Query: Absolute Head Pose Estimation From Overhead Wide-Angle Cameras

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The CMU Pose, Illumination, and Expression (PIE) Database <=SEP=> Between October 2000 and December 2000 we collected a database of over 40,000 facial images of 68 people. Using the CMU 3D Room we imaged each person across 13 different poses, under 43 different illumination conditions, and with 4 different expressions. We call this database the CMU Pose, Illumination, and Expression (PIE) database. In this paper we describe the imaging hardware, the collection procedure, the organization of the database, several potential uses of the database, and how to obtain the database.

From Few to Many: Generative Models for Recognition Under Variable Pose and Illumination <=SEP=> Image variability due to changes in pose and illumination can seriously impair object recognition. This paper presents appearance-based methods which, unlike previous appearance-based approaches, require only a small set of training images to generate a rich representation that models this variability. Speci cally, from as few as three images of an object in xed pose seen under slightly varying but unknown lighting, a surface and an albedo map are reconstructed. These are then used to generate synthetic images with large variations in pose and illumination and thus build a representation useful for object recognition. Our methods have been tested within the domain of face recognition on a subset of the Yale Face Database B containing 4050 images of 10 faces seen under variable pose and illumination. This database was speci cally gathered for testing these generative methods. Their performance is shown to exceed that of popular existing methods.

Model-based object tracking in monocular image sequences of road traffic scenes <=SEP=> Moving vehicles are detected and tracked automatically in monocular image sequences from road traffic scenes recorded by a stationary camera. In order to exploit the a priori knowledge about shape and motion of vehicles in traffic scenes, a parameterized vehicle model is used for an intraframe matching process and a recursive estimator based on a motion model is used for motion estimation. An interpretation cycle supports the intraframe matching process with a state MAP-update step. Initial model hypotheses are generated using an image segmentation component which clusters coherently moving image features into candidate representations of images of a moving vehicle. The inclusion of an illumination model allows taking shadow edges of the vehicle into account during the matching process. Only such an elaborate combination of various techniques has enabled us to track vehicles under complex illumination conditions and over long (over 400 frames) monocular image sequences. Results on various real-world road traffic scenes are presented and open problems as well as future work are outlined.

Pedestrian Detection Using Wavelet Templates <=SEP=> This paper presents a trainable object detection architecture that is applied to detecting people in static images of cluttered scenes. This problem poses several challenges. People are highly non-rigid objects with a high degree of variability in size, shape, color, and texture. Unlike previous approaches, this system learns from examples and does not rely on any a priori (hand-crafted) models or on motion. The detection technique is based on the novel idea of the wavelet template that deenes the shape of an object in terms of a subset of the wavelet coeecients of the image. It is invariant to changes in color and texture and can be used to robustly deene a rich and complex class of objects such as people. We show how the invariant properties and computational eeciency of the wavelet template make it an eeective tool for object detection.

Fitting Parameterized Three-Dimensional Models to Images <=SEP=> Model-based recognition and motion tracking depends upon the ability to solve for projection and model parameters that will best fit a 3-D model to matching 2-D image features. This paper extends current methods of parameter solving to handle objects with arbitrary curved surfaces and with any number of internal parameters representing articulations, variable dimensions, or surface deformations. Numerical stabilization methods are developed that take account of inherent inaccuracies in the image measurements and allow useful solutions to be determined even when there are fewer matches than unknown parameters. The LevenbergMarquardt method is used to always ensure convergence of the solution. These techniques allow model-based vision to be used for a much wider class of problems than was possible with previous methods. Their application is demonstrated for tracking the motion of curved, parameterized objects. This paper has been published in IEEE Transactions on Pattern Analysis and Machine Intelligence, 13, 5 (May 1991), pp. 441–450.

FA-RPN: Floating Region Proposals for Face Detection <=SEP=> We propose a novel approach for generating region proposals for performing face-detection. Instead of classifying anchor boxes using features from a pixel in the convolutional feature map, we adopt a pooling-based approach for generating region proposals. However, pooling hundreds of thousands of anchors which are evaluated for generating proposals becomes a computational bottleneck during inference. To this end, an efficient anchor placement strategy for reducing the number of anchor-boxes is proposed. We then show that proposals generated by our network (Floating Anchor Region Proposal Network, FA-RPN) are better than RPN for generating region proposals for face detection. We discuss several beneficial features of FA-RPN proposals like iterative refinement, placement of fractional anchors and changing anchors which can be enabled without making any changes to the trained model. Our face detector based on FA-RPN obtains 89.4% mAP with a ResNet-50 backbone on the WIDER dataset.

A human parietal face area contains aligned head-centered visual and tactile maps <=SEP=> Visually guided eating, biting and kissing, and avoiding objects moving toward the face and toward which the face moves require prompt, coordinated processing of spatial visual and somatosensory information in order to protect the face and the brain. Single-cell recordings in parietal cortex have identified multisensory neurons with spatially restricted, aligned visual and somatosensory receptive fields, but so far, there has been no evidence for a topographic map in this area. Here we mapped the organization of a multisensory parietal face area in humans by acquiring functional magnetic resonance images while varying the polar angle of facial air puffs and close-up visual stimuli. We found aligned maps of tactile and near-face visual stimuli at the highest level of human association cortex—namely, in the superior part of the postcentral sulcus. We show that this area may code the location of visual stimuli with respect to the face, not with respect to the retina.*NOTE: In the version of this article initially published online, there was an error in the affiliation in the html version. The first affiliation should read Department of Cognitive Science, University of California San Diego, La Jolla, California 92093, USA. The error has been corrected online.

An active volumetric model for 3D reconstruction <=SEP=> In this paper, we present an active volumetric model (AVM) for 3D reconstruction from multiple calibrated images of a scene. The AVM is a physically motivated 3D deformable model which shrinks actively under the influence of multiple simulated forces towards the real scene by throwing away some of its voxels. It provides a computational framework to integrate several constraints in an intelligible way. In the current work, we use three forces derived respectively from the smooth constraint, the compulsory silhouette constraint, and the color consistency constraint. Based on the composition of the 3 forces, our algorithm can significantly restrain holes and floating voxels, which plague voxel coloring algorithms, and produce precise and smooth models. We test our algorithm by experiments based on both synthetic and real data.

Image Segmentation by Nested Cuts <=SEP=> We present a new image segmentation algorithm based on graph cuts. Our main tool is separation of each pixel p from a special point outside the image by a cut of a minimum cost. Such a cut creates a group of pixels Cp around each pixel. We show that these groups Cp are either disjoint or nested in each other, and so they give a natural segmentation of the image. In addition this property allows an efficient implementation of the algorithm because for most pixelsp the computation of Cp is not performed on the whole graph. We inspect all Cp’s and discard those which are not interesting, for example if they are too small. This procedure automatically groups small components together or merges them into nearby large clusters. Effectively our segmentation is performed by extracting significant non-intersecting closed contours. We present interes ting segmentation results on real and artificial images.

A systematic literature review on Enterprise Architecture Implementation Methodologies <=SEP=> http://dx.doi.org/10.1016/j.infsof.2015.01.012 0950-5849/ 2015 Elsevier B.V. All rights reserved. ⇑ Corresponding author. E-mail addresses: drbabak2@live.utm.my (B.D. Rouhani), mdnazrim@utm.my (M.N. Mahrin), fa.nikpay@siswa.um.edu.my (F. Nikpay), rodina@um.edu.my (R.B. npourya2@live.utm.my (P. Nikfard). Babak Darvish Rouhani a,⇑, Mohd Naz’ri Mahrin , Fatemeh Nikpay , Rodina Binti Ahmad , Pourya Nikfard c

De-Anonymisierungsverfahren: Kategorisierung und Anwendung für Datenbankanfragen(De-Anonymization: Categorization and Use-Cases for Database Queries) <=SEP=> The project PArADISE deals with activity and intention recognition in smart environments. This can be used in apartments, for example, to recognize falls of elderly people. While doing this, the privacy concerns of the user should be kept. To reach this goal, the processing of the data is done as close as possible at those sensors collecting the data. Only in cases where the processing is not possible on local nodes the data will be transferred to the cloud. But before transferring, it is checked against the privacy concerns using some measures for the anonymity of the data. If the data is not valid against these checks, some additional anonymizations will be done. This anonymization of data must be done quite carefully. Mistakes might cause the problem that data can be reassigned to persons and anonymized data might be reproduced. This paper gives an overview about recent methods for anonymizing data while showing their weaknesses. How these weaknesses can be used to invert the anonymization (called de-anonymization) is shown as well. Our attacks representing the de-anonymization should help to find weaknesses in methods used to anonymize data and how these can be eliminated. Zusammenfassung: Im Projekt PArADISE sollen Aktivitätsund Intentionserkennungen in smarten Systemen, etwa Assistenzsystemen in Wohnungen, so durchgeführt werden, dass Privatheitsanforderungen des Nutzers gewahrt bleiben. Dazu werden einerseits Auswertungen der Daten sehr nah an den Sensoren, die die Daten erzeugen, vorgenommen. Eine Übertragung von Daten in die Cloud findet nur im Notfall statt. Zusätzlich werden aber vor der Übertragung der nicht vorausgewerteten Daten in die Cloud diese auf Privatheitsanforderungen hin geprüft, indem Anonymisierungsmaße getestet und eventuell weitere Anonymisierungen von Daten vorgenommen werden. Diese Anonymisierung von Datenbeständen muss mit großer Sorgfalt geschehen. Fehler können sehr schnell dazu führen, dass anonymisierte Datenbestände wieder personalisiert werden können und Daten, die eigentlich entfernt wurden, wieder zurückgewonnen werden können. Dieser Artikel betrachtet aktuelle Verfahren zur Anonymisierung und zeigt Schwachstellen auf, die zu Problemen oder gar der Umkehrung der Methoden führen können. Unsere künstlich erzeugten Angriffe durch De-Anonymisierungen sollen helfen, Schwachstellen in den Anonymisierungsverfahren zu entdecken und zu beheben.

An Ensemble Model for Diabetes Diagnosis in Large-scale and Imbalanced Dataset <=SEP=> Diabetes is becoming a more and more serious health challenge worldwide with the yearly rising prevalence, especially in developing countries. The vast majority of diabetes are type 2 diabetes, which has been indicated that about 80% of type 2 diabetes complications can be prevented or delayed by timely detection. In this paper, we propose an ensemble model to precisely diagnose the diabetic on a large-scale and imbalance dataset. The dataset used in our work covers millions of people from one province in China from 2009 to 2015, which is highly skew. Results on the real-world dataset prove that our method is promising for diabetes diagnosis with a high sensitivity, F3 and G --- mean, i.e, 91.00%, 58.24%, 86.69%, respectively.

A point-based MDP for robust single-lane autonomous driving behavior under uncertainties <=SEP=> In this paper, a point-based Markov Decision Process (QMDP) algorithm is used for robust single-lane autonomous driving behavior control under uncertainties. Autonomous vehicle decision making is modeled as a Markov Decision Process (MDP), then extended to a QMDP framework. Based on MDP/QMDP, three kinds of uncertainties are taken into account: sensor noise, perception constraints and surrounding vehicles' behavior. In simulation, the QMDP-based reasoning framework makes the autonomous vehicle perform with differing levels of conservativeness corresponding to different perception confidence levels. Road tests also indicate that the proposed algorithm helps the vehicle in avoiding potentially unsafe situations under these uncertainties. In general, the results indicate that the proposed QMDP-based algorithm makes autonomous driving more robust to limited sensing ability and occasional sensor failures.

Seeded watershed cut uncertainty estimators for guided interactive segmentation <=SEP=> Watershed cuts are among the fastest segmentation algorithms and therefore well suited for interactive segmentation of very large 3D data sets. To minimize the number of user interactions (“seeds”) required until the result is correct, we want the computer to actively query the human for input at the most critical locations, in analogy to active learning. These locations are found by means of suitable uncertainty measures. We propose various such measures for watershed cuts along with a theoretical analysis of some of their properties. Extensive evaluation on two types of 3D electron microscopic volumes of neural tissue shows that measures which estimate the non-local consequences of new user inputs achieve performance close to an oracle endowed with complete knowledge of the ground truth.

Nonlinear Component Analysis as a Kernel Eigenvalue Problem <=SEP=> A new method for performing a nonlinear form of principal component analysis is proposed. By the use of integral operator kernel functions, one can efficiently compute principal components in high-dimensional feature spaces, related to input space by some nonlinear mapfor instance, the space of all possible five-pixel products in 16 16 images. We give the derivation of the method and present experimental results on polynomial feature extraction for pattern recognition.

Electrodermal responses: what happens in the brain. <=SEP=> Electrodermal activity (EDA) is now the preferred term for changes in electrical conductance of the skin, including phasic changes that have been referred to as galvanic skin responses (GSR), that result from sympathetic neuronal activity. EDA is a sensitive psychophysiological index of changes in autonomic sympathetic arousal that are integrated with emotional and cognitive states. Until recently there was little direct knowledge of brain mechanisms governing generation and control of EDA in humans. However, studies of patients with discrete brain lesions and, more recently, functional imaging techniques have clarified the contribution of brain regions implicated in emotion, attention, and cognition to peripheral EDA responses. Moreover, such studies enable an understanding of mechanisms by which states of bodily arousal, indexed by EDA, influence cognition and bias motivational behavior.

Prepubertal hidradenitis suppurativa: two case reports and review of the literature. <=SEP=> Hidradenitis suppurativa (HS) is a chronic suppurative scarring disease of apocrine sweat gland-bearing skin in the axillary, anogenital, and, rarely, the breast and scalp regions. Females are more commonly affected than males and it is usually seen at puberty or later. We report two girls with prepubertal hidradenitis suppurativa whose initial presentation predated any signs of puberty. This early onset is very rare and its etiology remains unknown. Severe disease can be seen in prepubertal children and surgical intervention is effective in these cases.

Checking Business Process Evolution <=SEP=> Business processes support the modeling and the implementation of software as workflows of local and inter-process activities. Taking over structuring and composition, evolution has become a central concern in software development. We advocate it should be taken into account as soon as the modeling of business processes, which can thereafter be made executable using process engines or model-to-code transformations. We show here that business process evolution needs formal analysis in order to compare different versions of processes, identify precisely the differences between them, and ensure the desired consistency. To reach this objective, we first present a model transformation from the BPMN standard notation to the LNT process algebra. We then propose a set of relations for comparing business processes at the formal model level. With reference to related work, we propose a richer set of comparison primitives supporting renaming, refinement, propertyand context-awareness. Thanks to an implementation of our approach that can be used through a Web application, we put the checking of evolution within the reach of business process designers.

Tuning in to RF MEMS <=SEP=> RF MEMS technology was initially developed as a replacement for GaAs HEMT switches and p-i-n diodes for low-loss switching networks and X-band to mm-wave phase shifters. However, we have found that its very low loss properties (high device Q), its simple microwave circuit model and zero power consumption, its high power (voltage/current) handling capabilities, and its very low distortion properties, all make it the ideal tuning device for reconfigurable filters, antennas and impedance matching networks. In fact, reconfigurable networks are currently being funded at the same level-if not higher-than RF MEMS phase shifters, and in our opinion, are much more challenging for high-Q designs.

EEG-based automatic emotion recognition: Feature extraction, selection and classification methods <=SEP=> Automatic emotion recognition is an interdisciplinary research field which deals with the algorithmic detection of human affect, e.g. anger or sadness, from a variety of sources, such as speech or facial gestures. Apart from the obvious usage for industry applications in human-robot interaction, acquiring the emotional state of a person automatically also is of great potential for the health domain, especially in psychology and psychiatry. Here, evaluation of human emotion is often done using oral feedback or questionnaires during doctor-patient sessions. However, this can be perceived as intrusive by the patient. Furthermore, the evaluation can only be done in a noncontinuous manner, e.g. once a week during therapy sessions. In contrast, using automatic emotion detection, the affect state of a person can be evaluated in a continuous non-intrusive manner, for example to detect early on-sets of depression. An additional benefit of automatic emotion recognition is the objectivity of such an approach, which is not influenced by the perception of the patient and the doctor. To reach the goal of objectivity, it is important, that the source of the emotion is not easily manipulable, e.g. as in the speech modality. To circumvent this caveat, novel approaches in emotion detection research the potential of using physiological measures, such as galvanic skin sensors or pulse meters. In this paper we outline a way of detecting emotion from brain waves, i.e., EEG data. While EEG allows for a continuous, real-time automatic emotion recognition, it furthermore has the charm of measuring the affect close to the point of emergence: the brain. Using EEG data for emotion detection is nevertheless a challenging task: Which features, EEG channel locations and frequency bands are best suited for is an issue of ongoing research. In this paper we evaluate the use of state of the art feature extraction, feature selection and classification algorithms for EEG emotion classification using data from the de facto standard dataset, DEAP. Moreover, we present results that help choose methods to enhance classification performance while simultaneously reducing computational complexity.

Using the random forest classifier to assess and predict student learning of Software Engineering Teamwork <=SEP=> The overall goal of our Software Engineering Teamwork Assessment and Prediction (SETAP) project is to develop effective machine-learning-based methods for assessment and early prediction of student learning effectiveness in software engineering teamwork. Specifically, we use the Random Forest (RF) machine learning (ML) method to predict the effectiveness of software engineering teamwork learning based on data collected during student team project development. These data include over 100 objective and quantitative Team Activity Measures (TAM) obtained from monitoring and measuring activities of student teams during the creation of their final class project in our joint software engineering classes which ran concurrently at San Francisco State University (SFSU), Fulda University (Fulda) and Florida Atlantic University (FAU). In this paper we provide the first RF analysis results done at SFSU on our full data set covering four years of our joint SE classes. These data include 74 student teams with over 380 students, totaling over 30000 discrete data points. These data are grouped into 11 time intervals, each measuring important phases of project development during the class (e.g. early requirement gathering and design, development, testing and delivery). We briefly elaborate on the methods of data collection and describe the data itself. We then show prediction results of the RF analysis applied to this full data set. Results show that we are able to detect student teams who are bound to fail or need attention in early class time with good (about 70%) accuracy. Moreover, the variable importance analysis shows that the features (TAM measures) with high predictive power make intuitive sense, such as late delivery/late responses, time used to help each other, and surprisingly statistics on commit messages to the code repository, etc. In summary, we believe we demonstrate the viability of using ML on objective and quantitative team activity measures to predict student learning of software engineering teamwork, and point to easy-to-measure factors that can be used to guide educators and software engineering managers to implement early intervention for teams bound to fail. Details about the project and the complete ML training database are downloadable from the project web site.

A self biased operational amplifier at ultra low power supply voltage <=SEP=> This paper discusses the design of a self-biased folded cascode operational amplifier at an ultra low power supply voltage. The proposed design is first of its kind at 0.5 V where self-biasing techniques are used to reduce power and area overheads. The self-biasing scheme in this design is developed by using a current mirror for low voltage operation. This design is implemented in a 90 nm CMOS technology using Cadence General Purpose Design Kit (GPDK).

The Business Model as a Tool of Improving Value Creation in Complex Private Service System-Case : Value Network of Electric Mobility <=SEP=> This paper shows how the concept of business model can be used as an analysis tool when attempting to understand and describe the complexity of the value creation of a developing industry, namely electric mobility. The business model concept is applied as a theoretical framework in action research based and facilitated workshops with nine case companies operating in the field of electric mobility. The concept turned out to be a promising tool in creating a better understanding of the value creation in electric mobility, and thus being a potential framework for the development work for the actors in that field. The concept can help companies to improve cooperation with other players by being a common terminology.

Exact robot navigation using artificial potential functions <=SEP=> We present a new methodology for exact robot motion planning and control that unifies the purely kinematic path planning problem with the lower level feedback controller design. Complete information about the freespace and goal is encoded in the form of a special artificial potential function a navigation function that connects the kinematic planning problem with the dynamic execution problem in a provably correct fashion. The navigation function automatically gives rise to a bounded-torque feedback controller for the robot's actuators that guarantees collision-free motion and convergence to the destination from almost all initial free configurations. Since navigation functions exist for any robot and obstacle course, our methodology is completely general in principle. However, this paper is mainly concerned with certain constructive techniques for a particular class of motion planning problems. Specifically, we present a formula for navigation functions that guide a point-mass robot in a generalized sphere world. The simplest member of this family is a space obtained by puncturing a disc by an arbitrary number of smaller disjoint discs representing obstacles. The other spaces are obtained from this model by a suitable coordinate transformation that we show how to build. Our constructions exploit these coordinate transformations to adapt a navigation function on the model space to its more geometrically complicated (but topologically equivalent) instances. The formula that we present admits sphere-worlds of arbitrary dimension and is directly applicable to configuration spaces whose forbidden regions can be modeled by such generalized discs. We have implemented these navigation functions on planar scenarios, and simulation results are provided throughout the paper. Disciplines Robotics Comments Copyright 1992 IEEE. Reprinted from IEEE Transactions on Robotics and Automation, Volume 8, Issue 5, October 1992, pages 501-518. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it. NOTE: At the time of publication, Daniel Koditschek was affiliated with the University of Michigan. Currently, he is a faculty member at the School of Engineering of the University of Pennsylvania. This journal article is available at ScholarlyCommons: http://repository.upenn.edu/ese_papers/323

AmbiguityVis: Visualization of Ambiguity in Graph Layouts <=SEP=> Node-link diagrams provide an intuitive way to explore networks and have inspired a large number of automated graph layout strategies that optimize aesthetic criteria. However, any particular drawing approach cannot fully satisfy all these criteria simultaneously, producing drawings with visual ambiguities that can impede the understanding of network structure. To bring attention to these potentially problematic areas present in the drawing, this paper presents a technique that highlights common types of visual ambiguities: ambiguous spatial relationships between nodes and edges, visual overlap between community structures, and ambiguity in edge bundling and metanodes. Metrics, including newly proposed metrics for abnormal edge lengths, visual overlap in community structures and node/edge aggregation, are proposed to quantify areas of ambiguity in the drawing. These metrics and others are then displayed using a heatmap-based visualization that provides visual feedback to developers of graph drawing and visualization approaches, allowing them to quickly identify misleading areas. The novel metrics and the heatmap-based visualization allow a user to explore ambiguities in graph layouts from multiple perspectives in order to make reasonable graph layout choices. The effectiveness of the technique is demonstrated through case studies and expert reviews.

Definition Extraction with LSTM Recurrent Neural Networks <=SEP=> Definition extraction is the task to identify definitional sentences automatically from unstructured text. The task can be used in the aspects of ontology generation, relation extraction and question answering. Previous methods use handcraft features generated from the dependency structure of a sentence. During this process, only part of the dependency structure is used to extract features, thus causing information loss. We model definition extraction as a supervised sequence classification task and propose a new way to automatically generate sentence features using a Long Short-Term Memory neural network model. Our method directly learns features from raw sentences and corresponding part-ofspeech sequence, which makes full use of the whole sentence. We experiment on the Wikipedia benchmark dataset and obtain 91.2% on F1 score which outperforms the current state-of-the-art methods by 5.8%. We also show the effectiveness of our method in dealing with other languages by testing on a Chinese dataset and obtaining 85.7% on F1 score.

Path Planning with Phased Array SLAM and Voronoi Tessellation <=SEP=> Autonomous vehicles must often navigate environments that are at least partially unknown. They are faced with the tasks of creating a coordinate system to localize themselves on, identify the positions of obstacles, and chart safe paths through the environment. This process is known as Simultaneous Localization and Mapping, or SLAM. SLAM has traditionally been executed using measurements of distance to features in the environment. We propose an angle based methodology using a single phased array antenna and DSP.aimed to reduce this requirement to a single path for each data type. Additionally, our method makes use of rudimentary echo-location to discover reflective obstacles. Finally, our method uses Voronoi Tessellation for path planning.

Word-Formation in English <=SEP=> published by the press syndicate of the university of cambridge A catalogue record for this book is available from the British Library Library of Congress Cataloguing in Publication data Plag, Ingo. Word-formation in English / Ingo Plag. p. cm. – (Cambridge textbooks in linguistics) Includes bibliographical references (p.) and indexes. Contents Preface page xi Abbreviations and notational conventions xiii Introduction 1 1 Basic concepts 4 1.1 What is a word? 4 1.2 Studying word-formation 9 1.3 Inflection and derivation 14 1.4 Summary 17 Further reading 18 Exercises 18 2 Studying complex words 20 2.1 Identifying morphemes 20 2.1.1 The morpheme as the minimal linguistic sign 20 2.1.2 Problems with the morpheme: the mapping of form and meaning 22 2.2 Allomorphy 27 2.3 Establishing word-formation rules 30 2.4 Multiple affixation 38 2.5 Summary 41 Further reading 41 Exercises 41

Marginalized Denoising Dictionary Learning With Locality Constraint <=SEP=> Learning good representation for images is always a hot topic in machine learning and pattern recognition fields. Among the numerous algorithms, dictionary learning is a well-known strategy for effective feature extraction. Recently, more discriminative sub-dictionaries have been built by Fisher discriminative dictionary learning with specific class labels. Different types of constraints, such as sparsity, low rankness, and locality, are also exploited to make use of global and local information. On the other hand, as the basic building block of deep structure, the auto-encoder has demonstrated its promising performance in extracting new feature representation. To this end, we develop a unified feature learning framework by incorporating the marginalized denoising auto-encoder into a locality-constrained dictionary learning scheme, named marginalized denoising dictionary learning. Overall, we deploy low-rank constraint on each sub-dictionary and locality constraint instead of sparsity on coefficients, in order to learn a more concise and pure feature spaces meanwhile inheriting the discrimination from sub-dictionary learning. Finally, we evaluate our algorithm on several face and object data sets. Experimental results have demonstrated the effectiveness and efficiency of our proposed algorithm by comparing with several state-of-the-art methods.

Securing SMS Based One Time Password Technique from Man in the Middle Attack <=SEP=> Security of financial transactions in E-Commerce is difficult to implement and there is a risk that user’s confidential data over the internet may be accessed by hackers. Unfortunately, interacting with an online service such as a banking web application often requires certain degree of technical sophistication that not all Internet users possess. For the last couple of year such naive users have been increasingly targeted by phishing attacks that are launched by miscreants who are aiming to make an easy profit by means of illegal financial transactions. In this paper, we have proposed an idea for securing e-commerce transaction from phishing attack. An approach already exists where phishing attack is prevented using one time password which is sent on user’s registered mobile via SMS for authentication. But this method can be counter attacked by “Man in the Middle”. In our paper, a new idea is proposed which is more secure compared to the existing online payment system using OTP. In this mechanism OTP is combined with the secure key and is then passed through RSA algorithm to generate the Transaction password. A Copy of this password is maintained at the server side and is being generated at the user side using a mobile application; so that it is not transferred over the insecure network leading to a fraudulent transaction. Keywords—Phishing, Replay attack, MITM attack, RSA, Random Generator.