Query: Enablers and Barriers to the Organizational Adoption of Sustainable Business Practices

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IT doesn't matter <=SEP=> This publication contains reprint articles for which IEEE does not hold copyright. Full text is not available on IEEE Xplore for these articles.

Mixing Qualitative and Quantitative Methods : Triangulation in Action * <=SEP=> Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/about/terms.html. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a joumal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use.

STRATEGIC DECISION PROCESSES IN HIGH VELOCITY ENVIRONMENTS : FOUR CASES IN THE MICROCOMPUTER INDUSTRY * <=SEP=> Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use.

Research Commentary: Desperately Seeking the "IT" in IT Research - A Call to Theorizing the IT Artifact <=SEP=> The field of information systems is premised on the centrality of information technology in everyday socio-economic life. Yet, drawing on a review of the full set of articles published in Information Systems Research (ISR) over the past ten years, we argue that the field has not deeply engaged its core subject matter—the information technology (IT) artifact. Instead, we find that IS researchers tend to give central theoretical significance to the context (within which some usually unspecified technology is seen to operate), the discrete processing capabilities of the artifact (as separable from its context or use), or the dependent variable (that which is posited to be affected or changed as technology is developed, implemented, and used). The IT artifact itself tends to disappear from view, be taken for granted, or is presumed to be unproblematic once it is built and installed. After discussing the implications of our findings, we propose a research direction for the IS field that begins to take technology as seriously as its effects, context, and capabilities. In particular, we propose that IS researchers begin to theorize specifically about IT artifacts, and then incorporate these theories explicitly into their studies. We believe that such a research direction is critical if IS research is to make a significant contribution to the understanding of a world increasingly suffused with ubiquitous, interdependent, and emergent information technologies. (Information Systems Research; Information Technology; IT Research; IT Theory; Technological Artifacts; Technology Change)

Generalizing Generalizability in Information Systems Research <=SEP=> Generalizability is a major concern to those who do, and use, research. Statistical, sampling-based generalizability is well known, but methodologists have long been aware of conceptions of generalizability beyond the statistical. The purpose of this essay is to clarify the concept of generalizability by critically examining its nature, illustrating its use and misuse, and presenting a framework for classifying its different forms. The framework organizes the different forms into four types, which are defined by the distinction between empirical and theoretical kinds of statements. On the one hand, the framework affirms the bounds within which statistical, sampling-based generalizability is legitimate. On the other hand, the framework indicates ways in which researchers in information systems and other fields may properly lay claim to generalizability, and thereby broader relevance, even when their inquiry falls outside the bounds of sampling-based research. (Research Methodology; Positivist Research; Interpretive Research; Quantitative Research; Qualitative Research; Case Studies; Research Design; Generalizability )

ImageNet Classification with Deep Convolutional Neural Networks <=SEP=> We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively, which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overfitting in the fully connected layers we employed a recently developed regularization method called "dropout" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry.

Ranking Through Clustering: An Integrated Approach to Multi-Document Summarization <=SEP=> Multi-document summarization aims to create a condensed summary while retaining the main characteristics of the original set of documents. Under such background, sentence ranking has hitherto been the issue of most concern. Since documents often cover a number of topic themes with each theme represented by a cluster of highly related sentences, sentence clustering has been explored in the literature in order to provide more informative summaries. For each topic theme, the rank of terms conditional on this topic theme should be very distinct, and quite different from the rank of terms in other topic themes. Existing cluster-based summarization approaches apply clustering and ranking in isolation, which leads to incomplete, or sometimes rather biased, analytical results. A newly emerged framework uses sentence clustering results to improve or refine the sentence ranking results. Under this framework, we propose a novel approach that directly generates clusters integrated with ranking in this paper. The basic idea of the approach is that ranking distribution of sentences in each cluster should be quite different from each other, which may serve as features of clusters and new clustering measures of sentences can be calculated accordingly. Meanwhile, better clustering results can achieve better ranking results. As a result, ranking and clustering by mutually and simultaneously updating each other so that the performance of both can be improved. The effectiveness of the proposed approach is demonstrated by both the cluster quality analysis and the summarization evaluation conducted on the DUC 2004-2007 datasets.

The optimal training load for the development of dynamic athletic performance. <=SEP=> This study was performed to determine which of three theoretically optimal resistance training modalities resulted in the greatest enhancement in the performance of a series of dynamic athletic activities. The three training modalities included 1) traditional weight training, 2) plyometric training, and 3) explosive weight training at the load that maximized mechanical power output. Sixty-four previously trained subjects were randomly allocated to four groups that included the above three training modalities and a control group. The experimental groups trained for 10 wk performing either heavy squat lifts, depth jumps, or weighted squat jumps. All subjects were tested prior to training, after 5 wk of training and at the completion of the training period. The test items included 1) 30-m sprint, 2) vertical jumps performed with and without a countermovement, 3) maximal cycle test, 4) isokinetic leg extension test, and 5) a maximal isometric test. The experimental group which trained with the load that maximized mechanical power achieved the best overall results in enhancing dynamic athletic performance recording statistically significant (P < 0.05) improvements on most test items and producing statistically superior results to the two other training modalities on the jumping and isokinetic tests.

Hate Online : A Content Analysis of Extremist Internet Sites <=SEP=> Extremists, such as hate groups espousing racial supremacy or separation, have established an online presence. A content analysis of 157 extremist web sites selected through purposive sampling was conducted using two raters per site. The sample represented a variety of extremist groups and included both organized groups and sites maintained by apparently unaffiliated individuals. Among the findings were that the majority of sites contained external links to other extremist sites (including international sites), that roughly half the sites included multimedia content, and that half contained racist symbols. A third of the sites disavowed racism or hatred, yet one third contained material from supremacist literature. A small percentage of sites specifically urged violence. These and other findings suggest that the Internet may be an especially powerful tool for extremists as a means of reaching an international audience, recruiting members, linking diverse extremist groups, and allowing maximum image control.

Treatment of oral mucocele-scalpel versus CO2 laser. <=SEP=> OBJECTIVE
To compare the results obtained after oral mucocele resection with the scalpel versus the CO2 laser, based on the complications and recurrences after surgery.


PATIENTS AND METHODS
Of the 68 patients we studied who have mucocele, 38 were resected with a scalpel and the remaining 30 with the CO2 laser (5-7 W). Patient sex and age were documented, along with location of the lesion as well as size, symptoms, duration, etiological factors, type of treatment, complications and recurrences after surgical removal.


RESULTS
The sample comprised 40 males and 28 females, aged between 6-65 years. The histological diagnosis was extravasation mucocele in 95% of the cases. The most frequent location was the lower lip (73.5%). The mean lesion diameter was 9 mm, and in most cases no evident etiological factor was recorded. The mean duration of the lesion was 4 months. Among the cases of conventional surgical removal of mucocele, recurrence was recorded in 8.8% of the cases, and 13.2% of the patients suffered postoperative complications--the most frequent being the presence of fibrous scars. There were no complications or relapses after a minimum follow-up of 12 months in the cases subjected to CO2 laser treatment.


CONCLUSIONS
Oral mucocele ablation with the CO2 laser offers more predictable results and fewer complications and recurrences than conventional resection with the scalpel.

A neuromorphic VLSI design for spike timing and rate based synaptic plasticity <=SEP=> Triplet-based Spike Timing Dependent Plasticity (TSTDP) is a powerful synaptic plasticity rule that acts beyond conventional pair-based STDP (PSTDP). Here, the TSTDP is capable of reproducing the outcomes from a variety of biological experiments, while the PSTDP rule fails to reproduce them. Additionally, it has been shown that the behaviour inherent to the spike rate-based Bienenstock-Cooper-Munro (BCM) synaptic plasticity rule can also emerge from the TSTDP rule. This paper proposes an analogue implementation of the TSTDP rule. The proposed VLSI circuit has been designed using the AMS 0.35 μm CMOS process and has been simulated using design kits for Synopsys and Cadence tools. Simulation results demonstrate how well the proposed circuit can alter synaptic weights according to the timing difference amongst a set of different patterns of spikes. Furthermore, the circuit is shown to give rise to a BCM-like learning rule, which is a rate-based rule. To mimic an implementation environment, a 1000 run Monte Carlo (MC) analysis was conducted on the proposed circuit. The presented MC simulation analysis and the simulation result from fine-tuned circuits show that it is possible to mitigate the effect of process variations in the proof of concept circuit; however, a practical variation aware design technique is required to promise a high circuit performance in a large scale neural network. We believe that the proposed design can play a significant role in future VLSI implementations of both spike timing and rate based neuromorphic learning systems.

A Comparative Study on English and Chinese Word Uses with LIWC <=SEP=> This paper compared the linguistic and psychological word uses in English and Chinese languages with LIWC (Linguistic Inquiry and Word Count) programs. A Principal Component Analysis uncovered six linguistic and psychological components, among which five components were significantly correlated. The correlated components were ranked as Negative Valence (r=.92), Embodiment (r=.88), Narrative (r=.68), Achievement (r=.65), and Social Relation (r=.64). However, the results showed the order of the representative features differs in two languages and certain word categories co-occurred with different components in English and Chinese. The differences were interpreted from the perspective of distinctive eastern and western cultures.

Table-top spatially-augmented realty: bringing physical models to life with projected imagery <=SEP=> Despite the availability of high-quality graphics systems, architects and designers still build scaled physical models of buildings and products. These physical models have many advantages, however they are typically static in structure and surface characteristics. They are inherently lifeless. In contrast, high-quality graphics systems are tremendously flexible, allowing viewers to see alternative structures, facades, textures, cut-away views, and even dynamic effects such as changing lighting, moving automobiles, people, etc. We introduce a combination of these approaches that builds on our previously-published projector-based Spatially-Augmented Reality techniques. The basic idea is to aim multiple ceiling-mounted light projectors inward to graphically augment table-top scaled physical models of buildings or products. This approach promises to provide very compelling hybrid visualizations that afford the benefits of both traditional physical models, and modern computer graphics, effectively "bringing to life" table-top

Enhancing credit scoring model performance by a hybrid scoring matrix <=SEP=> Competition of the consumer credit market in Taiwan has become severe recently. Therefore, most financial institutions actively develop credit scoring models based on assessments of the credit approval of new customers and the credit risk management of existing customers. This study uses a genetic algorithm for feature selection and decision trees for customer segmentation. Moreover, it utilizes logistic regression to build the application and credit bureau scoring models where the two scoring models are combined for constructing the scoring matrix. The scoring matrix undergoes more accurate risk judgment and segmentation to further identify the parts required enhanced management or control within a personal loan portfolio. The analytical results demonstrate that the predictive ability of the scoring matrix outperforms both the application and credit bureau scoring models. Regarding the K-S value, the scoring matrix increases the prediction accuracy compared to the application and credit bureau scoring models by 18.40 and 5.70%, respectively. Regarding the AUC value, the scoring matrix increases the prediction accuracy compared to the application and credit bureau scoring models by 10.90 and 6.40%, respectively. Furthermore, this study applies the scoring matrix to the credit approval decisions for corresponding risk groups to strengthen bank’s risk management practices.

Tracking in cell and developmental biology. <=SEP=> The past decade has seen an unprecedented data explosion in biology. It has become evident that in order to take full advantage of the potential wealth of information hidden in the data produced by even a single experiment, visual inspection and manual analysis are no longer adequate. To ensure efficiency, consistency, and completeness in data processing and analysis, computational tools are essential. Of particular importance to many modern live-cell imaging experiments is the ability to automatically track and analyze the motion of objects in time-lapse microscopy images. This article surveys the recent literature in this area. Covering all scales of microscopic observation, from cells, down to molecules, and up to entire organisms, it discusses the latest trends and successes in the development and application of computerized tracking methods in cell and developmental biology.

Measurement-Based Harmonic Modeling of an Electric Vehicle Charging Station Using a Three-Phase Uncontrolled Rectifier <=SEP=> The harmonic pollution of electric vehicle chargers is increasing rapidly as the scale of electric vehicle charging stations enlarges to meet the rising demand for electric vehicles. This paper investigates the operating characteristics of a three-phase uncontrolled rectification electric vehicle charger with passive power factor correction. A method for estimating the equivalent circuit parameters of chargers is proposed based on the measured feature data of voltage and current at the ac side and on the circuit constraint during the conduction charging process. A harmonic analytical model of the charging station is then established by dividing the same charger types into groups. The parameter estimation method and the harmonic model of the charging station are verified through simulation, experiment, and field test. The parameter sensitivities of the equivalent circuit to the charging current harmonic are also studied.

Lemmatization and Morphological Tagging in German and Latin: A Comparison and a Survey of the State-of-the-art <=SEP=> This paper relates to the challenge of morphological tagging and lemmatization in morphologically rich languages by example of German and Latin. We focus on the question what a practitioner can expect when using state-of-the-art solutions out of the box. Moreover, we contrast these with old(er) methods and implementations for POS tagging. We examine to what degree recent efforts in tagger development pay out in improved accuracies — and at what cost, in terms of training and processing time. We also conduct in-domain vs. out-domain evaluation. Out-domain evaluations are particularly insightful because the distribution of the data which is being tagged by a user will typically differ from the distribution on which the tagger has been trained. Furthermore, two lemmatization techniques are evaluated. Finally, we compare pipeline tagging vs. a tagging approach that acknowledges dependencies between inflectional categories.

Why Neurons Have Thousands of Synapses, a Theory of Sequence Memory in Neocortex <=SEP=> Pyramidal neurons represent the majority of excitatory neurons in the neocortex. Each pyramidal neuron receives input from thousands of excitatory synapses that are segregated onto dendritic branches. The dendrites themselves are segregated into apical, basal, and proximal integration zones, which have different properties. It is a mystery how pyramidal neurons integrate the input from thousands of synapses, what role the different dendrites play in this integration, and what kind of network behavior this enables in cortical tissue. It has been previously proposed that non-linear properties of dendrites enable cortical neurons to recognize multiple independent patterns. In this paper we extend this idea in multiple ways. First we show that a neuron with several thousand synapses segregated on active dendrites can recognize hundreds of independent patterns of cellular activity even in the presence of large amounts of noise and pattern variation. We then propose a neuron model where patterns detected on proximal dendrites lead to action potentials, defining the classic receptive field of the neuron, and patterns detected on basal and apical dendrites act as predictions by slightly depolarizing the neuron without generating an action potential. By this mechanism, a neuron can predict its activation in hundreds of independent contexts. We then present a network model based on neurons with these properties that learns time-based sequences. The network relies on fast local inhibition to preferentially activate neurons that are slightly depolarized. Through simulation we show that the network scales well and operates robustly over a wide range of parameters as long as the network uses a sparse distributed code of cellular activations. We contrast the properties of the new network model with several other neural network models to illustrate the relative capabilities of each. We conclude that pyramidal neurons with thousands of synapses, active dendrites, and multiple integration zones create a robust and powerful sequence memory. Given the prevalence and similarity of excitatory neurons throughout the neocortex and the importance of sequence memory in inference and behavior, we propose that this form of sequence memory may be a universal property of neocortical tissue.

Gradient Flow in Recurrent Nets : the Difficulty of Learning Long-Term Dependencies <=SEP=> 

Defining Creativity: Finding Keywords for Creativity Using Corpus Linguistics Techniques <=SEP=> A computational system that evaluates creativity needs guidance on what creativity actually is. It is by no means straightforward to provide a computer with a formal definition of creativity; no such definition yet exists and viewpoints in creativity literature vary as to what the key components of creativity are considered to be. This work combines several viewpoints for a more general consensus of how we define creativity, using a corpus linguistics approach. 30 academic papers from various academic disciplines were analysed to extract the most frequently used words and their frequencies in the papers. This data was statistically compared with general word usage in written English. The results form a list of words that are significantly more likely to appear when talking about creativity in academic texts. Such words can be considered keywords for creativity, guiding us in uncovering key sub-components of creativity which can be used for computational assessment of creativity.

Weighted Task Regularization for Multitask Learning <=SEP=> Multitask Learning has been proven to be more effective than the traditional single task learning on many real-world problems by simultaneously transferring knowledge among different tasks which may suffer from limited labeled data. However, in order to build a reliable multitask learning model, nontrivial effort to construct the relatedness between different tasks is critical. When the number of tasks is not large, the learning outcome may suffer if there exists outlier tasks that inappropriately bias majority. Rather than identifying or discarding such outlier tasks, we present a weighted regularized multitask learning framework based on regularized multitask learning, which uses statistical metrics, such as Kullback-Leibler divergence, to assign weights prior to regularization process that robustly reduces the impact of outlier tasks and results in better learned models for all tasks. We then show that this formulation can be solved using dual form like optimizing a standard support vector machine with varied kernels. We perform experiments using both synthetic dataset and real-world dataset from petroleum industry which shows that our methodology outperforms existing methods.

A contextual-bandit approach to personalized news article recommendation <=SEP=> Personalized web services strive to adapt their services (advertisements, news articles, etc.) to individual users by making use of both content and user information. Despite a few recent advances, this problem remains challenging for at least two reasons. First, web service is featured with dynamically changing pools of content, rendering traditional collaborative filtering methods inapplicable. Second, the scale of most web services of practical interest calls for solutions that are both fast in learning and computation.
 In this work, we model personalized recommendation of news articles as a contextual bandit problem, a principled approach in which a learning algorithm sequentially selects articles to serve users based on contextual information about the users and articles, while simultaneously adapting its article-selection strategy based on user-click feedback to maximize total user clicks.
 The contributions of this work are three-fold. First, we propose a new, general contextual bandit algorithm that is computationally efficient and well motivated from learning theory. Second, we argue that any bandit algorithm can be reliably evaluated offline using previously recorded random traffic. Finally, using this offline evaluation method, we successfully applied our new algorithm to a Yahoo! Front Page Today Module dataset containing over 33 million events. Results showed a 12.5% click lift compared to a standard context-free bandit algorithm, and the advantage becomes even greater when data gets more scarce.

3D hand tracking using Kalman filter in depth space <=SEP=> Hand gestures are an important type of natural language used in many research areas such as human-computer interaction and computer vision. Hand gestures recognition requires the prior determination of the hand position through detection and tracking. One of the most efficient strategies for hand tracking is to use 2D visual information such as color and shape. However, visual-sensor-based hand tracking methods are very sensitive when tracking is performed under variable light conditions. Also, as hand movements are made in 3D space, the recognition performance of hand gestures using 2D information is inherently limited. In this article, we propose a novel real-time 3D hand tracking method in depth space using a 3D depth sensor and employing Kalman filter. We detect hand candidates using motion clusters and predefined wave motion, and track hand locations using Kalman filter. To verify the effectiveness of the proposed method, we compare the performance of the proposed method with the visual-based method. Experimental results show that the performance of the proposed method out performs visual-based method.

Classification of Monte Carlo tree search variants <=SEP=> Many variations of Monte Carlo tree search have been proposed and tested but relatively little comparison of these variants have occurred. In this study an Agent Case Embedding analysis and agglomorative hierarchical clustering was performed using eight variants of Monte Carlo Tree Search as agents and eight games as cases. This allowed us to compare the variant's abilities on each of the games to determine the type of games each are good at handling as well as which variants are similar to others. This comparison of variants exploits the ability of ACEs to compare different types of objects based on their behavior. By looking at the behavior of MCTS variants on a variety of games we obtain a good notion of the degree to which different MCTS variants exhibit different capabilities. A side effect of comparing MCTS variants with agent-case embeddings is that we also are able to compare the games used to test the MCTS variants.

Group awareness in distributed software development <=SEP=> Open-source software development projects are almost always collaborative and distributed. Despite the difficulties imposed by distance, these projects have managed to produce large, complex, and successful systems. However, there is still little known about how open-source teams manage their collaboration. In this paper we look at one aspect of this issue: how distributed developers maintain group awareness. We interviewed developers, read project communication, and looked at project artifacts from three successful open source projects. We found that distributed developers do need to maintain awareness of one another, and that they maintain both a general awareness of the entire team and more detailed knowledge of people that they plan to work with. Although there are several sources of information, this awareness is maintained primarily through text-based communication (mailing lists and chat systems). These textual channels have several characteristics that help to support the maintenance of awareness, as long as developers are committed to reading the lists and to making their project communication public.

Gland Segmentation and Computerized Gleason Grading of Prostate Histology by Integrating Low-, High-level and Domain Specific Information <=SEP=> In this paper we present a method of automatically detecting and segmenting glands in digitized images of prostate histology and to use features derived from gland morphology to distinguish between intermediate Gleason grades. Gleason grading is a method of describing prostate cancer malignancy on a numerical scale from grade 1 (early stage cancer) through grade 5 (highly infiltrative cancer). Studies have shown that gland morphology plays a significant role in discriminating Gleason grades. We present a method of automated detection and segmentation of prostate gland regions. A Bayesian classifier is used to detect candidate gland regions by utilizing low-level image features to find the lumen, epithelial cell cytoplasm, and epithelial nuclei of the tissue. False positive regions identified as glands are eliminated via use of domain-specific knowledge constraints. Following candidate region detection via low-level and empirical domain information, the lumen area is used to initialize a level-set curve, which is evolved to lie at the interior boundary of the nuclei surrounding the gland structure. Features are calculated from the boundaries that characterize the morphology of the lumen and the gland regions, including area overlap ratio, distance ratio, standard deviation and variance of distance, perimeter ratio, compactness, smoothness, and area. The feature space is reduced using a manifold learning scheme (Graph Embedding) that is used to embed objects that are adjacent to each other in the high dimensional feature space into a lower dimensional embedding space. Objects embedded in this low dimensional embedding space are then classified via a support vector machine (SVM) classifier as belonging to Gleason grade 3, grade 4 cancer, or benign epithelium. We evaluate the efficacy of the automated segmentation algorithm by comparing the classification accuracy obtained using the automated segmentation scheme to the accuracy obtained via a user assisted segmentation scheme. Using the automated scheme, the system achieves accuracies of 86.35% when distinguishing Gleason grade 3 from benign epithelium, 92.90% distinguishing grade 4 from benign epithelium, and 95.19% distinguishing between Gleason grades 3 and 4. The manual scheme returns accuracies of 95.14%, 95.35%, and 80.76% for the respective classification tasks, indicating that the automated segmentation algorithm and the manual scheme are comparable in terms of achieving the overall objective of grade classification.

VHDL Implementation of 8-Bit ALU <=SEP=> In this paper VHDL implementation of 8-bit arithmetic logic unit (ALU) is presented. The design was implemented using VHDL Xilinx Synthesis tool ISE 13.1 and targeted for Spartan device. ALU was designed to perform arithmetic operations such as addition and subtraction using 8-bit fast adder, logical operations such as AND, OR, XOR and NOT operations, 1’s and 2’s complement operations and compare. ALU consist of two input registers to hold the data during operation, one output register to hold the result of operation, 8-bit fast adder with 2’s complement circuit to perform subtraction and logic gates to perform logical operation. The maximum propagation delay is 13.588ns and power dissipation is 38mW. The ALU was designed for controller used in network interface card.

String similarity measures and joins with synonyms <=SEP=> A string similarity measure quantifies the similarity between two text strings for approximate string matching or comparison. For example, the strings "Sam" and "Samuel" can be considered similar. Most existing work that computes the similarity of two strings only considers syntactic similarities, e.g., number of common words or q-grams. While these are indeed indicators of similarity, there are many important cases where syntactically different strings can represent the same real-world object. For example, "Bill" is a short form of "William". Given a collection of predefined synonyms, the purpose of the paper is to explore such existing knowledge to evaluate string similarity measures more effectively and efficiently, thereby boosting the quality of string matching.
 In particular, we first present an expansion-based framework to measure string similarities efficiently while considering synonyms. Because using synonyms in similarity measures is, while expressive, computationally expensive (NP-hard), we propose an efficient algorithm, called selective-expansion, which guarantees the optimality in many real scenarios. We then study a novel indexing structure called SI-tree, which combines both signature and length filtering strategies, for efficient string similarity joins with synonyms. We develop an estimator to approximate the size of candidates to enable an online selection of signature filters to further improve the efficiency. This estimator provides strong low-error, high-confidence guarantees while requiring only logarithmic space and time costs, thus making our method attractive both in theory and in practice. Finally, the results from an empirical study of the algorithms verify the effectiveness and efficiency of our approach.

Dynamic 2D/3D registration for the Kinect <=SEP=> Image and geometry registration algorithms are essential components of many computer graphics and computer vision systems. With recent technological advances in RGB-D sensors, robust algorithms that combine 2D image and 3D geometry registration have become an active area of research.
 This course introduces the basics of 2D/3D registration algorithms and provides theoretical explanations and practical tools for designing computer vision and computer graphics systems based on RGB-D devices such as the Microsoft Kinect or Asus Xtion Live. To illustrate the theory and demonstrate practical relevance, the course briefly discusses three applications: rigid scanning, non-rigid modeling, and real-time face tracking.

Stylometry with R: a suite of tools <=SEP=> Stylometry today uses either stand-alone dedicated programs, custom-made by stylometrists, or applies existing software, often one for each stage of the analysis. Stylometry with R can be placed somewhere in-between, as the powerful open-source statistical programming environment provides, on the one hand, the opportunity of building statistical applications from scratch, and, on the other, allows less advanced researchers to use ready-made scripts and libraries. In our own stylometric adventure with R, one of the aims was to build a tool (or a set of tools) that would combine sophisticated state-of-the-art algorithms of classification and/or clustering with a user-friendly interface. In particular, we wanted to implement a number of multidimensional methods that could be used by scholars without programming skills. And more: it soon became evident that once our R scripts are made, provided with a graphic user interface and more or less documented, they are highly usable in class; experience shows that this is an excellent way to work around R’s normally steep learning curve without losing anything of the environment’s considerable computing power and speed.