title: Deep bottleneck classifiers in supervised dimension reduction

abstract: Deep autoencoder networks have successfully been applied in unsupervised dimension reduction. The autoencoder has a "bottleneck" middle layer of only a few hidden units, which gives a low dimensional representation for the data when the full network is trained to minimize reconstruction error. We propose using a deep bottlenecked neural network in supervised dimension reduction. Instead of trying to reproduce the data, the network is trained to perform classification. Pretraining with restricted Boltzmann machines is combined with supervised finetuning. Finetuning with supervised cost functions has been done, but with cost functions that scale quadratically. Training a bottleneck classifier scales linearly, but still gives results comparable to or sometimes better than two earlier supervised methods.


Efficient Generation of Linear Secret Sharing Scheme Matrices from Threshold Access Trees


title: A model of prehospital trauma training for lay persons devised in Africa

abstract: Methods: Over 300 commercial drivers attended a first aid and rescue course designed specifically for roadway trauma and geared to a low education level. The training programme has been evaluated twice at one and two year intervals by interviewing both trained and untrained drivers with regard to their experiences with injured persons. In conjunction with a review of prehospital care literature, lessons learnt from the evaluations were used in the revision of the training model. ::: ::: Results: Control of external haemorrhage was quickly learnt and used appropriately by the drivers. Areas identified needing emphasis in future trainings included consistent use of universal precautions and protection of airways in unconscious persons using the recovery position. ::: ::: Conclusion: In low income countries, prehospital trauma care for roadway casualties can be improved by training laypersons already involved in prehospital transport and care. Training should be locally devised, evidence based, educationally appropriate, and focus on practical demonstrations.


title: Handling Algebraic Effects

abstract: Algebraic effects are computational effects that can be represented by an ::: equational theory whose operations produce the effects at hand. The free model ::: of this theory induces the expected computational monad for the corresponding ::: effect. Algebraic effects include exceptions, state, nondeterminism, ::: interactive input/output, and time, and their combinations. Exception handling, ::: however, has so far received no algebraic treatment. We present such a ::: treatment, in which each handler yields a model of the theory for exceptions, ::: and each handling construct yields the homomorphism induced by the universal ::: property of the free model. We further generalise exception handlers to ::: arbitrary algebraic effects. The resulting programming construct includes many ::: previously unrelated examples from both theory and practice, including ::: relabelling and restriction in Milner's CCS, timeout, rollback, and stream ::: redirection.


title: Software reuse through information retrieval

abstract: There is widespread need for safe, verifiable, efficient, and reliable software that can be delivered in a timely manner. Software reuse can make a valuable contrbution toward this goal by increasing programmer productivity and software quality. Unfortunately, the amount of software reuse currently done is quite small. DeMarco [1] estimates that in the average software development environment only about five percent of code is reused.


Deep Poetry : Word-Level and Character-Level Language Models for Shakespearean Sonnet Generation


title: Wicked Problems in Design Thinking

abstract: Introduction Despite efforts to discover the foundations of design thinking in the fine arts, the natural sciences, or most recently, the social sciences, design eludes reduction and remains a surprisingly flexible activity. No single definition of design, or branches of professionalized practice such as industrial or graphic design, adequately covers the diversity of ideas and methods gathered together under the label. Indeed, the variety of research reported in conference papers, journal articles, and books suggests that design continues to expand in its meanings and connections, revealing unexpected dimensions in practice as well as understanding. This follows the trend of design thinking in the twentieth century, for we have seen design grow from a trade activity to a segmentedprofession to afield for technical research and to what now should be recognized as a new liberal art of technological culture. It may seem unusual to talk about design as a liberal art, particularly when many people are accustomed to identifying the liberal arts with the traditional "arts and sciences" that are institutionalized in colleges and universities. But the liberal arts are undergoing a revolutionary transformation in twentieth-century culture, and design is one of the areas in which this transformation is strikingly evident. To understand the change that is now underway, it is important to recognize that what are commonly regarded as the liberal arts today are not outside of history. They originated in the Renaissance and underwent prolonged development that culminated in the nineteenth century as a vision of an encyclopedic education of beaux arts, belles lettres, history, various natural sciences and mathematics, philosophy, and the fledgling social sciences. This circle of learning was divided into particular subject matters, each with a proper method or set of methods suitable to its exploration. At their peak as liberal arts, these subject matters provided an integrated understanding of human experience and the array of available knowledge. By the end of the nineteenth century, however, existing subjects were explored with progressively more refined methods, and new subjects were added to accord with advances in knowledge. As a This essay is based on a paper presented at 'Colloque Recherches sur le Design: Incitations, Implications, Interactions," the first French university symposium on design research held October 1990 at l'Universit6 de Technologie de Compiegne, Compiegne, France.


title: Robust non-local fuzzy c-means algorithm with edge preservation for SAR image segmentation

abstract: Fuzzy c-means (FCM) algorithm has been proven effective for image segmentation; nevertheless it is sensitive to different types of noises. Up to now, a series of improved FCM algorithms incorporating spatial information have been developed, which are robust for Gaussian, uniform, and salt and pepper noises. However, limited effort has been placed on tackling the problem of a large amount of intrinsic and undesired multiplicative speckle in synthetic aperture radar (SAR) images. A crucial problem for SAR image segmentation is to guarantee speckle insensitiveness and edge detail preservation simultaneously. To address this problem, a robust and specific non-local FCM algorithm with edge preservation for SAR image segmentation is proposed. In this study, a new image is constructed using the non-local information and rectifying the edge parts, which is robust for speckle without sacrificing edge sharpness. To measure the patch-similarity in non-local method effectively, a novel generalized ratio distance based on SAR multiplicative speckle is defined. To locate and rectify the edge parts, coefficient of variation (CV) based threshold and orientation based statistics methods are designed. At last, this new image is clustered by FCM algorithm. Compared with six improved FCM algorithms and two state-of-the-art segmentation algorithms (spectral clustering and normalized cuts), the proposed algorithm obtains the best performance in terms of region uniformity and boundary localization.


Verifying Mutual Authentication for the DLK Protocol Using ProVerif Tool


Proof-of-Work” proves not to work; version 0.2


title: Evaluation of Hybrid Memory Technologies Using SOT-MRAM for On-Chip Cache Hierarchy

abstract: Magnetic Random Access Memory (MRAM) is a very promising emerging memory technology because of its various advantages such as nonvolatility, high density and scalability. In particular, Spin Orbit Torque (SOT) MRAM is gaining interest as it comes along with all the benefits of its predecessor Spin Transfer Torque (STT) MRAM, but is supposed to eliminate some of its shortcomings. Especially the split of read and write paths in SOT-MRAM promises faster access times and lower energy consumption compared to STT-MRAM. In this paper, we provide a very detailed analysis of SOT-MRAM at both the circuit- and architecture-level. We present a detailed evaluation of performance and energy related parameters and compare the novel SOT-MRAM with several other memory technologies. Our architecture-level analysis shows that a hybrid -combination of SRAM for the L1-Data-cache, SOT-MRAM for the L1-Instruction-cache and L2-cache can reduce the energy consumption by 60% while the performance increases by 1% compared to an SRAM-only configuration. Moreover, the retention failure probability of SOT-MRAM is $27\boldsymbol {\times }$ smaller than the probability of radiation-induced Soft Errors in SRAM, for a 65nm technology node. All of these advantages together make SOT-MRAM a viable choice for microprocessor caches.


title: To See or not to See - Innovative Display Technologies as Enablers for Ergonomic Cockpit Concepts

abstract: Given the fact that the driving task will remarkably change by the increase of assistance and information systems in the future innovative display technologies play a central role for the ergonomic realization of the driver vehicle interaction. Increasing assistance and even automation of the driving task do not lead to a decrease or disappearance of visual information but instead request new and in some cases revolutionary concepts to close the loop between driver, vehicle and traffic environment. Augmenting information in contact analog head-up-displays for navigation and driver assistance is a very promising approach. Replacement of mirrors via camera monitor systems is a further example. Free programmable cluster instruments in combination with HUD promise to resolve the problem of information density produced by an increasing amount of ADAS and IVIS functionality. The driving task is by its nature very visual, therefore the integration of additional visual information needs specific care. Relevant requirements are compiled in ISO 15008 and guidelines like ESOP. From an ergonomic point of view the potential offered by innovative display technologies is huge. In many cases the solutions cited above show high potential for an ageing driver population and its visual limitations to ensure a safe and comfortable individual mobility. But for an efficient usage ergonomic rules and guidelines are necessary to avoid driver distraction and ensure readability and intuitive usage of these systems that are remarkable change for drivers that are suited to optical mirrors and static mechanical cluster instruments. In the following the UR:BAN MMI strategy shows an example how to formulate ergonomic guidelines for the integration of different display technologies. Furthermore the potential of digital human model RAMSIS cognitive for the early development phase of a camera-monitor system to replace the mirror will be shown. Finally the integration of augmenting information in a contact analogue head-up-display will be discussed from an ergonomic point of view.


title: A digital watermarking approach to secure and precise range query processing in sensor networks

abstract: Two-tiered wireless sensor networks offer good scalability, efficient power usage, and space saving. However, storage nodes are more attractive to attackers than sensors because they store sensor collected data and processing sink issued queries. A compromised storage node not only reveals sensor collected data, but also may reply incomplete or wrong query results. In this paper, we propose QuerySec, a protocol that enables storage nodes to process queries correctly while prevents them from revealing both data from sensors and queries from the sink. To protect privacy, we propose an order preserving function-based scheme to encode both sensor collected data and sink issued queries, which allows storage nodes to process queries correctly without knowing the actual values of both data and queries. To preserve integrity, we proposed a link watermarking scheme, where data items are formed into a link by the watermarks embedded in them so that any deletion in query results can be detected.


title: Deep learning of texture and structural features for multiclass Alzheimer's disease classification

abstract: This work proposes multiclass deep learning classification of Alzheimer's disease (AD) using novel texture and other associated features extracted from structural MRI. Two distinct learning models (Model 1 and 2) are presented where both include subcortical area specific feature extraction, feature selection and stacked auto-encoder (SAE) deep neural network (DNN). The models learn highly complex and subtle differences in spatial atrophy patterns using white matter volumes, gray matter volumes, cortical surface area, cortical thickness, and different types of Fractal Brownian Motion co-occurrence matrices for texture as features to classify AD from cognitive normal (CN) and mild cognitive impairment (MCI) in dementia patients. A five layer SAE with state-of-the-art dropout learning is trained on a publicly available ADNI dataset and the model performances are evaluated at two levels: one using in-house tenfold cross validation and another using the publicly available CADDementia competition. The in-house evaluations of our two models achieve 56.6% and 58.0% tenfold cross validation accuracies using 504 ADNI subjects. For the public domain evaluation, we are the first to report DNN to CADDementia and our methods yield competitive classification accuracies of 51.4% and 56.8%. Further, both of our proposed models offer higher True Positive Fraction (TPF) for AD class when compared to the top-overall ranked algorithm while Model 1 also ties for top diseased class sensitivity at 58.2% in the CADDementia challenge. Finally, Model 2 achieves strong disease class sensitivity with improvement in specificity and overall accuracy. Our algorithms have the potential to provide a rapid, objective, and non-invasive assessment of AD.


title: Vigilare: toward snoop-based kernel integrity monitor

abstract: In this paper, we present Vigilare system, a kernel integrity monitor that is architected to snoop the bus traffic of the host system from a separate independent hardware. This snoop-based monitoring enabled by the Vigilare system, overcomes the limitations of the snapshot-based monitoring employed in previous kernel integrity monitoring solutions. Being based on inspecting snapshots collected over a certain interval, the previous hardware-based monitoring solutions cannot detect transient attacks that can occur in between snapshots. We implemented a prototype of the Vigilare system on Gaisler's grlib-based system-on-a-chip (SoC) by adding Snooper hardware connections module to the host system for bus snooping. To evaluate the benefit of snoop-based monitoring, we also implemented similar SoC with a snapshot-based monitor to be compared with. The Vigilare system detected all the transient attacks without performance degradation while the snapshot-based monitor could not detect all the attacks and induced considerable performance degradation as much as 10% in our tuned STREAM benchmark test.


title: Sleep Stages Classification Using Spectral Based Statistical Moments as Features

abstract: In the pursuit of highly effective and efficient portable sleep classification systems, researchers have been testing a massive number of combinations of EEG features and classifiers. State of art sleep classification ensembles achieve accuracy in the order of 90%. However, there is presently no consensus regarding the best setof features for sleep staging with single channel EEG, leading researchers to modify feature selection according to the number of classification stages. This paper introduces a reduced set of frequency-domain features capable of yielding high classification accuracy (90.9%, 91.8%, 92.4%, 94.3% and 97.1%) for all 6- to 2-state sleep stages. The proposed system uses fast Fourier transform (FFT) to convert data from Pz-Oz EEG channel into the frequency domain. Afterwards, eight statistical features are extracted from specific frequency ranges and fed into a random forest classifier.


title: Classification and tracking of traffic scene objects with hybrid camera systems

abstract: In a hybrid camera system combining an omnidirectional and a Pan-Tilt-Zoom (PTZ) camera, the omnidirectional camera provides 360 degree horizontal field-of-view, whereas the PTZ camera provides high resolution at a certain direction. This results in a wide field-of-view and high resolution camera system. In this paper, we exploit this hybrid system for real-time object classification and tracking for traffic scenes. The omnidirectional camera detects the moving objects and performs an initial classification using shape-based features. Concurrently, the PTZ camera classifies the objects using high resolution frames and Histogram of Oriented Gradients (HOG) features. PTZ camera also performs high-resolution tracking for the objects classified as the target class by the omnidirectional camera. The object types we worked on are pedestrian, motorcycle, car and van. Extensive experiments were conducted to compare the classification accuracy of the hybrid system with single camera alternatives.


title: Object detection in adaptive cruise control using multi-class support vector machine

abstract: In this paper, we present a new object detection method in Adaptive Cruise Control (ACC) with the Support Vector Machine (SVM) algorithm using data from a radar system. ACC using Closest in Path Vehicle (CIPV) detects the object vehicle that comes in front of the vehicle's front radar. Therefore, if the object vehicle abruptly cuts into the lane ahead of ego vehicle, the speed of the ego vehicle should quickly reduce. This phenomenon makes passengers feel uncomfortable. To cope with this phenomenon, in this paper we propose multiple classifications of various driving situations using multi-class SVM. Classified data was used to detect the CIPV among nearby vehicles in ACC. The proposed method shows improved performance in predicting the motion of objects in advance over the conventional radar system so that it enable for the ACC system either to decelerate or to accelerate smoothly in advance. The performance of proposed method was validated via experimental results.


title: Three Factors in Language Design

abstract: The biolinguistic perspective regards the language faculty as an “organ of the body,” along with other cognitive systems. Adopting it, we expect to find three factors that interact to determine (I-) languages attained: genetic endowment (the topic of Universal Grammar), experience, and principles that are language- or even organism-independent. Research has naturally focused on I-languages and UG, the problems of descriptive and explanatory adequacy. The Principles-and-Parameters approach opened the possibility for serious investigation of the third factor, and the attempt to account for properties of language in terms of general considerations of computational efficiency, eliminating some of the technology postulated as specific to language and providing more principled explanation of linguistic phenomena


title: A CUDA-enabled parallel algorithm for accelerating retinex

abstract: Retinex is an image restoration approach used to restore the original appearance of an image. Among various methods, a center/surround retinex algorithm is favorable for parallelization because it uses the convolution operations with large-scale sizes to achieve dynamic range compression and color/lightness rendition. This paper presents a GPURetinex algorithm, which is a data parallel algorithm accelerating a modified center/surround retinex with GPGPU/CUDA. The GPURetinex algorithm exploits the massively parallel threading and heterogeneous memory hierarchy of a GPGPU to improve efficiency. Two challenging problems, irregular memory access and block size for data partition, are analyzed mathematically. The proposed mathematical models help optimally choose memory spaces and block sizes for maximal parallelization performance. The mathematical analyses are applied to three parallelization issues existing in the retinex problem: block-wise, pixel-wise, and serial operations. The experimental results conducted on GT200 GPU and CUDA 3.2 showed that the GPURetinex can gain 74 times acceleration, compared with an SSE-optimized single-threaded implementation on Core2 Duo for the images with 4,096 ? 4,096 resolution. The proposed method also outperforms the parallel retinex implemented with the nVidia Performance Primitives library. Our experimental results indicate that careful design of memory access and multithreading patterns for CUDA devices should acquire great performance acceleration for real-time processing of image restoration.


title: Multi-resolution Spatial Event Forecasting in Social Media

abstract: Social media has become a significant surrogate forspatial event forecasting. The accuracy and discernibility of aspatial event forecasting model are two key concerns, whichrespectively determine how accurate and how detailed themodel's predictions could be. Existing work pays most attentionon the accuracy alone, seldom considering the accuracyand discernibility simultaneously, because this would requiresa considerably more sophisticated model while still sufferingfrom several challenges: 1) the precise formulation of thetrade-off between accuracy and discernibility, 2) the scarcityof social media data with a high spatial resolution, and 3)the characterization of spatial correlation and heterogeneity. This paper proposes a novel feature learning model thatconcurrently addresses all the above challenges by formulatingprediction tasks for different locations with different spatialresolutions, allowing the heterogeneous relationships amongthe tasks to be characterized. This characterization is thenintegrated into our new model based on multitask learning, whose parameters are optimized by our proposed algorithmbased on the Alternative Direction Method of Multipliers(ADMM). Extensive experimental evaluations on 11 datasetsfrom different domains demonstrated the effectiveness of ourproposed approach.


title: A Lazy Concurrent List-Based Set Algorithm

abstract: List-based implementations of sets are a fundamental building block of many concurrent algorithms. A skiplist based on the lock-free list-based set algorithm of Michael will be included in the Java Concurrency Package of JDK 1.6.0. However, Michael's lock-free algorithm has several drawbacks, most notably that it requires all list traversal operations, including membership tests, to perform cleanup operations of logically removed nodes, and that it uses the equivalent of an atomically markable reference, a pointer that can be atomically marked, which is expensive in some languages and unavailable in others. We present a novel lazy list-based implementation of a concurrent set object. It is based on an optimistic locking scheme for inserts and removes, eliminating the need to use the equivalent of an atomically markable reference. It also has a novel wait-free membership test operation (as opposed to Michael's lock-free one) that does not need to perform cleanup operations and is more efficient than that of all previous algorithms. Empirical testing shows that the new lazy-list algorithm consistently outperforms all known algorithms, including Michael's lock-free algorithm, throughout the concurrency range. At high load, with 90% membership tests, the lazy algorithm is more than twice as fast as Michael's. This is encouraging given that typical search structure usage patterns include around 90% membership tests. By replacing the lock-free membership test of Michael's algorithm with our new wait-free one, we achieve an algorithm that slightly outperforms our new lazy-list (though it may not be as efficient in other contexts as it uses Java's RTTI mechanism to create pointers that can be atomically marked).


Combination of Convolutional and Recurrent Neural Network for Sentiment Analysis of Short Texts


title: Predicting the Direction of Stock Market Index Movement Using an Optimized Artificial Neural Network Model

abstract: In the business sector, it has always been a difficult task to predict the exact daily price of the stock market index; hence, there is a great deal of research being conducted regarding the prediction of the direction of stock price index movement. Many factors such as political events, general economic conditions, and traders’ expectations may have an influence on the stock market index. There are numerous research studies that use similar indicators to forecast the direction of the stock market index. In this study, we compare two basic types of input variables to predict the direction of the daily stock market index. The main contribution of this study is the ability to predict the direction of the next day’s price of the Japanese stock market index by using an optimized artificial neural network (ANN) model. To improve the prediction accuracy of the trend of the stock market index in the future, we optimize the ANN model using genetic algorithms (GA). We demonstrate and verify the predictability of stock price direction by using the hybrid GA-ANN model and then compare the performance with prior studies. Empirical results show that the Type 2 input variables can generate a higher forecast accuracy and that it is possible to enhance the performance of the optimized ANN model by selecting input variables appropriately.


title: Modulation of trail laying in the antLasius niger (Hymenoptera: Formicidae) and its role in the collective selection of a food source

abstract: Foragers of the ant Lasius nigerexploiting a 1 Msugar source were found to lay 43 %more trail marks than those exploiting a 0.05 or a 0.1 Msource. The trail laying per forager decreased during the course of individual recruitment episodes, and the mean lifetime of the trail pheromone was estimated to be 47 min. A mathematical function describing the probability that a forager chooses one of two paths in relation to the amount of trail pheromone on them closely fitted experimental data. These results were incorporated into a model describing the recruitment dynamics of L. niger.Simulations of this model showed that the observed modulation of trail laying with respect to food source quality is sufficient in itself to account for the systematic selection of the richer source seen in the experiments.


title: Phat - Persistent Homology Algorithms Toolbox

abstract: Phat is an open-source C++ library for the computation of persistent homology by matrix reduction, targeted towards developers of software for topological data analysis. We aim for a simple generic design that decouples algorithms from data structures without sacrificing efficiency or user-friendliness. We provide numerous different reduction strategies as well as data types to store and manipulate the boundary matrix. We compare the different combinations through extensive experimental evaluation and identify optimization techniques that work well in practical situations. We also compare our software with various other publicly available libraries for persistent homology.


A PERFORMANCE EVALUATION OF JPEG STEGANOGRAPHY TECHNIQUES


title: Closed-Form Solutions in Learning Probabilistic Logic Programs by Exact Score Maximization

abstract: We present an algorithm that learns acyclic propositional probabilistic logic programs from complete data, by adapting techniques from Bayesian network learning. Specifically, we focus on score-based learning and on exact maximum likelihood computations. Our main contribution is to show that by restricting any rule body to contain at most two literals, most needed optimization steps can be solved exactly. We describe experiments indicating that our techniques do produce accurate models from data with reduced numbers of parameters.


title: Analyzing Invariants in Cyber-Physical Systems using Latent Factor Regression

abstract: The analysis of large scale data logged from complex cyber-physical systems, such as microgrids, often entails the discovery of invariants capturing functional as well as operational relationships underlying such large systems. We describe a latent factor approach to infer invariants underlying system variables and how we can leverage these relationships to monitor a cyber-physical system. In particular we illustrate how this approach helps rapidly identify outliers during system operation.


Learning to See, to Experience, to Write: Peter Handke's Die Lehre der Sainte-Victoire as Narrative


title: Virtual reality simulation: using three-dimensional technology to teach nursing students.

abstract: The use of computerized technology is rapidly growing in the classroom and in healthcare. An emerging computer technology strategy for nursing education is the use of virtual reality simulation. This computer-based three-dimensional educational tool simulates real-life patient experiences in a risk-


title: Comparison of NRZ and RZ modulations in laser intersatellite communication systems

abstract: Laser intersatellite communication (LIC) is a promising choice for intersatellite communication due to the high data rate that can be achieved with the advantages of light weight, smaller size and lower power consumption. In this paper, we compare the maximum transmission distance with non-return-to-zero (NRZ) and return-to-zero (RZ) modulations for two different LIC scenarios. Simulation results show that for the long-range LIC system with a saturated booster optical amplifier, the RZ modulation scheme can offer a longer transmission distance than the NRZ modulation scheme. For the short-range LIC system without an optical amplifier, the RZ modulation scheme performs almost the same as the NRZ modulation scheme.


title: Five Popular Study Strategies: Their Pitfalls and Optimal Implementations:

abstract: Researchers’ and educators’ enthusiasm in applying cognitive principles to enhance educational practices has become more evident. Several published reviews have suggested that some potent strategie...


title: Simultaneously Discovering and Quantifying Risk Types from Textual Risk Disclosures

abstract: Managers and researchers alike have long recognized the importance of corporate textual risk disclosures. Yet it is a nontrivial task to discover and quantify variables of interest from unstructured text. In this paper, we develop a variation of the latent Dirichlet allocation topic model and its learning algorithm for simultaneously discovering and quantifying risk types from textual risk disclosures. We conduct comprehensive evaluations in terms of both conventional statistical fit and substantive fit with respect to the quality of discovered information. Experimental results show that our proposed method outperforms all competing methods, and could find more meaningful topics risk types. By taking advantage of our proposed method for measuring risk types from textual data, we study how risk disclosures in 10-K forms affect the risk perceptions of investors. Different from prior studies, our results provide support for all three competing arguments regarding whether and how risk disclosures affect the risk perceptions of investors, depending on the specific risk types disclosed. We find that around two-thirds of risk types lack informativeness and have no significant influence. Moreover, we find that the informative risk types do not necessarily increase the risk perceptions of investors---the disclosure of three types of systematic and liquidity risks will increase the risk perceptions of investors, whereas the other five types of unsystematic risks will decrease them. ::: ::: Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2014.1930 . ::: ::: This paper was accepted by Alok Gupta, special issue on business analytics.


The occasional carpal tunnel injection.


Malnutrition: causes and consequences


title: The Stable Marriage Problem: Structure and Algorithms

abstract: This book probes the stable marriage problem and its variants as a rich source of problems and ideas that illustrate both the design and analysis of efficient algorithms. It covers the most recent structural and algorithmic work on stable matching problems, simplifies and unifies many earlier proofs, strengthens several earlier results, and presents new results and more efficient algorithms.The authors develop the structure of the set of stable matchings in the stable marriage problem in a more general and algebraic context than has been done previously; they discuss the problem's structure in terms of rings of sets, which allows many of the most useful features to be seen as features of a more general set of problems. The relationship between the structure of the stable marriage problem and the more general stable roommates problem is demonstrated, revealing many commonalities.The results the authors obtain provide an algorithmic response to the practical, and political, problems created by the asymmetry inherent in the Gale Shapley solutions, leading to alternative methods and better compromises than are provided by the Gale Shapley method. And, in contrast to Donald Knuth's earlier work which primarily focused on the application of mathematics to the analysis of algorithms, this book illustrates the productive and almost inseparable relationship between mathematical insight and the design of efficient algorithms.Dan Gusfield is Associate Professor of Computer Science at the University of California, Davis. Robert W. Irving is Senior Lecturer in Computing Science at the University of Glasgow. The Stable Marriage Problem is included in the Foundations of Computing Series, edited by Michael Garey and Albert Meyer.


title: An IT Balance Scorecard Design under Service Management Philosophy

abstract: The present study proposes a design for an Information Technology Balanced Scorecard (IT BSC) that integrates with business and environment and balances and optimizes deployment and control of the Information Technology strategy. This Strategic and Tactical IT Planning model enhances IT's role in obtaining and measuring its contribution to business value: optimizing IT operational efficiency or proposing new business ideas with a high IT component. The proposed model stimulates innovation in each of the parts of the IT Services lifecycle, is based on IT Service Management best practices and uses ideas from the ITIL v3 standard.


title: A Lexical Semantic Approach to Interpreting and Bracketing English Noun Compounds

abstract: This paper presents a study on the interpretation and bracketing of noun compounds (‘NCs’) based on lexical semantics. Our primary goal is to develop a method to automatically interpret NCs through the use of semantic relations. Our NC interpretation method is based on lexical similarity with tagged NCs, based on lexical similarity measures derived from WordNet. We apply the interpretation method to both two- and three-term NC interpretation based on semantic roles. Finally, we demonstrate that our NC interpretation method can boost the coverage and accuracy of NC bracketing.


title: Improving Distantly Supervised Relation Extraction with Neural Noise Converter and Conditional Optimal Selector

abstract: Distant supervised relation extraction has been successfully applied to large corpus with thousands of relations. However, the inevitable wrong labeling problem by distant supervision will hurt the performance of relation extraction. In this paper, we propose a method with neural noise converter to alleviate the impact of noisy data, and a conditional optimal selector to make proper prediction. Our noise converter learns the structured transition matrix on logit level and captures the property of distant supervised relation extraction dataset. The conditional optimal selector on the other hand helps to make proper prediction decision of an entity pair even if the group of sentences is overwhelmed by no-relation sentences. We conduct experiments on a widely used dataset and the results show significant improvement over competitive baseline methods.


title: Inferring Trust Relationships in Web-Based Social Networks

abstract: The growth of web-based social networking and the properties of those networks have created great potential for producing intelligent software that integrates a user's social network and preferences. Our research looks particularly at assigning trust in web-based social nets and investigates how trust information can be mined and integrated into applications. This paper introduces a definition of trust suitable for use in web-based social networks with a discussion of the properties that will influence its use in computation. We then present two algorithms for inferring trust relationships between individuals that are not directly connected in the network. Both algorithms are shown theoretically and through simulation to produce calculated trust values that are highly accurate.. We then present TrustMail, a prototype email client that uses variations on these algorithms to score email messages in the user's inbox based on the user's participation and ratings in a trust network.


title: Telugu OCR Framework using Deep Learning

abstract: In this paper, we address the task of Optical Character Recognition(OCR) for the Telugu script. We present an end-to-end framework that segments the text image, classifies the characters and extracts lines using a language model. The segmentation is based on mathematical morphology. The classification module, which is the most challenging task of the three, is a deep convolutional neural network. The language is modelled as a third degree markov chain at the glyph level. Telugu script is a complex abugida and the language is agglutinative, making the problem hard. In this paper we apply the latest advances in neural networks to achieve acceptable error rates.


title: Relationship Between Religious Belief and Happiness: A Systematic Literature Review

abstract: Happiness is a feeling that is desired by every human being. To achieve happiness, human try various routes like, to gain financial superiority, fame, entertainment, assets and so on. But on the contrary, religiosity is claimed to be a technique to attain purpose in life, mental health, physical well-being and internal peace, which ultimately leads to happiness in life. This study analyses the studies conducted in last two decades toward understanding the relationship between religiousness and happiness. These studies have been organised in terms of the religions, geographic locations, scales and significance. The study shows that the claim has proven to be true by a vast majority of the surveys irrespective of religion, gender, nationality or race. Although Muslims seems to be the happiest, it requires further verification.


title: The Use of Gamification in Higher Education: An Empirical Study

abstract: The use of gamification in higher education has increased considerably over the past decades. An empirical study was conducted in Hungary with two groups of students to investigate their behaviour while interacting with Kahoot! The results were analyzed based on the technology acceptance model. They indicate that the positive attitude, good experience and ease of availability contributed to improve student performance which strengthened the intention to use the application. Besides these, the perceived utility was positively influenced by the ease of use as consequence.


Assessing Internet Enabled Business Value: An Exploratory Investigation,” Working Paper


title: Cuts to the offender’s own hand—unintentional self-infliction in the course of knife attacks

abstract: In a knife attack the perpetrator can unintentionally injure his own hand, if the knife does not have an adequate handguard and the tip of the blade hits a solid, mostly bony structure while being violently thrust into the victim’s body. The injuries occurring under these conditions are localized on the flexor side of the knife-holding hand and may include the index, middle, ring and little fingers. They are seen particularly often on the little finger at the level of the proximal phalanx and in the skin fold of the proximal interphalangeal joint. The majority of these cuts run transversely to the longitudinal axis of the fingers and can show a step-like arrangement with different distances to the metacarpophalangeal joints, often from ulnar-proximal to radial-distal. In the six cases presented the injuries were most pronounced on the ulnar side of the hand. When the flexor tendons of the fingers are also severed and the tendon stumps are strongly retracted this indicates that the fist was firmly closed at the time of the injury.


title: DCEP -Digital Corpus of the European Parliament

abstract: We are presenting a new highly multilingual document-aligned parallel corpus called DCEP Digital Corpus of the European Parliament. It consists of various document types covering a wide range of subject domains. With a total of 1.37 billion words in 23 languages (253 language pairs), gathered in the course of ten years, this is the largest single release of documents by a European Union institution. DCEP contains most of the content of the European Parliament's official Website. It includes different document types produced between 2001 and 2012, excluding only the documents already exist in the Europarl corpus to avoid overlapping. We are presenting the typical acquisition steps of the DCEP corpus: data access, document alignment, sentence splitting, normalisation and tokenisation, and sentence alignment efforts. The sentence-level alignment is still in progress but based on some first experiments; we showed that DCEP is very useful for NLP applications, in particular for Statistical Machine Translation.


title: Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs

abstract: The idea of computer vision as the Bayesian inverse problem to computer graphics has a long history and an appealing elegance, but it has proved difficult to directly implement. Instead, most vision tasks are approached via complex bottom-up processing pipelines. Here we show that it is possible to write short, simple probabilistic graphics programs that define flexible generative models and to automatically invert them to interpret real-world images. Generative probabilistic graphics programs (GPGP) consist of a stochastic scene generator, a renderer based on graphics software, a stochastic likelihood model linking the renderer's output and the data, and latent variables that adjust the fidelity of the renderer and the tolerance of the likelihood. Representations and algorithms from computer graphics are used as the deterministic backbone for highly approximate and stochastic generative models. This formulation combines probabilistic programming, computer graphics, and approximate Bayesian computation, and depends only on general-purpose, automatic inference techniques. We describe two applications: reading sequences of degraded and adversarially obscured characters, and inferring 3D road models from vehicle-mounted camera images. Each of the probabilistic graphics programs we present relies on under 20 lines of probabilistic code, and yields accurate, approximately Bayesian inferences about real-world images.


title: Recursive teaching dimension, VC-dimension and sample compression

abstract: This paper is concerned with various combinatorial parameters of classes that can be learned from a small set of examples. We show that the recursive teaching dimension, recently introduced by Zilles et al. (2008), is strongly connected to known complexity notions in machine learning, e.g., the self-directed learning complexity and the VC-dimension. To the best of our knowledge these are the first results unveiling such relations between teaching and query learning as well as between teaching and the VC-dimension. It will turn out that for many natural classes the RTD is upper-bounded by the VCD, e.g., classes of VC-dimension 1, intersection-closed classes and finite maximum classes. However, we will also show that there are certain (but rare) classes for which the recursive teaching dimension exceeds the VC-dimension. Moreover, for maximum classes, the combinatorial structure induced by the RTD, called teaching plan, is highly similar to the structure of sample compression schemes. Indeed one can transform any repetition-free teaching plan for a maximum class C into an unlabeled sample compression scheme for C and vice versa, while the latter is produced by (i) the corner-peeling algorithm of Rubinstein and Rubinstein (2012) and (ii) the tail matching algorithm of Kuzmin and Warmuth (2007).


title: A Hybrid Approach to Web Service Recommendation Based on QoS-Aware Rating and Ranking

abstract: As the number of Web services with the same or similar functions increases steadily on the Internet, nowadays more and more service consumers pay great attention to the non-functional properties of Web services, also known as quality of service (QoS), when finding and selecting appropriate Web services. For most of the QoS-aware Web service recommendation systems, the list of recommended Web services is generally obtained based on a rating-oriented prediction approach, aiming at predicting the potential ratings that an active user may assign to the unrated services as accurately as possible. However, in some application scenarios, high accuracy of rating prediction may not necessarily lead to a satisfactory recommendation result. In this paper, we propose a ranking-oriented hybrid approach by combining the item-based collaborative filtering and latent factor models to address the problem of Web services ranking. In particular, the similarity between two Web services is measured in terms of the correlation coefficient between their rankings instead of between the traditional QoS ratings. Besides, we also improve the measure NDCG (Normalized Discounted Cumulative Gain) for evaluating the accuracy of the top K recommendations returned in ranked order. Comprehensive experiments on the QoS data set composed of real-world Web services are conducted to test our approach, and the experimental results demonstrate that our approach outperforms other competing approaches.


title: The Critical Role of Substrate in Stabilizing Phosphorene Nanoflake: A Theoretical Exploration

abstract: Phosphorene, a new two-dimensional (2D) semiconductor, has received much interest due to its robust direct band gap and high charge mobility. Currently, however, phosphorene can only be produced by mechanical or liquid exfoliation, and it is still a significant challenge to directly epitaxially grow phosphorene, which greatly hinders its mass production and, thus, applications. In epitaxial growth, the stability of nanoscale cluster or flake on a substrate is crucial. Here, we perform ab initio energy optimizations and molecular dynamics simulations to explore the critical role of substrate on the stability of a representative phosphorene flake. Our calculations show that the stability of the phosphorene nanoflake is strongly dependent on the interaction strength between the nanoflake and substrate. Specifically, the strong interaction (0.75 eV/P atom) with Cu(111) substrate breaks up the phosphorene nanoflake, while the weak interaction (0.063 eV/P atom) with h-BN substrate fails to stabilize its 2D stru...


title: DHCRF: A Distributed Conditional Random Field Algorithm on a Heterogeneous CPU-GPU Cluster for Big Data

abstract: As one of the most recognized models in machine learning, the conditional random fields (CRF) has been widely used in many applications. As the parameter estimation of CRF is highly time-consuming, how to improve the performance of CRF has received significant attention, in particular in the big data environment. To deal with large-scale data, CPU-based or GPU-based parallelization solutions have been proposed to improve performance. However, the problem is an ongoing one. In this paper, we focus on the big data environment and propose a distributed CRF on a heterogeneous CPU-GPU cluster called DHCRF. Our approach differs from previous work. Specifically, it leverages a three-stage heterogeneous Map and Reduce operation to improve the performance, making full use of CPU-GPU collaborative computing capabilities in a big data environment. Furthermore, by combining elastic data partition and intermediate results multiplexing method, the distributed CRF is optimized. Elastic data partition is performed to keep the load balanced, and the intermediate results multiplexing method is adopted to reduce data communication. Experimental results show that the DHCRF outperforms the baseline CRF algorithm and the CPU-based parallel CRF algorithm with notable performance improvement while maintaining competitive correctness at the same time.


title: Variable Aperture Light Field Photography: Overcoming the Diffraction-Limited Spatio-Angular Resolution Tradeoff

abstract: Light fields have many applications in machine vision, consumer photography, robotics, and microscopy. However, the prevalent resolution limits of existing light field imaging systems hinder widespread adoption. In this paper, we analyze fundamental resolution limits of light field cameras in the diffraction limit. We propose a sequential, coded-aperture-style acquisition scheme that optimizes the resolution of a light field reconstructed from multiple photographs captured from different perspectives and fnumber settings. We also show that the proposed acquisition scheme facilitates high dynamic range light field imaging and demonstrate a proof-of-concept prototype system. With this work, we hope to advance our understanding of the resolution limits of light field photography and develop practical computational imaging systems to overcome them.


title: Maternal Depression and Child Psychopathology: A Meta-Analytic Review

abstract: Although the association between maternal depression and adverse child outcomes is well established, the strength of the association, the breadth or specificity of the outcomes, and the role of moderators are not known. This information is essential to inform not only models of risk but also the design of preventive interventions by helping to identify subgroups at greater risk than others and to elucidate potential mechanisms as targets of interventions. A meta-analysis of 193 studies was conducted to examine the strength of the association between mothers’ depression and children’s behavioral problems or emotional functioning. Maternal depression was significantly related to higher levels of internalizing, externalizing, and general psychopathology and negative affect/behavior and to lower levels of positive affect/behavior, with all associations small in magnitude. These associations were significantly moderated by theoretically and methodologically relevant variables, with patterns of moderation found to vary somewhat with each child outcome. Results are interpreted in terms of implications for theoretical models that move beyond main effects models in order to more accurately identify which children of depressed mothers are more or less at risk for specific outcomes.


title: MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation

abstract: Abstract In recent years Deep Learning has brought about a breakthrough in Medical Image Segmentation. In this regard, U-Net has been the most popular architecture in the medical imaging community. Despite outstanding overall performance in segmenting multimodal medical images, through extensive experimentations on some challenging datasets, we demonstrate that the classical U-Net architecture seems to be lacking in certain aspects. Therefore, we propose some modifications to improve upon the already state-of-the-art U-Net model. Following these modifications, we develop a novel architecture, MultiResUNet, as the potential successor to the U-Net architecture. We have tested and compared MultiResUNet with the classical U-Net on a vast repertoire of multimodal medical images. Although only slight improvements in the cases of ideal images are noticed, remarkable gains in performance have been attained for the challenging ones. We have evaluated our model on five different datasets, each with their own unique challenges, and have obtained a relative improvement in performance of 10.15%, 5.07%, 2.63%, 1.41%, and 0.62% respectively. We have also discussed and highlighted some qualitatively superior aspects of MultiResUNet over classical U-Net that are not really reflected in the quantitative measures.


title: Teaching word stress patterns of English using a musically-simulated technique

abstract: The correct placement of word-level stress is a main concern in EFL speech intelligibility training. It is particularly important for EFL learners who have a drastically different stress system in their L1 (e.g. Persian). These learners usually misplace the primary stress in their L2 which subsequently may interrupt the flow of communication and leads to unintelligibility. Therefore, the present study explored the effect of musically-simulated patterns, as a new teaching technique, on the learning of word stress patterns of English. Musically-simulated patterns were defined in the context of the study as impressionistic patterns which were acoustically similar to word stress patterns of English. 30 Iranian EFL elementary learners at a language institute participated in the study. The new technique was used to teach four stress patterns of two- and three-syllable English words. The results of the posttest indicated that the participants in the experimental group (who listened to musically-simulated patterns) obtained significantly higher scores. Moreover, two-syllable words appeared to be more positively influenced by these musical patterns. It is suggested that L1 negative transfer and the inherent cognitive complexity of the stress patterns may contribute to the varying effect of these musical patterns. Finally, the possible applications of using this technique at both levels of material development and classroom practice are discussed.


Analysis methods for numerical weather prediction


title: A Transition-based Algorithm for AMR Parsing

abstract: We present a two-stage framework to parse a sentence into its Abstract Meaning Representation (AMR). We first use a dependency parser to generate a dependency tree for the sentence. In the second stage, we design a novel transition-based algorithm that transforms the dependency tree to an AMR graph. There are several advantages with this approach. First, the dependency parser can be trained on a training set much larger than the training set for the tree-to-graph algorithm, resulting in a more accurate AMR parser overall. Our parser yields an improvement of 5% absolute in F-measure over the best previous result. Second, the actions that we design are linguistically intuitive and capture the regularities in the mapping between the dependency structure and the AMR of a sentence. Third, our parser runs in nearly linear time in practice in spite of a worst-case complexity ofO(n 2 ).


title: The Art and Science of Synthetic Character Design

abstract: Drawing from ideas in both traditional animation and modern philosophy, we present a methodology for designing synthetic characters. The goal of our approach is to construct intentional characters that are both compelling, in the sense that people can empathize with them, and understandable, in that their actions can be seen as attempts to satisfy their desires given their beliefs. We also present a simple, value-based framework that has the flexibility to implement the subsystems necessary for the construction of intentional characters.


title: Enabling Real Time Crime Intelligence Using Mobile GIS and Prediction Methods

abstract: A crime investigation is an official effort to uncover information about a crime. In recent years the number of crime cases has been on a rise. The traditional and age-old system of intelligence and criminal record maintenance has failed to live up to the requirements of the existing crime scenario. In this paper we propose a swift response system which can identify the most probable local suspects involved in a crime case, by analyzing the relevant case histories. We have looked into the mobile call detail records of suspects and victims to understand their presence in crime scenario. Records of Cell tower near crime scene have been analyzed to track the real perpetrators. With the knowledge of suspect's journey to crime and about the movements of people in the crime scene, we are able to model the system and to understand the probable suspects involved in the crime. Prediction techniques are used to filter and identify the different types of people present at the crime scene. To solve the case at a rapid pace, we have mapped the current location of the probable suspects using Mobile GIS. We have also evaluated the methods used in this system in comparison with traditional methods.


title: Storytelling with digital photographs

abstract: Photographs play a central role in many types of informal storytelling. This paper describes an easy-to-use device that enables digital photos to be used in a manner similar to print photos for sharing personal stories. A portable form factor combined with a novel interface supports local sharing like a conventional photo album as well as recording of stories that can be sent to distant friends and relatives. User tests validate the design and reveal that people alternate between “photo-driven” and “story-driven” strategies when telling stories about their photos.


title: A Review of 40 Years of Cognitive Architecture Research: Focus on Perception, Attention, Learning and Applications.

abstract: In this paper we present a broad overview of the last 40 years of research on cognitive architectures. Although the number of existing architectures is nearing several hundred, most of the existing surveys do not reflect this growth and focus on a handful of well-established architectures. While their contributions are undeniable, they represent only a part of the research in the field. Thus, in this survey we wanted to shift the focus towards a more inclusive and high-level overview of the research in cognitive architectures. Our final set of 86 architectures includes 55 that are still actively developed, and borrow from a diverse set of disciplines, spanning areas from psychoanalysis to neuroscience. To keep the length of this paper within reasonable limits we discuss only the core cognitive abilities, such as perception, attention mechanisms, learning and memory structure. To assess the breadth of practical applications of cognitive architectures we gathered information on over 700 practical projects implemented using the cognitive architectures in our list. We use various visualization techniques to highlight overall trends in the development of the field. Our analysis of practical applications shows that most architectures are very narrowly focused on a particular application domain. Furthermore, there is an apparent gap between general research in robotics and computer vision and research in these areas within the cognitive architectures field. It is very clear that biologically inspired models do not have the same range and efficiency compared to the systems based on engineering principles and heuristics. Another observation is related to a general lack of collaboration. Several factors hinder communication, such as the closed nature of the individual projects (only one-third of the reviewed here architectures are open-source) and terminological differences.


title: Iodine deficiency in the U.K.: an overlooked cause of impaired neurodevelopment?

abstract: This review describes historical iodine deficiency in the U.K., gives current information on dietary sources of iodine and summarises recent evidence of iodine deficiency and its association with child neurodevelopment. Iodine is required for the production of thyroid hormones that are needed for brain development, particularly during pregnancy. Iodine deficiency is a leading cause of preventable brain damage worldwide and is associated with impaired cognitive function. Despite a global focus on the elimination of iodine deficiency, iodine is a largely overlooked nutrient in the U.K., a situation we have endeavoured to address through a series of studies. Although the U.K. has been considered iodine-sufficient for many years, there is now concern that iodine deficiency may be prevalent, particularly in pregnant women and women of childbearing age; indeed we found mild-to-moderate iodine deficiency in pregnant women in Surrey. As the major dietary source of iodine in the U.K. is milk and dairy produce, it is relevant to note that we have found the iodine concentration of organic milk to be over 40% lower than that of conventional milk. In contrast to many countries, iodised table salt is unlikely to contribute to U.K. iodine intake as we have shown that its availability is low in grocery stores. This situation is of concern as the level of U.K. iodine deficiency is such that it is associated with adverse effects on offspring neurological development; we demonstrated a higher risk of low IQ and poorer reading-accuracy scores in U.K. children born to mothers who were iodine-deficient during pregnancy. Given our findings and those of others, iodine status in the U.K. population should be monitored, particularly in vulnerable subgroups such as pregnant women and children.


title: Computational systems biology

abstract: To understand complex biological systems requires the integration of experimental and computational research — in other words a systems biology approach. Computational biology, through pragmatic modelling and theoretical exploration, provides a powerful foundation from which to address critical scientific questions head-on. The reviews in this Insight cover many different aspects of this energetic field, although all, in one way or another, illuminate the functioning of modular circuits, including their robustness, design and manipulation. Computational systems biology addresses questions fundamental to our understanding of life, yet progress here will lead to practical innovations in medicine, drug discovery and engineering.


title: Alternative energy technologies

abstract: Fossil fuels currently supply most of the world's energy needs, and however unacceptable their long-term consequences, the supplies are likely to remain adequate for the next few generations. Scientists and policy makers must make use of this period of grace to assess alternative sources of energy and determine what is scientifically possible, environmentally acceptable and technologically promising.


title: Communication Over MIMO X Channels: Interference Alignment, Decomposition, and Performance Analysis

abstract: In a multiple-antenna system with two transmitters and two receivers, a scenario of data communication, known as the X channel, is studied in which each receiver receives data from both transmitters. In this scenario, it is assumed that each transmitter is unaware of the other transmitter's data (noncooperative scenario). This system can be considered as a combination of two broadcast channels (from the transmitters' points of view) and two multiple-access channels (from the receivers' points of view). Taking advantage of both perspectives, two signaling schemes for such a scenario are developed. In these schemes, some linear filters are employed at the transmitters and at the receivers which decompose the system into either two noninterfering multiple-antenna broadcast subchannels or two noninterfering multiple-antenna multiple-access subchannels. The main objective in the design of the filters is to exploit the structure of the channel matrices to achieve the highest multiplexing gain (MG). It is shown that the proposed noncooperative signaling schemes outperform other known noncooperative schemes in terms of the achievable MG. In particular, it is shown that in some specific cases, the achieved MG is the same as the MG of the system if full cooperation is provided either between the transmitters or between the receivers. In the second part of the paper, it is shown that by using mixed design schemes, rather than decomposition schemes, and taking the statistical properties of the interference terms into account, the power offset of the system can be improved. The power offset represents the horizontal shift in the curve of the sum-rate versus the total power in decibels.


title: Does monetary punishment crowd out pro-social motivation? A natural experiment on hospital length of stay

abstract: Abstract We study whether the use of monetary incentives might be counter-productive. In particular, we analyse the effect of fining owners of long-term care institutions who prolong length of stay at hospitals. Exploiting a unique natural experiment involving changes in the catchment areas of two large Norwegian hospitals, we find that hospital length of stay are longer in the hospital using fines to reduce length of stay compared with the hospital not using monetary punishment. We interpret these results as examples of monetary incentives crowding-out agents’ intrinsic motivation, leading to a reduction in effort.


title: Traveller, nomadic, and migrant education

abstract: Traveller, Nomadic and Migrant Education presents international accounts of approaches to educating mobile communities such as circus and fairground people, herders, hunters, Roma and Travellers. The chapters focus on three key dimensions of educational change: the client group moving from school to school; those schools having their demographics changed and seeking to change the mobile learners; and these learners contributing to fundamental change to the nature of schooling. ::: ::: The book brings together decades of research into the challenges and opportunities presented by mobile learners interacting with educational systems predicated on fixed residence. It identifies several obstacles to those learners receiving an equitable education, including negative stereotypes and centuries-old prejudice. Yet the book also explores a number of educational innovations that bring mobility and schooling together, ranging from specialised literacy programs and distance and online education to mobile schools and specially trained teachers. These innovations allow us to think differently about how education can and should be, for mobile and non-mobile learners alike.


title: Needle in a Haystack: Tracking Down Elite Phishing Domains in the Wild

abstract: Today's phishing websites are constantly evolving to deceive users and evade the detection. In this paper, we perform a measurement study on squatting phishing domains where the websites impersonate trusted entities not only at the page content level but also at the web domain level. To search for squatting phishing pages, we scanned five types of squatting domains over 224 million DNS records and identified 657K domains that are likely impersonating 702 popular brands. Then we build a novel machine learning classifier to detect phishing pages from both the web and mobile pages under the squatting domains. A key novelty is that our classifier is built on a careful measurement of evasive behaviors of phishing pages in practice. We introduce new features from visual analysis and optical character recognition (OCR) to overcome the heavy content obfuscation from attackers. In total, we discovered and verified 1,175 squatting phishing pages. We show that these phishing pages are used for various targeted scams, and are highly effective to evade detection. More than 90% of them successfully evaded popular blacklists for at least a month.


title: CAPJA - A Connector Architecture for Prolog and Java

abstract: Modern software often relies on the combination of several software modules that are developed independently. There are use cases where different software libraries from different programming languages are used, e.g., embedding DLL files in JAVA applications. Even more complex is the case when different programming paradigms are combined like within applications with database connections, for instance PHP and SQL. ::: ::: Such a diversification of programming languages and modules in just one software application is becoming more and more important, as this leads to a combination of the strengths of different programming paradigms. But not always, the developers are experts in the different programming languages or even in different programming paradigms. So, it is desirable to provide easy to use interfaces that enable the integration of programs from different programming languages and offer access to different programming paradigms. ::: ::: In this paper we introduce a connector architecture for two programming languages of different paradigms: JAVA as a representative of object oriented programming languages and PROLOG for logic programming. Our approach provides a fast, portable and easy to use communication layer between JAVA and PROLOG. The exchange of information is done via a textual term representation which can be used independently from a deployed PROLOG engine. The proposed connector architecture allows for Object Unification on the JAVA side. We provide an exemplary connector for JAVA and SWI-PROLOG, a well-known PROLOG implementation.


Factors affecting the accuracy of mean analysts' forecasts: with a focus on dispersion, and the implementationof a forecast improving model


title: Image analogies

abstract: This paper describes a new framework for processing images by example, called “image analogies.” The framework involves two stages: a design phase, in which a pair of images, with one image purported to be a “filtered” version of the other, is presented as “training data”; and an application phase, in which the learned filter is applied to some new target image in order to create an “analogous” filtered result. Image analogies are based on a simple multi-scale autoregression, inspired primarily by recent results in texture synthesis. By choosing different types of source image pairs as input, the framework supports a wide variety of “image filter” effects, including traditional image filters, such as blurring or embossing; improved texture synthesis, in which some textures are synthesized with higher quality than by previous approaches; super-resolution, in which a higher-resolution image is inferred from a low-resolution source; texture transfer, in which images are “texturized” with some arbitrary source texture; artistic filters, in which various drawing and painting styles are synthesized based on scanned real-world examples; and texture-by-numbers, in which realistic scenes, composed of a variety of textures, are created using a simple painting interface.


title: Real-Time Detection, Tracking, and Monitoring of Automatically Discovered Events in Social Media

abstract: We introduce ReDites, a system for realtime event detection, tracking, monitoring and visualisation. It is designed to assist Information Analysts in understanding and exploring complex events as they unfold in the world. Events are automatically detected from the Twitter stream. Then those that are categorised as being security-relevant are tracked, geolocated, summarised and visualised for the end-user. Furthermore, the system tracks changes in emotions over events, signalling possible flashpoints or abatement. We demonstrate the capabilities of ReDites using an extended use case from the September 2013 Westgate shooting incident. Through an evaluation of system latencies, we also show that enriched events are made available for users to explore within seconds of that event occurring.


God's Harp String : The Life and Legacy of the Benedictine Monk Swami Abhishiktananda


title: Empirical study of redo and undo logging in persistent memory

abstract: Atomic and durable transactions are widely used to ensure the crash consistency in persistent memory (PM). However, whether to use redo or undo logging is still a hotly debated topic in persistent memory systems. In this paper, we empirically study the performance of both redo and undo logging using NVML, a persistent memory transactional object store framework. Our results on an NVDIMM server show that redo logging significantly outperforms undo logging for workloads in which a transaction updates large number of different objects, while it underperforms undo logging for workloads with intensive read operations. Furthermore, undo logging is more sensitive to the read-to-write ratios, compared to redo logging. Finally, our experiments also demonstrate that asynchronous log truncation is much helpful in redo logging for log-heavy transactions.


Simulation of Progressive Cutting on Surface Mesh Model


title: Multivariate volumetric data analysis and visualization through bottom-up subspace exploration

abstract: Multivariate volumetric datasets are often encountered in results generated by scientific simulations. Compared to univariate datasets, analysis and visualization of multivariate datasets are much more challenging due to the complex relationships among the variables. As an effective way to visualize and analyze multivariate datasets, volume rendering has been frequently used, although designing good multivariate transfer functions is still non-trivial. In this paper, we present an interactive workflow to allow users to design multivariate transfer functions. To handle large scale datasets, in the preprocessing stage we reduce the number of data points through data binning and aggregation, and then a new set of data points with a much smaller size are generated. The relationship between all pairs of variables is presented in a matrix juxtaposition view, where users can navigate through the different subspaces. An entropy based method is used to help users to choose which subspace to explore. We proposed two weights: scatter weight and size weight that are associated with each projected point in those different subspaces. Based on those two weights, data point filter and kernel density estimation operations are employed to assist users to discover interesting features. For each user-selected feature, a Gaussian function is constructed and updated incrementally. Finally, all those selected features are visualized through multivariate volume rendering to reveal the structure of data. With our system, users can interactively explore different subspaces and specify multivariate transfer functions in an effective way. We demonstrate the effectiveness of our system with several multivariate volumetric datasets.


title: Producing and delivering a coursera MOOC on pattern-oriented software architecture for concurrent and networked software

abstract: A massive open online course (MOOC) is a web-based class environment aimed at large-scale global participation and open access via the Internet. MOOCs are also a disruptive trend changing how education is delivered and funded throughout the world. In the spring of 2013, we developed and taught Vanderbilt's first MOOC, entitled "Pattern-Oriented Software Architecture for Concurrent and Networked Software" (known as the POSA MOOC). This ten-week MOOC was an amalgamation of several courses on software design and programming taken by ~600 undergraduate and graduate students at Vanderbilt during the past decade. Enrollment in our POSA MOOC was more than 50 times (31,000+) that number, consisting of students with a wide range of backgrounds, interests, and expertise from scores of countries around the world. This paper describes our experiences producing and delivering the POSA MOOC. Where possible, we ground our observations in data from statistics collected via Coursera, which was the delivery platform we used for the POSA MOOC. We also discuss the broader implications of MOOCs on life-long learning and the role they play in improving the quality and productivity of software professionals in academia and industry.


title: Performance of Elephant Herding Optimization Algorithm on CEC 2013 Real Parameter Single Objective Optimization

abstract: Numerous real life problems represents hard optimization problems that cannot be solved by deterministic algorithm. In the past decades various different methods were proposed for these kind of problems and one of the methods are nature inspired algorithms especially swarm intelligence algorithms. Elephant herding optimization algorithm (EHO) is one of the recent swarm intelligence algorithm that has not been thoroughly researched. In this paper we tested EHO algorithm on 28 standard benchmark functions and compared results with particle swarm optimization algorithm. Comparison show that EHO has good characteristics and it outperformed other approach from literature.


title: Mass Murder: Causes, Classification, and Prevention

abstract: This article discusses common psychological and social factors in mass murders and outlines a proposed classification system to coordinate future research efforts. The final communications of two mass murderers are analyzed to demonstrate that the forensic psycholinguistic approach may assist in providing an enhanced understanding of the motives, psychopathology, and classification of mass murderers whose offenses can seem similar from a purely behavioral perspective. Language: en


title: Mining spam email to identify common origins for forensic application

abstract: In recent years, spam email has become a major tool for criminals to conduct illegal business on the Internet. Therefore, in this paper we describe a new research approach that uses data mining techniques to study spam emails with the focus on law enforcement forensic analysis. After we retrieve useful attributes from spam emails, we use a connected components clustering algorithm to form relationships between messages. These initial clusters are then refined by using a weighted edges model where membership in the cluster requires the weight to exceed a chosen threshold. The results of the cluster membership are validated by WHOIS data, by the IP address of the computer hosting the advertised sites, and through comparison of graphical images of website fetches. This technique has been successful in identifying relationships between spam campaigns that were not identified by human researchers, enabling additional data to be brought into a single investigation.


title: A Comprehensive Overview of Biometric Fusion

abstract: Abstract The performance of a biometric system that relies on a single biometric modality (e.g., fingerprints only) is often stymied by various factors such as poor data quality or limited scalability. Multibiometric systems utilize the principle of fusion to combine information from multiple sources in order to improve recognition accuracy whilst addressing some of the limitations of single-biometric systems. The past two decades have witnessed the development of a large number of biometric fusion schemes. This paper presents an overview of biometric fusion with specific focus on three questions: what to fuse, when to fuse, and how to fuse. A comprehensive review of techniques incorporating ancillary information in the biometric recognition pipeline is also presented. In this regard, the following topics are discussed: (i) incorporating data quality in the biometric recognition pipeline; (ii) combining soft biometric attributes with primary biometric identifiers; (iii) utilizing contextual information to improve biometric recognition accuracy; and (iv) performing continuous authentication using ancillary information. In addition, the use of information fusion principles for presentation attack detection and multibiometric cryptosystems is also discussed. Finally, some of the research challenges in biometric fusion are enumerated. The purpose of this article is to provide readers a comprehensive overview of the role of information fusion in biometrics.


title: How Secure is TextSecure?

abstract: Instant Messaging has gained popularity by users for both private and business communication as low-cost short message replacement on mobile devices. However, before releases about mass surveillance performed by intelligence services such as NSA and GCHQ and Facebook's acquisition of WhatsApp, most mobile messaging apps did not protect confidentiality or integrity of the messages. A messaging app that claims to provide secure instant messaging and has attracted a lot of attention is TextSecure. Besides numerous direct installations, its protocol is part of Android's most popular aftermarket firmware Cyanogen-Mod. TextSecure's successor Signal continues to use the underlying protocol for text messaging. In this paper, we present the first complete description of TextSecure's complex cryptographic protocol, provide a security analysis of its three main components (key exchange, key derivation and authenticated encryption), and discuss the main security claims of TextSecure. Furthermore, we formally prove that - if key registration is assumed to be secure - TextSecure's push messaging can indeed achieve most of the claimed security goals.


title: Rapid weak-perspective Structure from Motion with missing data

abstract: 3D reconstruction with missing data has been a very challenging computer vision task since the late 90s. This paper proposes the GPU-powered version of our weak-perspective Structure from Motion algorithm published in BMVC2008. Although this method is iterative, it is very rapid since all substeps in each iteration minimize the parameters optimally with respect to the reprojection error. We demonstrate here in both synthetic and real tests that the use of the GPU significantly reduces the time demand of the algorithm. Real-time 3D reconstruction is possible if the parameters of the optimization algorithm are set properly.


title: Hype and Dump Manipulation ∗

abstract: This paper introduces signaling in a standard market microstructure model so as to explore the economic circumstances under which hype and dump manipulation can be an equilibrium outcome. We consider a discrete time, multi-period model with stages of signaling and asset trading. A single informed trader contemplates whether or not to spread a (possibly dishonest) rumor on the asset payoff among uninformed traders. Dishonest rumor-mongering is costly due to regulatory enforcement, and the uninformed traders who access the rumor can be sophisticated or naive. The sophisticated traders correctly anticipate the relationship between the rumor and the asset payoff, whereas the naive ones take the rumor at its face value as if it truthfully reveals the asset payoff. The presence of sophisticated traders puts the informed trader off from rumor-mongering, because sophisticates fully infer the asset payoff from the rumor, reducing the informational rents enjoyed by the informed trader. Nevertheless we show that it can be optimal for an informed trader to create false hype among uninformed traders provided that there is at least one naive trader in the market and the cost of dishonest rumor-mongering is not too low. The false hype allows the informed trader to sell at an inflated price or buy at a deflated one. Intense regulatory enforcement, which makes dishonest rumor-mongering very costly, may not necessarily curb hype and dump schemes. Market depth and trading volume rise with â€œhype and dumpâ€ while market efficiency decreases.


title: Probability Models for Open Set Recognition

abstract: Real-world tasks in computer vision often touch upon open set recognition: multi-class recognition with incomplete knowledge of the world and many unknown inputs. Recent work on this problem has proposed a model incorporating an open space risk term to account for the space beyond the reasonable support of known classes. This paper extends the general idea of open space risk limiting classification to accommodate non-linear classifiers in a multi-class setting. We introduce a new open set recognition model called compact abating probability (CAP), where the probability of class membership decreases in value (abates) as points move from known data toward open space. We show that CAP models improve open set recognition for multiple algorithms. Leveraging the CAP formulation, we go on to describe the novel Weibull-calibrated SVM (W-SVM) algorithm, which combines the useful properties of statistical extreme value theory for score calibration with one-class and binary support vector machines. Our experiments show that the W-SVM is significantly better for open set object detection and OCR problems when compared to the state-of-the-art for the same tasks.


title: Wideband Single-Layer 90 $^{\circ}$ Phase Shifter Using Stepped Impedance Open Stub and Coupled-Line With Weak Coupling

abstract: This letter proposes a novel design approach of wideband 90 ° phase shifter, which comprises a stepped impedance open stub (SIOS) and a coupled-line with weak coupling. The result of analyses demonstrates that the bandwidths of return loss (RL) and phase deviation (PD) can be expanded by increasing the impedance ratio of the SIOS and the coupling strength of the coupled-line. For RL > 10 dB, insertion loss (IL) 1.1 dB, and PD of ±5°, the fabricated microstrip single-layer phase shifter exhibits bandwidth of 105% from 0.75 to 2.4 GHz.


title: Don’t miss patients with atypical FMR1 mutations: dysmorphism and clinical features in a boy with a partially methylated FMR1 full mutation

abstract: Fragile X syndrome characterized by intellectual disability (ID), facial dysmorphism, and postpubertal macroorchidism is the most common monogenic cause of ID. It is typically induced by an expansion of a CGG repeat in the fragile X mental retardation 1 (FMR1) gene on Xq27 to more than 200 repeats. Only rarely patients have atypical mutations in the FMR1 gene such as point mutations, deletions, or unmethylated/partially methylated full mutations. Most of these patients show a minor phenotype or even appear clinically healthy. Here, we report the dysmorphism and clinical features of a 17-year-old boy with a partially methylated full mutation of approximately 250 repeats. Diagnosis was made subsequently to the evaluation of a FMR1 premutation as the cause for maternal premature ovarian failure. Dysmorphic evaluation revealed no strikingly long face, no prominent forehead/frontal bossing, no prominent mandible, no macroorchidism, and a head circumference in the lower normal range. Acquisition of a driving license for mopeds and unaccompanied rides by public transport in his home province indicate rather mild ID (IQ = 58). Conclusion: This adolescent demonstrates that apart from only minor ID, patients with a partially methylated FMR1 full mutation present less to absent pathognomonic facial dysmorphism, thus emphasizing the impact of family history for a straightforward clinical diagnosis.


title: Copy-move detection of audio recording with pitch similarity

abstract: The widespread availability of audio editing software has made it very easy to create forgeries without perceptual trace. Copy-move is one of popular audio forgeries. It is very important to identify audio recording with duplicated segments. However, copy-move detection in digital audio with sample by sample comparison is invalid due to post-processing after forgeries. In this paper we present a method based on pitch similarity to detect copy-move forgeries. We use a robust pitch tracking method to extract the pitch of every syllable and calculate the similarities of these pitch sequences. Then we can use the similarities to detect copy-move forgeries of digital audio recording. Experimental result shows that our method is feasible and efficient.


title: Graphine: Programming Graph-Parallel Computation of Large Natural Graphs for Multicore Clusters

abstract: Graph-parallel computation has become a crucial component in emerging applications of web search, data analytics and machine learning. In practice, most graphs derived from real-world phenomena are very large and scale-free. Unfortunately, distributed graph-parallel computation of these natural graphs still suffers strong scalability issues on contemporary multicore clusters. To embrace the multicore architecture in distributed graph-parallel computation, we propose the framework Graphine, which features (i) A Scatter-Combine computation abstraction that is evolved from the traditional vertex-centric approach by fusing the paired scatter and gather operations, executed separately on two edge sides, into a one-sided scatter. Further coupled with active message mechanism, it potentially reduces intermediate message cost and enables fine-grained parallelism on multicore architecture. (ii) An Agent-Graph data model, which leverages an idea similar to vertex-cut but conceptually splits the remote replica into two agent types of scatter and combiner, resulting in less communication. We implement the Graphine framework and evaluate it using several representative algorithms on six large real-world graphs and a series of synthetic graphs with power-law degree distributions. We show that Graphine achieves sublinear scalability with the number of cores per node, number of nodes, and graph sizes (up to one billion vertices), and is 2 $\sim$ 15 times faster than the state-of-the-art PowerGraph on a cluster of 16 multicore nodes.


title: Loyalty-based management

abstract: Despite a flurry of activities aimed at serving customers better, few companies have systematically revamped their operations with customer loyalty in mind. Instead, most have adopted improvement programs ad hoc, and paybacks haven't materialized. Building a highly loyal customer base must be integral to a company's basic business strategy. Loyalty leaders like MBNA credit cards are successful because they have designed their entire business systems around customer loyalty--a self-reinforcing system in which the company delivers superior value consistently and reinvents cash flows to find and keep high-quality customers and employees. The economic benefits of high customer loyalty are measurable. When a company consistently delivers superior value and wins customer loyalty, market share and revenues go up, and the cost of acquiring new customers goes down. The better economics mean the company can pay workers better, which sets off a whole chain of events. Increased pay boosts employee moral and commitment; as employees stay longer, their productivity goes up and training costs fall; employees' overall job satisfaction, combined with their experience, helps them serve customers better; and customers are then more inclined to stay loyal to the company. Finally, as the best customers and employees become part of the loyalty-based system, competitors are left to survive with less desirable customers and less talented employees. To compete on loyalty, a company must understand the relationships between customer retention and the other parts of the business--and be able to quantify the linkages between loyalty and profits. It involves rethinking and aligning four important aspects of the business: customers, product/service offering, employees, and measurement systems.


title: Query Processing and Optimization in Modern Database Systems

abstract: This thesis focuses on the challenges for database systems posed by modern hardware. We present a concurrent transaction processing system and show how to synchronize data structures efficiently. We further design a parallel query engine for many-core CPUs that supports the important relational operators. Finally, we dissect the query optimization process in the main memory setting.


title: Color Portraits: From Color Picking to Interacting with Color

abstract: Although ubiquitous, color pickers have remained largely unchanged for 25 years. Based on contextual interviews with artists and designers, we created the Color Portraits design space to characterize five key color manipulation activities: sampling and tweaking individual colors, manipulating color relationships, combining colors with other elements, revisiting previous color choices, and revealing a design process through color. We found similar color manipulation requirements with scientists and engineers. We designed novel color interaction tools inspired by the design space, and used them as probes to identify specific design requirements, including: interactive palettes for sampling colors and exploring relationships; color composites for blending and decomposing colors with other elements; interactive histories to enable reuse of previous color choices; and providing color as a way to reveal underlying processes. We argue that color tools should allow users to interact with colors, not just pick or sample them.


title: Security of Cooperative Intelligent Transport Systems: Standards, Threats Analysis and Cryptographic Countermeasures

abstract: Due to the growing number of vehicles on the roads worldwide, road traffic accidents are currently recognized as a major public safety problem. In this context, connected vehicles are considered as the key enabling technology to improve road safety and to foster the emergence of next generation cooperative intelligent transport systems (ITS). Through the use of wireless communication technologies, the deployment of ITS will enable vehicles to autonomously communicate with other nearby vehicles and roadside infrastructures and will open the door for a wide range of novel road safety and driver assistive applications. However, connecting wireless-enabled vehicles to external entities can make ITS applications vulnerable to various security threats, thus impacting the safety of drivers. This article reviews the current research challenges and opportunities related to the development of secure and safe ITS applications. It first explores the architecture and main characteristics of ITS systems and surveys the key enabling standards and projects. Then, various ITS security threats are analyzed and classified, along with their corresponding cryptographic countermeasures. Finally, a detailed ITS safety application case study is analyzed and evaluated in light of the European ETSI TC ITS standard. An experimental test-bed is presented, and several elliptic curve digital signature algorithms (ECDSA) are benchmarked for signing and verifying ITS safety messages. To conclude, lessons learned, open research challenges and opportunities are discussed.


Weighted Log-odds-ratio, Informative Dirichlet Prior Method to Enhance Peer Review Feedback for Low- and High-scoring College Students in a Required First-year Writing Program


title: Single Aperture Spectral+ToF Compressive Camera: Toward Hyperspectral+Depth Imagery

abstract: Spectral imaging involves the sensing of a large amount of spatial information across a multitude of wavelengths. Conventional approaches rely on scanning techniques to construct a spectral data cube. Recently, compressive spectral imaging (CSI) has allowed to estimate spectral images with as few as a single coded snapshot. On a different front, 3-D ranging imaging often involves scanning along one of the spatial dimensions to estimate the depth of an scene using structured light, or the use of two cameras as required by stereo-imaging techniques. Recently, Time-of-Flight (ToF) snapshot imaging has gained considerable attention, due to its accuracy and speed. To date, however, these imaging modalities (CSI and ToF) have been realized and implemented by separate independent imaging sensors. This paper presents the development of a single aperture compressive spectral + depth imaging camera that employs a commodity 3-D range ToF sensor as the sensing device of a coded-aperture-based compressive spectral imager. The proposed system uses a single aperture/single sensor; thus, representing a significant improvement over existing RGB+D cameras that integrate two separate image sensors, one for RGB and another for depth. In addition, the observable wavelength range of the CSI device is expanded from the visible to the near-infrared, resolving up to as many as 16 independent channels. The proposed system allows the addition of side-information by placing a grayscale or RGB camera in the same path of the single-aperture system, such that the quality of the spectral estimation is improved, while maintaining high-frame rates. We demonstrate the proposed ideas through real experimentation conducted on an assembled CSI+ToF testbed camera system.


title: Inferring preferences in ontology-based recommender systems using WOWA

abstract: In content-based semantic recommender systems the items to be considered are defined in terms of a set of semantic attributes, which may take as values the concepts of a domain ontology. The aim of these systems is to suggest to the user the items that fit better with his/her preferences, stored in the user profile. When large ontologies are considered it is unrealistic to expect to have complete information about the user preference on each concept. In this work, we explain how the Weighted Ordered Weighted Averaging operator may be used to deduce the user preferences on all concepts, given the structure of the ontology and some partial preferential information. The parameters of the WOWA operator enable to establish the desired aggregation policy, which ranges from a full conjunction to a full disjunction. Different aggregation policies have been analyzed in a case study involving the recommendation of touristic activities in the city of Tarragona. Several profiles have been compared and the results indicate that different aggregation policies should be used depending on the type of user. The amount of information available in the ontology must be also taken into account in order to establish the parameters of the proposed algorithm.