Analysis of Flux Leakage in a Segmented Core Brushless Permanent Magnet Motor

This paper presents an analytical model to analyze the leakage flux associated with a brushless permanent magnet motor that utilizes segmented stator core. Due to the presence of a segmented stator structure, the leakage of flux through the tooth tips becomes worthy of analysis and consideration in such a motor. A leakage parameter alpha is developed to show the effects of various design parameters on tooth-tip flux leakage that is essential in accurately predicting the air gap flux density and other output parameters in the motor. Finite-element analysis and experimental verifications are also done to validate the analytical model.


Double-Input Bidirectional DC/DC Converter Using Cell-Voltage Equalizer With Flyback Transformer

In this paper, a double-input bidirectional dc/dc converter that uses a rechargeable battery and an ultracapacitor (UC) is proposed. This converter is connected to a cell-voltage equalizer between the battery and UC. The cell-voltage equalizer enables cell-voltage equalization and energy transfer between the battery and UC. This converter has six operational modes. These modes are investigated by reduced-power-scale circuit experiment. In addition, the circuit operation under the combination of the six modes is verified using a PSIM simulator in a large power scale.


An Improved Multiobjective Optimization Evolutionary Algorithm Based on Decomposition for Complex Pareto Fronts

The multiobjective evolutionary algorithm based on decomposition (MOEA/D) has been shown to be very efficient in solving multiobjective optimization problems (MOPs). In practice, the Pareto-optimal front (POF) of many MOPs has complex characteristics. For example, the POF may have a long tail and sharp peak and disconnected regions, which significantly degrades the performance of MOEA/D. This paper proposes an improved MOEA/D for handling such kind of complex problems. In the proposed algorithm, a two-phase strategy (TP) is employed to divide the whole optimization procedure into two phases. Based on the crowdedness of solutions found in the first phase, the algorithm decides whether or not to delicate computational resources to handle unsolved subproblems in the second phase. Besides, a new niche scheme is introduced into the improved MOEA/D to guide the selection of mating parents to avoid producing duplicate solutions, which is very helpful for maintaining the population diversity when the POF of the MOP being optimized is discontinuous. The performance of the proposed algorithm is investigated on some existing benchmark and newly designed MOPs with complex POF shapes in comparison with several MOEA/D variants and other approaches. The experimental results show that the proposed algorithm produces promising performance on these complex problems.


Towards a truly mobile auditory brain-computer interface: exploring the P300 to take away.

In a previous study we presented a low-cost, small, and wireless 14-channel EEG system suitable for field recordings (Debener et al., 2012, psychophysiology). In the present follow-up study we investigated whether a single-trial P300 response can be reliably measured with this system, while subjects freely walk outdoors. Twenty healthy participants performed a three-class auditory oddball task, which included rare target and non-target distractor stimuli presented with equal probabilities of 16%. Data were recorded in a seated (control condition) and in a walking condition, both of which were realized outdoors. A significantly larger P300 event-related potential amplitude was evident for targets compared to distractors (p<.001), but no significant interaction with recording condition emerged. P300 single-trial analysis was performed with regularized stepwise linear discriminant analysis and revealed above chance-level classification accuracies for most participants (19 out of 20 for the seated, 16 out of 20 for the walking condition), with mean classification accuracies of 71% (seated) and 64% (walking). Moreover, the resulting information transfer rates for the seated and walking conditions were comparable to a recently published laboratory auditory brain-computer interface (BCI) study. This leads us to conclude that a truly mobile auditory BCI system is feasible.


Exploring Recurrent Neural Networks for On-Line Handwritten Signature Biometrics

Systems based on deep neural networks have made a breakthrough in many different pattern recognition tasks. However, the use of these systems with traditional architectures seems not to work properly when the amount of training data is scarce. This is the case of the on-line signature verification task. In this paper, we propose a novel writer-independent on-line signature verification systems based on Recurrent Neural Networks (RNNs) with a Siamese architecture whose goal is to learn a dissimilarity metric from the pairs of signatures. To the best of our knowledge, this is the first time these recurrent Siamese networks are applied to the field of on-line signature verification, which provides our main motivation. We propose both Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) systems with a Siamese architecture. In addition, a bidirectional scheme (which is able to access both past and future context) is considered for both LSTM- and GRU-based systems. An exhaustive analysis of the system performance and also the time consumed during the training process for each recurrent Siamese network is carried out in order to compare the advantages and disadvantages for practical applications. For the experimental work, we use the BiosecurID database comprised of 400 users who contributed a total of 11,200 signatures in four separated acquisition sessions. Results achieved using our proposed recurrent Siamese networks have outperformed the state-of-the-art on-line signature verification systems using the same database.


OpenHAR: A Matlab Toolbox for Easy Access to Publicly Open Human Activity Data Sets

This study introduces OpenHAR, a free Matlab toolbox to combine and unify publicly open data sets. It provides an easy access to accelerometer signals of ten publicly open human activity data sets. Data sets are easy to access as OpenHAR provides all the data sets in the same format. In addition, units, measurement range and labels are unified, as well as, body position IDs. Moreover, data sets with different sampling rates are unified using downsampling. What is more, data sets have been visually inspected to find visible errors, such as sensor in wrong orientation. OpenHAR improves re-usability of data sets by fixing these errors. Altogether OpenHAR contains over 65 million labeled data samples. This is equivalent to over 280 hours of data from 3D accelerometers. This includes data from 211 study subjects performing 17 daily human activities and wearing sensors in 14 different body positions.


Predicting Sequences of Clinical Events by Using a Personalized Temporal Latent Embedding Model

As a result of the recent trend towards digitization -- which increasingly affects evidence-based medicine, accountable care, personalized medicine, and medical "Big Data" analysis -- growing amounts of clinical data are becoming available for analysis. In this paper, we follow the idea that one can model clinical processes based on clinical data, which can then be the basis for many useful applications. We model the whole clinical evolution of each individual patient, which is composed of thousands of events such as ordered tests, lab results and diagnoses. Specifically, we base our work on a dataset provided by the Charité University Hospital of Berlin which is composed of patients that suffered from kidney failure and either obtained an organ transplant or are still waiting for one. These patients face a lifelong treatment and periodic visits to the clinic. Our goal is to develop a system to predict the sequence of events recorded in the electronic medical record of each patient, and thus to develop the basis for a future clinical decision support system. For modelling, we use machine learning approaches which are based on a combination of the embedding of entities and events in a multidimensional latent space, in combination with Neural Network predictive models. Similar approaches have been highly successful in statistical models for recommendation systems, language models, and knowledge graphs. We extend existing embedding models to the clinical domain, in particular with respect to temporal sequences, long-term memories and personalization. We compare the performance of our proposed models with standard approaches such as K-nearest neighbors method, Naïve Bayes classifier and Logistic Regression, and obtained favorable results with our proposed model.


A survey of video datasets for anomaly detection in automated surveillance

Automated anomaly detection in surveillance system demands to address various problems of real-time monitoring. Due to these challenges, the researchers from computer vision community focused on automation of the surveillance system. The research carried out in this domain has been proposed many datasets which are suitable for different applications. Though many prominent datasets are available, there is lack of description and categorization of these datasets according to the application demand. A survey on anomaly detection video datasets is necessary for researchers to understand and progress in this domain. This paper presents various datasets description briefly.


Logic and Artificial Intelligence

Nilsson, N.J., Logic and artificial intelligence, Artificial Intelligence 47 (1990) 31-56. The theoretical foundations of the logical approach to artificial intelligence are presented. Logical languages are widely used for expressing the declarative knowledge needed in artificial intelligence systems. Symbolic logic also provides a clear semantics for knowledge representation languages and a methodology for analyzing and comparing deductive inference techniques. Several observations gained from experience with the approach are discussed. Finally, we confront some challenging problems for artificial intelligence and describe what is being done in an attempt to solve them.


Complete mesocolic excision with central vascular ligation produces an oncologically superior specimen compared with standard surgery for carcinoma of the colon.

PURPOSE
The plane of surgery in colonic cancer has been linked to patient outcome although the optimal extent of mesenteric resection is still unclear. Surgeons in Erlangen, Germany, routinely perform complete mesocolic excision (CME) with central vascular ligation (CVL) and report 5-year survivals of higher than 89%. We aimed to further investigate the importance of CME and CVL surgery for colonic cancer by comparison with a series of standard specimens.

METHODS
The fresh photographs of 49 CME and CVL specimens from Erlangen and 40 standard specimens from Leeds, United Kingdom, for primary colonic adenocarcinoma were collected. Precise tissue morphometry and grading of the plane of surgery were performed before comparison to histopathologic variables.

RESULTS
CME and CVL surgery removed more tissue compared with standard surgery in terms of the distance between the tumor and the high vascular tie (median, 131 v 90 mm; P < .0001), the length of large bowel (median, 314 v 206 mm; P < .0001), and ileum removed (median, 83 v 63 mm; P = .003), and the area of mesentery (19,657 v 11,829 mm(2); P < .0001). In addition, CME and CVL surgery was associated with more mesocolic plane resections (92% v 40%; P < .0001) and a greater lymph node yield (median, 30 v 18; P < .0001).

CONCLUSION
Surgeons in Erlangen routinely practicing CME and CVL surgery remove more mesocolon and are more likely to resect in the mesocolic plane when compared with standard excisions. This, along with the associated greater lymph node yield, may partially explain the high 5-year survival rates reported in Erlangen.


Simple broadband Gysel combiner with a single coupled line

A coupled-Gysel broadband combiner/divider is proposed and demonstrated. The new concept relies on using a single coupled line segment in the design. Significant improvement in bandwidth are realized while maintaining low-loss, ease of design, and flexibility. The coupled-Gysel is demonstrated with a 2.5 - 8 GHz (105% fractional bandwidth) divider with 0.1 dB divider loss, and a 3.4 - 10.2 GHz (100% fractional bandwidth) with 0.2 dB divider loss.


A Deep Learning Approach for Population Estimation from Satellite Imagery

Knowing where people live is a fundamental component of many decision making processes such as urban development, infectious disease containment, evacuation planning, risk management, conservation planning, and more. While bottom-up, survey driven censuses can provide a comprehensive view into the population landscape of a country, they are expensive to realize, are infrequently performed, and only provide population counts over broad areas. Population disaggregation techniques and population projection methods individually address these shortcomings, but also have shortcomings of their own. To jointly answer the questions of "where do people live" and "how many people live there," we propose a deep learning model for creating high-resolution population estimations from satellite imagery. Specifically, we train convolutional neural networks to predict population in the USA at a 0.01°x0.01° resolution grid from 1-year composite Landsat imagery. We validate these models in two ways: quantitatively, by comparing our model's grid cell estimates aggregated at a county-level to several US Census county-level population projections, and qualitatively, by directly interpreting the model's predictions in terms of the satellite image inputs. We find that aggregating our model's estimates gives comparable results to the Census county-level population projections and that the predictions made by our model can be directly interpreted, which give it advantages over traditional population disaggregation methods. In general, our model is an example of how machine learning techniques can be an effective tool for extracting information from inherently unstructured, remotely sensed data to provide effective solutions to social problems.


High Voltage Pulsed Power Supply Using IGBT Stacks

High voltage pulsed power supply using IGBT (insulated gate bipolar transistor) stacks and pulse transformer for plasma source ion implantation is proposed. To increase voltage rating, twelve IGBTs are used in series at each IGBT stack and a step-up pulse transformer is utilized. To increase the current rating, the proposed system makes use of synchronized three pulse generator modules composed of diodes, capacitors and IGBT stacks. The proposed pulsed power supply uses semiconductor switches as main switches. Hence, the system is compact, and has semi-infinite lifetime. In addition, it has high flexibility in parameters such as voltage magnitude (10-60 kV), pulse repetition rate (PRR) (10-2000 pps), and pulse width (2-5 muS).


Super-resolution from image sequences-a review

Growing interest in super-resolution (SR) restoration of video sequences and the closely related problem of construction of SR still images from image sequences has led to the emergence of several competing methodologies. We review the state of the art of SR techniques using a taxonomy of existing techniques. We critique these methods and identify areas which promise performance improvements.


AppFusion: Interactive Appearance Acquisition Using a Kinect Sensor

We present an interactive material acquisition system for average users to capture the spatially-varying appearance of daily objects. While an object is being scanned, our system computes its appearance on-the-fly and provides quick visual feedback. We build the system entirely on low-end, off-the-shelf components: a Kinect, a mirror ball and printed markers. We exploit the Kinect infra-red emitter/receiver, originally designed for depth computation, as an active hand-held reflectometer, to segment the object into clusters of similar specular materials and estimate the roughness parameters of BRDFs simultaneously. Next, the diffuse albedo and specular intensity of the spatially-varying materials are rapidly computed in an inverse rendering framework, using data from the Kinect RGB camera. We demonstrate captured results of a range of materials, and physically validate our system. CR Categories: I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism—Color, shading, shadowing, and texture


Gaussian Process Priors with Uncertain Inputs - Application to Multiple-Step Ahead Time Series Forecasting

We consider the problem of multi-step ahead prediction in time series analysis using the non-parametric Gaussian process model. -step ahead forecasting of a discrete-time non-linear dynamic system can be performed by doing repeated one-step ahead predictions. For a state-space model of the form , the prediction of at time is based on the point estimates of the previous outputs. In this paper, we show how, using an analytical Gaussian approximation, we can formally incorporate the uncertainty about intermediate regressor values, thus updating the uncertainty on the current prediction.


Minimum Complexity Echo State Network

Reservoir computing (RC) refers to a new class of state-space models with a fixed state transition structure (the reservoir) and an adaptable readout form the state space. The reservoir is supposed to be sufficiently complex so as to capture a large number of features of the input stream that can be exploited by the reservoir-to-output readout mapping. The field of RC has been growing rapidly with many successful applications. However, RC has been criticized for not being principled enough. Reservoir construction is largely driven by a series of randomized model-building stages, with both researchers and practitioners having to rely on a series of trials and errors. To initialize a systematic study of the field, we concentrate on one of the most popular classes of RC methods, namely echo state network, and ask: What is the minimal complexity of reservoir construction for obtaining competitive models and what is the memory capacity (MC) of such simplified reservoirs? On a number of widely used time series benchmarks of different origin and characteristics, as well as by conducting a theoretical analysis we show that a simple deterministically constructed cycle reservoir is comparable to the standard echo state network methodology. The (short-term) of linear cyclic reservoirs can be made arbitrarily close to the proved optimal value.


The''echo state''approach to analysing and training recurrent neural networks

The report introduces a constructive learning algorithm for recurrent neural networks, which modifies only the weights to output units in order to achieve the learning task. key words: recurrent neural networks, supervised learning Zusammenfassung. Der Report führt ein konstruktives Lernverfahren für rekurrente neuronale Netze ein, welches zum Erreichen des Lernzieles lediglich die Gewichte der zu den Ausgabeneuronen führenden Verbindungen modifiziert. Stichwörter: rekurrente neuronale Netze, überwachtes Lernen


Reweighted Wake-Sleep

Training deep directed graphical models with many hidden variables and performing inference remains a major challenge. Helmholtz machines and deep belief networks are such models, and the wake-sleep algorithm has been proposed to train them. The wake-sleep algorithm relies on training not just the directed generative model but also a conditional generative model (the inference network) that runs backward from visible to latent, estimating the posterior distribution of latent given visible. We propose a novel interpretation of the wake-sleep algorithm which suggests that better estimators of the gradient can be obtained by sampling latent variables multiple times from the inference network. This view is based on importance sampling as an estimator of the likelihood, with the approximate inference network as a proposal distribution. This interpretation is confirmed experimentally, showing that better likelihood can be achieved with this reweighted wake-sleep procedure. Based on this interpretation, we propose that a sigmoid belief network is not sufficiently powerful for the layers of the inference network, in order to recover a good estimator of the posterior distribution of latent variables. Our experiments show that using a more powerful layer model, such as NADE, yields substantially better generative models.


Design and Synthesis of Synchronization Skeletons Using Branching Time Temporal Logic

We propose a method of constructing concurrent programs in which the synchronization skeleton of the program ~s automatically synthesized from a high-level (branching time) Temporal Logic specification. The synchronization skeleton is an abstraction of the actual program where detail irrelevant to synchronization is suppressed. For example, in the synchronization skeleton for a solution to the critical section problem each process's critical section may be viewed as a single node since the internal structure of the critical section is unimportant. Most solutions to synchronization problems in the literature are in fact given as synchronization skeletons. Because synchronization skeletons are in general finite state, the propositional version of Temporal Logic can be used to specify their properties. Our synthesis method exploits the (bounded) finite model property for an appropriate propositional Temporal Logic which asserts that if a formula of the logic is satisfiable, it is satisfiable in a finite model (of size bounded by a function of the length of the formula). Decision procedures have been devised which, given a formula of Temporal Logic, f, will decide whether f is satisfiable or unsatisfiable. If f is satisfiable, a finite model of f is constructed, in our application, unsatisfiability of f means that the specification is inconsistent (and must be reformulated). If the formula f is satisfiable, then the specification it expresses is consistent. A model for f with a finite number of states is constructed by the decision procedure. The synchronization skeleton of a program meeting the specification can be read from this model. The finite model property ensures that any program whose synchronization properties can be expressed in propositional Temporal Logic can be realized by a system of concurrently running processes, each of which is a finite state machine. initially, the synchronization skeletons we synthesize wi]] be for concurrent programs running in a shared-memory environment and for monitors. However, we believe that it is also possible to extend these techniques to synthesize distributed programs. One such application would be the automatic synthesis of network communication protocols from propositional Temporal Logic specifications. Previous efforts toward parallel program synthesis can be found in the work of [LA78] and iRK80]. [LA78] uses a specification language that is essentially predicate


Cross-Domain Perceptual Reward Functions

In reinforcement learning, we often define goals by specifying rewards within desirable states. One problem with this approach is that we typically need to redefine the rewards each time the goal changes, which often requires some understanding of the solution in the agent’s environment. When humans are learning to complete tasks, we regularly utilize alternative sources that guide our understanding of the problem. Such task representations allow one to specify goals on their own terms, thus providing specifications that can be appropriately interpreted across various environments. This motivates our own work, in which we represent goals in environments that are different from the agent’s. We introduce Cross-Domain Perceptual Reward (CDPR) functions, learned rewards that represent the visual similarity between an agent’s state and a cross-domain goal image. We report results for learning the CDPRs with a deep neural network and using them to solve two tasks with deep reinforcement learning.


Height and gradient from shading

The method described here for recovering the shape of a surface from a shaded image can deal with complex, wrinkled surfaces. Integrability can be enforced easily because both surface height and gradient are represented. (A gradient field is integrable if it is the gradient of some surface height function.) The robustness of the method stems in part from linearization of the reflectance map about the current estimate of the surface orientation at each picture cell. (The reflectance map gives the dependence of scene radiance on surface orientation.) The new scheme can find an exact solution of a given shape-from-shading problem even though a regularizing term is included. The reason is that the penalty term is needed only to stabilize the iterative scheme when it is far from the correct solution; it can be turned off as the solution is approached. This is a reflection of the fact that shape-from-shading problems are not ill posed when boundary conditions are available, or when the image contains singular points. This article includes a review of previous work on shape from shading and photoclinometry. Novel features of the new scheme are introduced one at a time to make it easier to see what each contributes. Included is a discussion of implementation details that are important if exact algebraic solutions of synthetic shape-from-shading problems are to be obtained. The hope is that better performance on synthetic data will lead to better performance on real data.


Origami Pop-up Card Generation from 3D Models Using a Directed Acyclic Graph

Origami is a paper art used to create three-dimensional (3D) objects by cutting and folding a single sheet of paper. Origami is labor-intensive and requires a high skill level to generate two-dimensional (2D) objects that pop-up into realistic 3D objects. However, this special feature makes designing an origami architecture procedure challenging. This paper provides a novel algorithm to create an origami paper card from a 3D model with a user-specified folding line. The algorithm segments the 2D shape from a 3D model and creates layers of the pop-up paper card using a directed acyclic graph. After applying layers to the layout, the algorithm creates connections between layers, and then outputs the origami layout. Based on a directed acyclic graph, the algorithm computes a class of paper architectures containing two sets of layers and connections that approximate the input geometry while guaranteeing that a pop up card is physically realizable. The proposed method is demonstrated with a number of paper pop-ups, and physical experimental results are presented.


Misplaced Trust ? Exploring the Structure of the E-Government-Citizen Trust Relationship

A growing body of research focuses on the relationship between e-government, the relatively new mode of citizen-to-government contact founded in information and communications technologies, and citizen trust in government. For many, including both academics and policy makers, e-government is seen as a potentially transformational medium, a mode of contact that could dramatically improve citizen perceptions of government service delivery and possibly reverse the long-running decline in citizen trust in government. To date, however, the literature has left significant gaps in our understanding of the e-government-citizen trust relationship. This study intends to fill some of these gaps. Using a cross-sectional sample of 787 end users of US federal government services, data from the American Customer Satisfaction Index study, and structural equation modeling statistical techniques, this study explores the structure of the e-government-citizen trust relationship. Included in the model are factors influencing the decision to adopt e-government, as well as prior expectations, overall satisfaction, and outcomes including both confidence in the particular agency experienced and trust in the federal government overall. The findings suggest that although e-government The authors would like to thank Professor Claes Fornell of the University ofMichigan for granting access to the ACSI data that made this study possible. Thanks also to Jason Joyner of CFI Group for his extensive and helpful comments on an early version of the article. Forrest V. Morgeson III is a Research Scientist at the ACSI in Ann Arbor, MI. He has a PhD in Political Science from the University of Pittsburgh. His recent research focuses on the financial impact of consumer satisfaction and citizen satisfaction with federal government services—both online and offline—and has recently been published or is forthcoming in journals such as Public Administration Review, Electronic Government: an International Journal, Journal of Marketing, Marketing Science, and the International Journal of Research in Marking. David VanAmburg is Director of the American Customer Satisfaction Index. Mr VanAmburg has acted as Director for the ACSI’s Federal government-wide satisfaction measurement since its inception in 1999 and of the ACSI as a whole since 2001. He has lectured extensively both in the United States and abroad on topics related to customer satisfaction, quality, customer loyalty, and shareholder value, and co-authored The American Customer Satisfaction Index at Ten Years, ACSI 1994–2004: A Summary of Findings: Implications for the Economy, Stock Returns andManagement. Sunil Mithas is an Assistant Professor at Robert H. Smith School of Business at University of Maryland. He has a PhD in Business from the Ross School of Business at the University of Michigan and an Engineering degree from Indian Institute of Technology, Roorkee, India. Prior to pursuing the PhD, he worked for about 10 years with the Tata group. His research focuses on strategic management and the impact of information technology resources and has appeared in journals that includeManagement Science, Information Systems Research, MIS Quarterly,Marketing Science, Journal of Marketing, and Production and Operations Management. His articles have won best article awards and best article nominations and have been featured in practice-oriented publications such as Harvard Business Review, Sloan Management Review, Bloomberg, Computerworld, and InformationWeek. Address correspondence to the author at morgeson@theacsi.org. doi:10.1093/jopart/muq006 a The Author 2010. Published by Oxford University Press on behalf of the Journal of Public Administration Research and Theory, Inc. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org Journal of Public Administration Research and Theory Advance Access published April 26, 2010 by on A ril 7, 2010 http://jpaordjournals.org D ow nladed fom may help improve citizens’ confidence in the future performance of the agency experienced, it does not yet lead to greater satisfaction with an agency interaction nor does it correlate with greater generalized trust in the federal government overall. Explanations for these findings, including an assessment of the potential of e-government to help rebuild trust in government in the future, are offered.


Death Due to Decapitation in Two Motorcyclists: A Description of a Unique Case and a Brief Review of the Literature.

Deaths due to decapitation, especially related to traffic accidents, are rarely found in forensic practice. The present case involves a man and a woman who died from decapitation due to an unusual mechanism while they were riding on a motorbike down a mountain road. The autopsy, which was completed as a physics study, allowed the accident to be reconstructed as follows: A plastic cable that had detached from a timber-transporting machine whipped the road and hit the two motorcyclists. The impact resulted in the complete severing of both riders' heads. Involving different scientists in this accident investigation was crucial to understanding the dynamics of the accident. In fact, in addition to scene inspection and autopsy, a physics study was carried out on the cable and the clamp involved, which led to an explanation for the abrupt movement of the cable and, thus, to a thorough reconstruction of the accident.


Revisiting Requirements Elicitation Techniques

The importance of Requirements Engineering (RE) has been well recognized by the research community in the last decade. There is no doubt to say that requirements phase is the foundation of the entire Software Development Life Cycle (SDLC) on which the entire software is built. With the proper management of various activities falling under requirements phase, a project can deliver the right solution within the time and budget. Requirement elicitation, requirement specification, and requirement validation are the important stages to assure the quality of requirement documentations. Out of these, elicitation is the first major activity, which requires proper attention by Requirement Engineers and other related stakeholders. Literature reveals various elicitation techniques, which can be used for the purpose depending upon the nature of project/s. In this paper, an attempt is made to cover all the major elicitation techniques along with their significant aspects at one place. Such a review would enable the concerned stakeholders to understand and select the most appropriate technique to be used for their project/s.


Video Gaming and Children’s Psychosocial Wellbeing: A Longitudinal Study

The effects of video games on children's psychosocial development remain the focus of debate. At two timepoints, 1 year apart, 194 children (7.27-11.43 years old; male = 98) reported their gaming frequency, and their tendencies to play violent video games, and to game (a) cooperatively and (b) competitively; likewise, parents reported their children's psychosocial health. Gaming at time one was associated with increases in emotion problems. Violent gaming was not associated with psychosocial changes. Cooperative gaming was not associated with changes in prosocial behavior. Finally, competitive gaming was associated with decreases in prosocial behavior, but only among children who played video games with high frequency. Thus, gaming frequency was related to increases in internalizing but not externalizing, attention, or peer problems, violent gaming was not associated with increases in externalizing problems, and for children playing approximately 8 h or more per week, frequent competitive gaming may be a risk factor for decreasing prosocial behavior. We argue that replication is needed and that future research should better distinguish between different forms of gaming for more nuanced and generalizable insight.


Classification of Substitution Ciphers using Neural Networks

Most of the time of a cryptanalyst is spent on finding the cipher technique used for encryption rather than the finding the key/ plaintext of the received cipher text. The Strength of the classical substitution cipher‟s success lie on the variety of characters employed to represent a single character. More, the characters employed more the complexity. Thus, in order to reduce the work of the cryptanalyst, neural network based identification is done based on the features of the cipher methods. In this paper, classical substitution ciphers, namely, Playfair, Vigenère and Hill ciphers are considered. The features of the cipher methods under consideration were extracted and a backpropagation neural network was trained. The network was tested for random texts with random keys of various lengths. The cipher text size was fixed as 1Kb. The results so obtained were encouraging.


Efficient Verification of Holograms Using Mobile Augmented Reality

Paper documents such as passports, visas and banknotes are frequently checked by inspection of security elements. In particular, optically variable devices such as holograms are important, but difficult to inspect. Augmented Reality can provide all relevant information on standard mobile devices. However, hologram verification on mobiles still takes long and provides lower accuracy than inspection by human individuals using appropriate reference information. We aim to address these drawbacks by automatic matching combined with a special parametrization of an efficient goal-oriented user interface which supports constrained navigation. We first evaluate a series of similarity measures for matching hologram patches to provide a sound basis for automatic decisions. Then a re-parametrized user interface is proposed based on observations of typical user behavior during document capture. These measures help to reduce capture time to approximately 15 s with better decisions regarding the evaluated samples than what can be achieved by untrained users.


Rethinking Feelings: An fMRI Study of the Cognitive Regulation of Emotion

The ability to cognitively regulate emotional responses to aversive events is important for mental and physical health. Little is known, however, about neural bases of the cognitive control of emotion. The present study employed functional magnetic resonance imaging to examine the neural systems used to reappraise highly negative scenes in unemotional terms. Reappraisal of highly negative scenes reduced subjective experience of negative affect. Neural correlates of reappraisal were increased activation of the lateral and medial prefrontal regions and decreased activation of the amygdala and medial orbito-frontal cortex. These findings support the hypothesis that prefrontal cortex is involved in constructing reappraisal strategies that can modulate activity in multiple emotion-processing systems.


Metacognitive Control of the Spacing of Study Repetitions

Rememberers play an active role in learning, not only by committing material more or less faithfully to memory, but also by selecting judicious study strategies (or not). In three experiments, subjects chose whether to mass or space the second presentation of to-be-learned paired-associate terms that were either normatively difficult or easy to remember, under the constraint that subjects needed to space exactly half of the items (and mass the other half). In contrast with recent findings that implemented no such constraint (Son, 2004), subjects chose to space more of the difficult pairs (in Experiments 1 and 2). Reduction in exposure time eliminated but did not reverse this effect (Experiment 3). Subjects who spaced more of the difficult pairs were more likely to exhibit superior memory performance, but, because subjects who made spacing selections that had no effect on the actual scheduling of items also showed this effect (Experiment 2), that enhancement in performance is more likely to reflect subject selection than strategy efficacy. Overall, these results suggest that choice constraints strongly elicit a discrepancy-reduction approach (Dunlosky & Hertzog, 1998) to strategic decisionmaking, but that reduced study time can eliminate this effect. Metacognitive control of spacing 3 The question of how people use their metacognitive knowledge to regulate their behaviors has been of much interest in recent years, particularly with regard to the implementation of study strategies. Metacognition plays an integral role in tasks such as selfdirected learning (Koriat & Goldsmith, 1996; Nelson & Narens, 1990), and understanding the means by which metacognitions guide learning processes is essential to facilitate and optimize the learning process itself (see Bjork, 1994). This article pursues that question by examining the strategies that subjects employ in the scheduling of learning events. Recent evidence revealed conditions under which subjects prefer to space easier materials and mass more difficult ones (Son, 2004). That result is fascinating because it either reveals that subjects choose to apply more effective study conditions to easier materials—a result in conflict with the vast majority of findings from study-time allocation experiments—or it reveals a fundamental misappreciation of the greater effectiveness of spacing in promoting learning (e.g., Baddeley & Longman, 1978). However, the present experiments reveal the opposite effect—subjects choose to space difficult and mass easy items. These results thus suggest that, under some conditions, subjects do understand the beneficial effects of spacing and also choose to selectively utilize them with difficult materials. Self-regulation of learning Theories of self-regulated study claim that active learners use assessments of item difficulty and their own degree of learning in deciding whether to allocate further cognitive resources toward study of that item or to move on to other items (e.g. Dunlosky & Hertzog, 1998; Mazzoni, Cornoldi, & Marchitelli, 1990; Metcalfe, 2002; Nelson & Leonesio, 1988; Nelson & Narens, 1990; Thiede & Dunlosky, 1999). There is some debate, however, with regard to how difficulty and resource allocation – specifically, study time allotment – are related. Metacognitive control of spacing 4 Discrepancy reduction. One theory emphasizes a discrepancy-reduction mechanism (Dunlosky & Hertzog, 1998). According to this theory, the learner compares their perceived degree of learning of a to-be-learned item to the their desired level of mastery for that item, also known as the norm of study (Le Ny, Denhiere, & Le Taillanter, 1972; see also Nelson & Narens, 1990), and if the degree of learning does not reach that criterion, additional study of that item ensues. Therefore, in reducing the discrepancy between an item’s current and desired degree of learning, the model predicts an inverse relationship between the perceived (prior) degree of learning and study time, and hence suggests that people will allot more study time to judgeddifficult than judged-easy items (Dunlosky & Hertzog, 1998; see also Thiede & Dunlosky, 1999). Indeed, a multitude of experiments have shown that people tend to study more difficult items for longer than they study easier items. In a comprehensive review, Son and Metcalfe (2000) reported that, of 46 treatment combinations in 19 published experiments in which subjects controlled the allocation of study time, 35 revealed a strategy of devoting more time to difficult items, and none showed the opposite strategy of devoting more time to easy items. These studies included subjective as well as objective measures of difficulty, and the results were consistently found across age groups and study materials. Proximal learning. However, Son and Metcalfe (2000) showed that total time constraints caused subjects to apportion more study time to judged-easy items than to judged-difficult items. That is, when the total study time allotted was likely insufficient to master all items, subjects chose to allocate their limited time to items that individually take less time to master, rather than slowly learn fewer difficult items. Similarly, Thiede and Dunlosky (1999) found that if their task Metacognitive control of spacing 5 was to remember only a small portion of the to-be-learned items, rather than the full set, subjects devoted more study time to easy items. Metcalfe (2002) surmised that the discrepancy-reduction model adequately accounted for subjects’ allocation strategies only under certain conditions, and forwarded a more comprehensive model to incorporate the newer data. She argued that study time should be devoted to those items that are just beyond the current grasp of the individual, in the region of proximal learning (Metcalfe, 2002; see also Metcalfe & Kornell, 2003). In the case that those just-unlearned items are the most difficult to-be-learned items, the discrepancy-reduction and region of proximal learning models agree on what the appropriate strategy should be. However, in cases where easy items are still unlearned, the predictions of the two theories are in opposition. Whereas the discrepancy-reduction model suggests that learners will always devote more time to the difficult items, the proximal learning hypothesis implies that individual differences in expertise within a domain should influence study-time allocation. Metcalfe (2002) demonstrated this effect using English-Spanish vocabulary pairs. To monolingual speakers, even relatively easy items can be difficult to learn, and thus those speakers allocated more study time to those easy pairs accordingly. Experts, on the other hand, spent more time studying the difficult word pairs, and Metcalfe (2002) attributed the group differences in item selection to the difference between the two groups’ regions of proximal learning. Novices chose to spend more time studying the easy, yet still unlearned, items before moving on to more difficult items, a result that is not predicted by the discrepancy-reduction model. Effects of strategy choice. In addition to identifying the strategies used in allocating study time, determining which strategy ultimately leads to superior performance on subsequent Metacognitive control of spacing 6 recall tests is of importance as well. The actual quality of any study strategy, after all, can only be evaluated by the outcome it produces on the subsequent test. As Son and Metcalfe (2000) pointed out, even though much of the previous literature suggests a tendency for subjects to study the difficult items for longer than the easy items, there are no data showing that subjects who employ such a strategy outperform subjects who spend equal amounts of time on easy and difficult items. While it is intuitive that increased duration of study should lead to higher recall performance for any given item, previous findings have suggested that once study time for an item has been sufficient for initial acquisition, continued immediate study of that item leads to little or no increase in the probability of future recall. This null increase in performance despite substantial increases in study time has been termed the “labor-in-vain effect” (Nelson & Leonesio, 1988, p. 680). Metcalfe and Kornell (2003) systematically investigated the effects of allocating study time to a particular subset of items on later recall performance. Their results showed that allocating study time to items of medium difficulty was more helpful than was allocating study time to easy or difficult items, presumably because learning of the easy items had already plateaued and would not benefit from additional study, whereas learning of the difficult items would require even larger amounts of additional study time to reach a level sufficient to improve later recall. The plateau occurs sooner for easy items than for medium items, and likewise sooner for medium items than for difficult items. If an optimal strategy of study-time allocation is one in which study of a particular item should be discontinued at the point when further study is not as advantageous as study of another item, a critical question for the metacognizer is where (or when) that point is. If information uptake has slowed sufficiently for an item, immediate further study of that item at the cost of a Metacognitive control of spacing 7 different item may be detrimental to overall recall performance, whereas further study at some later time may boost one’s memory for that item and may benefit future recall. Such a strategy has already been shown in the context of semantic retrieval tasks in which subjects tried to name as many items as possible from two semantic categories in a short time. When retrieval for items in one category slowed, and the other category became relatively more appealing, subjects switched to the second category (Young, 2004). To test whether subjects employ this type of strategy in a learning task, a pa


Undefined By Data: A Survey of Big Data Definitions

The term big data has become ubiquitous. Owing to a shared origin between academia, industry and the media there is no single unified definition, and various stakeholders provide diverse and often contradictory definitions. The lack of a consistent definition introduces ambiguity and hampers discourse relating to big data. This short paper attempts to collate the various definitions which have gained some degree of traction and to furnish a clear and concise definition of an otherwise ambiguous term.


Explaining the Poor Performance of Consumption-Based Asset Pricing Models

The poor performance of consumption-based asset pricing models relative to traditional portfoliobased asset pricing models is one of the great disappointments of the empirical asset pricing literature. We show that the external habit-formation model economy of Campbell and Cochrane (1999) can explain this puzzle. Though artificial data from that economy conform to a consumption-based model by construction, the CAPM and its extensions are much better approximate models than is the standard power utility specification of the consumption-based model. Conditioning information is the central reason for this result. The model economy has one shock, so when returns are measured at sufficiently high frequency the consumption-based model and the CAPM are equivalent and perfect conditional asset pricing models. However, the model economy also produces time-varying expected returns, tracked by the dividend-price ratio. Portfolio-based models capture some of this variation in state variables, which a state-independent function of consumption cannot capture, and so portfolio-based models are better approximate unconditional asset pricing models. John Y. Campbell John H. Cochrane Department of Economics Graduate School of Business Harvard University University of Chicago Littauer Center 213 1101 E. 58th. St. Cambridge, MA 02138 Chicago, IL 60637 and NBER and NBER john_campbell@harvard.edu john.cochrane@gsb.uchicago.edu


Assessing Organizational Capabilities: Reviewing and Guiding the Development of Maturity Grids

Managing and improving organizational capabilities is a significant and complex issue for many companies. To support management and enable improvement, performance assessments are commonly used. One way of assessing organizational capabilities is by means of maturity grids. While maturity grids may share a common structure, their content differs and very often they are developed anew. This paper presents both a reference point and guidance for developing maturity grids. This is achieved by reviewing 24 existing maturity grids and by suggesting a roadmap for their development. The review places particular emphasis on embedded assumptions about organizational change in the formulation of the maturity ratings. The suggested roadmap encompasses four phases: planning, development, evaluation, and maintenance. Each phase discusses a number of decision points for development, such as the selection of process areas, maturity levels, and the delivery mechanism. An example demonstrating the roadmap's utility in industrial practice is provided. The roadmap can also be used to evaluate existing approaches. In concluding the paper, implications for management practice and research are presented.


Modelling out-of-vocabulary words for robust speech recognition

This thesis concerns the problem of unknown or out-of-vocabulary (OOV) words in continuous speech recognition. Most of today's state-of-the-art speech recognition systems can recognize only words that belong to some predefined finite word vocabulary. When encountering an OOV word, a speech recognizer erroneously substitutes the OOV word with a similarly sounding word from its vocabulary. Furthermore, a recognition error due to an OOV word tends to spread errors into neighboring words; dramatically degrading overall recognition performance. In this thesis we propose a novel approach for handling OOV words within a single-stage recognition framework. To achieve this goal, an explicit and detailed model of OOV words is constructed and then used to augment the closed-vocabulary search space of a standard speech recognizer. This OOV model achieves open-vocabulary recognition through the use of more flexible subword units that can be concatenated during recognition to form new phone sequences corresponding to potential new words. Examples of such subword units are phones, syllables, or some automatically-learned multi-phone sequences. Subword units have the attractive property of being a closed set, and thus are able to cover any new words, and can conceivably cover most utterances with partially spoken words as well. The main challenge with such an approach is ensuring that the OOV model does not absorb portions of the speech signal corresponding to in-vocabulary (IV) words. In dealing with this challenge, we explore several research issues related to designing the subword lexicon, language model, and topology of the OOV model. We present a dictionary-based approach for estimating subword language models. Such language models are utilized within the subword search space to help recognize the underlying phonetic transcription of OOV words. We also propose a data-driven iterative bottom-up procedure for automatically creating a multi-phone subword inventory. Starting with individual phones, this procedure uses the maximum mutual information principle to successively merge phones to obtain longer subword units. The thesis also extends this OOV approach to modelling multiple classes of OOV words. Instead of augmenting the word search space with a single model, we add several models, one for each class of words. We present two approaches for designing the OOV word classes. The first approach relies on using common part-of-speech tags. The second approach is a data-driven two-step clustering procedure, where the first step uses agglomerative clustering to derive an initial class assignment, while the second step uses iterative clustering to move words from one class to another in order to reduce the model perplexity.


A Digital Input Controller for Audio Class-D Amplifiers with 100W 0.004% THD+N and 113dB DR

A digital input controller for audio class-D amplifiers is presented. The controller utilizes specially configured integrated DAC and power stage feedback loop to suppress distortion components coming from power-stage switching with digital input capability. The class-D amplifier system with the controller and an existing power stage achieves 113dB DR, 0.0018% THD+N with 10W output power, and 0.004% THD+N with 100W output power into 4Omega load


Understanding Latency Variation in Modern DRAM Chips: Experimental Characterization, Analysis, and Optimization

Long DRAM latency is a critical performance bottleneck in current systems. DRAM access latency is defined by three fundamental operations that take place within the DRAM cell array: (i) activation of a memory row, which opens the row to perform accesses; (ii) precharge, which prepares the cell array for the next memory access; and (iii) restoration of the row, which restores the values of cells in the row that were destroyed due to activation. There is significant latency variation for each of these operations across the cells of a single DRAM chip due to irregularity in the manufacturing process. As a result, some cells are inherently faster to access, while others are inherently slower. Unfortunately, existing systems do not exploit this variation.
 The goal of this work is to (i) experimentally characterize and understand the latency variation across cells within a DRAM chip for these three fundamental DRAM operations, and (ii) develop new mechanisms that exploit our understanding of the latency variation to reliably improve performance. To this end, we comprehensively characterize 240 DRAM chips from three major vendors, and make several new observations about latency variation within DRAM. We find that (i) there is large latency variation across the cells for each of the three operations; (ii) variation characteristics exhibit significant spatial locality: slower cells are clustered in certain regions of a DRAM chip; and (iii) the three fundamental operations exhibit different reliability characteristics when the latency of each operation is reduced.
 Based on our observations, we propose Flexible-LatencY DRAM (FLY-DRAM), a mechanism that exploits latency variation across DRAM cells within a DRAM chip to improve system performance. The key idea of FLY-DRAM is to exploit the spatial locality of slower cells within DRAM, and access the faster DRAM regions with reduced latencies for the fundamental operations. Our evaluations show that FLY-DRAM improves the performance of a wide range of applications by 13.3%, 17.6%, and 19.5%, on average, for each of the three different vendors' real DRAM chips, in a simulated 8-core system. We conclude that the experimental characterization and analysis of latency variation within modern DRAM, provided by this work, can lead to new techniques that improve DRAM and system performance.


Autoimmune disorders: nail signs and therapeutic approaches.

Systemic sclerosis (scleroderma, SSc) is an autoimmune disease that targets small and medium-sized arteries and arterioles in the involved tissues, resulting in a fibrotic vasculopathy and tissue fibrosis. Several prominent nail and periungual changes are apparent in scleroderma. Examination of the nail fold capillaries can reveal the nature and extent of microvascular pathology in patients with collagen vascular disease and Raynaud's phenomenon. Among the complications stemming from Raynaud's phenomenon can be painful ischemic digital ulcers. This can be managed, and potentially prevented, through pharmacologic and nonpharmacologic means. Whereas oral calcium channel blockers remain the most convenient therapy, oral endothelin receptor antagonists and intravenous prostaglandins may be important therapeutic advances for ischemic digital vascular lesions.


An introduction to convex optimization for communications and signal processing

Convex optimization methods are widely used in the design and analysis of communication systems and signal processing algorithms. This tutorial surveys some of recent progress in this area. The tutorial contains two parts. The first part gives a survey of basic concepts and main techniques in convex optimization. Special emphasis is placed on a class of conic optimization problems, including second-order cone programming and semidefinite programming. The second half of the survey gives several examples of the application of conic programming to communication problems. We give an interpretation of Lagrangian duality in a multiuser multi-antenna communication problem; we illustrate the role of semidefinite relaxation in multiuser detection problems; we review methods to formulate robust optimization problems via second-order cone programming techniques


Fading correlation and its effect on the capacity of multielement antenna systems

We investigate the effects of fading correlations in multielement antenna (MEA) communication systems. Pioneering studies showed that if the fades connecting pairs of transmit and receive antenna elements are independently, identically distributed, MEA’s offer a large increase in capacity compared to single-antenna systems. An MEA system can be described in terms of spatial eigenmodes, which are single-input single-output subchannels. The channel capacity of an MEA is the sum of capacities of these subchannels. We will show that the fading correlation affects the MEA capacity by modifying the distributions of the gains of these subchannels. The fading correlation depends on the physical parameters of MEA and the scatterer characteristics. In this paper, to characterize the fading correlation, we employ an abstract model, which is appropriate for modeling narrow-band Rayleigh fading in fixed wireless systems.


Shifting the MIMO Paradigm

Multi-user MIMO (MU-MIMO) networks reveal the unique opportunities arising from a joint optimization of antenna combining techniques with resource allocation protocols. Furthermore, it brings robustness with respect to multipath richness, allowing for compact antenna spacing at the BS and, crucially, yielding the diversity and multiplexing gains without the need for multiple antenna user terminals. To realize these gains, however, the BS should be informed with the user's channel coefficients, which may limit practical application to TDD or low-mobility settings. To circumvent this problem and reduce feedback load, combining MU-MIMO with opportunistic scheduling seems a promising direction. The success for this type of scheduler is strongly traffic and QoS-dependent, however.


On Limits of Wireless Communications in a Fading Environment when Using Multiple Antennas

This paper is motivated by the need for fundamental understanding of ultimate limits of bandwidth efficient delivery of higher bit-rates in digital wireless communications and to also begin to look into how these limits might be approached. We examine exploitation of multi-element array (MEA) technology, that is processing the spatial dimension (not just the time dimension) to improve wireless capacities in certain applications. Specifically, we present some basic information theory results that promise great advantages of using MEAs in wireless LANs and building to building wireless communication links. We explore the important case when the channel characteristic is not available at the transmitter but the receiver knows (tracks) the characteristic which is subject to Rayleigh fading. Fixing the overall transmitted power, we express the capacity offered by MEA technology and we see how the capacity scales with increasing SNR for a large but practical number, , of antenna elements at bothtransmitter and receiver. We investigate the case of independent Rayleigh faded paths between antenna elements and find that with high probability extraordinary capacity is available. Compared to the baseline n = 1 case, which by Shannon’s classical formula scales as one more bit/cycle for every 3 dB of signal-to-noise ratio (SNR) increase, remarkably with MEAs, the scaling is almost like n more bits/cycle for each 3 dB increase in SNR. To illustrate how great this capacity is, even for small n, take the cases n = 2, 4 and 16 at an average received SNR of 21 dB. For over 99% of the channels the capacity is about 7, 19 and 88 bits/cycle respectively, while if n = 1 there is only about 1.2 bit/cycle at the 99% level. For say a symbol rate equal to the channel bandwith, since it is the bits/symbol/dimension that is relevant for signal constellations, these higher capacities are not unreasonable. The 19 bits/cycle for n = 4 amounts to 4.75 bits/symbol/dimension while 88 bits/cycle for n = 16 amounts to 5.5 bits/symbol/dimension. Standard approaches such as selection and optimum combining are seen to be deficient when compared to what will ultimately be possible. New codecs need to be invented to realize a hefty portion of the great capacity promised.


Detecting A Continuum Of Compositionality In Phrasal Verbs


Growth and morphological development of laboratory-reared larval and juvenile giant gourami Osphronemus goramy (Perciformes: Osphronemidae)

Morphological development in laboratory-reared larval and juvenile Osphronemus goramy is described. Body lengths were 4.8 ± 0.1 (mean ± SD) mm just after hatching, and reached 9.8 ± 0.8 mm by day 16 and 15.3 ± 0.6 mm by day 40. Aggregate fin ray numbers attained full complements by 11.9 mm BL on day 22. All larvae began feeding by day 5 (ca. 6.7 mm BL). Conical jaw teeth appeared on day 7 (ca. 7.0 mm BL). Notochord flexion began on day 3 (ca. 6.5 mm BL), the yolk being absorbed during days 14–15 (ca. 8.0–10.0 mm BL). Pale melanophores, initially visible on the upper yolksac surface during days 0 and 1, became more intense and numerous with growth, and covered almost the entire body in juveniles. Seven or eight vertical bars appeared on the body on day 13 (ca. 9.0 mm BL), thereafter becoming more discernible. Pre-anal length, body depth, and eye diameter proportions became relatively constant after yolk absorption, with those of the head/snout/upper jaw lengths subsequently increasing. The labyrinth organ began to develop in juveniles larger than ca. 14.5 mm BL (day 30), and air-breathing behavior was first observed on days 35–40.


Optimal power allocation for DL NOMA systems

Non-orthogonal multiple access (NOMA) is an important candidate for 5G radio access technology. In NOMA transmitter, different users' signals are superposed on the same radio resource with different power allocation factors. The receiver removes other users' signals from the received signal before decoding its own signal. In this work, an iterative gradient ascent-based power allocation method is proposed for downlink NOMA transmitter. It maximizes the geometric mean of the throughputs of users who share the same radio resource to provide proportional fairness between users. Simulation results show that the method achieves theoretical best results in terms of the suggested metric. Also it is shown that it increases the efficiency as much as 80% when compared to orthogonal multiple access (OMA) and it gives better results than NOMA that uses fixed and fractional power allocation methods.


The PeerRank Method for Peer Assessment

We propose the PeerRank method for peer assessment. This constructs a grade for an agent based on the grades proposed by the agents evaluating the agent. Since the grade of an agent is a measure of their ability to grade correctly, the PeerRank method weights grades by the grades of the grading agent. The PeerRank method also provides an incentive for agents to grade correctly. As the grades of an agent depend on the grades of the grading agents, and as these grades themselves depend on the grades of other agents, we define the PeerRank method by a fixed point equation similar to the PageRank method for ranking web-pages.


Critical Issues Affecting an ERP Implementation

Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content.


Analysis of Semipermeable Containment Sleeve Technology for High-Speed Permanent Magnet Machines

This paper presents an alternative permanent magnet rotor containment technology. The use of a semipermeable material in a laminated structure is shown to result in a significant improvement in magnetic loading for a given thickness of containment compared to the use of magnetically inert materials such as carbon fiber or Inconel, while minimizing eddy current losses in the containment layer. An analytical model is presented for determining the air-gap field delivered from a permanent magnet array with a semipermeable containment. The analysis is validated through finite element analysis. The fabrication of a prototype machine is detailed and the results presented show good agreement with the analysis. The validated modeling process is used to assess the potential of the new technology.


A scalable end-to-end Gaussian process adapter for irregularly sampled time series classification

We present a general framework for classification of sparse and irregularly-sampled time series. The properties of such time series can result in substantial uncertainty about the values of the underlying temporal processes, while making the data difficult to deal with using standard classification methods that assume fixeddimensional feature spaces. To address these challenges, we propose an uncertaintyaware classification framework based on a special computational layer we refer to as the Gaussian process adapter that can connect irregularly sampled time series data to any black-box classifier learnable using gradient descent. We show how to scale up the required computations based on combining the structured kernel interpolation framework and the Lanczos approximation method, and how to discriminatively train the Gaussian process adapter in combination with a number of classifiers end-to-end using backpropagation.


Characterization of a RS-LiDAR for 3D Perception

High precision 3D LiDARs are still expensive and hard to acquire. This paper presents the characteristics of RSLiDAR, a model of low-cost LiDAR with sufficient supplies, in comparison with VLP-16. The paper also provides a set of evaluations to analyze the characterizations and performances of LiDARs sensors. This work analyzes multiple properties, such as drift effects, distance effects, color effects and sensor orientation effects, in the context of 3D perception. By comparing with Velodyne LiDAR, we found RS-LiDAR as a cheaper and acquirable substitute of VLP-16 with similar efficiency.


Analytical Design of Decoupling Internal Model Control ( IMC ) Scheme for Two-Input-Two-Output ( TITO ) Processes with Time Delays

In this paper, a new decoupling control scheme, in terms of an internal model control (IMC) structure, is proposed for two-input -two-output (TITO) processes with time delays. Noteworthy improvement of the decoupling regulation can be achieved for the nominal system output responses, and moreover, either of the system output responses can be quantitatively regulated by virtue of the analytical relationship between the adjustable parameters of the decoupling controller matrix and the nominal system transfer matrix. The ideally optimal controller matrix is analytically derived by proposing the practically desired diagonal system transfer matrix, in terms of the robust H 2 optimal performance objective. Because of the difficulty of physical implementation, its practical form is carefully configured according to whether there exist any complex righthalf-plane (RHP) zeros in the process transfer matrix determinant. At the same time, tuning constraints for the proposed decoupling controller matrix to hold the control system robust stability are analyzed in the presence of the process additive and multiplicative uncertainties, and, accordingly, the on-line tuning rule is provided to cope with the process unmodeled dynamics in practice. Finally, illustrative simulation examples are included to demonstrate the remarkable superiority of the proposed method.


Benchmarking open government: An open data perspective

Available online 3 April 2014


Gesture recognition in smart home using passive RFID technology

Gesture recognition is a well-establish topic of research that is widely adopted for a broad range of applications. For instance, it can be exploited for the command of a smart environment without any remote control unit or even for the recognition of human activities from a set of video cameras deployed in strategic position. Many researchers working on assistive smart home, such as our team, believe that the intrusiveness of that technology will prevent the future adoption and commercialization of smart homes. In this paper, we propose a novel gesture recognition algorithm that is solely based on passive RFID technology. This technology enables the localization of small tags that can be embedded in everyday life objects (a cup or a book, for instance) while remaining non intrusive. However, until now, this technology has been largely ignored by researchers on gesture recognition, mostly because it is easily disturbed by noise (metal, human, etc.) and offer limited precision. Despite these issues, the localization algorithms have improved over the years, and our recent efforts resulted in a real-time tracking algorithm with a precision approaching 14cm. With this, we developed a gesture recognition algorithm able to perform segmentation of gestures and prediction on a spatio-temporal data series. Our new model, exploiting works on qualitative spatial reasoning, achieves recognition of 91%. Our goal is to ultimately use that knowledge for both human activity recognition and errors detection.


Dirichlet Processes for Joint Learning of Morphology and PoS Tags

This paper presents a joint model for learning morphology and part-of-speech (PoS) tags simultaneously. The proposed method adopts a finite mixture model that groups words having similar contextual features thereby assigning the same PoS tag to those words. While learning PoS tags, words are analysed morphologically by exploiting similar morphological features of the learned PoS tags. The results show that morphology and PoS tags can be learned jointly in a fully unsupervised setting.


Social media driven image retrieval

People often try to find an image using a short query and images are usually indexed using short annotations. Matching the query vocabulary with the indexing vocabulary is a difficult problem when little text is available. Textual user generated content in Web 2.0 platforms contains a wealth of data that can help solve this problem. Here we describe how to use Wikipedia and Flickr content to improve this match. The initial query is launched in Flickr and we create a query model based on co-occurring terms. We also calculate nearby concepts using Wikipedia and use these to expand the query. The final results are obtained by ranking the results for the expanded query using the similarity between their annotation and the Flickr model. Evaluation of these expansion and ranking techniques, over the Image CLEF 2010 Wikipedia Collection containing 237,434 images and their multilingual textual annotations, shows that a consistent improvement compared to state of the art methods.


Pose-Driven Deep Convolutional Model for Person Re-identification

Feature extraction and matching are two crucial components in person Re-Identification (ReID). The large pose deformations and the complex view variations exhibited by the captured person images significantly increase the difficulty of learning and matching of the features from person images. To overcome these difficulties, in this work we propose a Pose-driven Deep Convolutional (PDC) model to learn improved feature extraction and matching models from end to end. Our deep architecture explicitly leverages the human part cues to alleviate the pose variations and learn robust feature representations from both the global image and different local parts. To match the features from global human body and local body parts, a pose driven feature weighting sub-network is further designed to learn adaptive feature fusions. Extensive experimental analyses and results on three popular datasets demonstrate significant performance improvements of our model over all published state-of-the-art methods.


Probabilistic aggregation for data dissemination in VANETs

We propose an algorithm for the hierarchical aggregation of observations in dissemination-based, distributed traffic information systems. Instead of carrying specific values (e.g., the number offree parking places in a given area), our aggregates contain a modified Flajolet-Martin sketch as a probabilistic approximation. The main advantage of this approach is that the aggregates are duplicate insensitive. This overcomes two central problems of existing aggregation schemes for VANET applications. First, when multiple aggregates of observations for the same area are available, it is possible to combine them into an aggregate containing all information from the original aggregates. This is fundamentally different from existing approaches where typically one of the aggregates is selected for further use while the rest is discarded. Second, any observation or aggregate can be included into higher level aggregates, regardless if it has already been previously - directly or indirectly - added. As a result of those characteristics the quality of the aggregates is high, while their construction is very flexible. We demonstrate these traits of our approach by a simulation study.


Drugs of Abuse and Stress Trigger a Common Synaptic Adaptation in Dopamine Neurons

Drug seeking and drug self-administration in both animals and humans can be triggered by drugs of abuse themselves or by stressful events. Here, we demonstrate that in vivo administration of drugs of abuse with different molecular mechanisms of action as well as acute stress both increase strength at excitatory synapses on midbrain dopamine neurons. Psychoactive drugs with minimal abuse potential do not cause this change. The synaptic effects of stress, but not of cocaine, are blocked by the glucocorticoid receptor antagonist RU486. These results suggest that plasticity at excitatory synapses on dopamine neurons may be a key neural adaptation contributing to addiction and its interactions with stress and thus may be an attractive therapeutic target for reducing the risk of addiction.


Personalizing Similar Product Recommendations in Fashion E-commerce

In fashion e-commerce platforms, product discovery is one of the key components of a good user experience. There are numerous ways using which people find the products they desire. Similar product recommendations is one of the popular modes using which users find products that resonate with their intent. Generally these recommendations are not personalized to a specific user. Traditionally, collaborative filtering based approaches have been popular in the literature for recommending non-personalized products given a query product. Also, there has been focus on personalizing the product listing for a given user. In this paper, we marry these approaches so that users will be recommended with personalized similar products. Our experimental results on a large fashion ecommerce platform (Myntra) show that we can improve the key metrics by applying personalization on similar product recommendations.


Reader-Aware Multi-Document Summarization via Sparse Coding

We propose a new MDS paradigm called readeraware multi-document summarization (RA-MDS). Specifically, a set of reader comments associated with the news reports are also collected. The generated summaries from the reports for the event should be salient according to not only the reports but also the reader comments. To tackle this RAMDS problem, we propose a sparse-coding-based method that is able to calculate the salience of the text units by jointly considering news reports and reader comments. Another reader-aware characteristic of our framework is to improve linguistic quality via entity rewriting. The rewriting consideration is jointly assessed together with other summarization requirements under a unified optimization model. To support the generation of compressive summaries via optimization, we explore a finer syntactic unit, namely, noun/verb phrase. In this work, we also generate a data set for conducting RA-MDS. Extensive experiments on this data set and some classical data sets demonstrate the effectiveness of our proposed approach.


Foundations of Cryptography: Basic Tools

If you really want to be smarter, reading can be one of the lots ways to evoke and realize. Many people who like reading will have more knowledge and experiences. Reading can be a way to gain information from economics, politics, science, fiction, literature, religion, and many others. As one of the part of book categories, foundations of cryptography basic tools always becomes the most wanted book. Many people are absolutely searching for this book. It means that many love to read this kind of book.


An early fire-detection method based on image processing

The paper presents an early fire-alarm raising method based on video processing. The basic idea of the proposed of fire-detection is to adopt a RGB (red, green, blue) model based chromatic and disorder measurement for extracting fire-pixels and smoke-pixels. The decision function of fire-pixels is mainly deduced by the intensity and saturation of R component. The extracted fire-pixels will be verified if it is a real fire by both dynamics of growth and disorder, and further smoke. Based on iterative checking on the growing ratio of flames, a fire-alarm is given when the alarm-raising condition is met. Experimental results show that the developed technique can achieve fully automatic surveillance of fire accident with a lower false alarm rate and thus is very attractive for the important military, social security, commercial applications, and so on, at a general cost.


A Fire-Alarming Method Based on Video Processing

This paper presents a fire-alarming method based on video processing. We propose a system that uses color and motion information extracted from video sequences to detect fire. Flame can be recognized according to its color which is a primary element of fire images. Thus choosing a suitable color model is the key to detect flames from fire images. An effective fire detection criterion based on color model is proposed in this paper by intensive experiments and trainings. The detection criterion is used to make a raw localization of fire regions first. However, color alone is not enough for fire detection. To identify a real burning fire, in addition to chromatic features, dynamic features are usually adopted to distinguish other fire aliases. In this paper, both the growth of fire region and the invariability of flame are utilized to further detect the fire regions as a complement of the detection criterion. The effectiveness of the proposed fire-alarming method is demonstrated by the experiments implemented on a large number of scenes.


Adaptive Background Mixture Models for Real-Time Tracking

A common method for real-time segmentation of moving regions in image sequences involves “background subtraction,” or thresholding the error between an estimate of the image without moving objects and the current image. The numerous approaches to this problem differ in the type of background model used and the procedure used to update the model. This paper discusses modeling each pixel as a mixture of Gaussians and using an on-line approximation to update the model. The Gaussian distributions of the adaptive mixture model are then evaluated to determine which are most likely to result from a background process. Each pixel is classified based on whether the Gaussian distribution which represents it most effectively is considered part of the background model. This results in a stable, real-time outdoor tracker which reliably deals with lighting changes, repetitive motions from clutter, and long-term scene changes. This system has been run almost continuously for 16 months, 24 hours a day, through rain and snow.


Antenna in package design for WLAN using MCM-D manufacturing technology

This paper introduces an antenna design based on MCM-D manufacturing technology to realize an antenna-integrated package for IEEE 802.11b/g application. Co-design guidelines are employed to include the parasitic effects caused by the integration of the antenna and the RF module. The loop antenna is located on the second layer of the MCM-D substrate. The antenna incorporates the capacitively feed strip which is fed by the coplanar waveguide (CPW). By the coupling feed technique, the size of the proposed antenna is only 3.8 mm × 4.7 mm over the WLAN band (2.4–2.484 GHz). Furthermore, the resonant frequency can be adjusted by tuning the length of the coupling strip. The results show that the coupling-fed loop antenna achieved a gain of 1.6 dBi and radiation efficiency of 85 % at 2.45 GHz in a very compact size (0.03 λ0 × 0.04 λ0). In addition, the occupied area of the antenna is very small (4.4%) compared to the overall area of the package; therefore, the proposed method is very useful for package antenna design. The detailed parameters studies are presented, which demonstrate the feasibility of the proposed method.


Relationship between procrastination and academic performance among a group of undergraduate dental students in India.

Procrastination, generally defined as a voluntary, irrational delay of behavior, is a prevalent phenomenon among college students throughout the world and occurs at alarmingly high rates. For this study, a survey was conducted of 209 second-, third-, and fourth-year undergraduate dental students of Bapuji Dental College and Hospital, Davangere, India, to identify the relationship between their level of procrastination and academic performance. A sixteen-item questionnaire was used to assess the level of procrastination among these students. Data related to their academic performance were also collected. Spearman's correlation coefficient test was used to assess the relationship between procrastination and academic performance. It showed a negative correlation of -0.63 with a significance level of p<0.01 (two-tailed test), indicating that students who showed high procrastination scores performed below average in their academics. In addition, analysis with the Mann-Whitney U test found a significant difference in procrastination scores between the two gender groups (p<0.05). Hence, among the Indian undergraduate dental students evaluated in this study, it appeared that individuals with above average and average academic performance had lower scores of procrastination and vice versa.


Detecting cyberbullying: query terms and techniques

In this paper we describe a close analysis of the language used in cyberbullying. We take as our corpus a collection of posts from Formspring.me. Formspring.me is a social networking site where users can ask questions of other users. It appeals primarily to teens and young adults and the cyberbullying content on the site is dense; between 7% and 14% of the posts we have analyzed contain cyberbullying content.
 The results presented in this article are two-fold. Our first experiments were designed to develop an understanding of both the specific words that are used by cyberbullies, and the context surrounding these words. We have identified the most commonly used cyberbullying terms, and have developed queries that can be used to detect cyberbullying content. Five of our queries achieve an average precision of 91.25% at rank 100.
 In our second set of experiments we extended this work by using a supervised machine learning approach for detecting cyberbullying. The machine learning experiments identify additional terms that are consistent with cyberbullying content, and identified an additional querying technique that was able to accurately assign scores to posts from Formspring.me. The posts with the highest scores are shown to have a high density of cyberbullying content.


The Magma Algebra System I: The User Language

Magma is a new software system for computational algebra, the design of which is based on the twin concepts of algebraic structure and morphism. The design is intended to provide a mathematically rigorous environment for computing with algebraic structures (groups, rings, fields, modules and algebras), geometric structures (varieties, special curves) and combinatorial structures (graphs, designs and codes). The philosophy underlying the design of Magma is based on concepts from Universal Algebra and Category Theory. Key ideas from these two areas provide the basis for a general scheme for the specification and representation of mathematical structures. The user language includes three important groups of constructors that realize the philosophy in syntactic terms: structure constructors, map constructors and set constructors. The utility of Magma as a mathematical tool derives from the combination of its language with an extensive kernel of highly efficient C implementations of the fundamental algorithms for most branches of computational algebra. In this paper we outline the philosophy of the Magma design and show how it may be used to develop an algebraic programming paradigm for language design. In a second paper we will show how our design philosophy allows us to realize natural computational “environments” for different branches of algebra. An early discussion of the design of Magma may be found in Butler and Cannon (1989, 1990). A terse overview of the language together with a discussion of some of the implementation issues may be found in Bosma et al. (1994).


Quadcopter control system

The paper presents two different types of approach for mathematical modeling of quadcopter kinematics and dynamics. The first one is based on the equations of classical mechanics and the other one is derived from Denavit-Hartenberg formalism and Lagrangian mechanics. The obtained models were used to design the control of the quadcopter motion on one rotation axis. The paper also offers some details of the physical and software implementation of the system on which tests have been made.


Recognition of Information Leakage of Computer via Conducted Emanations on the Power Line

The security problem of screen image leakage from a display unit has become serious with the rapid speed of signal transmission technology. This paper presents a novel investigation on the characteristics of the power line compromising channel. Moreover, a measurement system has been actually developed for the leakage signal analyzing and image reconstruction. In order to overcome the degradation of reconstructed motion images and enhance the reconstructed image quality, a multi-image blind deconvolution method was proposed and test experiments were carried out to verify the effectiveness of the multi-image blind deconvolution algorithm based on the conducted signal from the power line.


Out-of-domain FrameNet Semantic Role Labeling

Domain dependence of NLP systems is one of the major obstacles to their application in large-scale text analysis, also restricting the applicability of FrameNet semantic role labeling (SRL) systems. Yet, current FrameNet SRL systems are still only evaluated on a single in-domain test set. For the first time, we study the domain dependence of FrameNet SRL on a wide range of benchmark sets. We create a novel test set for FrameNet SRL based on user-generated web text and find that the major bottleneck for out-of-domain FrameNet SRL is the frame identification step. To address this problem, we develop a simple, yet efficient system based on distributed word representations. Our system closely approaches the state-of-the-art in-domain while outperforming the best available frame identification system out-of-domain. We publish our system and test data for research purposes.1


S-Match: an algorithm and an implementation of semantic matching

We think of Match as an operator which takes two graph-like structures (e.g., conceptual hierarchies or ontologies) and produces a mapping between those nodes of the two graphs that correspond semantically to each other. Semantic matching is a novel approach where semantic correspondences are discovered by computing, and returning as a result, the semantic information implicitly or explicitly codified in the labels of nodes and arcs. In this paper we present an algorithm implementing semantic matching, and we discuss its implementation within the S-Match system. We also test S-Match against three state of the art matching systems. The results, though preliminary, look promising, in particular for what concerns precision and recall.


Behavioral Experiments for Assessing the Abstract Argumentation Semantics of Reinstatement

Argumentation is a very fertile area of research in Artificial Intelligence, and various semantics have been developed to predict when an argument can be accepted, depending on the abstract structure of its defeaters and defenders. When these semantics make conflicting predictions, theoretical arbitration typically relies on ad hoc examples and normative intuition about what prediction ought to be the correct one. We advocate a complementary, descriptive-experimental method, based on the collection of behavioral data about the way human reasoners handle these critical cases. We report two studies applying this method to the case of reinstatement (both in its simple and floating forms). Results speak for the cognitive plausibility of reinstatement and yet show that it does not yield the full expected recovery of the attacked argument. Furthermore, results show that floating reinstatement yields comparable effects to that of simple reinstatement, thus arguing in favor of preferred argumentation semantics, rather than grounded argumentation semantics. Besides their theoretical value for validating and inspiring argumentation semantics, these results have applied value for developing artificial agents meant to argue with human users.


An integrated framework for human activity recognition

This poster presents an integrated framework to enable using standard non-sequential machine learning tools for accurate multi-modal activity recognition. Our framework contains simple pre- and post-classification strategies such as class-imbalance correction on the learning data using structure preserving oversampling, leveraging the sequential nature of sensory data using smoothing of the predicted label sequence and classifier fusion, respectively, for improved performance. Through evaluation on recent publicly-available OPPORTUNITY activity datasets comprising of a large amount of multi-dimensional, continuous-valued sensory data, we show that our proposed strategies are effective in improving the performance over common techniques such as One Nearest Neighbor (1NN) and Support Vector Machines (SVM). Our framework also shows better performance over sequential probabilistic models, such as Conditional Random Field (CRF) and Hidden Markov Models (HMM) and when these models are used as meta-learners.


The five-factor model of personality and managerial performance: validity gains through the use of 360 degree performance ratings.

This study investigated the usefulness of the five-factor model (FFM) of personality in predicting two aspects of managerial performance (task vs. contextual) assessed by utilizing the 360 degree performance rating system. The authors speculated that one reason for the low validity of the FFM might be the failure of single-source (e.g., supervisor) ratings to comprehensively capture the construct of managerial performance. The operational validity of personality was found to increase substantially (50%-74%) across all of the FFM personality traits when both peer and subordinate ratings were added to supervisor ratings according to the multitrait-multimethod approach. Furthermore, the authors responded to the recent calls to validate tests via a multivariate (e.g., multitrait-multimethod) approach by decomposing overall managerial performance into task and contextual performance criteria and by using multiple rating perspectives (sources). Overall, this study contributes to the evidence that personality may be even more useful in predicting managerial performance if the performance criteria are less deficient.


A bias for posterior α-band power suppression versus enhancement during shifting versus maintenance of spatial attention

Voluntarily directing visual attention to a cued position in space leads to improved processing of forthcoming visual stimuli at the attended position, and attenuated processing of competing stimuli elsewhere, due to anticipatory tuning of visual cortex activity. In EEG, recent evidence points to a determining role of modulations of posterior alpha-band activity (8-14 Hz) in such anticipatory facilitation (alpha-power decreases) versus inhibition (alpha-power increases). Yet, while such alpha-modulations are a common finding, the direction of modulation varies to a great extent across studies implying dependence on task demands. Here, we reveal opposite modulation of posterior alpha-power with early/initiation versus later/sustained processes of anticipatory attention orienting. Marked alpha-decreases were observed during shifting of attention (initial 700 ms) over occipito-parietal areas processing to-be-attended visual space, while alpha-increases dominated in the subsequent maintenance phase (>700 ms) over occipito-parietal cortex tuned to unattended positions. Notably, the presence of alpha-modulation strongly depended on individual resting alpha-power. Overall, this provides further support to an active facilitative versus inhibitory role of alpha-power decreases and increases and suggests that these attention-related changes are differentially deployed during anticipatory attention orienting to prepare versus maintain the cortex for optimal target processing.


A Novel Method for Accurate and Efficient Barcode Detection with Morphological Operations

Barcode technology is the pillar of automatic identification, that is used in a wide range of real-time applications with various types of codes. The different types of codes and applications impose special problems, so there is a continuous need for solutions with improved effectiveness. There are several methods for barcode localization, that are well characterized by accuracy and speed. Particularly, high-speed processing places need automatic barcode localization, e.g. conveyor belts, automated production, where missed detections cause loss of profit. In this paper, we mainly deal with segmentation of images with 1D barcode, but also analyze the operation of different methods for 2D barcode images as well. Our goal is to detect automatically, rapidly and accurately the barcode location by the help of extracted features. We compare some published method from the literature, which basically rely on the contrast between the background and the shape that represent the code. We also propose a novel algorithm, that outperforms the others in both accuracy and efficiency in detecting 1D codes.


A Two-Layered Permission-Based Android Malware Detection Scheme

Android platform has become the main target of the malware developers in the past few years. One of Android's main defense mechanisms against malicious apps is a permission-based access control mechanism. It is a feasible approach to detect a potential malicious application based on the permissions it requested. In this paper, we proposed a two-layered permission based detection scheme for detecting malicious Android applications. Comparing with previous researches, we consider the apps requested permission pairs as the additional condition, and we also consider used permissions to improve detection accuracy. The result of an evaluation, performed using 28548 benign apps and 1536 malicious apps, indicates that a two-layered permission-based detector has high detection rate of malware.