the technique of conditional expectation maximization. Our experimental results show that our approach leads to improved performance on three real world tasks on four different data sets from the natural language processing domain.

Augmenting paper-based reading activity with direct access to digital materials and scaffolded questioning <=SEP=> Comprehension is the goal of reading. However, students often encounter reading difficulties due to the lack of background knowledge and proper reading strategy. Unfortunately, print text provides very limited assistance to one’s reading comprehension through its static knowledge representations such as symbols, charts, and graphs. Integrating digital materials and reading strategy into paper-based reading activities may bring opportunities for learners to make meaning of the print material. In this study, QR codes were adopted in association with mobile technology to deliver supplementary materials and questions to support students’ reading. QR codes were printed on paper prints to provide direct access to digital materials and scaffolded questions. Smartphones were used to scan the printed QR codes to fetch predesigned digital resources and scaffolded questions over the Internet. A quasi-experiment was conducted to evaluate the effectiveness of direct access to the digital materials prepared by the instructor using QR codes and that of scaffolded questioning in improving students’ reading comprehension. The results suggested that direct access to digital resources using QR codes does not significantly influence students’ reading comprehension; however, the reading strategy of scaffolded questioning significantly improves students’ understanding about the text. The survey showed that most students agreed that the integrated print-and-digital-materialbased learning system benefits English reading comprehension but may not be as efficient as expected. The implications of the findings shed light on future improvement of the system. 2011 Elsevier Ltd. All rights reserved.

Effective Identification of Similar Patients Through Sequential Matching over ICD Code Embedding <=SEP=> Evidence-based medicine often involves the identification of patients with similar conditions, which are often captured in ICD (International Classification of Diseases (World Health Organization 2013)) code sequences. With no satisfying prior solutions for matching ICD-10 code sequences, this paper presents a method which effectively captures the clinical similarity among routine patients who have multiple comorbidities and complex care needs. Our method leverages the recent progress in representation learning of individual ICD-10 codes, and it explicitly uses the sequential order of codes for matching. Empirical evaluation on a state-wide cancer data collection shows that our proposed method achieves significantly higher matching performance compared with state-of-the-art methods ignoring the sequential order. Our method better identifies similar patients in a number of clinical outcomes including readmission and mortality outlook. Although this paper focuses on ICD-10 diagnosis code sequences, our method can be adapted to work with other codified sequence data.

Anger detection in call center dialogues <=SEP=> We present a method to classify fixed-duration windows of speech as expressing anger or not, which does not require speech recognition, utterance segmentation, or separating the utterances of different speakers and can, thus, be easily applied to real-world recordings. We also introduce the task of ranking a set of spoken dialogues by decreasing percentage of anger duration, as a step towards helping call center supervisors and analysts identify conversations requiring further action. Our work is among the very few attempts to detect emotions in spontaneous human-human dialogues recorded in call centers, as opposed to acted studio recordings or human-machine dialogues. We show that despite the non-perfect performance (approx. 70% accuracy) of the window-level classifier, its decisions help produce a ranking of entire conversations by decreasing percentage of anger duration that is clearly better than a random ranking, which represents the case where supervisors and analysts randomly select conversations to inspect.

Kathará: A container-based framework for implementing network function virtualization and software defined networks <=SEP=> Network Function Virtualization (NFV) and Software-Defined Networking (SDN) are deeply changing the networking field by introducing software at any level, aiming at decoupling the logic from the hardware. Together, they bring several benefits, mostly in terms of scalability and flexibility. Up to now, SDN has been used to support NFV from the routing and the architectural point of view. In this paper we present Kathará, a framework based on con­tainers, that allows network operators to deploy Virtual Network Functions (VNFs) through the adoption of emerging data-plane programmable capabilities, such as P4-compliant switches. It also supports the coexistence of SDN and traditional routing protocols in order to set up arbitrarily complex networks. As a side effect, thanks to Kathará, we demonstrate that implementing NFV by means of specific-purpose equipment is feasible and it provides a gain in performance while preserving the benefits of NFV. We measure the resource consumption of Kathará and we show that it performs better than frameworks that implement virtual networks using virtual machines by several orders of magnitude.

An innovative ontology-driven system supporting personnel selection: the OntoHR case <=SEP=> This paper describes the initial development of an HRM system that aims to decrease the gap between higher vocational education and the labour market for a specific job in the ICT sector. This paper focuses specifically on the delineation of a process model and the selection of a suitable job role (information system analyst) that is valid across organisational and cultural boundaries. The process model implies various applied uses of this ontology-based system, including mapping qualifications in vocational education to current and valid job roles, testing and evaluating the student applicant on the basis of labour market driven competencies, and providing ad-hoc support to educational institutions by elucidating the weaknesses of particular VET curricula.

Joint Learning of CNN and LSTM for Image Captioning <=SEP=> In this paper, we describe the details of our methods for the participation in the subtask of the ImageCLEF 2016 Scalable Image Annotation task: Natural Language Caption Generation. The model we used is the combination of a procedure of encoding and a procedure of decoding, which includes a Convolutional neural network(CNN) and a Long Short-Term Memory(LSTM) based Recurrent Neural Network. We first train a model on the MSCOCO dataset and then fine tune the model on different target datasets collected by us to get a more suitable model for the natural language caption generation task. Both of the parameters of CNN and LSTM are learned together.

The Stable Model Semantics for Logic Programming <=SEP=> This paper studies the stable model semantics of logic progr ams with (abstract) constraint atoms and their properties. We introduce a succinct abstract represe ntation of these constraint atoms in which a constraint atom is represented compactly. We show two appli cations. First, under this representation of constraint atoms, we generalize the Gelfond-Lifschitz t ransformation and apply it to define stable models (also called answer sets) for logic programs with arb itr y constraint atoms. The resulting semantics turns out to coincide with the one defined by Son et a l., which is based on a fixpoint approach. One advantage of our approach is that it can be appl ied, in a natural way, to define stable models for disjunctive logic programs with constraint atom s, which may appear in the disjunctive head as well as in the body of a rule. As a result, our approach t o the stable model semantics for logic programs with constraint atoms generalizes a number of prev ious approaches. Second, we show that our abstract representation of constraint atoms provides a me ns to characterize dependencies of atoms in a program with constraint atoms, so that some standa rd characterizations and properties relying on these dependencies in the past for logic programs with ordinary atoms can be extended to logic programs with constraint atoms.

Improving late life depression and cognitive control through the use of therapeutic video game technology: A proof-of-concept randomized trial. <=SEP=> BACKGROUND
Existing treatments for depression are known to have only modest effects, are insufficiently targeted, and are inconsistently utilized, particularly in older adults. Indeed, older adults with impaired cognitive control networks tend to demonstrate poor response to a majority of existing depression interventions. Cognitive control interventions delivered using entertainment software have the potential to not only target the underlying cerebral dysfunction associated with depression, but to do so in a manner that is engaging and engenders adherence to treatment protocol.


METHODS
In this proof-of-concept trial (Clinicaltrials.gov #: NCT02229188), individuals with late life depression (LLD) (22; 60+ years old) were randomized to either problem solving therapy (PST, n = 10) or a neurobiologically inspired digital platform designed to enhance cognitive control faculties (Project: EVO™, n = 12). Given the overlapping functional neuroanatomy of mood disturbances and executive dysfunction, we explored the impact of an intervention targeting cognitive control abilities, functional disability, and mood in older adults suffering from LLD, and how those outcomes compare to a therapeutic gold standard.


RESULTS
EVO participants demonstrated similar improvements in mood and self-reported function after 4 weeks of treatment to PST participants. The EVO participants also showed generalization to untrained measures of working memory and attention, as well as negativity bias, a finding not evident in the PST condition. Individuals assigned to EVO demonstrated 100% adherence.


CONCLUSIONS
This study provides preliminary findings that this therapeutic video game targeting cognitive control deficits may be an efficacious LLD intervention. Future research is needed to confirm these findings.

Enhancing scholarly use of digital libraries: A comparative survey and review of bibliographic metadata ontologies <=SEP=> The HathiTrust Research Center (HTRC) is engaged in the development of tools that will give scholars the ability to analyze the HathiTrust digital library's 14 million volume corpus. A cornerstone of the HTRC's digital infrastructure is the workset -- a kind of scholar-built research collection intended for use with the HTRC's analytics platform. Because more than 66% of the digital corpus is subject to copyright restrictions, scholarly users remain dependent upon the descriptive accounts provided by traditional metadata records in order to identify and gather together bibliographic resources for analysis. This paper compares the MADSRDF/MODSRDF, Bibframe, schema.org, BIBO, and FaBiO ontologies by assessing their suitability for employment by the HTRC to meet scholars' needs. These include distinguishing among multiple versions of the same work; representing the complex historical and physical relationships among those versions; and identifying and providing access to finer grained bibliographic entities, e.g., poems, chapters, sections, and even smaller segments of content.

XYZ Indoor Navigation through Augmented Reality: A Research in Progress <=SEP=> We present an overall framework of services for indoor navigation, which includes Indoor Mapping, Indoor Positioning, Path Planning, and En-route Assistance. Within such framework we focus on an augmented reality (AR) solution for en-route assistance. AR assists the user walking in a multi-floor building by displaying a directional arrow under a camera view, thus freeing the user from knowing his/her position. Our AR solution relies on geomagnetic positioning and north-oriented space coordinates transformation. Therefore, it can work without infrastructure and without relying on GPS. The AR visual interface and the integration with magnetic positioning is the main novelty of our solution, which has been validated by experiments and shows a good performance.

Weakly supervised detection with decoupled attention-based deep representation <=SEP=> Training object detectors with only image-level annotations is an important problem with a variety of applications. However, due to the deformable nature of objects, a target object delineated by a bounding box always includes irrelevant context and occlusions, which causes large intra-class object variations and ambiguity in object-background distinction. For this reason, identifying the object of interest from a substantial amount of cluttered backgrounds is very challenging. In this paper, we propose a decoupled attention-based deep model to optimize region-based object representation. Different from existing approaches posing object representation in a single-tower model, our proposed network decouples object representation into two separate modules, i.e., image representation and attention localization. The image representation module captures content-based semantic representation, while the attention localization module regresses an attention map which simultaneously highlights the locations of the discriminative object parts and down weights the irrelevant backgrounds presented in the image. The combined representation alleviates the impact from the noisy context and occlusions inside an object bounding box. As a result, object-background ambiguity can be largely reduced and background regions can be suppressed effectively. In addition, the proposed object representation model can be seamlessly integrated into a state-of-the-art weakly supervised detection framework, and the entire model can be trained end-to-end. We extensively evaluate the detection performance on the PASCAL VOC 2007, VOC 2010 and VOC2012 datasets. Experimental results demonstrate that our approach effectively improves weakly supervised object detection.

The mass appraisal of the real estate by computational intelligence <=SEP=> Mass appraisal is the systematic appraisal of groups of properties as of a given date using standardized procedures and statistical testing. Mass appraisal is commonly used to compute real estate tax. There are three traditional real estate valuation methods: the sales comparison approach, income approach, and the cost approach. Mass appraisal models are commonly based on the sales comparison approach. The ordinary least squares (OLS) linear regression is the classical method used to build models in this approach. The method is compared with computational intelligence approaches – support vector machine (SVM) regression, multilayer perceptron (MLP), and a committee of predictors in this paper. All the three predictors are used to build a weighted data-depended committee. A self-organizing map (SOM) generating clusters of value zones is used to obtain the data-dependent aggregation weights. The experimental investigations performed using data cordially provided by the Register center of Lithuania have shown very promising results. The performance of the computational intelligence-based techniques was considerably higher than that obtained using the official real estate models of the Register center. The performance of the committee using the weights based on zones obtained from the SOM was also higher than of that exploiting the real estate value zones provided by the Register center. © 2009 Elsevier B.V. All rights reserved.

Beyond the Turing Test <=SEP=> SPRING 2016 3 Alan Turing’s renowned test on intelligence, commonly known as the Turing test, is an inescapable signpost in AI. To people outside the field, the test — which hinges on the ability of machines to fool people into thinking that they (the machines) are people — is practically synonymous with the quest to create machine intelligence. Within the field, the test is widely recognized as a pioneering landmark, but also is now seen as a distraction, designed over half a century ago, and too crude to really measure intelligence. Intelligence is, after all, a multidimensional variable, and no one test could possibly ever be definitive truly to measure it. Moreover, the original test, at least in its standard implementations, has turned out to be highly gameable, arguably an exercise in deception rather than a true measure of anything especially correlated with intelligence. The much ballyhooed 2015 Turing test winner Eugene Goostman, for instance, pretends to be a thirteen-year-old foreigner and proceeds mainly by ducking questions and returning canned one-liners; it cannot see, it cannot think, and it is certainly a long way from genuine artificial general intelligence. Our hope is to see a new suite of tests, part of what we have

Extending the Business Model Canvas: A Dynamic Perspective <=SEP=> When designing and assessing a business model, a more visual and practical ontology and framework is necessary. We show how an academic theory such as Business Model Ontology has evolved into the Business Model Canvas (BMC) that is used by practitioners around the world today. We draw lessons from usage and define three maturity level. We propose new concepts to help design the dynamic aspect of a business model. On the first level, the BMC supports novice users as they elicit their models; it also helps novices to build coherent models. On the second level, the BMC allows expert users to evaluate the interaction of business model elements by outlining the key threads in the business models’ story. On the third level, master users are empowered to create multiple versions of their business models, allowing them to evaluate alternatives and retain the history of the business model’s evolution. These new concepts for the BMC which can be supported by Computer-Aided Design tools provide a clearer picture of the business model as a strategic planning tool and are the basis for further research.

Agent-Based Modeling of the Spread of Influenza-Like Illness in an Emergency Department: A Simulation Study <=SEP=> The objective of this paper was to develop an agent based modeling framework in order to simulate the spread of influenza virus infection on a layout based on a representative hospital emergency department in Winnipeg, Canada. In doing so, the study complements mathematical modeling techniques for disease spread, as well as modeling applications focused on the spread of antibiotic-resistant nosocomial infections in hospitals. Twenty different emergency department scenarios were simulated, with further simulation of four infection control strategies. The agent based modeling approach represents systems modeling, in which the emergency department was modeled as a collection of agents (patients and healthcare workers) and their individual characteristics, behaviors, and interactions. The framework was coded in C + + using Qt4 libraries running under the Linux operating system. A simple ordinary least squares (OLS) regression was used to analyze the data, in which the percentage of patients that be came infected in one day within the simulation was the dependent variable. The results suggest that within the given instance con text, patient-oriented infection control policies (alternate treatment streams, masking symptomatic patients) tend to have a larger effect than policies that target healthcare workers. The agent-based modeling framework is a flexible tool that can be made to reflect any given environment; it is also a decision support tool for practitioners and policymakers to assess the relative impact of infection control strategies. The framework illuminates scenarios worthy of further investigation, as well as counterintuitive findings.

Communicating with Cost-based Implicature : a Game-Theoretic Approach to Ambiguity <=SEP=> A game-theoretic approach to linguistic communication predicts that speakers can meaningfully use ambiguous forms in a discourse context in which only one of several available referents has a costly unambiguous form and in which rational interlocutors share knowledge of production costs. If a speaker produces a low-cost ambiguous form to avoid using the high-cost unambiguous form, a rational listener will infer that the high-cost entity was the intended entity, or else the speaker would not have risked ambiguity. We report data from two studies in which pairs of speakers show alignment of their use of ambiguous forms based on this kind of shared knowledge. These results extend the analysis of cost-based pragmatic inferencing beyond that previously associated only with fixed lexical hosts.

Smoothing LUT classifiers for robust face detection <=SEP=> Look-up table (LUT) classifiers are often used to construct concise classifiers for rapid object detection due to their favorable convergent ability. However, their poor generalization ability imposes restrictions on their applications. A novel improvement to LUT classifiers is proposed in this paper where the new confident of each partition is recalculated by smoothing the old ones within its neighbor partitions. The new confidents are more generalizable than the old ones because each of the new predicts is supported by more training samples and the high frequency components in the old predict sequences are suppressed greatly through smoothing operation. Both weight sum smoothing method and confident smoothing method are introduced here, which all bring negligible extra computation cost in training time and no extra cost in test time. Experimental results in the domain of upright frontal face detection using smoothed LUT classifiers with identical smoothing width and smoothing factor for all partitions based on Haar-like rectangle features show that smoothed LUT classifiers generalize much better and also converge more or less worse than unsmooth LUT classifiers. Specifically, smoothed LUT classifiers can delicately balance between generalization ability and convergent ability through carefully set smoothing parameters.

Multiple-output ZCS resonant inverter for multi-coil induction heating appliances <=SEP=> This paper presents a multiple-output resonant inverter for multi-coil systems featuring high efficiency and flexible output power control for modern induction heating appliances. By adopting a matrix structure, the number of controlled devices can be reduced to the square root of the number of controlled induction heating loads. Therefore, an elevated component count reduction can be achieved compared to classical multi-outputs solutions. The proposed converter is analyzed and verified experimentally, proving the feasibility of this proposal and benefits compared with previous alternatives.

ExpNet: Landmark-Free, Deep, 3D Facial Expressions <=SEP=> We describe a deep learning based method for estimating 3D facial expression coefficients. Unlike previous work, our process does not relay on facial landmark detection methods as a proxy step. Recent methods have shown that a CNN can be trained to regress accurate and discriminative 3D morphable model (3DMM) representations, directly from image intensities. By foregoing landmark detection, these methods were able to estimate shapes for occluded faces appearing in unprecedented viewing conditions. We build on those methods by showing that facial expressions can also be estimated by a robust, deep, landmark-free approach. Our ExpNet CNN is applied directly to the intensities of a face image and regresses a 29D vector of 3D expression coefficients. We propose a unique method for collecting data to train our network, leveraging on the robustness of deep networks to training label noise. We further offer a novel means of evaluating the accuracy of estimated expression coefficients: by measuring how well they capture facial emotions on the CK+ and EmotiW-17 emotion recognition benchmarks. We show that our ExpNet produces expression coefficients which better discriminate between facial emotions than those obtained using state of the art, facial landmark detectors. Moreover, this advantage grows as image scales drop, demonstrating that our ExpNet is more robust to scale changes than landmark detectors. Finally, our ExpNet is orders of magnitude faster than its alternatives.

A First Look at Android Malware Traffic in First Few Minutes <=SEP=> With the advent of mobile era, mobile terminals are going through a trend of surpassing PC to become the most popular computing device. Meanwhile, the hackers and viruswriters are paying close attention to the mobile terminals, especially the Android platform. The growing of malwares on the Android system has drawn attentions from both the academia and security industry. Recently, mobile network traffic analysis has been used to identify the malware. But due to the lack of a large-scale malware repository and a systematic analysis of network traffic features, the existing research mostly remain in theory. In this paper, we design an Android malware traffic behavior monitoring scheme to capture traffic data generated by malware samples in a real Internet environment. We capture the network traffic from 5560 malware samples in the first 5 minutes, and analyze the major compositions of the traffic data. We discover that HTTP and DNS traffic are accounted for more than 99% on the application layer traffic. We then present an analysis of related network features: DNS query, HTTP packet length, ratio of downlink to uplink traffic amount, HTTP request and Ad traffic feature. Our statistical results illustrate that: (1) more than 70% malwares generate malicious traffic in the first 5 minutes, (2) DNS query and HTTP request can be used to identify the malware, and the detection rate reaches 69.55% and 40.89% respectively, (3) Ad traffic can greatly affect the malware detection. We believe our research provides an in-depth analysis into mobile malwares' network behaviors.

Continuous Representation of Location for Geolocation and Lexical Dialectology using Mixture Density Networks <=SEP=> We propose a method for embedding twodimensional locations in a continuous vector space using a neural network-based model incorporating mixtures of Gaussian distributions, presenting two model variants for text-based geolocation and lexical dialectology. Evaluated over Twitter data, the proposed model outperforms conventional regression-based geolocation and provides a better estimate of uncertainty. We also show the effectiveness of the representation for predicting words from location in lexical dialectology, and evaluate it using the DARE dataset.

Restoration of Endodontically Treated Molars Using All Ceramic Endocrowns <=SEP=> Clinical success of endodontically treated posterior teeth is determined by the postendodontic restoration. Several options have been proposed to restore endodontically treated teeth. Endocrowns represent a conservative and esthetic restorative alternative to full coverage crowns. The preparation consists of a circular equigingival butt-joint margin and central retention cavity into the entire pulp chamber constructing both the crown and the core as a single unit. The case reports discussed here are moderately damaged endodontically treated molars restored using all ceramic endocrowns fabricated using two different systems, namely, CAD/CAM and pressed ceramic.

Efficient lineage tracking for scientific workflows <=SEP=> Data lineage and data provenance are key to the management of scientific data. Not knowing the exact provenance and processing pipeline used to produce a derived data set often renders the data set useless from a scientific point of view. On the positive side, capturing provenance information is facilitated by the widespread use of workflow tools for processing scientific data. The workflow process describes all the steps involved in producing a given data set and, hence, captures its lineage. On the negative side, efficiently storing and querying workflow based data lineage is not trivial. All existing solutions use recursive queries and even recursive tables to represent the workflows. Such solutions do not scale and are rather inefficient. In this paper we propose an alternative approach to storing lineage information captured as a workflow process. We use a space and query efficient interval representation for dependency graphs and show how to transform arbitrary workflow processes into graphs that can be stored using such representation. We also characterize the problem in terms of its overall complexity and provide a comprehensive performance evaluation of the approach.

A novel tri-state boost PFC converter with fast dynamic performance <=SEP=> Dynamic response of boost power factor correction (PFC) converter operating in continuous-conduction mode (CCM) is heavily influenced by low-bandwidth of voltage control loop. A novel tri-state boost PFC converter operating in pseudo-continuous-conduction mode (PCCM) is proposed in this paper. An additional degree of control-freedom introduced by a freewheeling switching control interval helps to achieve PFC control. A simple and fast voltage control loop can be used to maintain a constant output voltage. Furthermore, compared with boost PFC converter operating in conventional discontinuous-conduction mode (DCM), boost PFC converter operating in PCCM demonstrates greatly improved current handling capability with reduced current and voltage ripples. Analytical and simulation results of the tri-state boost PFC converter have been presented and compared with those of the boost PFC converter operating in conventional CCM and DCM. Simulation results show excellent dynamic performance of the tri-state boost PFC converter.

A NoSQL Data Model for Scalable Big Data Workflow Execution <=SEP=> While big data workflows haven been proposed recently as the next-generation data-centric workflow paradigm to process and analyze data of ever increasing in scale, complexity, and rate of acquisition, a scalable distributed data model is still missing that abstracts and automates data distribution, parallelism, and scalable processing. In the meanwhile, although NoSQL has emerged as a new category of data models, they are optimized for storing and querying of large datasets, not for ad-hoc data analysis where data placement and data movement are necessary for optimized workflow execution. In this paper, we propose a NoSQL data model that: 1) supports high-performance MapReduce-style workflows that automate data partitioning and data-parallelism execution. In contrast to the traditional MapReduce framework, our MapReduce-style workflows are fully composable with other workflows enabling dataflow applications with a richer structure, 2) automates virtual machine provisioning and deprovisioning on demand according to the sizes of input datasets, 3) enables a flexible framework for workflow executors that take advantage of the proposed NoSQL data model to improve the performance of workflow execution. Our case studies and experiments show the competitive advantages of our proposed data model. The proposed NoSQL data model is implemented in a new release of DATAVIEW, one of the most usable big data workflow systems in the community.