Query: Fingerprint verification by fusion of optical and capacitive sensors

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Filterbank-based fingerprint matching <=SEP=> With identity fraud in our society reaching unprecedented proportions and with an increasing emphasis on the emerging automatic personal identification applications, biometrics-based verification, especially fingerprint-based identification, is receiving a lot of attention. There are two major shortcomings of the traditional approaches to fingerprint representation. For a considerable fraction of population, the representations based on explicit detection of complete ridge structures in the fingerprint are difficult to extract automatically. The widely used minutiae-based representation does not utilize a significant component of the rich discriminatory information available in the fingerprints. Local ridge structures cannot be completely characterized by minutiae. Further, minutiae-based matching has difficulty in quickly matching two fingerprint images containing a different number of unregistered minutiae points. The proposed filter-based algorithm uses a bank of Gabor filters to capture both local and global details in a fingerprint as a compact fixed length FingerCode. The fingerprint matching is based on the Euclidean distance between the two corresponding FingerCodes and hence is extremely fast. We are able to achieve a verification accuracy which is only marginally inferior to the best results of minutiae-based algorithms published in the open literature. Our system performs better than a state-of-the-art minutiae-based system when the performance requirement of the application system does not demand a very low false acceptance rate. Finally, we show that the matching performance can be improved by combining the decisions of the matchers based on complementary (minutiae-based and filter-based) fingerprint information.

On-line fingerprint verification <=SEP=> Fingerprint verification is one of the most reliable personal identification methods. However, manual fingerprint verification is so tedious, time-consuming, and expensive that it is incapable of meeting today’s increasing performance requirements. An automatic fingerprint identification system (AFIS) is widely needed. It plays a very important role in forensic and civilian applications such as criminal identification, access control, and ATM card verification. This paper describes the design and implementation of an on-line fingerprint verification system which operates in two stages: minutia extraction and minutia matching. An improved version of the minutia extraction algorithm proposed by Ratha et al., which is much faster and more reliable, is implemented for extracting features from an input fingerprint image captured with an on-line inkless scanner. For minutia matching, an alignment-based elastic matching algorithm has been developed. This algorithm is capable of finding the correspondences between minutiae in the input image and the stored template without resorting to exhaustive search and has the ability of adaptively compensating for the nonlinear deformations and inexact pose transformations between fingerprints. The system has been tested on two sets of fingerprint images captured with inkless scanners. The verification accuracy is found to be acceptable. Typically, a complete fingerprint verification procedure takes, on an average, about eight seconds on a SPARC 20 workstation. These experimental results show that our system meets the response time requirements of on-line verification with high accuracy. Index Terms —Biometrics, fingerprints, matching, verification, minutia, orientation field, ridge extraction. —————————— ✦ ——————————

Handbook of Fingerprint Recognition <=SEP=> label(s) Label Class c) b) a) 7.3 Integration Strategies 241 Combination strategy A combination strategy (also called combination scheme) is a technique used to combine the output of the individual classifiers. The most popular combination strategies at the abstract level are based on majority vote rules, which simply assign an input pattern to the most voted class (see Section 7.2). When two classifiers are combined, either a logical AND or a logical OR operator is typically used. When more than two classifiers are integrated, the AND/OR rules can be combined. For example, a biometric system may work on “fingerprint OR (face AND hand geometry)”; that is, it requires a user to present either a fingerprint or both face and hand geometry for recognition. Class set reduction, logistic regression, and Borda counts are the most commonly used approaches in combining classifiers based on the rank labels (Ho, Hull, and Srihari, 1994). In class set reduction, a subset of the classes is selected with the aim that the subset be as small as possible and still contain the true class. Multiple subsets from multiple modalities are typically combined using either a union or an intersection of the subsets. The logistic regression and Borda count methods are collectively called the class set reordering methods. The objective here is to derive a consensus ranking of the given classes such that the true class is ranked at the top. Rank labels are very useful for integration in an indexing/retrieval system. A biometric retrieval system typically outputs an ordered list of candidates (most likely matches). The top element of this ordered list is the most likely to be a correct match and the bottom of the list is the least likely match. The most popular combination schemes for combining confidence values from multiple modalities are sum, mean, median, product, minimum, and maximum rules. Kittler et al. (1998) have developed a theoretical framework in an attempt to understand the underlying mathematical basis of these popular schemes. Their experiments demonstrated that the sum or mean scheme typically performs very well in practice. A problem in using the sum rule is that the confidences (or scores) from different modalities should be normalized. This normalization typically involves mapping the confidence measures from different modalities into a common domain. For example, a biometric system may output a distance score (the lower the score, the more similar the patterns) whereas another may output a similarity score (the higher the score, the more similar the patterns) and thus the scores cannot be directly combined using the sum rule. In its simplest form, this normalization may only include inverting the sign of distance scores such that a higher score corresponds to a higher similarity. In a more complex form, the normalization may be non-linear which can be learned from training data by estimating distributions of confidence values from each modality. The scores are then translated and scaled to have zero mean, unit variance, and then remapped to a fixed interval of (0,1) using a hyperbolic tangent function. Note that it is tempting to parameterize the estimated distributions for normalization. However, such parameterization of distributions should be used with care, because the error rates of biometric systems are typically very small and a small error in estimating the tails of the distributions may result in a significant change in the error estimates (see Figure 7.3). Another common practice is to compute different scaling factors (weights) for each modality from training data, such that the accuracy of the combined classifier is maxi7 Multimodal Biometric Systems 242 mized. This weighted sum rule is expected to work better than the simple sum rule when the component classifiers have different strengths (i.e., different error rates). Figure 7.3. a) Genuine and impostor distributions for a fingerprint verification system (Jain et al., 2000) and a Normal approximation for the impostor distribution. Visually, the Normal approximation seems to be good, but causes significant decrease in performance compared to the non-parametric estimate as shown in the ROCs in b), where FMR is referred to as FAR (False Acceptance Rate) and (1-FNMR) as Genuine Acceptance Rate. ©Elsevier. Some schemes to combine multiple modalities in biometric systems have also been studied from a theoretical point of view. Through a theoretical analysis, Daugman (1999b) showed that if a strong biometric and a weak biometric are combined with an abstract level combination using either the AND or the OR voting rules, the performance of the combination will be worse than the better of the two individual biometrics. Hong, Jain, and Pankanti’s (1999) theoretical analysis that AND/OR voting strategies can improve performance only when certain conditions are satisfied confirmed Daugman’s findings. Their analysis further showed that a confidence level fusion is expected to significantly improve overall performance even in the case of combining a weak and a strong biometric. Kittler et al. (1998) introduced a sensitivity analysis to explain why the sum (or average) rule outperforms the other rules. They showed that the sum rule is less sensitive than the other similar rules (such as the “product” rule) to the error rates of individual classifiers in estimating posterior probabilities (confidence values). They claim that the sum rule is the most appropriate for combining different estimates of the same posterior probabilities (e.g., resulting from different classifier initializations). Prabhakar and Jain (2002) compared the sum and the product rules with the Neyman−Pearson combination scheme and showed that the product rule is worse than the sum rule when combining correlated features and both the sum rule and the product rules are inferior to the Neyman− Pearson combination scheme when combining weak and strong classifiers. 0 20 40 60 80 100 0 1 2 3 4 5 6 7 Normalized Matching Score Pe rc en ta ge ( % ) Imposter Genuine Nonparametric Imposter Distribution Normal Imposter Distribution Genuine Distribution 0 1 2 3 4 5 50 55 60 65 70 75 80 85 90 95 100 False Acceptance Rate (%) G en ui ne A cc ep ta nc e R at e (% ) Using Nonparametric Imposter Distribution Using Normal Imposter Distribution

FVC2002: Second Fingerprint Verification Competition <=SEP=> Two years after the first edition, a new Fingerprint Verification Competition (FVC2002) was organized by the authors, with the aim of determining the state-of-theart in this challenging pattern recognition application. The experience and the feedback received from FVC2000 allowed the authors to improve the organization of FVC2002 and to capture the attention of a significantly higher number of academic and commercial organizations (33 algorithms were submitted). This paper discusses the FVC2002 database, the test protocol and the main differences between FVC2000 and FVC2002. The algorithm performance evaluation will be presented at the 16 ICPR.

Introduction to Algorithms <=SEP=> problems To understand the class of polynomial-time solvable proble ms, we must first have a formal notion of what a “problem” is. We define anbstract problemQ to be a binary relation on a set I of probleminstancesand a setS of problemsolutions. For example, an instance for SHORTEST-PATH is a triple consi sting of a graph and two vertices. A solution is a sequence of vertices in the g raph, with perhaps the empty sequence denoting that no path exists. The problem SHORTEST-PATH itself is the relation that associates each instance of a gra ph and two vertices with a shortest path in the graph that connects the two vertices. S ince shortest paths are not necessarily unique, a given problem instance may have mo r than one solution. This formulation of an abstract problem is more general than is required for our purposes. As we saw above, the theory of NP-completeness res tricts attention to decision problems : those having a yes/no solution. In this case, we can view an abstract decision problem as a function that maps the instan ce setI to the solution set {0, 1}. For example, a decision problem related to SHORTEST-PATH i s the problem PATH that we saw earlier. If i = 〈G,u, v,k〉 is an instance of the decision problem PATH, then PATH(i ) = 1 (yes) if a shortest path fromu to v has at mostk edges, and PATH (i ) = 0 (no) otherwise. Many abstract problems are not decision problems, but rather optimization problems , in which some value must be minimized or maximized. As we saw above, however, it is usual ly a simple matter to recast an optimization problem as a decision problem that is no harder. 1See Hopcroft and Ullman [156] or Lewis and Papadimitriou [20 4] for a thorough treatment of the Turing-machine model. 34.1 Polynomial time 973

Cover your ACKs: pitfalls of covert channel censorship circumvention <=SEP=> In response to increasingly sophisticated methods of blocking access to censorship circumvention schemes such as Tor, recently proposed systems such as Skypemorph, FreeWave, and CensorSpoofer have used voice and video conferencing protocols as "cover channels" to hide proxy connections. We demonstrate that even with perfect emulation of the cover channel, these systems can be vulnerable to attacks that detect or disrupt the covert communications while having no effect on legitimate cover traffic. Our attacks stem from differences in the channel requirements for the cover protocols, which are peer-to-peer and loss tolerant, and the covert traffic, which is client-proxy and loss intolerant. These differences represent significant limitations and suggest that such protocols are a poor choice of cover channel for general censorship circumvention schemes.

Internet Addiction: The Emergence of a New Clinical Disorder <=SEP=> Anecdotal reports indicated that some on-line users were becoming addicted to the Internet in much that same way that others became addicted to drugs or alcohol which resulted in academic, social, and occupational impairment. However, research among sociologists, psychologists, or psychiatrists has not formally identified addictive use of the Internet as a problematic behavior. This study investigated the existence of Internet addiction and the extent of problems caused by such potential misuse. This study utilized an adapted version of the criteria for pathological gambling defined by the DSM-IV (APA, 1994). On the basis of this criteria, case studies of 396 dependent Internet users (Dependents) and a control group of 100 non-dependent Internet users (Non-Dependents) were classified. Qualitative analyses suggests significant behavioral and functional usage differences between the two groups. Clinical and social implications of pathological Internet use and future directions for research are discussed.

Smart Agents in Industrial Cyber–Physical Systems <=SEP=> Future industrial systems can be realized using the cyber-physical systems (CPSs) that advocate the coexistence of cyber and physical counterparts in a network structure to perform the system's functions in a collaborative manner. Multiagent systems share common ground with CPSs and can empower them with a multitude of capabilities in their efforts to achieve complexity management, decentralization, intelligence, modularity, flexibility, robustness, adaptation, and responsiveness. This work surveys and analyzes the current state of the industrial application of agent technology in CPSs, and provides a vision on the way agents can effectively enable emerging CPS challenges.

Katyusha: the first direct acceleration of stochastic gradient methods <=SEP=> Nesterovâs momentum trick is famously known for accelerating gradient descent, and has been proven useful in building fast iterative algorithms. However, in the stochastic setting, counterexamples exist and prevent Nesterovâs momentum from providing similar acceleration, even if the underlying problem is convex. 
We introduce Katyusha, a direct, primal-only stochastic gradient method to fix this issue. It has a provably accelerated convergence rate in convex (off-line) stochastic optimization. The main ingredient is Katyusha momentum, a novel ânegative momentumâ on top of Nesterovâs momentum that can be incorporated into a variance-reduction based algorithm and speed it up. Since variance reduction has been successfully applied to a growing list of practical problems, our paper suggests that in each of such cases, one could potentially give Katyusha a hug.

A maximum entropy approach to adaptive statistical language modelling <=SEP=> An adaptive statistical language model is described, which successfully integrates long distance linguistic information with other knowledge sources. Most existing statistical language models exploit only the immediate history of a text. To extract information from further back in the document’s history, we propose and use trigger pairs as the basic information bearing elements. This allows the model to adapt its expectations to the topic of discourse. Next, statistical evidence from multiple sources must be combined. Traditionally, linear interpolation and its variants have been used, but these are shown here to be seriously deficient. Instead, we apply the principle of Maximum Entropy (ME). Each information source gives rise to a set of constraints, to be imposed on the combined estimate. The intersection of these constraints is the set of probability functions which are consistent with all the information sources. The function with the highest entropy within that set is the ME solution. Given consistent statistical evidence, a unique ME solution is guaranteed to exist, and an iterative algorithm exists which is guaranteed to converge to it. The ME framework is extremely general: any phenomenon that can be described in terms of statistics of the text can be readily incorporated. An adaptive language model based on the ME approach was trained on the Wall Street Journal corpus, and showed a 32–39% perplexity reduction over the baseline. When interfaced to SPHINX-II, Carnegie Mellon’s speech recognizer, it reduced its error rate by 10–14%. This thus illustrates the feasibility of incorporating many diverse knowledge sources in a single, unified statistical framework.  1996 Academic Press Limited

Expressing Interactivity with States and Constraints <=SEP=> ......................................................................................................................... iii Acknowledgements ......................................................................................................... iv Table of

Cascade Residual Learning: A Two-Stage Convolutional Neural Network for Stereo Matching <=SEP=> Leveraging on the recent developments in convolutional neural networks (CNNs), matching dense correspondence from a stereo pair has been cast as a learning problem, with performance exceeding traditional approaches. However, it remains challenging to generate high-quality disparities for the inherently ill-posed regions. To tackle this problem, we propose a novel cascade CNN architecture composing of two stages. The first stage advances the recently proposed DispNet by equipping it with extra up-convolution modules, leading to disparity images with more details. The second stage explicitly rectifies the disparity initialized by the first stage; it couples with the first-stage and generates residual signals across multiple scales. The summation of the outputs from the two stages gives the final disparity. As opposed to directly learning the disparity at the second stage, we show that residual learning provides more effective refinement. Moreover, it also benefits the training of the overall cascade network. Experimentation shows that our cascade residual learning scheme provides state-of-the-art performance for matching stereo correspondence. By the time of the submission of this paper, our method ranks first in the KITTI 2015 stereo benchmark, surpassing the prior works by a noteworthy margin.

Sensory gain control (amplification) as a mechanism of selective attention: electrophysiological and neuroimaging evidence. <=SEP=> Both physiological and behavioral studies have suggested that stimulus-driven neural activity in the sensory pathways can be modulated in amplitude during selective attention. Recordings of event-related brain potentials indicate that such sensory gain control or amplification processes play an important role in visual-spatial attention. Combined event-related brain potential and neuroimaging experiments provide strong evidence that attentional gain control operates at an early stage of visual processing in extrastriate cortical areas. These data support early selection theories of attention and provide a basis for distinguishing between separate mechanisms of attentional suppression (of unattended inputs) and attentional facilitation (of attended inputs).

An anatomically-constrained local deformation model for monocular face capture <=SEP=> We present a new anatomically-constrained local face model and fitting approach for tracking 3D faces from 2D motion data in very high quality. In contrast to traditional global face models, often built from a large set of blendshapes, we propose a local deformation model composed of many small subspaces spatially distributed over the face. Our local model offers far more flexibility and expressiveness than global blendshape models, even with a much smaller model size. This flexibility would typically come at the cost of reduced robustness, in particular during the under-constrained task of monocular reconstruction. However, a key contribution of this work is that we consider the face anatomy and introduce subspace skin thickness constraints into our model, which constrain the face to only valid expressions and helps counteract depth ambiguities in monocular tracking. Given our new model, we present a novel fitting optimization that allows 3D facial performance reconstruction from a single view at extremely high quality, far beyond previous fitting approaches. Our model is flexible, and can be applied also when only sparse motion data is available, for example with marker-based motion capture or even face posing from artistic sketches. Furthermore, by incorporating anatomical constraints we can automatically estimate the rigid motion of the skull, obtaining a rigid stabilization of the performance for free. We demonstrate our model and single-view fitting method on a number of examples, including, for the first time, extreme local skin deformation caused by external forces such as wind, captured from a single high-speed camera.

ESD reliability improvement of the 0.25-µm 60-V power nLDMOS with discrete embedded SCRs separated by STI structures <=SEP=> In this paper, the electrostatic-discharge (ESD) robustness improvement by modulating the drain-side embedded SCR of an HV nLDMOS device is investigated via a TSMC 0.25 μm 60 V process. After a systematic layout design and data analysis, it can be found that the holding voltage (Vh) of an nLDMOS with a parasitic SCR “npn”-arranged type & thin oxide (OD) discrete (i.e. separated by the shallow-trench isolation (STI) structure) distribution in the drain-side have greatly increased with the parasitic SCR OD decreasing. Therefore, a high Vh value in the OD discrete parameter 2 (DIS-2) can be obtained about 13.3 V. On the other hand, the trigger voltage (Vt1) values and the holding voltage (Vh) values of the nLDMOS DUTs with a parasitic SCR “pnp”-arranged type & OD discrete distribution in the drain-side are slowly increased with the parasitic-SCR OD decreasing. The best Vh value in the “pnp”-arranged type & OD discrete parameter 2 (DIS-2) is about 14.4 V. Meanwhile, the secondary breakdown current (It2) values of this type are greater than 7 A except for the OD discrete parameter 2 and 3. Therefore, an appropriate layout of nLDMOS embedded with a drain-side “pnp” arranged type & OD discrete distribution that can yield high ESD and latch-up (LU) robustness levels.

The huge Package for High-dimensional Undirected Graph Estimation in R <=SEP=> We describe an R package named huge which provides easy-to-use functions for estimating high dimensional undirected graphs from data. This package implements recent results in the literature, including Friedman et al. (2007), Liu et al. (2009, 2012) and Liu et al. (2010). Compared with the existing graph estimation package glasso, the huge package provides extra features: (1) instead of using Fortan, it is written in C, which makes the code more portable and easier to modify; (2) besides fitting Gaussian graphical models, it also provides functions for fitting high dimensional semiparametric Gaussian copula models; (3) more functions like data-dependent model selection, data generation and graph visualization; (4) a minor convergence problem of the graphical lasso algorithm is corrected; (5) the package allows the user to apply both lossless and lossy screening rules to scale up large-scale problems, making a tradeoff between computational and statistical efficiency.

Semantic Attachments for Domain-Independent Planning Systems <=SEP=> Solving real-world problems using symbolic planning often requires a simplified formulation of the original problem, since certain subproblems cannot be represented at all or only in a way leading to inefficiency. For example, manipulation planning may appear as a subproblem in a robotic planning context or a packing problem can be part of a logistics task. In this paper we propose an extension of PDDL for specifying semantic attachments. This allows the evaluation of grounded predicates as well as the change of fluents by externally specified functions. Furthermore, we describe a general schema of integrating semantic attachments into a forward-chaining planner and report on our experience of adding this extension to the planners FF and Temporal Fast Downward. Finally, we present some preliminary experiments using semantic attach-

Visualising convolutional neural network decisions in automated sleep scoring <=SEP=> Current sleep medicine relies on the supervised analysis of polysomnographic recordings, which comprise amongst others electroencephalogram (EEG), electromyogram (EMG), and electrooculogram (EOG) signals. Convolutional neural networks (CNN) provide an interesting framework for automated sleep classification, however, the lack of interpretability of its results has hampered CNN’s further use in medicine. In this study, we train a CNN using as input Continuous Wavelet transformed EEG, EOG and EMG recordings from a publicly available dataset. The network achieved a 10-fold cross-validation Cohen’s Kappa score of κ = 0.71±0.01. Further, we provide insights on how this network classifies individual epochs of sleep using Guided Gradient-weighted Class Activation Maps (Guided Grad-CAM). The proposed approach is able to produce fine-grained activation maps on time-frequency domain for each signal providing a useful tool for identifying relevant features in CNNs.

Long-Term Treatment with High-Dose Thiamine in Parkinson Disease: An Open-Label Pilot Study. <=SEP=> OBJECTIVES
To investigate the potential clinical, restorative, and neuroprotective effects of long-term treatment with thiamine in Parkinson disease (PD).


DESIGN
Observational open-label pilot study.


SETTING
Outpatient neurologic rehabilitation clinic.


PATIENTS AND METHODS
Starting in June 2012, we have recruited 50 patients with PD (33 men and 17 women; mean age, 70.4 ± 12.9 years; mean disease duration, 7.3 ± 6.7 years). All the patients were assessed at baseline with the Unified Parkinson's Disease Rating Scale (UPDRS) and the Fatigue Severity Scale (FSS) and began treatment with 100 mg of thiamine administered intramuscularly twice a week, without any change to personal therapy. All the patients were re-evaluated after 1 month and then every 3 months during treatment.


RESULTS
Thiamine treatment led to significant improvement of motor and nonmotor symptoms: mean UPDRS scores (parts I-IV) improved from 38.55 ± 15.24 to 18.16 ± 15.08 (p = 2.4 × 10(-14), t test for paired data) within 3 months and remained stable over time; motor UPDRS part III score improved from 22.01 ± 8.57 to 9.92 ± 8.66 (p = 3.1 × 10(-22)). Some patients with a milder phenotype had complete clinical recovery. FSS scores, in six patients who had fatigue, improved from 53.00 ± 8.17 to 23.60 ± 7.77 (p < 0.0001, t test for paired data). Follow-up duration ranged from 95 to 831 days (mean, 291.6 ± 207.2 days).


CONCLUSIONS
Administration of parenteral high-dose thiamine was effective in reversing PD motor and nonmotor symptoms. The clinical improvement was stable over time in all the patients. From our clinical evidence, we hypothesize that a dysfunction of thiamine-dependent metabolic processes could cause selective neural damage in the centers typically affected by this disease and might be a fundamental molecular event provoking neurodegeneration. Thiamine could have both restorative and neuroprotective action in PD.

Information quality in information fusion <=SEP=> Designing fusion systems for decision support in complex dynamic situations calls for an integrated human-machine information environment, in which some processes are best executed automatically while for others the judgment and guidance of human experts and end-users are critical. Thus decision making in such environment requires constant information exchange between human and automated agents that utilize operational data, data obtained from sensors, intelligence reports, and open source information. The quality of decision making strongly depends on the success of being aware of, and compensating for, insufficient information quality at each step of information exchange. Designing the methods of representing and incorporating information quality into this environment is a relatively new and a rather difficult problem. The paper discusses major challenges and suggests some approaches to address this problem.

An Intelligent Tutoring System for Entity Relationship Modelling <=SEP=> The paper presents KERMIT, a Knowledge-based Entity Relationship Modelling Intelligent Tutor. KERMIT is a problem-solving environment for the university-level students, in which they can practise conceptual database design using the Entity-Relationship data model. KERMIT uses Constraint-Based Modelling (CBM) to model the domain knowledge and generate student models. We have used CBM previous in tutors that teach SQL and English punctuation rules. The research presented in this paper is significant because we show that CBM can be used to support students learning design tasks, which are very different from domains we dealt with in earlier tutors. The paper describes the system’s architecture and functionality. The system observes students’ actions and adapts to their knowledge and learning abilities. KERMIT has been evaluated in the context of genuine teaching activities. We present the results of two evaluation studies with students taking database courses, which show that KERMIT is an effective system. The students have enjoyed the system’s adaptability and found it a valuable asset to their learning.

Optical character recognition of the Orthodox Hellenic Byzantine Music notation <=SEP=> In this paper we present for the first time, the development of a new system for the off-line optical recognition of the characters used in the Orthodox Hellenic Byzantine Music Notation, that has been established since 1814. We describe the structure of the new system and propose algorithms for the recognition of the 71 distinct character classes, based on Wavelets, 4-projections and other structural and statistical features. Using a Nearest Neighbor classifier, combined with a post classification schema and a tree-structured classification philosophy, an accuracy of 99.4 % was achieved, in a database of about 18,000 byzantine character patterns that have been developed for the needs of the system. Optical music recognition Off-line character recognition, Byzantine Music, Byzantine Music Notation, Wavelets, Projections, Neural Networks Contour processing, Nearest Neighbor Classifier Byzantine Music Data Base

A binary linear programming formulation of the graph edit distance <=SEP=> A binary linear programming formulation of the graph edit distance for unweighted, undirected graphs with vertex attributes is derived and applied to a graph recognition problem. A general formulation for editing graphs is used to derive a graph edit distance that is proven to be a metric, provided the cost function for individual edit operations is a metric. Then, a binary linear program is developed for computing this graph edit distance, and polynomial time methods for determining upper and lower bounds on the solution of the binary program are derived by applying solution methods for standard linear programming and the assignment problem. A recognition problem of comparing a sample input graph to a database of known prototype graphs in the context of a chemical information system is presented as an application of the new method. The costs associated with various edit operations are chosen by using a minimum normalized variance criterion applied to pairwise distances between nearest neighbors in the database of prototypes. The new metric is shown to perform quite well in comparison to existing metrics when applied to a database of chemical graphs

Blogging as social activity, or, would you let 900 million people read your diary? <=SEP=> "Blogging" is a Web-based form of communication that is rapidly becoming mainstream. In this paper, we report the results of an ethnographic study of blogging, focusing on blogs written by individuals or small groups, with limited audiences. We discuss motivations for blogging, the quality of social interactivity that characterized the blogs we studied, and relationships to the blogger's audience. We consider the way bloggers related to the known audience of their personal social networks as well as the wider "blogosphere" of unknown readers. We then make design recommendations for blogging software based on these findings.

Wideband printed rectangular monopole antenna for circularly polarization <=SEP=> In this paper, a printed monopole antenna for circular polarization has been proposed. The relationship between the geometry of the antenna and the axial ratio is investigated. A wideband circularly polarized antenna is designed according to the parametric studies. The simulated bandwidth of 3dB-axial ratio is from 2.06GHz to 6.04GHz (98%).

Domain Adaptation for Statistical Machine Translation with Domain Dictionary and Monolingual Corpora <=SEP=> tra Statistical machine translation systems are usually trained on large amounts of bilingual text and monolingual text. In this paper, we propose a method to perform domain adaptation for statistical machine translation, where in-domain bilingual corpora do not exist. This method first uses out-of-domain corpora to train a baseline system and then uses in-domain translation dictionaries and in-domain monolingual corpora to improve the indomain performance. We propose an algorithm to combine these different resources in a unified framework. Experimental results indicate that our method achieves absolute improvements of 8.16 and 3.36 BLEU scores on Chinese to English translation and English to French translation respectively, as compared with the baselines using only out-ofdomain corpora.

Spatial Planning: A Configuration Space Approach <=SEP=> This paper presents algorithms for computing constraints on the position of an object due to the presence of ther objects. This problem arises in applications that require choosing how to arrange or how to move objects without collisions. The approach presented here is based on characterizing the position and orientation of an object as a single point in a configuration space, in which each coordinate represents a degree of freedom in the position or orientation of the object. The configurations forbidden to this object, due to the presence of other objects, can then be characterized as regions in the configuration space, called configuration space obstacles. The paper presents algorithms for computing these configuration space obstacles when the objects are polygons or polyhedra.

Recommendations for designers and researchers resulting from the world-wide failure exercise <=SEP=> The World-Wide Failure Exercise (WWFE) contained a detailed assessment of 19 theoretical approaches for predicting the deformation and failure response of polymer composite laminates when subjected to complex states of stress. The leading five theories are explored in greater detail to demonstrate their strengths and weaknesses in predicting various types of structural failure. Recommendations are then derived, as to how the theories can be best utilised to provide safe and economic predictions in a wide range of engineering design applications. Further guidance is provided for designers on the level of confidence and bounds of applicability of the current theories. The need for careful interpretation of initial failure predictions is emphasised, as is the need to allow for multiple sources of non-linearity (including progressive damage) where accuracy is sought for certain classes of large deformation and final failure strength predictions. Aspects requiring further experimental and theoretical investigation are identified. Direction is also provided to the research community by highlighting specific, tightly focussed, experimental and theoretical studies that, if carried out in the very near future, would pay great dividends from the designer’s perspective, by increasing their confidence in the theoretical foundations. # 2003 QinetiQ Ltd. Published by Elsevier Ltd. All rights reserved.

A case study of cooperative learning and communication pedagogy : Does working in teams make a difference ? <=SEP=> Cooperative learning has increasingly become a popular form of active pedagogy employed in academic institutions. This case study explores the relationship between cooperative learning and academic performance in higher education, specifically in the field of communication. Findings from a questionnaire administered to undergraduate students in a communication research course indicate that involvement in cooperative learning is a strong predictor of a student’s academic performance. A significant positive relationship was found between the degree to which grades are important to a student and his or her active participation in cooperative learning. Further, the importance of grades and sense of achievement are strong predictors of performance on readiness assessment tests.

Generative and Discriminative Voxel Modeling with Convolutional Neural Networks <=SEP=> When working with three-dimensional data, choice of representation is key. We explore voxel-based models, and present evidence for the viability of voxellated representations in applications including shape modeling and object classification. Our key contributions are methods for training voxel-based variational autoencoders, a user interface for exploring the latent space learned by the autoencoder, and a deep convolutional neural network architecture for object classification. We address challenges unique to voxel-based representations, and empirically evaluate our models on the ModelNet benchmark, where we demonstrate a 51.5% relative improvement in the state of the art for object classification.