Understanding Difficulties and Resulting Confusion in Learning: An Integrative Review. Difficulties are often an unavoidable but important part of the learning process. This seems particularly so for complex conceptual learning. Challenges in the learning process are however, particularly difficult to detect and respond to in educational environments where growing class sizes and the increased use of digital technologies mean that teachers are unable to provide nuanced and personalized feedback and support to help students overcome their difficulties. Individual differences, the specifics of the learning activity, and the difficulty of giving individual feedback in large classes and digital environments all add to the challenge of responding to student difficulties and confusion. In this integrative review, we aim to explore difficulties and resulting emotional responses in learning. We will review the primary principles of cognitive disequilibrium and contrast these principles with work on desirable difficulties, productive failure, impasse driven learning, and pure discovery-based learning. We conclude with a theoretical model of the zones of optimal and sub-optimal confusion as a way of conceptualizing the parameters of productive and non-productive difficulties experienced by students while they learn.

Question: What is the difference between cognitive disequilibrium and desirable difficulties?


Design of Multimode Net-Type Resonators and Their Applications to Filters and Multiplexers. Net-type resonators with dual- and tri-mode electrical behaviors have been presented and analyzed theoretically in this paper. The proposed net-type resonator is constructed by a short-ended and several open-ended transmission-line sections, which has the advantages of small size and more flexible resonant frequency allocation and is therefore particularly suitable for applications to the design of microwave devices. To verify the usefulness of the proposed resonators, three experimental examples, including a dual-mode bandpass filter, a dual-passband filter, and a triplexer, have been designed and fabricated with microstrip technology. Each of the designed circuits occupies a very small size and has a good upper stop and performance. All measured results are in good agreement with the full-wave simulation results.

Question: What is a net-type resonator?


THE RESTORATIVE BENEFITS OF NATURE : TOWARD AN INTEGRATIVE. Directed attention plays an important role in human information processing; its fatigue, in turn, has far reaching consequences. Attention Restoration Theory provides an analysis of the kinds of experiences that lead to recovery from such fatigue. Natural environments turn out to be particularly rich in the character istics necessary for restorative experiences. An integrative framework is proposed that places both directed attention and stress in the larger context of human-environment relationships. © 1995 Academic Press Limited

Question: What is the restorative benefits of nature?


SASI: A New Ultralightweight RFID Authentication Protocol Providing Strong Authentication and Strong Integrity. As low-cost RFIDs become more and more popular, it is imperative to design ultralightweight RFID authentication protocols to resist all possible attacks and threats. However, all of the previous ultralightweight authentication schemes are vulnerable to various attacks. In this paper, we propose a new ultralightweight RFID authentication protocol that provides strong authentication and strong integrity protection of its transmission and of updated data. The protocol requires only simple bit-wise operations on the tag and can resist all the possible attacks. These features make it very attractive to low-cost RFIDs and very low-cost RFIDs.

Question: What is the ultralightweight authentication protocol?


End-to-End Waveform Utterance Enhancement for Direct Evaluation Metrics Optimization by Fully Convolutional Neural Networks. Speech enhancement model is used to map a noisy speech to a clean speech. In the training stage, an objective function is often adopted to optimize the model parameters. However, in the existing literature, there is an inconsistency between the model optimization criterion and the evaluation criterion for the enhanced speech. For example, in measuring speech intelligibility, most of the evaluation metric is based on a short-time objective intelligibility STOI measure, while the frame based mean square error MSE between estimated and clean speech is widely used in optimizing the model. Due to the inconsistency, there is no guarantee that the trained model can provide optimal performance in applications. In this study, we propose an end-to-end utterance-based speech enhancement framework using fully convolutional neural networks FCN to reduce the gap between the model optimization and the evaluation criterion. Because of the utterance-based optimization, temporal correlation information of long speech segments, or even at the entire utterance level, can be considered to directly optimize perception-based objective functions. As an example, we implemented the proposed FCN enhancement framework to optimize the STOI measure. Experimental results show that the STOI of a test speech processed by the proposed approach is better than conventional MSE-optimized speech due to the consistency between the training and the evaluation targets. Moreover, by integrating the STOI into model optimization, the intelligibility of human subjects and automatic speech recognition system on the enhanced speech is also substantially improved compared to those generated based on the minimum MSE criterion.

Question: How to optimize the model parameters?


An empirical comparison of classification algorithms for mortgage default prediction: evidence from a distressed mortgage market. This paper evaluates the performance of a number of modelling approaches for future mortgage default status. Boosted regression trees, random forests, penalised linear and semi-parametric logistic regression models are applied to four portfolios of over 300,000 Irish owner-occupier mortgages. The main findings are that the selected approaches have varying degrees of predictive power and that boosted regression trees significantly outperform logistic regression. This suggests that boosted regression trees can be a useful addition to the current toolkit for mortgage credit risk assessment by banks and regulators.

Question: What is the main finding of this paper?


Multi-agent quadrotor testbed control design: integral sliding mode vs. reinforcement learning. The Stanford Testbed of Autonomous Rotorcraft for Multi-Agent Control (STARMAC) is a multi-vehicle testbed currently comprised of two quadrotors, also called X4-flyers, with capacity for eight. This paper presents a comparison of control design techniques, specifically for outdoor altitude control, in and above ground effect, that accommodate the unique dynamics of the aircraft. Due to the complex airflow induced by the four interacting rotors, classical linear techniques failed to provide sufficient stability. Integral sliding mode and reinforcement learning control are presented as two design techniques for accommodating the nonlinear disturbances. The methods both result in greatly improved performance over classical control techniques.

Question: What is the difference between integral sliding mode and reinforcement learning?


A Bayesian Classification Model for Fraud Detection over ATM Platforms Milgo , Carolyne. The banking system relies greatly on the use of Automated Teller Machines, debit and credit cards as a vital element of its payment processing systems. The efficient functioning of payment processing systems allows transactions to be completed safely and on time thereby contributing to operational performance. Customer accounts are equally exposed to risk with respect to fraud. The major security risk identified is identity and access management of customer funds. This research sought to use classification techniques to implement a novel fraud detection solution to establish legitimacy of customer transactions. It also sought to identify the main security issues encountered with use of ATM cards and establish internal control mechanisms which can be implemented to deter possibility of card fraud. The data was obtained from a local bank and was consequently preprocessed and fed into WEKA which was used to develop the training model which was to be used for classification of incoming transactions.

Question: What is a Bayesian Classification Model?


Proactive avoidance of moving obstacles for a service robot utilizing a behavior-based control. A main challenge in the application of service robotics is safe and reliable navigation of robots in human everyday environments. Supermarkets, which are chosen here as an example, pose a challenging scenario because they usually have a cluttered and nested character. The robot has to avoid collisions with static and even with moving obstacles while interacting with nearby humans or a dedicated user respectively. This paper presents a hierarchical approach for the proactive avoidance of moving objects as it is used on the robot shopping trolley InBOT. The behavior-based control (bbc) of InBOT is extended by a reflex and a reactive behavior to ensure adequate reaction times when confronted with a possible collision. On top of the bbc a spatio-temporal planner is situated which is able to predict environmental changes and therefore can generate a safe movement sequence accordingly.

Question: What is a behavior-based control?


Lightweight eye capture using a parametric model. Facial scanning has become ubiquitous in digital media, but so far most efforts have focused on reconstructing the skin. Eye reconstruction, on the other hand, has received only little attention, and the current state-of-the-art method is cumbersome for the actor, time-consuming, and requires carefully setup and calibrated hardware. These constraints currently make eye capture impractical for general use. We present the first approach for high-quality lightweight eye capture, which leverages a database of pre-captured eyes to guide the reconstruction of new eyes from much less constrained inputs, such as traditional single-shot face scanners or even a single photo from the internet. This is accomplished with a new parametric model of the eye built from the database, and a novel image-based model fitting algorithm. Our method provides both automatic reconstructions of real eyes, as well as artistic control over the parameters to generate user-specific eyes.

Question: What is the first approach for high-quality lightweight eye capture?


Structured Feature Learning for Pose Estimation. In this paper, we propose a structured feature learning framework to reason the correlations among body joints at the feature level in human pose estimation. Different from existing approaches of modeling structures on score maps or predicted labels, feature maps preserve substantially richer descriptions of body joints. The relationships between feature maps of joints are captured with the introduced geometrical transform kernels, which can be easily implemented with a convolution layer. Features and their relationships are jointly learned in an end-to-end learning system. A bi-directional tree structured model is proposed, so that the feature channels at a body joint can well receive information from other joints. The proposed framework improves feature learning substantially. With very simple post processing, it reaches the best mean PCP on the LSP and FLIC datasets. Compared with the baseline of learning features at each joint separately with ConvNet, the mean PCP has been improved by 18% on FLIC. The code is released to the public.

Question: How to learn features for human pose estimation?


Deep Reinforcement Learning for Multi-Resource Multi-Machine Job Scheduling. Minimizing job scheduling time is a fundamental issue in data center networks that has been extensively studied in recent years. The incoming jobs require different CPU and memory units, and span different number of time slots. The traditional solution is to design efficient heuristic algorithms with performance guarantee under certain assumptions. In this paper, we improve a recently proposed job scheduling algorithm using deep reinforcement learning and extend it to multiple server clusters. Our study reveals that deep reinforcement learning method has the potential to outperform traditional resource allocation algorithms in a variety of complicated environments.

Question: What is the potential of deep reinforcement learning in job scheduling?


Students' opinions on blended learning and its implementation in terms of their learning styles. The purpose of this article is to examine students' views on the blended learning method and its use in relation to the students' individual learning style. The study was conducted with 31 senior students. Web based media together with face to face classroom settings were used in the blended learning framework. A scale of Students' Views on Blended Learning and its implementation, Kolb's Learning Style Inventory, Pre-Information Form and open ended questions were used to gather data. The majority of the students' fell into assimilators, accommodators and convergers learning styles. Results revealed that students' views on blended learning method and its use are quite positive.

Question: What are the students' views on blended learning?


Ravenclaw: dialog management using hierarchical task decomposition and an expectation agenda. We describe RavenClaw, a new dialog management framework developed as a successor to the Agenda [1] architecture used in the CMU Communicator. RavenClaw introduces a clear separation between task and discourse behavior specification, and allows rapid development of dialog management components for spoken dialog systems operating in complex, goal-oriented domains. The system development effort is focused entirely on the specification of the dialog task, while a rich set of domain-independent conversational behaviors are transparently generated by the dialog engine. To date, RavenClaw has been applied to five different domains allowing us to draw some preliminary conclusions as to the generality of the approach. We briefly describe our experience in developing these systems.

Question: What is a spoken dialog system?


DPP-Net: Device-Aware Progressive Search for Pareto-Optimal Neural Architectures. Recent breakthroughs in Neural Architectural Search (NAS) have achieved state-of-the-art performances in applications such as image classification and language modeling. However, these techniques typically ignore device-related objectives such as inference time, memory usage, and power consumption. Optimizing neural architecture for devicerelated objectives is immensely crucial for deploying deep networks on portable devices with limited computing resources. We propose DPPNet: Device-aware Progressive Search for Pareto-optimal Neural Architectures, optimizing for both device-related (e.g., inference time and memory usage) and device-agnostic (e.g., accuracy and model size) objectives. DPP-Net employs a compact search space inspired by current state-of-the-art mobile CNNs, and further improves search efficiency by adopting progressive search (Liu et al. 2017). Experimental results on CIFAR-10 are poised to demonstrate the effectiveness of Pareto-optimal networks found by DPP-Net, for three different devices: (1) a workstation with Titan X GPU, (2) NVIDIA Jetson TX1 embedded system, and (3) mobile phone with ARM Cortex-A53. Compared to CondenseNet and NASNet (Mobile), DPP-Net achieves better performances: higher accuracy & shorter inference time on various devices. Additional experimental results show that models found by DPP-Net also achieve considerablygood performance on ImageNet as well.

Question: What is the difference between NASNet and DPP-Net?


Visual Descriptors for Dense Tensor Fields in Computational Turbulent Combustion: A Case Study. Simulation and modeling of turbulent flow, and of turbulent reacting flow in particular, involve solving for and analyzing time-dependent and spatially dense tensor quantities, such as turbulent stress tensors. The interactive visual exploration of these tensor quantities can effectively steer the computational modeling of combustion systems. In this article, the authors analyze the challenges in dense symmetric-tensor visualization as applied to turbulent combustion calculation; most notable among these challenges are the dataset size and density. They analyze, together with domain experts, the feasibility of using several established tensor visualization techniques in this application domain. They further examine and propose visual descriptors for volume rendering of the data. Of these novel descriptors, one is a density-gradient descriptor which results in Schlieren-style images, and another one is a classification descriptor inspired by machine-learning techniques. The result is a hybrid visual analysis tool to be utilized in the debugging, benchmarking and verification of models and solutions in turbulent combustion. The authors demonstrate this analysis tool on two example configurations, report feedback from combustion researchers, and summarize the design lessons learned. c © 2016 Society for Imaging Science and Technology. [DOI: 10.2352/J.ImagingSci.Technol.2016.60.1.010404] INTRODUCTION Computational simulation of turbulent combustion for gas turbine design has become increasingly important in the last two decades, due in part to environmental concerns and regulations on toxic emissions. Such modern gas turbine designs feature a variety of mixing fuel compositions and possible flow configurations,1,2 which make non-computational simulations difficult. The focus of the computational research effort in this direction is on the development of computational tools fo

Question: What is the difference between a computational and non-computational simulation?


Grammar Inference Using Recurrent Neural Networks. This paper compares the performance of two recurrent neural network models learning the grammar of simple English sentences and introduces a method for creating useful negative training examples from a set of grammatical sentences. The learning task is cast as a classification problem as opposed to a prediction problem; networks are taught to classify a given sequence as grammatical or ungrammatical. The two neural network models compared are the simple architecture proposed by Elman in [1], and the Long Short-Term Memory (LSTM) network from [4]. For comparison, a Naive Bayes (NB) classifier is also trained with bigrams to accomplish this task. Only the Elman network learns the training data better than NB, and neither network generalizes better than NB on sentences longer than the training sentences. The LSTM network does not generalize as well as the Elman network on a simple regular grammar, but shows comparable ability to generalize knowledge of English Grammar.

Question: What is the difference between a naive bayes classifier and a recurrent neural network?


Does Twitter language reliably predict heart disease? A commentary on Eichstaedt et al. (2015a). We comment on Eichstaedt et al.'s (2015a) claim to have shown that language patterns among Twitter users, aggregated at the level of US counties, predicted county-level mortality rates from atherosclerotic heart disease (AHD), with "negative" language being associated with higher rates of death from AHD and "positive" language associated with lower rates. First, we examine some of Eichstaedt et al.'s apparent assumptions about the nature of AHD, as well as some issues related to the secondary analysis of online data and to considering counties as communities. Next, using the data files supplied by Eichstaedt et al., we reproduce their regression- and correlation-based models, substituting mortality from an alternative cause of death-namely, suicide-as the outcome variable, and observe that the purported associations between "negative" and "positive" language and mortality are reversed when suicide is used as the outcome variable. We identify numerous other conceptual and methodological limitations that call into question the robustness and generalizability of Eichstaedt et al.'s claims, even when these are based on the results of their ridge regression/machine learning model. We conclude that there is no good evidence that analyzing Twitter data in bulk in this way can add anything useful to our ability to understand geographical variation in AHD mortality rates.

Question: What is the difference between a commentary and a critique?


Recognition of Offline Handwritten Chinese Characters Using the Tesseract Open Source OCR Engine. Due to the complex structure and handwritten deformation, the offline handwritten Chinese characters recognition has been one of the most challenging problems. In this paper, an offline handwritten Chinese character recognition tool has been developed based on the Tesseract open source OCR engine. The tool mainly contributes on the following two points: First, a handwritten Chinese character features library is generated, which is independent of a specific user's writing style, Second, by preprocessing the input image and adjusting the Tesseract engine, multiple candidate recognition results are output based on weight ranking. The recognition accuracy rate of this tool is above 88% for both known user test set and unknown user test set. It has shown that the Tesseract engine is feasible for offline handwritten Chinese character recognition to a certain degree.

Question: What is the offline handwritten Chinese character recognition?


Holographic artificial impedance surfaces for conformal antennas. We have developed a method for generating arbitrary radiation patterns from antennas on complex objects. The object is coated with an artificial impedance surface consisting of a lattice of sub-wavelength metal patches on a grounded dielectric substrate. The effective surface impedance depends on the size of the patches, and can be varied as a function of position. Using holography, the surface impedance is designed to generate any desired radiation pattern from currents in the surface. With this technique we can create antennas with novel properties such as radiation toward angles that would otherwise be shadowed

Question: What is a holographic artificial impedance surface?


Paraphrasing Out-of-Vocabulary Words with Word Embeddings and Semantic Lexicons for Low Resource Statistical Machine Translation. Out-of-vocabulary (OOV) word is a crucial problem in statistical machine translation (SMT) with low resources. OOV paraphrasing that augments the translation model for the OOV words by using the translation knowledge of their paraphrases has been proposed to address the OOV problem. In this paper, we propose using word embeddings and semantic lexicons for OOV paraphrasing. Experiments conducted on a low resource setting of the OLYMPICS task of IWSLT 2012 verify the effectiveness of our proposed method.

Question: What is the OLYMPICS task of IWSLT 2012?


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 re

Question: What is the difference between DRAM and FLY-DRAM?


Stability and Generalization. We define notions of stability for learning algorithms and show how to use these notions to derive generalization error bounds based on the empirical error and the leave-one-out error. The methods we use can be applied in the regression framework as well as in the classification one when the classifier is obtained by thresholding a real-valued function. We study the stability properties of large classes of learning algorithms such as regularization based algorithms. In particular we focus on Hilbert space regularization and Kullback-Leibler regularization. We demonstrate how to apply the results to SVM for regression and classification.

Question: What is the stability of learning algorithms?


An Introduction to Independent Component Analysis: InfoMax and FastICA algorithms. This paper presents an introduction to independent component analysis (ICA). Unlike principal component analysis, which is based on the assumptions of uncorrelatedness and normality, ICA is rooted in the assumption of statistical independence. Foundations and basic knowledge necessary to understand the technique are provided hereafter. Also included is a short tutorial illustrating the implementation of two ICA algorithms (FastICA and InfoMax) with the use of the Mathematica software.

Question: What is the difference between ICA and PCA?


Analysis of a Wavelength-Scaled Array (WSA) Architecture. Wavelength-scaled array architectures use scaled elements to achieve ultrawideband performance with significantly fewer overall radiators than traditional ultrawideband arrays based on a single element type. Compared to a conventional ultrawideband array with 8:1 bandwidth, a wavelength-scaled array that uses elements of three different sizes creates an aperture with fewer than 16% of the original element count, i.e., 6.4-times fewer elements, and by extension a comparable reduction in electronics required to feed the array. In this paper, a study of an asymmetric wavelength-scaled array architecture is presented for finite arrays of offset-centered dual-polarized flared-notch radiators. The unique element transitions within the finite array structure are modeled via a non-matching grid Domain Decomposition-Finite Element Method that allows for rigorous impedance and radiation pattern prediction of full-sized wavelength-scaled arrays. This design study shows that the wavelength-scaled array has comparable performance to traditional ultrawideband arrays in terms of VSWR, radiation patterns, array mismatch efficiency, and cross-polarization levels.

Question: What is the difference between a wavelength-scaled array and a traditional ultrawideband array?


Blockchain as a Platform for Secure Inter-Organizational Business Processes. Today, most of the services one may think of are based on a collaborative paradigm (e.g., social media services, IoT-based services, etc.). One of the most relevant representative of such class of services are inter-organizational processes, where an organized group of joined activities is carried out by two or more organizations to achieve a common business goal. Inter-organizational processes are therefore vital to achieve business partnerships among different organizations. However, they may also pose serious security and privacy threats to the data each organization exposes. This is mainly due to the weak trust relationships that may hold among the collaborating parties, which result in a potential lack of trust on how data/operations are managed. In this paper, we discuss, how blockchain, one of today hottest technology, can be used in support of secure inter-organizational processes. We further point out which additional security issues the use of blockchain can bring, illustrate the ongoing research projects in the area and discuss future research directions.

Question: How can blockchain be used in support of secure inter-organizational processes?


Nursing bedside clinical handover - an integrated review of issues and tools.. AIMS AND OBJECTIVES This article reviews the available literature that supports implementing bedside clinical handover in nursing clinical practice and then seeks to identify key issues if any.   BACKGROUND Clinical handover practices are recognised as being an essential component in the effective transfer of clinical care between health practitioners. It is recognised that the point where a patient is 'handed over' from one clinician to another is significant in maintaining continuity of care and that doing this poorly can have significant safety issues for the patient.   DESIGN An integrated literature review.   METHOD A literature review of 45 articles was undertaken to understand bedside clinical handover and the issues related to the implementation of this process.   RESULTS It was identified that there are a number of clinical handover mnemonics available that provide structure to the process and that areas such as confidentiality, inclusion of the patient/carer and involving the multidisciplinary team remain topical issues for practitioners in implementing good clinical handover practices.   CONCLUSIONS This literature review identified a lack of literature available about the transfer of responsibility and accountability during clinical handover and auditing practices of the clinical handover process. The nurses were more concerned about confidentiality issues than were patients. The use of a structured tool was strongly supported; however, no one singular tool was considered suitable for all clinical areas.   RELEVANCE TO CLINICAL PRACTICE Nursing clinicians seeking to implement best practice within their professional speciality should consider some of the issues raised within this article and seek to address these issues by developing strategies to overcome them.

Question: What is the difference between a mnemonic and a tool?


Music Synthesis with Reconstructive Phrase Modeling. This article describes a new synthesis technology called reconstructive phrase modeling (RPM). A goal of RPM is to combine the realistic sound quality of sampling with the performance interaction of functional synthesis. Great importance is placed on capturing the dynamics of note transitions-slurs, legato, bow changes, etc. Expressive results are achieved with conventional keyboard controllers. Mastery of special performance techniques is not needed. RPM is an analysis-synthesis system that is related to two important trends in computer music research. The first is a form of additive synthesis in which sounds are represented as a sum of time-varying harmonics plus noise elements. RPM creates expressive performances by searching a database of idiomatic instrumental phrases and combining modified fragments of these phrases to form a new expressive performance. This approach is related to another research trend called concatenative synthesis

Question: What is a database of idiomatic instrumental phrases?


Unsupervised Training Set Generation for Automatic Acquisition of Technical Terminology in Patents. NLP methods for automatic information access to rich technological knowledge sources like patents are of great value. One important resource for accessing this knowledge is the technical terminology of the patent domain. In this paper, we address the problem of automatic terminology acquisition (ATA), i.e., the problem of automatically identifying all technical terms in a document. We analyze technical terminology in patents and define the concept of technical term based on the analysis. We present a novel method for labeling large amounts of high-quality training data for ATA in an unsupervised fashion. We train two ATA methods on this training data, a term candidate classifier and a conditional random field (CRF), and investigate the utility of different types of features. Finally, we show that our method of automatically generating training data is effective and the two ATA methods successfully generalize, considerably increasing recall while preserving high precision relative to a state-of-the-art baseline.

Question: What is the difference between a term candidate classifier and a conditional random field?


Micropower energy harvesting. More than a decade of research in the field of thermal, motion, vibration and electromagnetic radiation energy harvesting has yielded increasing power output and smaller embodiments. Power management circuits for rectification and DC–DC conversion are becoming able to efficiently convert the power from these energy harvesters. This paper summarizes recent energy harvesting results and their power man-

Question: What is the power of a micro power harvester?


Direct Torque and Indirect Flux Control of Brushless DC Motor. In this paper, the position-sensorless direct torque and indirect flux control of brushless dc (BLDC) motor with nonsinusoidal back electromotive force (EMF) has been extensively investigated. In the literature, several methods have been proposed for BLDC motor drives to obtain optimum current and torque control with minimum torque pulsations. Most methods are complicated and do not consider the stator flux linkage control, therefore, possible high-speed operations are not feasible. In this study, a novel and simple approach to achieve a low-frequency torque ripple-free direct torque control (DTC) with maximum efficiency based on dq reference frame is presented. The proposed sensorless method closely resembles the conventional DTC scheme used for sinusoidal ac motors such that it controls the torque directly and stator flux amplitude indirectly using d-axis current. This method does not require pulsewidth modulation and proportional plus integral regulators and also permits the regulation of varying signals. Furthermore, to eliminate the low-frequency torque oscillations, two actual and easily available line-to-line back EMF constants ( kba and kca) according to electrical rotor position are obtained offline and converted to the dq frame equivalents using the new line-to-line park transformation. Then, they are set up in the look-up table for torque estimation. The validity and practical applications of the proposed sensorless three-phase conduction DTC of BLDC motor drive scheme are verified through simulations and experimental results.

Question: How to control the torque ripple of a brushless dc motor?


Some Digital Communication Fundamentals for Physicists and Others. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission.

Question: How to make a digital copy of a book?


Simple Method for High-Performance Digit Recognition Based on Sparse Coding. In this brief paper, we propose a method of feature extraction for digit recognition that is inspired by vision research: a sparse-coding strategy and a local maximum operation. We show that our method, despite its simplicity, yields state-of-the-art classification results on a highly competitive digit-recognition benchmark. We first employ the unsupervised Sparsenet algorithm to learn a basis for representing patches of handwritten digit images. We then use this basis to extract local coefficients. In a second step, we apply a local maximum operation to implement local shift invariance. Finally, we train a support vector machine (SVM) on the resulting feature vectors and obtain state-of-the-art classification performance in the digit recognition task defined by the MNIST benchmark. We compare the different classification performances obtained with sparse coding, Gabor wavelets, and principal component analysis (PCA). We conclude that the learning of a sparse representation of local image patches combined with a local maximum operation for feature extraction can significantly improve recognition performance.

Question: What is the simple method for high-performance digit recognition based on sparse coding?


Deep reinforcement learning with experience replay based on SARSA. SARSA, as one kind of on-policy reinforcement learning methods, is integrated with deep learning to solve the video games control problems in this paper. We use deep convolutional neural network to estimate the state-action value, and SARSA learning to update it. Besides, experience replay is introduced to make the training process suitable to scalable machine learning problems. In this way, a new deep reinforcement learning method, called deep SARSA is proposed to solve complicated control problems such as imitating human to play video games. From the experiments results, we can conclude that the deep SARSA learning shows better performances in some aspects than deep Q learning.

Question: What is the difference between deep reinforcement learning and deep Q learning?


Basic local alignment search tool.. A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score. Recent mathematical results on the stochastic properties of MSP scores allow an analysis of the performance of this method as well as the statistical significance of alignments it generates. The basic algorithm is simple and robust; it can be implemented in a number of ways and applied in a variety of contexts including straightforward DNA and protein sequence database searches, motif searches, gene identification searches, and in the analysis of multiple regions of similarity in long DNA sequences. In addition to its flexibility and tractability to mathematical analysis, BLAST is an order of magnitude faster than existing sequence comparison tools of comparable sensitivity.

Question: What is the basic local alignment search tool?


Foot Step Based Person Identification Using Histogram Similarity and Wavelet Decomposition. Research in person identification and authentication has attracted significant attention from the researchers and scientists. This paper presents a biometric user authentication based on a person's foot step. The advantage of this recognition method over other types of biometrics is that it enables unobtrusive user authentication where other types of biometrics are not available. Firstly the ground reaction force data was extracted using force plate to gather ground reaction force for individuals. Later we utilized the discrete wavelet transform to de-noise the experimental data and in the final step, histograms were used to identify different person's foot step. The experimental results show improvements in identification accuracies compared to previously reported work.

Question: How to extract ground reaction force data?


The synthesis and rendering of eroded fractal terrains. In standard fractal terrain models based on fractional Brownian motion the statistical character of the surface is, by design, the same everywhere. A new approach to the synthesis of fractal terrain height fields is presented which, in contrast to previous techniques, features locally independent control of the frequencies composing the surface, and thus local control of fractal dimension and other statistical characteristics. The new technique, termed noise synthesis, is intermediate in difficulty of implementation, between simple stochastic subdivision and Fourier filtering or generalized stochastic subdivision, and does not suffer the drawbacks of creases or periodicity. Varying the local crossover scale of fractal character or the fractal dimension with altitude or other functions yields more realistic first approximations to eroded landscapes. A simple physical erosion model is then suggested which simulates hydraulic and thermal erosion processes to create gloabl stream/valley networks and talus slopes. Finally, an efficient ray tracing algorithm for general height fields, of which most fractal terrains are a subset, is presented.

Question: How do you make a fractal terrain?


A new approach for robot motion planning using rapidly-exploring Randomized Trees. In the last few years, car-like robots became increasingly important. Thus, motion planning algorithms for this kind of problem are needed more than ever. Unfortunately, this problem is computational difficult and so probabilistic approaches like Probabilistic Roadmaps or Rapidly-exploring Randomized Trees are often used in this context. This paper introduces a new concept for robot motion planning especially for car-like robots based on Rapidly-exploring Randomized Trees. In contrast to the conventional method, the presented approach uses a pre-computed auxiliary path to improve the distribution of random states. The main contribution of this approach is the significantly increased quality of the computed path. A proof-of-concept implementation evaluates the quality and performance of the proposed concept.

Question: What is a robot?


Unsupervised Learning with Mixed Numeric and Nominal Data. ÐThis paper presents a Similarity-Based Agglomerative Clustering (SBAC) algorithm that works well for data with mixed numeric and nominal features. A similarity measure, proposed by Goodall for biological taxonomy [15], that gives greater weight to uncommon feature value matches in similarity computations and makes no assumptions of the underlying distributions of the feature values, is adopted to define the similarity measure between pairs of objects. An agglomerative algorithm is employed to construct a dendrogram and a simple distinctness heuristic is used to extract a partition of the data. The performance of SBAC has been studied on real and artificially generated data sets. Results demonstrate the effectiveness of this algorithm in unsupervised discovery tasks. Comparisons with other clustering schemes illustrate the superior performance of this approach. Index TermsÐAgglomerative clustering, conceptual clustering, feature weighting, interpretation, knowledge discovery, mixed numeric and nominal data, similarity measures, 2 aggregation.

Question: What is the difference between a clustering algorithm and a clustering method?


Understanding mobility based on GPS data. Both recognizing human behavior and understanding a user's mobility from sensor data are critical issues in ubiquitous computing systems. As a kind of user behavior, the transportation modes, such as walking, driving, etc., that a user takes, can enrich the user's mobility with informative knowledge and provide pervasive computing systems with more context information. In this paper, we propose an approach based on supervised learning to infer people's motion modes from their GPS logs. The contribution of this work lies in the following two aspects. On one hand, we identify a set of sophisticated features, which are more robust to traffic condition than those other researchers ever used. On the other hand, we propose a graph-based post-processing algorithm to further improve the inference performance. This algorithm considers both the commonsense constraint of real world and typical user behavior based on location in a probabilistic manner. Using the GPS logs collected by 65 people over a period of 10 months, we evaluated our approach via a set of experiments. As a result, based on the change point-based segmentation method and Decision Tree-based inference model, the new features brought an eight percent improvement in inference accuracy over previous result, and the graph-based post-processing achieve a further four percent enhancement.

Question: What is the difference between a GPS log and a GPS log?


Line-of-sight guidance for formation control of quadrotor. In the broad class of unmanned aerial vehicles (UAV), Quadrotor is an important member. Cooperative control of UAVs are currently being researched for a wide range of applications. An active and important research area of cooperative control of multiple UAVs is the formation control. Formation flying is the disciplined flight of two or more aircraft under the command of a flight leader. The main goal is to coordinate a group of UAVs to achieve a desired formation pattern and avoid inter-vehicle collisions at the same time. Formation flight of a team of UAVs has broad applications such as aerial mapping, traffic monitoring, terrain and utilities inspection, search and rescue operations, perimeter planning, disaster monitoring, reconnaissance mission, surveillance etc. Here we propose a Line Of Sight (LOS) guidance based flight formation control strategy for multiple quad rotor UAVs in leader-follower framework. The guidance laws for 2D formation are implemented with the help of LOS angles, between adjacent quadrotors to keep the current quadrotor's position in formation. The full dynamic model of quadrotor incorporating LOS guidance law is considered for designing PID controller and the simulation analysis was done using MATLAB/ Simulink.

Question: How to control multiple quadrotors?


Road Network Extraction and Intersection Detection From Aerial Images by Tracking Road Footprints. In this paper, a new two-step approach (detecting and pruning) for automatic extraction of road networks from aerial images is presented. The road detection step is based on shape classification of a local homogeneous region around a pixel. The local homogeneous region is enclosed by a polygon, called the footprint of the pixel. This step involves detecting road footprints, tracking roads, and growing a road tree. We use a spoke wheel operator to obtain the road footprint. We propose an automatic road seeding method based on rectangular approximations to road footprints and a toe-finding algorithm to classify footprints for growing a road tree. The road tree pruning step makes use of a Bayes decision model based on the area-to-perimeter ratio (the A/P ratio) of the footprint to prune the paths that leak into the surroundings. We introduce a lognormal distribution to characterize the conditional probability of A/P ratios of the footprints in the road tree and present an automatic method to estimate the parameters that are related to the Bayes decision model. Results are presented for various aerial images. Evaluation of the extracted road networks using representative aerial images shows that the completeness of our road tracker ranges from 84% to 94%, correctness is above 81%, and quality is from 82% to 92%.

Question: How to extract road networks from aerial images?


The Spherical Coil as an Inductor, Shield, or Antenna. The spherical coil is an idealized form of inductor having, on a spherical surface, a single-layer winding of constant axial pitch. Its magnetic field inside is uniform and outside is that of a small coil or magnetic dipole. Its properties exemplify exactly some of the rules that are approximately applicable to practical inductors. Simple formulas are given for self-inductance, mutual inductance, coupling coefficient, effect of iron core, and radiation power factor in free space or sea water. This coil is the basis for evaluating the shielding effect of a closed conductive (nonmagnetic) metal shell. A special winding is described which enables simple and exact computation of self-resonance (the length of wire being just 1/2 wavelength in some cases).

Question: What is the spherical coil?


3D printing of robotic soft actuators with programmable bioinspired architectures. Soft actuation allows robots to interact safely with humans, other machines, and their surroundings. Full exploitation of the potential of soft actuators has, however, been hindered by the lack of simple manufacturing routes to generate multimaterial parts with intricate shapes and architectures. Here, we report a 3D printing platform for the seamless digital fabrication of pneumatic silicone actuators exhibiting programmable bioinspired architectures and motions. The actuators comprise an elastomeric body whose surface is decorated with reinforcing stripes at a well-defined lead angle. Similar to the fibrous architectures found in muscular hydrostats, the lead angle can be altered to achieve elongation, contraction, or twisting motions. Using a quantitative model based on lamination theory, we establish design principles for the digital fabrication of silicone-based soft actuators whose functional response is programmed within the material's properties and architecture. Exploring such programmability enables 3D printing of a broad range of soft morphing structures. 3D-printed soft actuators have limited motion and are far from reaching the level of complexity found in biological systems. Here the authors present a multimaterial 3D printing platform for the fabrication of soft actuators displaying a wide range of motions that are programmable.

Question: What is the difference between a soft actuator and a soft robot?


Gender differences in participation and reward on Stack Overflow. Programming is a valuable skill in the labor market, making the underrepresentation of women in computing an increasingly important issue. Online question and answer platforms serve a dual purpose in this field: they form a body of knowledge useful as a reference and learning tool, and they provide opportunities for individuals to demonstrate credible, verifiable expertise. Issues, such as male-oriented site design or overrepresentation of men among the site's elite may therefore compound the issue of women's underrepresentation in IT. In this paper we audit the differences in behavior and outcomes between men and women on Stack Overflow, the most popular of these Q&A sites. We observe significant differences in how men and women participate in the platform and how successful they are. For example, the average woman has roughly half of the reputation points, the primary measure of success on the site, of the average man. Using an Oaxaca-Blinder decomposition, an econometric technique commonly applied to analyze differences in wages between groups, we find that most of the gap in success between men and women can be explained by differences in their activity on the site and differences in how these activities are rewarded. Specifically, 1) men give more answers than women and 2) are rewarded more for their answers on average, even when controlling for possible confounders such as tenure or buy-in to the site. Women ask more questions and gain more reward per question. We conclude with a hypothetical redesign of the site's scoring system based on these behavioral differences, cutting the reputation gap in half.

Question: What is the gender gap in participation and reward on stackoverflow?


Test-potentiated learning: three independent replications, a disconfirmed hypothesis, and an unexpected boundary condition.. Arnold and McDermott [(2013). Test-potentiated learning: Distinguishing between direct and indirect effects of testing. Journal of Experimental Psychology: Learning, Memory, and Cognition, 39, 940-945] isolated the indirect effects of testing and concluded that encoding is enhanced to a greater extent following more versus fewer practice tests, referred to as test-potentiated learning. The current research provided further evidence for test-potentiated learning and evaluated the covert retrieval hypothesis as an alternative explanation for the observed effect. Learners initially studied foreign language word pairs and then completed either one or five practice tests before restudy occurred. Results of greatest interest concern performance on test trials following restudy for items that were not correctly recalled on the test trials that preceded restudy. Results replicate Arnold and McDermott (2013) by demonstrating that more versus fewer tests potentiate learning when trial time is limited. Results also provide strong evidence against the covert retrieval hypothesis concerning why the effect occurs (i.e., it does not reflect differential covert retrieval during pre-restudy trials). In addition, outcomes indicate that the magnitude of the test-potentiated learning effect decreases as trial length increases, revealing an unexpected boundary condition to test-potentiated learning.

Question: What is test-potentiated learning?


Self-Attentional Acoustic Models. Self-attention is a method of encoding sequences of vectors by relating these vectors to each-other based on pairwise similarities. These models have recently shown promising results for modeling discrete sequences, but they are non-trivial to apply to acoustic modeling due to computational and modeling issues. In this paper, we apply self-attention to acoustic modeling, proposing several improvements to mitigate these issues: First, self-attention memory grows quadratically in the sequence length, which we address through a downsampling technique. Second, we find that previous approaches to incorporate position information into the model are unsuitable and explore other representations and hybrid models to this end. Third, to stress the importance of local context in the acoustic signal, we propose a Gaussian biasing approach that allows explicit control over the context range. Experiments find that our model approaches a strong baseline based on LSTMs with networkin-network connections while being much faster to compute. Besides speed, we find that interpretability is a strength of selfattentional acoustic models, and demonstrate that self-attention heads learn a linguistically plausible division of labor.

Question: What is self-attention?


The survival of direct composite restorations in the management of severe tooth wear including attrition and erosion: A prospective 8-year study.. OBJECTIVES Survival of directly placed composite to restore worn teeth has been reported in studies with small sample sizes, short observation periods and different materials. This study aimed to estimate survival for a hybrid composite placed by one clinician up to 8-years follow-up.   METHODS All patients were referred and recruited for a prospective observational cohort study. One composite was used: Spectrum(®) (DentsplyDeTrey). Most restorations were placed on the maxillary anterior teeth using a Dahl approach.   RESULTS A total of 1010 direct composites were placed in 164 patients. Mean follow-up time was 33.8 months (s.d. 27.7). 71 of 1010 restorations failed during follow-up. The estimated failure rate in the first year was 5.4% (95% CI 3.7-7.0%). Time to failure was significantly greater in older subjects (p=0.005) and when a lack of posterior support was present (p=0.003). Bruxism and an increase in the occlusal vertical dimension were not associated with failure. The proportion of failures was greater in patients with a Class 3 or edge-to-edge incisal relationship than in Class 1 and Class 2 cases but this was not statistically significant. More failures occurred in the lower arch (9.6%) compared to the upper arch (6%) with the largest number of composites having been placed on the maxillary incisors (n=519).   CONCLUSION The worn dentition presents a restorative challenge but composite is an appropriate restorative material.   CLINICAL SIGNIFICANCE This study shows that posterior occlusal support is necessary to optimise survival.

Question: How many composite restorations are placed in Canada each year?


The Role of Smart Meters in Enabling Real-Time Energy Services for Households : The Italian Case. The Smart Meter (SM) is an essential tool for successful balancing the demand-offer energy curve. It allows the linking of the consumption and production measurements with the time information and the customer's identity, enabling the substitution of flat-price billing with smarter solutions, such as Time-of-Use or Real-Time Pricing. In addition to sending data to the energy operators for billing and monitoring purposes, Smart Meters must be able to send the same data to customer devices in near-real-time conditions, enabling new services such as instant energy awareness and home automation. In this article, we review the ongoing situation in Europe regarding real-time services for the final customers. Then, we review the architectural and technological options that have been considered for the roll-out phase of the Italian second generation of Smart Meters. Finally, we identify a collection of use cases, along with their functional and performance requirements, and discuss what architectures and communications technologies can meet these requirements.

Question: What is the role of smart meters in enabling real-time energy services for households?


Phishing detection based Associative Classification data mining. Website phishing is considered one of the crucial security challenges for the online community due to the massive numbers of online transactions performed on a daily basis. Website phishing can be described as mimicking a trusted website to obtain sensitive information from online users such as usernames and passwords. Black lists, white lists and the utilisation of search methods are examples of solutions to minimise the risk of this problem. One intelligent approach based on data mining called Associative Classification (AC) seems a potential solution that may effectively detect phishing websites with high accuracy. According to experimental studies, AC often extracts classifiers containing simple ''If-Then'' rules with a high degree of predictive accuracy. In this paper, we investigate the problem of website phishing using a developed AC method called Multi-label Classifier based Associative Classification (MCAC) to seek its applicability to the phishing problem. We also want to identify features that distinguish phishing websites from legitimate ones. In addition, we survey intelligent approaches used to handle the phishing problem. Experimental results using real data collected from different sources show that AC particularly MCAC detects phishing websites with higher accuracy than other intelligent algorithms. Further, MCAC generates new hidden knowledge (rules) that other algorithms are unable to find and this has improved its classifiers predictive performance. 2014 Elsevier Ltd. All rights reserved.

Question: What is phishing?


ETHEREUM: A SECURE DECENTRALISED GENERALISED TRANSACTION LEDGER. The blockchain paradigm when coupled with cryptographically-secured transactions has demonstrated its utility through a number of projects, with Bitcoin being one of the most notable ones. Each such project can be seen as a simple application on a decentralised, but singleton, compute resource. We can call this paradigm a transactional singleton machine with shared-state. Ethereum implements this paradigm in a generalised manner. Furthermore it provides a plurality of such resources, each with a distinct state and operating code but able to interact through a message-passing framework with others. We discuss its design, implementation issues, the opportunities it provides and the future hurdles we envisage.

Question: What is the difference between a singleton machine and a generalised machine?


Changes in adult attachment styles in American college students over time: a meta-analysis.. The current article examines changes over time in a commonly used measure of adult attachment style. A cross-temporal meta-analysis was conducted on 94 samples of American college students (total N = 25,243, between 1988 and 2011) who chose the most representative description of four possible attachment styles (Secure, Dismissing, Preoccupied, and Fearful) on the Relationship Questionnaire. The percentage of students with Secure attachment styles has decreased in recent years (1988: 48.98%; 2011: 41.62%), whereas the percentage of students with Insecure attachment styles (sum of Dismissing, Preoccupied, Fearful) has increased in recent years (1988: 51.02%; 2011: 58.38%). The percentage of students with Dismissing attachment styles has increased over time (1988: 11.93%; 2011: 18.62%), even after controlling for age, gender, race, and publication status. Positive views of others have declined across the same time period. We discuss possible implications and explanations for these changes.

Question: What is the percentage of students with secure attachment styles?


Gabor-based kernel PCA with fractional power polynomial models for face recognition. This paper presents a novel Gabor-based kernel principal component analysis (PCA) method by integrating the Gabor wavelet representation of face images and the kernel PCA method for face recognition. Gabor wavelets first derive desirable facial features characterized by spatial frequency, spatial locality, and orientation selectivity to cope with the variations due to illumination and facial expression changes. The kernel PCA method is then extended to include fractional power polynomial models for enhanced face recognition performance. A fractional power polynomial, however, does not necessarily define a kernel function, as it might not define a positive semidefinite Gram matrix. Note that the sigmoid kernels, one of the three classes of widely used kernel functions (polynomial kernels, Gaussian kernels, and sigmoid kernels), do not actually define a positive semidefinite Gram matrix either. Nevertheless, the sigmoid kernels have been successfully used in practice, such as in building support vector machines. In order to derive real kernel PCA features, we apply only those kernel PCA eigenvectors that are associated with positive eigenvalues. The feasibility of the Gabor-based kernel PCA method with fractional power polynomial models has been successfully tested on both frontal and pose-angled face recognition, using two data sets from the FERET database and the CMU PIE database, respectively. The FERET data set contains 600 frontal face images of 200 subjects, while the PIE data set consists of 680 images across five poses (left and right profiles, left and right half profiles, and frontal view) with two different facial expressions (neutral and smiling) of 68 subjects. The effectiveness of the Gabor-based kernel PCA method with fractional power polynomial models is shown in terms of both absolute performance indices and comparative performance against the PCA method, the kernel P

Question: What is the difference between kernel PCA and kernel PCA with fractional power polynomial models?


A File Comparison Program. A file comparison program produces a list of differences between two files. These differences can be couched in terms of lines, e.g. by telling which lines must be inserted, deleted or moved to convert the first file to the second. Alternatively, the list of differences can identify individual bytes. Byte-oriented comparisons are useful with non-text files, such as compiled programs, that are not divided into lines. The approach adopted here is to generate only instructions to insert or delete entire lines. Since lines are treated as indivisible objects, files can be treated as containing lines consisting of a single symbol. In other words, an n-line file is modelled by a string of n symbols. In more formal terms, the file comparison problem can be rephrased as follows. The edit distance between two strings of symbols is the length of a shortest sequence of insertions and deletions that will convert the first string to the second. T h e goal, then, is to write a program that computes the edit distance between two arbitrary strings of symbols. In addition, the program must explicitly produce a shortest possible edit script (i.e. sequence of edit commands) for the given strings. Other approaches have been tried. For example, Tichy ' discusses a file-comparison tool that determines how one file can be constructed from another by copying blocks of lines and appending lines. However, the ability to economically generate shortestpossible edit scripts depends critically on the repertoire of instructions that are allowed in the scripts.2 File comparison algorithms have a number of potential uses beside merely producing a set of edit commands to be read by someone trying to understand the evolution of a program or document. For example, the edit scripts might be text editor instructions that are saved to avoid the expense of storing nearly identical files. Rather than storing

Question: What is the difference between a file comparison program and a file comparison algorithm?


Syntax Parsing: Implementation Using Grammar-Rules for English Language. Syntactic parsing deals with syntactic structure of a sentence. The word 'syntax' refers to the grammatical arrangement of words in a sentence and their relationship with each other. The objective of syntactic analysis is to find syntactic structure of a sentence which is usually depicted as a tree. Identifying the syntactic structure is useful in determining the meaning of a sentence. Natural language processing is an arena of computer science and linguistics, concerned with the dealings amongst computers and human languages. It processes the data through lexical analysis, Syntax analysis, Semantic analysis, Discourse processing, Pragmatic analysis. This paper gives various parsing methods. The algorithm in this paper splits the English sentences into parts using POS tagger, It identifies the type of sentence (Facts, active, passive etc.) and then parses these sentences using grammar rules of Natural language. The algorithm has been tested on real sentences of English and it accomplished an accuracy of 81%.

Question: What is the algorithm in this paper?


The role of debriefing in simulation-based learning.. The aim of this paper is to critically review what is felt to be important about the role of debriefing in the field of simulation-based learning, how it has come about and developed over time, and the different styles or approaches that are used and how effective the process is. A recent systematic review of high fidelity simulation literature identified feedback (including debriefing) as the most important feature of simulation-based medical education.1 Despite this, there are surprisingly few papers in the peer-reviewed literature to illustrate how to debrief, how to teach or learn to debrief, what methods of debriefing exist and how effective they are at achieving learning objectives and goals. This review is by no means a systematic review of all the literature available on debriefing, and contains information from both peer and nonpeer reviewed sources such as meeting abstracts and presentations from within the medical field and other disciplines versed in the practice of debriefing such as military, psychology, and business. It also contains many examples of what expert facilitators have learned over years of practice in the area. We feel this would be of interest to novices in the field as an introduction to debriefing, and to experts to illustrate the gaps that currently exist, which might be addressed in further research within the medical simulation community and in collaborative ventures between other disciplines experienced in the art of debriefing.

Question: What is the role of debriefing in simulation-based learning?


Credibilist occupancy grids for vehicle perception in dynamic environments. In urban environments, moving obstacles detection and free space determination are key issues for driving assistance systems and autonomous vehicles. When using lidar sensors scanning in front of the vehicle, uncertainty arises from ignorance and errors. Ignorance is due to the perception of new areas and errors come from imprecise pose estimation and noisy measurements. Complexity is also increased when the lidar provides multi-echo and multi-layer information. This paper presents an occupancy grid framework that has been designed to manage these different sources of uncertainty. A way to address this problem is to use grids projected onto the road surface in global and local frames. The global one generates the mapping and the local one is used to deal with moving objects. A credibilist approach is used to model the sensor information and to do a global fusion with the world-fixed map. Outdoor experimental results carried out with a precise positioning system show that such a perception strategy increases significantly the performance compared to a standard approach.

Question: How to deal with uncertainty in perception?


The Foreign Language Effect on Moral Judgment: The Role of Emotions and Norms. We investigated whether and why the use of a foreign language influences moral judgment. We studied the trolley and footbridge dilemmas, which propose an action that involves killing one individual to save five. In line with prior work, the use of a foreign language increased the endorsement of such consequentialist actions for the footbridge dilemma, but not for the trolley dilemma. But contrary to recent theorizing, this effect was not driven by an attenuation of emotions. An attenuation of emotions was found in both dilemmas, and it did not mediate the foreign language effect on moral judgment. An examination of additional scenarios revealed that foreign language influenced moral judgment when the proposed action involved a social or moral norm violation. We propose that foreign language influences moral judgment by reducing access to normative knowledge.

Question: What is the role of emotions in moral judgment?


Simultaneous algebraic reconstruction technique (SART): a superior implementation of the art algorithm.. In this paper we have discussed what appears to be a superior implementation of the Algebraic Reconstruction Technique (ART). The method is based on 1) simultaneous application of the error correction terms as computed by ART for all rays in a given projection; 2) longitudinal weighting of the correction terms back-distributed along the rays; and 3) using bilinear elements for discrete approximation to the ray integrals of a continuous image. Since this implementation generates a good reconstruction in only one iteration, it also appears to have a computational advantage over the more traditional implementation of ART. Potential applications of this implementation include image reconstruction in conjunction with ray tracing for ultrasound and microwave tomography in which the curved nature of the rays leads to a non-uniform ray density across the image.

Question: What is the difference between the art algorithm and the simultaneous algebraic reconstruction technique?


Mechanisms linking obesity to insulin resistance and type 2 diabetes. Obesity is associated with an increased risk of developing insulin resistance and type 2 diabetes. In obese individuals, adipose tissue releases increased amounts of non-esterified fatty acids, glycerol, hormones, pro-inflammatory cytokines and other factors that are involved in the development of insulin resistance. When insulin resistance is accompanied by dysfunction of pancreatic islet β-cells — the cells that release insulin — failure to control blood glucose levels results. Abnormalities in β-cell function are therefore critical in defining the risk and development of type 2 diabetes. This knowledge is fostering exploration of the molecular and genetic basis of the disease and new approaches to its treatment and prevention.

Question: What is the molecular and genetic basis of type 2 diabetes?


LaneQuest: An accurate and energy-efficient lane detection system. Current outdoor localization techniques fail to provide the required accuracy for estimating the car's lane. In this paper, we present LaneQuest: a system that leverages the ubiquitous and low-energy inertial sensors available in commodity smart-phones to provide an accurate estimate of the car's current lane. LaneQuest leverages hints from the phone sensors about the surrounding environment to detect the car's lane. For example, a car making a right turn most probably will be in the right-most lane, a car passing by a pothole will be in a specific lane, and the car's angular velocity when driving through a curve reflects its lane. Our investigation shows that there are amble opportunities in the environment, i.e. lane "anchors", that provide cues about the car's lane. To handle the ambiguous location, sensors noise, and fuzzy lane anchors; LaneQuest employs a novel probabilistic lane estimation algorithm. Furthermore, it uses an unsupervised crowd-sourcing approach to learn the position and lane-span distribution of the different lane-level anchors. Our evaluation results from implementation on different android devices and 260Km driving traces by 13 drivers in different cities shows that LaneQuest can detect the different lane-level anchors with an average precision and recall of more than 90%. This leads to an accurate detection of the exact car's lane position 80% of the time, increasing to 89% of the time to within one lane. This comes with a low-energy footprint, allowing LaneQuest to be implemented on the energy-constrained mobile devices.

Question: What is the accuracy of lane detection?


Are More Data Always Better for Factor Analysis ?. Factors estimated from large macroeconomic panels are being used in an increasing number of applications. However, little is known about how the size and composition of the data affect the factor estimates. In this paper, we question whether it is possible to use more series to extract the factors and that yet the resulting factors are less useful for forecasting, and the answer is yes. Such a problem tends to arise when the idiosyncratic errors are cross-correlated. It can also arise if forecasting power is provided by a factor that is dominant in a small dataset but is a dominated factor in a larger dataset. In a real time forecasting exercise, we find that factors extracted from as few as 40 pre-screened series often yield satisfactory or even better results than using all 147 series. Our simulation analysis is unique in that special attention is paid to cross-correlated idiosyncratic errors, and we also allow the factors to have weak loadings on groups of series. It thus allows us to better understand the properties of the principal components estimator in empirical applications. ∗Graduate School of Business, 719 Uris Hall, Columbia University, New York, NY. Email: jb903@columbia.edu †Corresponding author: Department of Economics, Johns Hopkins University, 3400 N. Charles St., Baltimore, MD 21218. Email: Serena.Ng@jhu.edu The authors would like to thank National Science Foundation for financial support. We thank Pritha Mitra for useful research assistance.

Question: Are more data always better for factor analysis?


Where to Buy It: Matching Street Clothing Photos in Online Shops. In this paper, we define a new task, Exact Street to Shop, where our goal is to match a real-world example of a garment item to the same item in an online shop. This is an extremely challenging task due to visual differences between street photos (pictures of people wearing clothing in everyday uncontrolled settings) and online shop photos (pictures of clothing items on people, mannequins, or in isolation, captured by professionals in more controlled settings). We collect a new dataset for this application containing 404,683 shop photos collected from 25 different online retailers and 20,357 street photos, providing a total of 39,479 clothing item matches between street and shop photos. We develop three different methods for Exact Street to Shop retrieval, including two deep learning baseline methods, and a method to learn a similarity measure between the street and shop domains. Experiments demonstrate that our learned similarity significantly outperforms our baselines that use existing deep learning based representations.

Question: How to match street photos to online shop photos?


Knowledge Sharing in the Workplace: A Social Networking Site Assessment. Enterprise executives are taking note that great potential exists for enhancing knowledge sharing and linking experts within the organization when incorporating social software technologies into their operations. But there are warnings of potential negative effects if ineffective use or even misuse of these technologies occurs. There is relatively little empirical research examining the business impact of social software implementations evolving from Web 2.0 applications when considering the effects of trust on knowledge sharing. This paper attempts to fill this gap in the research by proposing a theoretical framework to study the effects of trust, risk and benefits, critical mass, and social influence on knowledge sharing intentions of employees using social media technology in the organization.

Question: What is the impact of social media on knowledge sharing?


THE SERIAL POSITION EFFECT OF FREE RECALL. Recently Murdock (1960) has shown that in free recall RI, the total number of words recalled after one presentation, is a linear function of t, total presentation time. Nothing was said about the serial position effect, though this is a well-known phenomenon of free recall (e.g., Deese & Kaufman, 1957). However, given that there is a serial position effect, the simple linear relationship between RI and t is rather surprising. In the customary serial position curve of free recall, probability of recall is plotted as a function of serial position. This means, then, that the area under the serial position curve is equal to RI, the number of words recalled after one presentation. If R j is a linear function of t then it must follow that the area under the serial position curve is also a linear function of t. However, it is not immediately apparent how the serial position curve varies with t in such a way as to maintain this simple linear relationship. The present experiment was designed as an attempt to determine how the serial position curve varied with list length and presentation rate while still maintaining this linear relationship. Unfortunately, at the end of the experiment it was still not clear how this relationship came about or, for that matter, whether the relationship was even linear after all. The basic reason for this failure was

Question: What is the serial position effect?


Opensmile: the munich versatile and fast open-source audio feature extractor. We introduce the openSMILE feature extraction toolkit, which unites feature extraction algorithms from the speech processing and the Music Information Retrieval communities. Audio low-level descriptors such as CHROMA and CENS features, loudness, Mel-frequency cepstral coefficients, perceptual linear predictive cepstral coefficients, linear predictive coefficients, line spectral frequencies, fundamental frequency, and formant frequencies are supported. Delta regression and various statistical functionals can be applied to the low-level descriptors. openSMILE is implemented in C++ with no third-party dependencies for the core functionality. It is fast, runs on Unix and Windows platforms, and has a modular, component based architecture which makes extensions via plug-ins easy. It supports on-line incremental processing for all implemented features as well as off-line and batch processing. Numeric compatibility with future versions is ensured by means of unit tests. openSMILE can be downloaded from http://opensmile.sourceforge.net/.

Question: What is the munich versatile and fast open-source audio feature extractor?


The comparison of soil sensors for integrated creation of IOT-based Wetting front detector (WFD) with an efficient irrigation system to support precision farming. This study investigates a prototyping of integrated system of Internet of Things based Wetting front detector (IOT-WFD) which focuses on how to enhance the IOT based Wetting front detector design for smart irrigation system. The empirical study was conducted with 2 sensors type to detect the wetting fronts which are the Frequency Domain Reflectrometry sensor (FDR) and Resistor-based sensor (RB) integrated and design with low-cost WFD. The results of this study point toward the IOT-WFD as an appropriated technology providing real time wetting front information in soil positively for application in terms of agricultural water management, with precision agriculture and efficient irrigation domain with a related decision knowledge that matches with the technology trend and smart farmer requirements. Evidence of positive results of this prototyping summary has been provided.

Question: What is the difference between the two sensors?


Auto-directed video stabilization with robust L1 optimal camera paths. We present a novel algorithm for automatically applying constrainable, L1-optimal camera paths to generate stabilized videos by removing undesired motions. Our goal is to compute camera paths that are composed of constant, linear and parabolic segments mimicking the camera motions employed by professional cinematographers. To this end, our algorithm is based on a linear programming framework to minimize the first, second, and third derivatives of the resulting camera path. Our method allows for video stabilization beyond the conventional filtering of camera paths that only suppresses high frequency jitter. We incorporate additional constraints on the path of the camera directly in our algorithm, allowing for stabilized and retargeted videos. Our approach accomplishes this without the need of user interaction or costly 3D reconstruction of the scene, and works as a post-process for videos from any camera or from an online source.

Question: How to stabilize a video?


Conceptual modeling and development of an intelligent agent-assisted decision support system for anti-money laundering. Criminal elements in today's technology-driven society are using every means available at their disposal to launder the proceeds from their illegal activities. In response, international anti-money laundering (AML) efforts are being made. The events of September 11, 2001, highlighted the need for more sophisticated AML and anti-terrorist financing programs across the industry and nation. In the wake of this, regulators are focusing on the role that technology can play in compliance with laws and ultimately in law enforcement. Banks will have to employ or enhance AML tools and technology to satisfy rising regulatory expectations. While many AML solutions have been in place for some time within the banks, they are faced with the challenge of adapting to the ever-changing risks and methods related to money laundering. In order to provide support for AML decisions, we have formulated an AML conceptual model by following Simon [Simon, H. A. (1977). The new science of management decision. Englewood Cliffs, NJ: Prentice-Hall] decision-making process model. Based on this model, a novel and open multi-agent AML system prototype has been designed and developed. Intelligent agents with their properties of autonomy, reactivity, and proactivity are well suited for dynamic, ill-structured, and complex ML prevention controls. The advanced architecture is able to provide more adaptive, intelligent, and flexible solution for AML. This paper is the first attempt at intelligent agent financial application in the AML domain, with a decision-making/problem-solving process model, an innovative agent-based architecture, and a prototype system. 2007 Elsevier Ltd. All rights reserved.

Question: What is the role of intelligent agents in anti-money laundering?


Cervical radiculopathy.. Cervical radiculopathy is the result of irritation and/or compression of nerve root as it exits the cervical spine. Pain is a common presenting symptom and may be accompanied by motor or sensory deficits in areas innervated by the affected nerve root. Diagnosis is suggested by history and corresponding physical examination findings. Confirmation is achieved with MRI. A multimodal approach to treatment helps patients improve. Medications may be used to alleviate symptoms and manage pain. Physical therapy and manipulation may improve neck discomfort. Guided corticosteroid injections and selected nerve blocks may help control nerve root pain. Most patients improve with a conservative, nonoperative treatment course.

Question: What is cervical radiculopathy?


Refining the relationship between personality and subjective well-being.. Understanding subjective well-being (SWB) has historically been a core human endeavor and presently spans fields from management to mental health. Previous meta-analyses have indicated that personality traits are one of the best predictors. Still, these past results indicate only a moderate relationship, weaker than suggested by several lines of reasoning. This may be because of commensurability, where researchers have grouped together substantively disparate measures in their analyses. In this article, the authors review and address this problem directly, focusing on individual measures of personality (e.g., the Neuroticism-Extroversion-Openness Personality Inventory; P. T. Costa & R. R. McCrae, 1992) and categories of SWB (e.g., life satisfaction). In addition, the authors take a multivariate approach, assessing how much variance personality traits account for individually as well as together. Results indicate that different personality and SWB scales can be substantively different and that the relationship between the two is typically much larger (e.g., 4 times) than previous meta-analyses have indicated. Total SWB variance accounted for by personality can reach as high as 39% or 63% disattenuated. These results also speak to meta-analyses in general and the need to account for scale differences once a sufficient research base has been generated.

Question: What is the relationship between personality and subjective well-being?


Textile Materials for the Design of Wearable Antennas: A Survey. In the broad context of Wireless Body Sensor Networks for healthcare and pervasive applications, the design of wearable antennas offers the possibility of ubiquitous monitoring, communication and energy harvesting and storage. Specific requirements for wearable antennas are a planar structure and flexible construction materials. Several properties of the materials influence the behaviour of the antenna. For instance, the bandwidth and the efficiency of a planar microstrip antenna are mainly determined by the permittivity and the thickness of the substrate. The use of textiles in wearable antennas requires the characterization of their properties. Specific electrical conductive textiles are available on the market and have been successfully used. Ordinary textile fabrics have been used as substrates. However, little information can be found on the electromagnetic properties of regular textiles. Therefore this paper is mainly focused on the analysis of the dielectric properties of normal fabrics. In general, textiles present a very low dielectric constant that reduces the surface wave losses and increases the impedance bandwidth of the antenna. However, textile materials are constantly exchanging water molecules with the surroundings, which affects their electromagnetic properties. In addition, textile fabrics are porous, anisotropic and compressible materials whose thickness and density might change with low pressures. Therefore it is important to know how these characteristics influence the behaviour of the antenna in order to minimize unwanted effects. This paper presents a survey of the key points for the design and development of textile antennas, from the choice of the textile materials to the framing of the antenna. An analysis of the textile materials that have been used is also presented.

Question: What is the dielectric constant of a textile?


A Novel Implementation of Phase Control Technique for Speed Control of Induction Motor Using ARDUINO. Induction motors are the most widely used motors for appliances, industrial control and automation. However there arises a problem in voltage levels, which affects the speed of induction motor. As the voltage (V) is directly proportional to speed (N), we need to control the stator voltage which controls the speed proportionally. In this Paper a novel Open loop phase control method is developed by coding a program using ARDUINO software in which ARDUINO controller takes input from the user and generates firing pulses for the TRIAC which controls the speed of the Induction motor. The total process is executed with the help of an ARDUINO controller kit where ARDUINO and Tera-Term softwares are used for Micro Controller and for serial monitor. This results in variable speed control of Induction motor.

Question: How to control the speed of induction motor?


Applying Model Management to Classical Meta Data Problems. Model management is a new approach to meta data management that offers a higher level programming interface than current techniques. The main abstractions are models (e.g., schemas, interface definitions) and mappings between models. It treats these abstractions as bulk objects and offers such operators as Match, Merge, Diff, Compose, Apply, and ModelGen. This paper extends earlier treatments of these operators and applies them to three classical meta data management problems: schema integration, schema evolution, and round-trip engineering.

Question: What is model management?


The earth is flat (p > 0.05): significance thresholds and the crisis of unreplicable research. The widespread use of 'statistical significance' as a license for making a claim of a scientific finding leads to considerable distortion of the scientific process (according to the American Statistical Association). We review why degrading p-values into 'significant' and 'nonsignificant' contributes to making studies irreproducible, or to making them seem irreproducible. A major problem is that we tend to take small p-values at face value, but mistrust results with larger p-values. In either case, p-values tell little about reliability of research, because they are hardly replicable even if an alternative hypothesis is true. Also significance (p ≤ 0.05) is hardly replicable: at a good statistical power of 80%, two studies will be 'conflicting', meaning that one is significant and the other is not, in one third of the cases if there is a true effect. A replication can therefore not be interpreted as having failed only because it is nonsignificant. Many apparent replication failures may thus reflect faulty judgment based on significance thresholds rather than a crisis of unreplicable research. Reliable conclusions on replicability and practical importance of a finding can only be drawn using cumulative evidence from multiple independent studies. However, applying significance thresholds makes cumulative knowledge unreliable. One reason is that with anything but ideal statistical power, significant effect sizes will be biased upwards. Interpreting inflated significant results while ignoring nonsignificant results will thus lead to wrong conclusions. But current incentives to hunt for significance lead to selective reporting and to publication bias against nonsignificant findings. Data dredging, p-hacking, and publication bias should be addressed by removing fixed significance thresholds. Consistent with the recommendations of the late Ronald Fisher, p-values should be interp

Question: What is the crisis of unreplicable research?


Data quality management, data usage experience and acquisition intention of big data analytics. Big data analytics associated with database searching, mining, and analysis can be seen as an innovative IT capability that can improve firm performance. Even though some leading companies are actively adopting big data analytics to strengthen market competition and to open up new business opportunities, many firms are still in the early stage of the adoption curve due to lack of understanding of and experience with big data. Hence, it is interesting and timely to understand issues relevant to big data adoption. In this study, a research model is proposed to explain the acquisition intention of big data analytics

Question: What is the research model of big data analytics?


Pathways for irony detection in tweets. Posts on Twitter allow users to express ideas and opinions in a very dynamic way. The volume of data available is incredibly high in this support, and it may provide relevant clues regarding the judgement of the public on certain product, event, service etc. While in standard sentiment analysis the most common task is to classify the utterances according to their polarity, it is clear that detecting ironic senses represent a big challenge for Natural Language Processing. By observing a corpus constitued by tweets, we propose a set of patterns that might suggest ironic/sarcastic statements. Thus, we developed special clues for irony detection, through the implementation and evaluation of a set of patterns.

Question: How to detect irony in tweets?


Ranking , Boosting , and Model Adaptation. We present a new ranking algorithm that combines the strengt hs of two previous methods: boosted tree classification, and LambdaR ank, which has been shown to be empirically optimal for a widely used inform ation retrieval measure. The algorithm is based on boosted regression trees , although the ideas apply to any weak learners, and it is significantly fast er in both train and test phases than the state of the art, for comparable accu racy. We also show how to find the optimal linear combination for any two ran kers, and we use this method to solve the line search problem exactly du ring boosting. In addition, we show that starting with a previously tra ined model, and boosting using its residuals, furnishes an effective techn ique for model adaptation, and we give results for a particularly pressing prob lem in Web Search training rankers for markets for which only small amounts o f labeled data are available, given a ranker trained on much more data from a larger market.

Question: What is the difference between a ranker and a classifier?


Providing Adaptive Courses in Learning Management Systems with Respect to Learning Styles. Learning management systems (LMS) are commonly used in e-learning but provide little, or in most cases, no adaptivity. However, courses which adapt to the individual needs of students make learning easier for them and lead to a positive effect in learning. In this paper, we introduce a concept for providing adaptivity based on learning styles in LMS. In order to show the effectiveness of our approach, Moodle was extended by an add-on and an experiment with 437 students was performed. From the analysis of the students' performance and behaviour in the course, we found out that students who learned from a course that matches their learning styles spent significantly less time in the course and achieved in average the same marks than students who got a course that either mismatched with their learning styles or included all available learning objects. Therefore, providing adaptive courses in LMS according to the proposed concept can be seen as effective in supporting students in learning.

Question: How can we adapt a course to the individual needs of students?


Norepinephrine transporter occupancy by antidepressant in human brain using positron emission tomography with (S,S)-[18F]FMeNER-D2. Central norepinephrine transporter (NET) is one of the main targets of antidepressants. Although the measurement of NET occupancy has been attempted in humans, the outcomes have been inconclusive. In this study, the occupancy of NET by different doses of an antidepressant, nortriptyline, was measured using positron emission tomography (PET) with (S,S)-[18F]FMeNER-D2. PET scans using (S,S)-[18F]FMeNER-D2 were performed on six healthy men before and after oral administration of a single oral dose of nortriptyline (10–75 mg). After a bolus i.v. injection of (S,S)-[18F]FMeNER-D2, dynamic scanning was performed for 0–90 min, followed by scanning for 120–180 min. The ratio of the thalamus-to-caudate areas under the curve (120–180 min) minus 1 was used as the binding potential (BPND) for NET. NET occupancy was calculated as the percentage reduction of BPND. Venous blood samples were taken to measure the concentrations of nortriptyline just before injection of the tracer and at 180 min after the injection. Mean NET occupancies by nortriptyline were 16.4% at 10 mg, 33.2% at 25 mg, and 41.1% at 75 mg. The mean plasma concentration of nortriptyline was less than the lower limit of detection at 10 mg, 23.7 ng/mL at 25 mg, and 50.5 ng/mL at 75 mg. Estimated ED50 was 76.8 mg of administration dose and 59.8 ng/mL of plasma concentration. NET occupancy by nortriptyline corresponding to the administration dose of 10–75 mg or plasma concentration was observed from 16% to 41%.

Question: What is the ED50 of nortriptyline?


Segmentation quality evaluation based on multi-scale convolutional neural networks. Segmentation quality evaluation is an important task in image segmentation. The existing evaluation methods formulate segmentation quality as regression model, and recent Convolutional Neural Network (CNN) based evaluation methods show superior performance. However, designing efficient CNN-based segmentation evaluation model is still under exploited. In this paper, we propose two types of CNN structures such as double-net and multi-scale network for segmentation quality evaluation. We observe that learning the local and global information and considering multi-scale image are useful for segmentation quality evaluation. To train and verify the proposed networks, we construct a novel objective segmentation quality evaluation dataset with large amount of data by combining several proposal generation methods. The experimental results demonstrate that the proposed method obtains larger Linear Correlation Coefficient (LCC) value than several state-of-art segmentation quality evaluation methods.

Question: How to evaluate segmentation quality?