A comparative study of game mechanics and control laws for an adaptive physiological game. We present an adaptive biofeedback game that aims to maintain the player's arousal by modifying game difficulty in response to the player's physiological state, as measured with wearable sensors. Our approach models the interaction between human physiology and game difficulty during gameplay as a control problem, where game difficulty is the system input and player arousal its output. We validate the approach on a car-racing game with real-time adaptive game mechanics. Specifically, we use (1) car speed, road visibility, and steering jitter as three mechanisms to manipulate game difficulty, (2) electrodermal activity as physiological correlate of arousal, and (3) two types of control law: proportional (P) control, and proportional-integral-derivative (PID) control. We also propose quantitative measures to characterize the effectiveness of these game adaptations and controllers in manipulating the player's arousal. Experimental trials with 25 subjects in both open-loop (no feedback) and closed-loop (negative feedback) conditions show statistically significant differences in effectiveness among the three game mechanics and also between the two control laws. Specifically, manipulating car speed provides higher control of arousal levels than changing road visibility or vehicle steering. Our results also confirm that PID control leads to lower error and reduced oscillations in the closed-loop response compared to proportional-only control. Finally, we discuss the theoretical and practical implications of our approach.

Question: What is a physiological game?


A Double-Sided LCLC-Compensated Capacitive Power Transfer System for Electric Vehicle Charging. A double-sided LCLC-compensated capacitive power transfer (CPT) system is proposed for the electric vehicle charging application. Two pairs of metal plates are utilized to form two coupling capacitors to transfer power wirelessly. The LCLC-compensated structure can dramatically reduce the voltage stress on the coupling capacitors and maintain unity power factor at both the input and output. A 2.4-kW CPT system is designed with four 610-mm × 610-mm copper plates and an air gap distance of 150 mm. The experimental prototype reaches a dc-dc efficiency of 90.8% at 2.4-kW output power. At 300-mm misalignment case, the output power drops to 2.1 kW with 90.7% efficiency. With a 300-mm air gap distance, the output power drops to 1.6 kW with 89.1% efficiency.

Question: What is a double-sided LCLC-compensated capacitive power transfer system?


A Survey on Query Processing and Optimization in Relational Database Management System. The query processer and optimizer is an important component in today's relational database management system. This component is responsible for translating a user query, usually written in a non-procedural language like SQL – into an efficient query evaluation program that can be executed against database. In this paper, we identify many of the common issues, themes, and approaches that extend this work and the settings in which each piece of work is most appropriate. Our goal with this paper is to be a "value-add" over the existing papers on the material, providing not only a brief overview of each technique, but also a basic framework for understating the field of query processing and optimization in general.

Question: What is the difference between a query processor and an optimizer?


Similarity Breeds Proximity: Pattern Similarity within and across Contexts Is Related to Later Mnemonic Judgments of Temporal Proximity. Experiences unfold over time, but little is known about the mechanisms that support the formation of coherent episodic memories for temporally extended events. Recent work in animals has provided evidence for signals in hippocampus that could link events across temporal gaps; however, it is unknown whether and how such signals might be related to later memory for temporal information in humans. We measured patterns of fMRI BOLD activity as people encoded items that were separated in time and manipulated the presence of shared or distinct context across items. We found that hippocampal pattern similarity in the BOLD response across trials predicted later temporal memory decisions when context changed. By contrast, pattern similarity in lateral occipital cortex was related to memory only when context remained stable. These data provide evidence in humans that representational stability in hippocampus across time may be a mechanism for temporal memory organization.

Question: How does the brain represent time?


Neural Kinematic Networks for Unsupervised Motion Retargetting. We propose a recurrent neural network architecture with a Forward Kinematics layer and cycle consistency based adversarial training objective for unsupervised motion retargetting. Our network captures the high-level properties of an input motion by the forward kinematics layer, and adapts them to a target character with different skeleton bone lengths (e.g., shorter, longer arms etc.). Collecting paired motion training sequences from different characters is expensive. Instead, our network utilizes cycle consistency to learn to solve the Inverse Kinematics problem in an unsupervised manner. Our method works online, i.e., it adapts the motion sequence on-the-fly as new frames are received. In our experiments, we use the Mixamo animation data1 to test our method for a variety of motions and characters and achieve state-of-the-art results. We also demonstrate motion retargetting from monocular human videos to 3D characters using an off-the-shelf 3D pose estimator.

Question: How does our method work?


Active Learning Literature Survey. The key idea behind active learning is that a machine learning algorithm can achieve greater accuracy with fewer labeled training instances if it is allowed to choose the data from which is learns. An active learner may ask queries in the form of unlabeled instances to be labeled by an oracle (e.g., a human annotator). Active learning is well-motivated in many modern machine learning problems, where unlabeled data may be abundant but labels are difficult, time-consuming, or expensive to obtain. This report provides a general introduction to active learning and a survey of the literature. This includes a discussion of the scenarios in which queries can be formulated, and an overview of the query strategy frameworks proposed in the literature to date. An analysis of the empirical and theoretical evidence for active learning, a summary of several problem setting variants, and a discussion of related topics in machine learning research are also presented.

Question: What is the key idea behind active learning?


Extensive Benchmark and Survey of Modeling Methods for Scene Background Initialization. Scene background initialization is the process by which a method tries to recover the background image of a video without foreground objects in it. Having a clear understanding about which approach is more robust and/or more suited to a given scenario is of great interest to many end users or practitioners. The aim of this paper is to provide an extensive survey of scene background initialization methods as well as a novel benchmarking framework. The proposed framework involves several evaluation metrics and state-of-the-art methods, as well as the largest video data set ever made for this purpose. The data set consists of several camera-captured videos that: 1) span categories focused on various background initialization challenges; 2) are obtained with different cameras of different lengths, frame rates, spatial resolutions, lighting conditions, and levels of compression; and 3) contain indoor and outdoor scenes. The wide variety of our data set prevents our analysis from favoring a certain family of background initialization methods over others. Our evaluation framework allows us to quantitatively identify solved and unsolved issues related to scene background initialization. We also identify scenarios for which state-of-the-art methods systematically fail.

Question: What is the largest video data set ever made for this purpose?


Engineering Time-Expanded Graphs for Faster Timetable Information. We present an extension of the well-known time-expanded approach for timetable information. By remodeling unimportant stations, we are able to obtain faster query times with less space consumption than the original model. Moreover, we show that our extensions harmonize well with speed-up techniques whose adaption to timetable networks is more challenging than one might expect.

Question: How to obtain faster timetable information?


A Formal Analysis of the ISO 9241-210 Definition of User Experience. User Experience (UX) is a major concept in HCI and a variety of different UX definitions have been suggested within the scientific community. An ISO UX definition has been presented to standardize the term from an industry perspective. We introduce methods from formal logic in order to formalize and analyze the ISO UX definition with regard to consistency and ambiguities and present recommendations for an improved version. Although this kind of formalization is not common within the CHI community, we show that quasi-formal methods provide an alternative way of analyzing widely discussed HCI terms, such as UX, to deepen its understanding.

Question: What is the definition of user experience?


AutoMoDe: A novel approach to the automatic design of control software for robot swarms. We introduce AutoMoDe: a novel approach to the automatic design of control software for robot swarms. The core idea in AutoMoDe recalls the approach commonly adopted in machine learning for dealing with the bias–variance tradedoff: to obtain suitably general solutions with low variance, an appropriate design bias is injected. AutoMoDe produces robot control software by selecting, instantiating, and combining preexisting parametric modules—the injected bias. The resulting control software is a probabilistic finite state machine in which the topology, the transition rules and the values of the parameters are obtained automatically via an optimization process that maximizes a task-specific objective function. As a proof of concept, we define AutoMoDe-Vanilla, which is a specialization of AutoMoDe for the e-puck robot. We use AutoMoDe-Vanilla to design the robot control software for two different tasks: aggregation and foraging. The results show that the control software produced by AutoMoDe-Vanilla (i) yields good results, (ii) appears to be robust to the so called reality gap, and (iii) is naturally human-readable.

Question: What is the e-puck robot?


Prediction of click frauds in mobile advertising. Click fraud represents a serious drain on advertising budgets and can seriously harm the viability of the internet advertising market. This paper proposes a novel framework for prediction of click fraud in mobile advertising which consists of feature selection using Recursive Feature Elimination (RFE) and classification through Hellinger Distance Decision Tree (HDDT).RFE is chosen for the feature selection as it has given better results as compared to wrapper approach when evaluated using different classifiers. HDDT is also selected as classifier to deal with class imbalance issue present in the data set. The efficiency of proposed framework is investigated on the data set provided by Buzzcity and compared with J48, Rep Tree, logitboost, and random forest. Results show that accuracy achieved by proposed framework is 64.07 % which is best as compared to existing methods under study.

Question: What is click fraud?


Expanded Semantic Graph Representation for Matching Related Information of Interest across Free Text Documents. This research proposes an expanded semantic graph definition that serves as a basis for an expanded semantic graph representation and graph matching approach. This representation separates the content and context and adds a number of semantic structures that encapsulate inferred information. The expanded semantic graph approach facilitates finding additional matches, identifying and eliminating poor matches, and prioritizing matches based on how much new knowledge is provided. By focusing on information of interest, doing pre-processing, and reducing processing requirements, the approach is applicable to problems where related information of interest is sought across a massive body of free text documents. Key aspects of the approach include (1) expanding the nodes and edges through inference using DL-Safe rules, abductive hypotheses, and syntactic patterns, (2) separating semantic content into nodes and semantic context into edges, and (3) applying relatedness measures on a node, edge, and sub graph basis. Results from tests using a ground-truthed subset of a large dataset of law enforcement investigator emails illustrate the benefits of these approaches.

Question: What is the expanded semantic graph representation?


A broadband compact low-loss 4×4 Butler Matrix in CMOS with stacked transformer based quadrature couplers. This paper presents an ultra-broadband ultracompact Butler Matrix design scheme. The design employs stacked transformer based couplers and lumped LC π-network phase shifters for substantial size reduction. As a proof-of-concept design, a 4×4 Butler Matrix is implemented in a standard 130nm bulk CMOS process at a center frequency of 2.0 GHz. Compared with reported fully integrated 2.0 GHz 4×4 Butler Matrix designs in CMOS, the proposed design achieves the lowest insertion loss of 1.10dB, the smallest amplitude mismatch of 0.3 dB, the largest fractional bandwidth of 34.6%, and the smallest chip core area of 0.635×1.122 mm2. Based on the measured S-parameters, the four concurrent electrical array patterns of the Butler Matrix achieve array peak-to-null ratio (PNR) of 29.5 dB at 2.0 GHz and better than 15.0 dB between 1.55 GHz and 2.50 GHz.

Question: What is the smallest chip core area of a 4×4 Butler Matrix in CMOS?


Driving-Style-Based Codesign Optimization of an Automated Electric Vehicle: A Cyber-Physical System Approach. This paper studies the codesign optimization approach to determine how to optimally adapt automatic control of an intelligent electric vehicle to driving styles. A cyber-physical system (CPS)-based framework is proposed for codesign optimization of the plant and controller parameters for an automated electric vehicle, in view of vehicle's dynamic performance, drivability, and energy along with different driving styles. System description, requirements, constraints, optimization objectives, and methodology are investigated. Driving style recognition algorithm is developed using unsupervised machine learning and validated via vehicle experiments. Adaptive control algorithms are designed for three driving styles with different protocol selections. Performance exploration method is presented. Parameter optimizations are implemented based on the defined objective functions. Test results show that an automated vehicle with optimized plant and controller can perform its tasks well under aggressive, moderate, and conservative driving styles, further improving the overall performance. The results validate the feasibility and effectiveness of the proposed CPS-based codesign optimization approach.

Question: How to optimize the plant and controller parameters for an automated electric vehicle?


The Whale Optimization Algorithm. This paper proposes a novel nature-inspired meta-heuristic optimization algorithm, called Whale Optimization Algorithm (WOA), which mimics the social behavior of humpback whales. The algorithm is inspired by the bubble-net hunting strategy. WOA is tested with 29 mathematical optimization problems and 6 structural design problems. Optimization results prove that the WOA algorithm is very competitive compared to the state-of-art meta-heuristic algorithms as well as conventional methods. The source codes of the WOA algorithm are publicly available at http://www.alimirjalili.com/WOA.html © 2016 Elsevier Ltd. All rights reserved.

Question: What is the source code of the whale optimization algorithm?


Deceiving Others: Distinct Neural Responses of the Prefrontal Cortex and Amygdala in Simple Fabrication and Deception with Social Interactions. Brain mechanisms for telling lies have been investigated recently using neuroimaging techniques such as functional magnetic resonance imaging and positron emission tomography. Although the advent of these techniques has gradually enabled clarification of the functional contributions of the prefrontal cortex in deception with respect to executive function, the specific roles of subregions within the prefrontal cortex and other brain regions responsible for emotional regulation or social interactions during deception are still unclear. Assuming that the processes of falsifying truthful responses and deceiving others are differentially associated with the activities of these regions, we conducted a positron emission tomography experiment with 2 (truth, lie) 2 (honesty, dishonesty) factorial design. The main effect of falsifying the truthful responses revealed increased brain activity of the left dorsolateral and right anterior prefrontal cortices, supporting the interpretation of previous studies that executive functions are related to making untruthful responses. The main effect of deceiving the interrogator showed activations of the ventromedial prefrontal (medial orbitofrontal) cortex and amygdala, adding new evidence that the brain regions assumed to be responsible for emotional processing or social interaction are active during deceptive behavior similar to that in real-life situations. Further analysis revealed that activity of the right anterior prefrontal cortex showed both effects of deception, indicating that this region has a pivotal role in telling lies. Our results provide clear evidence of functionally dissociable roles of the prefrontal subregions and amygdala for human deception.

Question: What is the difference between telling a lie and deceiving others?


Practical Variational Inference for Neural Networks. Variational methods have been previously explored as a tractable approximation to Bayesian inference for neural networks. However the approaches proposed so far have only been applicable to a few simple network architectures. This paper introduces an easy-to-implement stochastic variational method (or equivalently, minimum description length loss function) that can be applied to most neural networks. Along the way it revisits several common regularisers from a variational perspective. It also provides a simple pruning heuristic that can both drastically reduce the number of network weights and lead to improved generalisation. Experimental results are provided for a hierarchical multidimensional recurrent neural network applied to the TIMIT speech corpus.

Question: What is a variational method?


Mode-Seeking on Hypergraphs for Robust Geometric Model Fitting. In this paper, we propose a novel geometric model fitting method, called Mode-Seeking on Hypergraphs (MSH), to deal with multi-structure data even in the presence of severe outliers. The proposed method formulates geometric model fitting as a mode seeking problem on a hypergraph in which vertices represent model hypotheses and hyperedges denote data points. MSH intuitively detects model instances by a simple and effective mode seeking algorithm. In addition to the mode seeking algorithm, MSH includes a similarity measure between vertices on the hypergraph and a "weight-aware sampling" technique. The proposed method not only alleviates sensitivity to the data distribution, but also is scalable to large scale problems. Experimental results further demonstrate that the proposed method has significant superiority over the state-of-the-art fitting methods on both synthetic data and real images.

Question: What is the difference between the proposed method and the state-of-the-art fitting methods?


On Plant Responses to D-Amino Acids Features of Growth , Root Behaviour and Selection for Plant Transformation. Amino acids have been regarded as potential plant nitrogen sources for more than a century. Some amino acids have been shown to support growth while others have growth-retarding effects. The D-isomers with notably adverse effects on plants' growth and development include D-serine and D-alanine. Recently, D-serine has been recognised as an endogenous ligand of receptor channels mediating calcium fluxes in plants, but otherwise little is known about endogenous roles of D-amino acids in plants. In the studies underlying this thesis, the negative responses to D-serine and D-alanine were converted to positive effects in Arabidopsis thaliana (Arabidopsis) plants by introducing either of two D-amino acid-metabolising enzymes. Transgenic Arabidopsis lines expressing either the D-serine dehydratase (dsdA) gene from Escherichia coli or the D-amino acid oxidase (DAO1) gene from Rhodotorula gracilis grew with otherwise toxic D-amino acids as the sole nitrogen source. I also expressed a transporter specific for D-amino acids, which further increased the transgenic plants' growth with D-serine as sole nitrogen source. Hence, both assimilation and uptake restrictions can limit plant growth on D-amino acids. The growth of transgenic lines on D-serine or D-alanine provides an unambiguous and highly visible phenotype, which is essential for a selectable marker. Thus, expressing of either the dsdA or DAO1 genes generated transformants that are easy to screen. Furthermore, the DAO1 gene can be readily used for either positive or negative selection, depending on the substrate, thus it provides a unique conditional, substrate-dependent positive/negative selectable marker for plant transformation. In summary, the presented work demonstrates that introducing the ability to catalyse a single metabolic step can allow plants to exploit an otherwise inaccessible or toxic form of orga

Question: What is the difference between a positive and negative selectable marker?


A-PNR: Automatic Plate Number Recognition. Automatic Plate Number Recognition (APNR) has important applications in traffic surveillance, toll booth, protected parking lot, No parking zone, etc. It is a challenging problem, especially when the license plates have varying sizes, the number of lines, fonts, background diversity etc. This work aims to address APNR using deep learning method for real-time traffic images. We first extract license plate candidates from each frame using edge information and geometrical properties, ensuring high recall using one class SVM. The verified candidates are used for NP recognition purpose along with a spatial transformer network (STN) for character recognition. Our system is evaluated on several traffic images with vehicles having different license plate formats in terms of tilt, distances, colors, illumination, character size, thickness etc. Also, the background was very challenging. Results demonstrate robustness to such variations and impressive performance in both the localization and recognition.

Question: What is the name of the method used in this paper?


Motivation and Second Language Acquisition 1. This paper posits four stages of language acquisition, identified as elemental, consolidation, conscious expression, and automaticity and thought, and considers the role of motivation in this process. It distinguishes between two types of motivation, language learning motivation and classroom motivation, indicating how these relate to two distinct contexts, the cultural and the educational through their influence on integrativeness and attitudes toward the learning situation. It discusses how the two types of motivation are differentially involved in the four stages, and empirical support for this perspective is presented in the form of path analyses of two samples of students from Catalonia.

Question: What is the difference between motivation and second language acquisition?


A Statistical Approach to Anaphora Resolution. This paper presents an algorithm for identifying pronominal anaphora and two experiments based upon this algorithm. We incorporate multiple anaphora resolution factors into a statistical framework specifically the distance between the pronoun and the proposed antecedent, gender/number/animaticity of the proposed antecedent, governing head information and noun phrase repetition. We combine them into a single probability that enables us to identify the referent. Our first experiment shows the relative contribution of each source Of information and demonstrates a success rate of 82.9% for all sources combined. The second experiment investigates a method for unsupervised learning of gender/number/animaticity information. We present some experiments illustrating the accuracy of the method and note that with this information added, our pronoun resolution method achieves 84.2% accuracy. 1 I n t r o d u c t i o n We present a statistical method for determining pronoun anaphora. This program differs from earlier work in its almost complete lack of hand-crafting, relying instead on a very small corpus of Penn Wall Street Journal Tree-bank text (Marcus et al., 1993) that has been marked with co-reference information. The first sections of this paper describe this program: the probabilistic model behind it, its implementation, and its performance. The second half of the paper describes a method for using (portions of) t~e aforementioned program to learn automatically the typical gender of English words, information that is itself used in the pronoun resolution program. In particular, the scheme infers the gender of a referent from the gender of the pronouns that 161 refer to it and selects referents using the pronoun anaphora program. We present some typical results as well as the more rigorous results of a blind evaluation of its output. 2 A P r o b a b i l i s t i c M o d e l There are many factors, both syntactic and semantic, u

Question: What is the size of the canadian military?


Employing U.S. Military Families to Provide Business Process Outsourcing Services: A Case study of Impact Sourcing and Reshoring. This paper describes how a startup business process outsourcing (BPO) provider named Liberty Source helped a large U.S.-based client reshore business services from an established Indian BPO provider. Founded in 2014, Liberty Source is a for-profit firm that provides a competitive alternative to offshoring while fulfilling its social mission to launch and sustain the careers of U.S. military spouses and veterans who face various employment disadvantages. Thus, the case describes reshoring in the context of impact sourcing. It addresses key impact sourcing issues pertaining to workforce development, scalability, and impact on employees. The impact was positive: the workers found the employment and stable salary were beneficial, "the military" culture fit well with the workers, and workers received considerable flexibility and greater career options. Liberty Source was able to reduce a client's costs after reshoring the client's processes because Liberty Source's U.S. site had about 20 percent fewer full time equivalents (FTEs) FTEs than the original India location and because Liberty Source received subsidies. We found evidence that the offshore BPO provider and Liberty source experienced difficulties with finding enough skilled staff for the wages offered and both firms experienced attrition problems, although attrition was greater in India.

Question: What is a case study?


"Good job, you're so smart": The effects of inconsistency of praise type on young children's motivation.. Previous research has demonstrated that generic praise ("good drawer") is related to children giving up after failure because failure implies the lack of a critical trait (e.g., drawing ability). Conversely, nongeneric praise ("good job drawing") is related to mastery motivation because it implies that success is related to effort. Yet children may receive a mixture of these praise types (i.e., inconsistent praise), the effects of which are unclear. We tested how inconsistent praise influenced two components of motivation: self-evaluation and persistence. Kindergarteners (N=135) were randomly assigned to one of five conditions in which consistency of praise type was varied. After two failure scenarios, children reported self-evaluations and persistence. Results indicated that more nongeneric praise related linearly to greater motivation, yet self-evaluation and persistence were impacted differently by inconsistent praise types. Hearing even a small amount of generic praise reduced persistence, whereas hearing a small amount of nongeneric praise preserved self-evaluation.

Question: How does praise affect children's motivation?


Compliance with a structured bedside handover protocol : An observational , multicentred study ☆. Background: Bedside handover is the delivery of the nurse-to-nurse shift handover at the patient's bedside. The method is increasingly used in nursing, but the evidence concerning the implementation process and compliance to the method is limited. Objectives: To determine the compliance with a structured bedside handover protocol following ISBARR and if there were differences in compliance between wards. Design: A multicentred observational study with unannounced and non-participatory observations (n=638) one month after the implementation of a structured bedside handover protocol. Settings and participants: Observations of individual patient handovers between nurses from the morning shift and the afternoon shift in 12 nursing wards in seven hospitals in Flanders, Belgium. Methods: A tailored and structured bedside handover protocol following ISBARR was developed, and nurses were trained accordingly. One month after implementation, a minimum of 50 observations were performed with a checklist, in each participating ward. To enhance reliability, 20% of the observations were conducted by two researchers, and inter-rater agreement was calculated. Data were analysed using descriptive statistics, one-way ANOVAs and multilevel analysis. Results: Average compliance rates to the structured content protocol during bedside handovers were high (83.63%; SD 11.44%), and length of stay, the type of ward and the nursing care model were influencing contextual factors. Items that were most often omitted included identification of the patient (46.27%), the introduction of nurses (36.51%), hand hygiene (35.89%), actively involving the patient (34.44%), and using the call light (21.37%). Items concerning the exchange of clinical information (e.g., test results, reason for admittance, diagnoses) were omitted less (8.09%–1.45%). Absence of the patients (27.29%) and staffing issues (26.70%) ac

Question: What is the compliance with a structured bedside handover protocol?


Laryngeal Tumor Detection and Classification in Endoscopic Video. The development of the narrow-band imaging (NBI) has been increasing the interest of medical specialists in the study of laryngeal microvascular network to establish diagnosis without biopsy and pathological examination. A possible solution to this challenging problem is presented in this paper, which proposes an automatic method based on anisotropic filtering and matched filter to extract the lesion area and segment blood vessels. Lesion classification is then performed based on a statistical analysis of the blood vessels' characteristics, such as thickness, tortuosity, and density. Here, the presented algorithm is applied to 50 NBI endoscopic images of laryngeal diseases and the segmentation and classification accuracies are investigated. The experimental results show the proposed algorithm provides reliable results, reaching an overall classification accuracy rating of 84.3%. This is a highly motivating preliminary result that proves the feasibility of the new method and supports the investment in further research and development to translate this study into clinical practice. Furthermore, to our best knowledge, this is the first time image processing is used to automatically classify laryngeal tumors in endoscopic videos based on tumor vascularization characteristics. Therefore, the introduced system represents an innovation in biomedical and health informatics.

Question: What is the classification accuracy of the proposed algorithm?


Evaluating collaborative filtering recommender systems. Recommender systems have been evaluated in many, often incomparable, ways. In this article, we review the key decisions in evaluating collaborative filtering recommender systems: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole. In addition to reviewing the evaluation strategies used by prior researchers, we present empirical results from the analysis of various accuracy metrics on one content domain where all the tested metrics collapsed roughly into three equivalence classes. Metrics within each equivalency class were strongly correlated, while metrics from different equivalency classes were uncorrelated.

Question: What is the difference between accuracy and precision?


A network in a laptop: rapid prototyping for software-defined networks. Mininet is a system for rapidly prototyping large networks on the constrained resources of a single laptop. The lightweight approach of using OS-level virtualization features, including processes and network namespaces, allows it to scale to hundreds of nodes. Experiences with our initial implementation suggest that the ability to run, poke, and debug in real time represents a qualitative change in workflow. We share supporting case studies culled from over 100 users, at 18 institutions, who have developed Software-Defined Networks (SDN). Ultimately, we think the greatest value of Mininet will be supporting collaborative network research, by enabling self-contained SDN prototypes which anyone with a PC can download, run, evaluate, explore, tweak, and build upon.

Question: What is a software-defined network?


Look who's talking: speech style and social context in language input to infants are linked to concurrent and future speech development.. Language input is necessary for language learning, yet little is known about whether, in natural environments, the speech style and social context of language input to children impacts language development. In the present study we investigated the relationship between language input and language development, examining both the style of parental speech, comparing 'parentese' speech to standard speech, and the social context in which speech is directed to children, comparing one-on-one (1:1) to group social interactions. Importantly, the language input variables were assessed at home using digital first-person perspective recordings of the infants' auditory environment as they went about their daily lives (N =26, 11- and 14-months-old). We measured language development using (a) concurrent speech utterances, and (b) word production at 24 months. Parentese speech in 1:1 contexts is positively correlated with both concurrent speech and later word production. Mediation analyses further show that the effect of parentese speech-1:1 on infants' later language is mediated by concurrent speech. Our results suggest that both the social context and the style of speech in language addressed to children are strongly linked to a child's future language development.

Question: What is the relationship between language input and language development?


Comparative accuracies of artificial neural networks and discriminant analysis in predicting forest cover types from cartographic variables. This study compared two alternative techniques for predicting forest cover types from cartographic variables. The study evaluated four wilderness areas in the Roosevelt National Forest, located in the Front Range of northern Colorado. Cover type data came from US Forest Service inventory information, while the cartographic variables used to predict cover type consisted of elevation, aspect, and other information derived from standard digital spatial data processed in a geographic information system (GIS). The results of the comparison indicated that a feedforward artificial neural network model more accurately predicted forest cover type than did a traditional statistical model based on Gaussian discriminant analysis. © 1999 Elsevier Science B.V. All rights reserved.

Question: What is the difference between a neural network and a discriminant analysis?


Web-based expert systems: benefits and challenges. Convergence of technologies in the Internet and the field of expert systems has offered new ways of sharing and distributing knowledge. However, there has been a general lack of research in the area of web-based expert systems (ES). This paper addresses the issues associated with the design, development, and use of web-based ES from a standpoint of the benefits and challenges of developing and using them. The original theory and concepts in conventional ES were reviewed and a knowledge engineering framework for developing them was revisited. The study considered three web-based ES: WITS-Advisor – for e-business strategy development, Fish-Expert – For fish disease diagnosis, and IMIS – to promote intelligent interviews. The benefits and challenges in developing and using ES are discussed by comparing them with traditional standalone systems from development and application perspectives.

Question: What is a web-based expert system?


A High-Quality Denoising Dataset for Smartphone Cameras. The last decade has seen an astronomical shift from imaging with DSLR and point-and-shoot cameras to imaging with smartphone cameras. Due to the small aperture and sensor size, smartphone images have notably more noise than their DSLR counterparts. While denoising for smartphone images is an active research area, the research community currently lacks a denoising image dataset representative of real noisy images from smartphone cameras with high-quality ground truth. We address this issue in this paper with the following contributions. We propose a systematic procedure for estimating ground truth for noisy images that can be used to benchmark denoising performance for smartphone cameras. Using this procedure, we have captured a dataset - the Smartphone Image Denoising Dataset (SIDD) - of ~30,000 noisy images from 10 scenes under different lighting conditions using five representative smartphone cameras and generated their ground truth images. We used this dataset to benchmark a number of denoising algorithms. We show that CNN-based methods perform better when trained on our high-quality dataset than when trained using alternative strategies, such as low-ISO images used as a proxy for ground truth data.

Question: What is a denoising dataset?


Texas 3D Face Recognition Database. We make the Texas 3D Face Recognition Database available to researchers in three dimensional (3D) face recognition and other related areas. This database contains 1149 pairs of high resolution, pose normalized, preprocessed, and perfectly aligned color and range images of 118 adult human subjects acquired using a stereo camera. The images are accompanied with information about the subjects' gender, ethnicity, facial expression, and the locations of 25 manually located anthropometric facial fiducial points. Specific partitions of the data for developing and evaluating 3D face recognition algorithms are also included.

Question: What is the size of the texas 3d face database?


LogicBlox, Platform and Language: A Tutorial. The modern enterprise software stack—a collection of applications supporting bookkeeping, analytics, planning, and forecasting for enterprise data—is in danger of collapsing under its own weight. The task of building and maintaining enterprise software is tedious and laborious; applications are cumbersome for end-users; and adapting to new computing hardware and infrastructures is difficult. We believe that much of the complexity in today's architecture is accidental, rather than inherent. This tutorial provides an overview of the LogicBlox platform, a ambitious redesign of the enterprise software stack centered around a unified declarative programming model, based on an extended version of Datalog. 1 The Enterprise Hairball Modern enterprise applications involve an enormously complex technology stack composed of many disparate systems programmed in a hodgepodge of programming languages. We refer to this stack, depicted in Figure 1, as "the enterprise hairball." First, there is an online transaction processing (OLTP) layer that performs bookkeeping of the core business data for an enterprise. Such data could include the current product catalog, recent sales figures, current outstanding invoices, customer account balances, and so forth. This OLTP layer typically includes a relational DBMS—programmed in a combination of a query language (SQL), a stored procedure language (like PL/SQL or TSQL), and a batch programming language like Pro*C—an application server, such as Oracle WebLogic [35], IBM WebSphere [36], or SAP NetWeaver [26]—programmed in an object-oriented language like Java, C#, or ABAP—and a web browser front-end, programmed using HTML and Javascript. In order to track the performance of the enterprise over time, a second business intelligence (BI) layer typically holds five to ten years of historical information that was originally recorded in the OLTP layer and performs read-only analyses on this information. Thi

Question: What is the enterprise hairball?


THE INSTITUTIONAL FOUNDATIONS OF PUBLIC POLICY : A TRANSACTIONS APPROACH WITH APPLICATION TO ARGENTINA *. Public policies are the outcomes of complex intertemporal exchanges among politicians. The basic institutional characteristics of a country constitute the framework within which those transactions are accomplished. We develop a transactions theory to understand the ways in which political institutions affect the transactions that political actors are able to undertake, and hence the policies that emerge. We argue that Argentina is a case in which the functioning of political institutions has been such that it prevented the capacity to undertake efficient intertemporal political exchanges. We use positive political theory and transaction cost economics to explain the workings of Argentine political institutions, and to show how that maps into low-quality policies.

Question: What is the institutional foundations of public policy?


Health chair: implicitly sensing heart and respiratory rate. People interact with chairs frequently, making them a potential location to perform implicit health sensing that requires no additional effort by users. We surveyed 550 participants to understand how people sit in chairs and inform the design of a chair that detects heart and respiratory rate from the armrests and backrests of the chair respectively. In a laboratory study with 18 participants, we evaluated a range of common sitting positions to determine when heart rate and respiratory rate detection was possible (32% of the time for heart rate, 52% for respiratory rate) and evaluate the accuracy of the detected rate (83% for heart rate, 73% for respiratory rate). We discuss the challenges of moving this sensing to the wild by evaluating an in-situ study totaling 40 hours with 11 participants. We show that, as an implicit sensor, the chair can collect vital signs data from its occupant through natural interaction with the chair.

Question: How do you measure the accuracy of a heart rate sensor?


Semi-Supervised Classification with Graph Convolutional Networks. We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs. We motivate the choice of our convolutional architecture via a localized first-order approximation of spectral graph convolutions. Our model scales linearly in the number of graph edges and learns hidden layer representations that encode both local graph structure and features of nodes. In a number of experiments on citation networks and on a knowledge graph dataset we demonstrate that our approach outperforms related methods by a significant margin.

Question: What is a citation network?


Semantic Labeling: A Domain-Independent Approach. Semantic labeling is the process of mapping attributes in data sources to classes in an ontology and is a necessary step in heterogeneous data integration. Variations in data formats, attribute names and even ranges of values of data make this a very challenging task. In this paper, we present a novel domain-independent approach to automatic semantic labeling that uses machine learning techniques. Previous approaches use machine learning to learn a model that extracts features related to the data of a domain, which requires the model to be re-trained for every new domain. Our solution uses similarity metrics as features to compare against labeled domain data and learns a matching function to infer the correct semantic labels for data. Since our approach depends on the learned similarity metrics but not the data itself, it is domainindependent and only needs to be trained once to work effectively across multiple domains. In our evaluation, our approach achieves higher accuracy than other approaches, even when the learned models are trained on domains other than the test domain.

Question: What is semantic labeling?


Functional reactive programming from first principles. Functional Reactive Programming, or FRP, is a general framework for programming hybrid systems in a high-level, declarative manner. The key ideas in FRP are its notions of behaviors and events. Behaviors are time-varying, reactive values, while events are time-ordered sequences of discrete-time event occurrences. FRP is the essence of Fran, a domain-specific language embedded in Haskell for programming reactive animations, but FRP is now also being used in vision, robotics and other control systems applications.  In this paper we explore the formal semantics of FRP and how itrelates to an implementation based on streams that represent (and therefore only approximate) continuous behaviors. We show that, in the limit as the sampling interval goes to zero, the implementation is faithful to the formal, continuous semantics, but only when certain constraints on behaviors are observed. We explore the nature of these constraints, which vary amongst the FRP primitives. Our results show both the power and limitations of this approach to language design and implementation. As an example of a limitation, we show that streams are incapable of representing instantaneous predicate events over behaviors.

Question: What is the difference between FRP and streams?


Group awareness in distributed software development. Open-source software development projects are almost always collaborative and distributed. Despite the difficulties imposed by distance, these projects have managed to produce large, complex, and successful systems. However, there is still little known about how open-source teams manage their collaboration. In this paper we look at one aspect of this issue: how distributed developers maintain group awareness. We interviewed developers, read project communication, and looked at project artifacts from three successful open source projects. We found that distributed developers do need to maintain awareness of one another, and that they maintain both a general awareness of the entire team and more detailed knowledge of people that they plan to work with. Although there are several sources of information, this awareness is maintained primarily through text-based communication (mailing lists and chat systems). These textual channels have several characteristics that help to support the maintenance of awareness, as long as developers are committed to reading the lists and to making their project communication public.

Question: How do distributed developers maintain group awareness?


Janus: a general purpose WebRTC gateway. This paper deals with the design and implementation of Janus, a general purpose, open source WebRTC gateway. Details will be provided on the architectural choices we took for Janus, as well as on the APIs we made available to extend and make use of it. Examples of how the gateway can be used for complex WebRTC applications are presented, together with some experimental results we collected during the development process.

Question: What is a general purpose WebRTC gateway?


Goalie Actions Attackers and Defenders Goalie Role Allocation Layer Action Selection Layer Vector Field Action Shoot Action Action Selection Layer Vector Field Action Shoot Action Action Selection Layer Vector Field Action Shoot Action Action Selection Layer Vector Field Action. The robot soccer system is being used as a test bed to develop the next generation of field robots. In the multiagent system, action selection is important for the cooperation and coordination among agents. There are many techniques in choosing a proper action of the agent. As the environment is dynamic, reinforcement learning is more suitable than supervised learning. Reinforcement learning is based on trial and error method through experience. Because of this, reinforcement learning is applied to many practical problems. However, straightforward application of reinforcement learning algorithm may not successfully scale up to more complex multi-agent learning problems. To solve this problem, modular Q-learning is employed for multi-agent learning in reinforcement methods. A modified uni-vector field is used for robot navigation. This paper discusses the Y2K2 NaroSot(Nano-Robot World Cup Soccer Tournament) robot soccer system.

Question: What is the Y2K2 NaroSot(Nano-Robot World Cup Soccer Tournament) robot soccer system?


Video-based emotion recognition in the wild using deep transfer learning and score fusion. Multimodal recognition of affective states is a difficult problem, unless the recording conditions are carefully controlled. For recognition "in the wild", large variances in face pose and illumination, cluttered backgrounds, occlusions, audio and video noise, as well as issues with subtle cues of expression are some of the issues to target. In this paper, we describe a multimodal approach for video-based emotion recognition in the wild. We propose using summarizing functionals of complementary visual descriptors for video modeling. These features include deep convolutional neural network (CNN) based features obtained via transfer learning, for which we illustrate the importance of flexible registration and fine-tuning. Our approach combines audio and visual features with least squares regression based classifiers and weighted score level fusion. We report state-of-the-art results on the EmotiW Challenge for "in the wild" facial expression recognition. Our approach scales to other problems, and ranked top in the ChaLearn-LAP First Impressions Challenge 2016 from video clips collected in the wild.

Question: How does the canadian military work?


Personalized Online Education - A Crowdsourcing Challenge. Interest in online education is surging, as dramatized by the success of Khan Academy and recent Stanford online courses, but the technology for online education is in its infancy. Crowdsourcing mechanisms will likely be essential in order to reach the full potential of this medium. This paper sketches some of the challenges and directions we hope HCOMP researchers will ad-

Question: What is the full potential of online education?


Geographical Routing With Location Service in Intermittently Connected MANETs. Combining mobile platforms such as manned or unmanned vehicles and peer-assisted wireless communication is an enabler for a vast number of applications. A key enabler for the applications is the routing protocol that directs the packets in the network. Routing packets in fully connected mobile ad hoc networks (MANETs) has been studied to a great extent, but the assumption on full connectivity is generally not valid in a real system. This case means that a practical routing protocol must handle intermittent connectivity and the absence of end-to-end connections. In this paper, we propose a geographical routing algorithm called location-aware routing for delay-tolerant networks (LAROD), enhanced with a location service, location dissemination service (LoDiS), which together are shown to suit an intermittently connected MANET (IC-MANET). Because location dissemination takes time in IC-MANETs, LAROD is designed to route packets with only partial knowledge of geographic position. To achieve low overhead, LAROD uses a beaconless strategy combined with a position-based resolution of bids when forwarding packets. LoDiS maintains a local database of node locations, which is updated using broadcast gossip combined with routing overhearing. The algorithms are evaluated under a realistic application, i.e., unmanned aerial vehicles deployed in a reconnaissance scenario, using the low-level packet simulator ns-2. The novelty of this paper is the illustration of sound design choices in a realistic application, with holistic choices in routing, location management, and the mobility model. This holistic approach justifies that the choice of maintaining a local database of node locations is both essential and feasible. The LAROD-LoDiS scheme is compared with a leading delay-tolerant routing algorithm (spray and wait) and is shown to have a competitive edge, both in terms of delivery ratio and overhead. For

Question: What is a realistic application?


Comprehensive Database for Facial Expression Analysis. Within the past decade, significant effort has occurred in developing methods of facial expression analysis. Because most investigators have used relatively limited data sets, the generalizability of these various methods remains unknown. We describe the problem space for facial expression analysis, which includes level of description, transitions among expression, eliciting conditions, reliability and validity of training and test data, individual differences in subjects, head orientation and scene complexity, image characteristics, and relation to non-verbal behavior. We then present the CMU-Pittsburgh AU-Coded Face Expression Image Database, which currently includes 2105 digitized image sequences from 182 adult subjects of varying ethnicity, performing multiple tokens of most primary FACS action units. This database is the most comprehensive test-bed to date for comparative studies of facial expression

Question: What is the most comprehensive database for facial expression analysis?


Intelligent Device-to-Device Communication in the Internet of Things. Analogous to the way humans use the Internet, devices will be the main users in the Internet of Things (IoT) ecosystem. Therefore, device-to-device (D2D) communication is expected to be an intrinsic part of the IoT. Devices will communicate with each other autonomously without any centralized control and collaborate to gather, share, and forward information in a multihop manner. The ability to gather relevant information in real time is key to leveraging the value of the IoT as such information will be transformed into intelligence, which will facilitate the creation of an intelligent environment. Ultimately, the quality of the information gathered depends on how smart the devices are. In addition, these communicating devices will operate with different networking standards, may experience intermittent connectivity with each other, and many of them will be resource constrained. These characteristics open up several networking challenges that traditional routing protocols cannot solve. Consequently, devices will require intelligent routing protocols in order to achieve intelligent D2D communication. We present an overview of how intelligent D2D communication can be achieved in the IoT ecosystem. In particular, we focus on how state-of-the-art routing algorithms can achieve intelligent D2D communication in the IoT.

Question: What is the Internet of Things?


57.5GHz bandwidth 4.8Vpp swing linear modulator driver for 64GBaud m-PAM systems. A novel series-stacked large swing push-pull MOS-HBT driver was implemented in 55nm SiGe BiCMOS technology. The circuit achieves 4.8Vpp differential swing, 57.5GHz band-width and has an output compression point of 12 dBm per side. 4-PAM and 8-PAM eye diagrams were measured at 56 GBaud for a record data rate of 168 Gb/s. 4-PAM 64GBaud eye diagrams were also demonstrated. The circuit consumes 820/600 mW with/without the predriver, for an energy efficiency of 4.88/3.57 pJ/b.

Question: What is the maximum data rate of a 4-PAM system?


Information-centric sensor networks for cognitive IoT: an overview. Information-centric sensor networks (ICSNs) are a paradigm of wireless sensor networks that focus on delivering information from the network based on user requirements, rather than serving as a point-to-point data communication network. Introducing learning in such networks can help to dynamically identify good data delivery paths by correlating past actions and results, make intelligent adaptations to improve the network lifetime, and also improve the quality of information delivered by the network to the user. However, there are several factors and limitations that must be considered while choosing a learning strategy. In this paper, we identify some of these factors and explore various learning techniques that have been applied to sensor networks and other applications with similar requirements in the past. We provide our recommendation on the learning strategy based on how well it complements the needs of ICSNs, while keeping in mind the cost, computation, and operational overhead limitations.

Question: What is an information-centric sensor network?


Pattern codification strategies in structured light systems. Coded structured light is considered one of the most reliable techniques for recovering the surface of objects. This technique is based on projecting a light pattern and viewing the illuminated scene from one or more points of view. Since the pattern is coded, correspondences between image points and points of the projected pattern can be easily found. The decoded points can be triangulated and 3D information is obtained. We present an overview of the existing techniques, as well as a new and de nitive classi cation of patterns for structured light sensors. We have implemented a set of representative techniques in this eld and present some comparative results. The advantages and constraints of the di5erent patterns are also discussed. ? 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

Question: What is the difference between a coded structured light system and a coded structured light sensor?


Momentum and Credit Rating. This paper establishes a strong link between momentum and firm credit rating. Momentum profitability is statistically significant and economically large among low-grade firms, but it is nonexistent among high-grade firms. The momentum payoffs documented in the literature are generated by low rated firms that account for less than four percent of the overall market capitalization of rated firms. For loser (winner) stocks in the low rating category, profit margins, sales growth, operating cash flows, and interest coverage decrease (increase) over the formation and holdings periods, while illiquidity and volatility increase (decrease). This operating performance of the winner and loser stocks is not anticipated by the market as evidenced by the earnings surprises and analyst forecast revisions. As the market observes the deteriorating (improving) conditions there is a pressure to sell (buy) losers (winners). Indeed, we show that institutional investors buy winners and sell losers during one year into the holding period. Such buying and selling activity accompanied with high illiquidity precipitates the gains among high credit-risk winners and losses among high credit-risk losers.

Question: What is the link between momentum and credit rating?


A Deep Learning Architecture for Image Representation, Visual Interpretability and Automated Basal-Cell Carcinoma Cancer Detection. This paper presents and evaluates a deep learning architecture for automated basal cell carcinoma cancer detection that integrates (1) image representation learning, (2) image classification and (3) result interpretability. A novel characteristic of this approach is that it extends the deep learning architecture to also include an interpretable layer that highlights the visual patterns that contribute to discriminate between cancerous and normal tissues patterns, working akin to a digital staining which spotlights image regions important for diagnostic decisions. Experimental evaluation was performed on set of 1,417 images from 308 regions of interest of skin histopathology slides, where the presence of absence of basal cell carcinoma needs to be determined. Different image representation strategies, including bag of features (BOF), canonical (discrete cosine transform (DCT) and Haar-based wavelet transform (Haar)) and proposed learned-from-data representations, were evaluated for comparison. Experimental results show that the representation learned from a large histology image data set has the best overall performance (89.4% in F-measure and 91.4% in balanced accuracy), which represents an improvement of around 7% over canonical representations and 3% over the best equivalent BOF representation.

Question: What is the best representation for image classification?


Interlocking obfuscation for anti-tamper hardware. Tampering and Reverse Engineering of a chip to extract the hardware Intellectual Property (IP) core or to inject malicious alterations is a major concern. Digital systems susceptible to tampering are of immense concern to defense organizations. First, offshore chip manufacturing allows the design secrets of the IP cores to be transparent to the foundry and other entities along the production chain. Second, small malicious modifications to the design may not be detectable after fabrication without anti-tamper mechanisms. Some techniques have been developed in the past to improve the defense against such attacks but they tend to fall prey to the increasing power of the attacker. We present a new way to protect against tampering by a clever obfuscation of the design, which can be unlocked with a specific, dynamic path traversal. Hence, the functional mode of the controller is hidden with the help of obfuscated states, and the functional mode is made operational only on the formation of a specific interlocked Code-Word during state transition. No comparator is needed as the obfuscation is embedded within the transition function of the state machine itself. A side benefit is that any small alteration will be magnified via the obfuscated design. In other words, an alteration to the design will manifest itself as a large difference in the circuit's functionality. Experimental results on an Advanced Encryption Standard (AES) circuit from the open-source IP-cores suite suggest that the proposed method provides better active defense mechanisms against attacks with nominal (7.8%) area overhead.

Question: How to protect against tampering?


Part-of-Speech Tagging for Twitter: Annotation, Features, and Experiments. We address the problem of part-of-speech tagging for English data from the popular microblogging service Twitter. We develop a tagset, annotate data, develop features, and report tagging results nearing 90% accuracy. The data and tools have been made available to the research community with the goal of enabling richer text analysis of Twitter and related social media data sets.

Question: What is a microblogging service?


The Synthetic Data Vault. The goal of this paper is to build a system that automatically creates synthetic data to enable data science endeavors. To achieve this, we present the Synthetic Data Vault (SDV), a system that builds generative models of relational databases. We are able to sample from the model and create synthetic data, hence the name SDV. When implementing the SDV, we also developed an algorithm that computes statistics at the intersection of related database tables. We then used a state-of-the-art multivariate modeling approach to model this data. The SDV iterates through all possible relations, ultimately creating a model for the entire database. Once this model is computed, the same relational information allows the SDV to synthesize data by sampling from any part of the database. After building the SDV, we used it to generate synthetic data for five different publicly available datasets. We then published these datasets, and asked data scientists to develop predictive models for them as part of a crowdsourced experiment. By analyzing the outcomes, we show that synthetic data can successfully replace original data for data science. Our analysis indicates that there is no significant difference in the work produced by data scientists who used synthetic data as opposed to real data. We conclude that the SDV is a viable solution for synthetic data generation.

Question: What is the goal of this paper?


Privacy and trust in Facebook photo sharing: age and gender differences. Purpose – The purpose of this paper is to examine gender and age differences regarding various aspects of privacy, trust, and activity on one of the most popular Facebook activity – "photo sharing." Design/methodology/approach – The data were collected using an online survey hosted by a web-based survey service for three weeks during December 2014-January 2015. The target audience comprised of Facebook users over 18 years engaged in sharing their photos on the platform. Findings –Women and young Facebook users are significantly more concerned about the privacy of their shared photos. Meanwhile, users from older age groups are less active in using the site, in sharing photos, and in taking privacy-related protective measures. Interestingly, despite having more privacy concerns, young Facebook users display higher trust levels toward the platform than older users. Overall, in the study, there was an extremely significant difference in privacy attitudes among people under and over 35 years of age. Originality/value – The main contribution of this study is new knowledge regarding the gender and age differences in various privacy-related aspects, trust, and activity. Findings from the study broadens the overall understanding of how these issues positively/negatively influence the photosharing activity on Facebook.

Question: What is the difference between privacy and trust?


The Evolution of Culturally-Variable Sex Differences : Men and Women Are Not Always Different , but When They Are ... It Appears Not to Result from Patriarchy or Sex Role Socialization. © Springer International Publishing Switzerland 2014 T. K. Shackelford, R. D. Hansen (eds.), The Evolution of Sexuality, Evolutionary Psychology DOI 10.1007/978-3-319-09384-0_11 D. P. Schmitt () Department of Psychology, Bradley University, 75 Bradley Hall, Peoria, IL 61625, USA e-mail: dps@bradley.edu Just like all other sexually reproducing species, male and female humans are more similar than different. Even so, men's and women's psychological traits sometimes differ in important ways, both in terms of typical or average levels (Buss 1989; Del Giudice 2009; Ellis 2011) and in terms of variability (Archer and Mehdikhani 2003; Borkenau et al. 2013; Lippa 2009). Sex differences in numerous traits have been well-established as moderate to large in size1 and as culturally pervasive. For example, sex differences in negative emotion-related traits have been documented across several meta-analyses (Feingold 1994; Miettunen et al. 2007; Whissell 1996), integrative neuroscientific reviews (Hyde et al. 2008; Stevens and Hamann 2012), and large cross-cultural surveys (Costa et al. 2001; Hopcroft and McLaughlin 2012; Lippa 2009; McCrae and Terracciano 2005; Schmitt et al. 2008; Van de Velde et al. 2010). Using a multivariate approach, Del Giudice et al. (2012) documented across 16 personality traits—ranging from dominance and liveliness to perfectionism and tension—that sex differences in personality are astonishingly large, with only 10 % overlap in men's and women's overall distributions. Beyond sex differences in personality traits, psychologists have uncovered dozens of ways that men and women differ in affect, behavior, and cognition across most cultures (Archer 2014; Browne 1998; Mealey 2000). In one comprehensive review, Ellis (2011) identified 63 psychological sex differences that hav

Question: What is the difference between men and women?


It's All Fun and Games until Someone Annotates: Video Games with a Purpose for Linguistic Annotation. Annotated data is prerequisite for many NLP applications. Acquiring large-scale annotated corpora is a major bottleneck, requiring significant time and resources. Recent work has proposed turning annotation into a game to increase its appeal and lower its cost; however, current games are largely text-based and closely resemble traditional annotation tasks. We propose a new linguistic annotation paradigm that produces annotations from playing graphical video games. The effectiveness of this design is demonstrated using two video games: one to create a mapping from WordNet senses to images, and a second game that performs Word Sense Disambiguation. Both games produce accurate results. The first game yields annotation quality equal to that of experts and a cost reduction of 73% over equivalent crowdsourcing; the second game provides a 16.3% improvement in accuracy over current state-of-the-art sense disambiguation games with WordNet.

Question: How to annotate video games?


Definition and Multidimensionality of Security Awareness: Close Encounters of the Second Order. This study proposes and examines a multidimensional definition of information security awareness. We also investigate its antecedents and analyze its effects on compliance with organizational information security policies. The above research goals are tested through the theoretical lens of technology threat avoidance theory and protection motivation theory. Information security awareness is defined as a second-order construct composed of the elements of threat and coping appraisals supplemented by the responsibilities construct to account for organizational environment. The study was executed in two stages. First, the participants (employees of a municipality) were exposed to a series of phishing messages. Second, the same individuals were asked to participate in a survey designed to examine their security awareness. The research model was tested using PLS-SEM approach. The results indicate that security awareness is in fact a second-order formative construct composed of six components. There are significant differences in security awareness levels between the victims of the phishing experiment and the employees who maintain compliance with security policies. Our study extends the theory by proposing and validating a general, yet practical definition of security awareness. It also bridges the gap between theory and practice - our contextualization of security awareness draws heavily on both fields.

Question: What is the difference between a phishing attack and a phishing email?


Data quality management in the public domain: a case study within the Dutch justice system. The need for anonymity preservation within the justice domain requires the introduction of a trusted third party as an intermediary, while integrating individual databases within its boundaries. After the trusted third party encrypts records, it is no longer possible to perform checks on the data quality and correct data anomalies. Therefore, this research examines the concepts of data quality, data integration, record linkage and trusted third party and, then, combines these with four expert interviews in order to identify ways to assess and improve data quality while linking privacy-sensitive data. Next, the trusted data linkage framework (TDLF) is presented to aid data quality management while combining citizens' privacy-sensitive data from different organisations. Finally, we evaluate the framework in a case study to demonstrate how the quality of structured judicial data can be managed prior to its encryption, while using multiple databases as sources of data and a different final recipient.

Question: What is the trusted third party?


A virtual translation technique to improve current regulator for salient-pole AC machines. This paper presents a synchronous frame current regulator for salient-pole AC machines, which is insensitive to both parameters and synchronous frequencies. The transformation from stationary to synchronous frame is known to produce a frame-dependent cross-coupling term, which results in degradation in current response at high speeds and is represented as an asymmetric pole in the complex vector form of such systems. The asymmetric pole close to right half plane (RHP) in the complex vector s-domain cannot be perfectly canceled by parameter-based design of PI controllers due to parameter variation. Complex vector design synchronous frame PI current regulator with a virtually translated asymmetric pole is shown in this paper to reduce the parameter sensitivity and to improve disturbance rejection. The current regulator with the virtually translated pole have been analyzed in complex vector form for nonsalient-pole AC machine and in scalar form for salient-pole AC machines. It has been shown to yield comparable performances in both cases. This paper includes experimental verification of the performance of the proposed current regulator for an IPMSM.

Question: What is a virtual translation technique?


Medusa: Simplified Graph Processing on GPUs. Graphs are common data structures for many applications, and efficient graph processing is a must for application performance. Recently, the graphics processing unit (GPU) has been adopted to accelerate various graph processing algorithms such as BFS and shortest paths. However, it is difficult to write correct and efficient GPU programs and even more difficult for graph processing due to the irregularities of graph structures. To simplify graph processing on GPUs, we propose a programming framework called Medusa which enables developers to leverage the capabilities of GPUs by writing sequential C/C++ code. Medusa offers a small set of user-defined APIs and embraces a runtime system to automatically execute those APIs in parallel on the GPU. We develop a series of graph-centric optimizations based on the architecture features of GPUs for efficiency. Additionally, Medusa is extended to execute on multiple GPUs within a machine. Our experiments show that 1) Medusa greatly simplifies implementation of GPGPU programs for graph processing, with many fewer lines of source code written by developers and 2) the optimization techniques significantly improve the performance of the runtime system, making its performance comparable with or better than manually tuned GPU graph operations.

Question: What is a graph processing unit?


Network bandwidth predictor (NBP): a system for online network performance forecasting. The applicability of network-based computing depends on the availability of the underlying network bandwidth. However, network resources are shared and the available network bandwidth varies with time. There is no satisfactory solution available for network performance predictions. In this research, we propose, design, and implement the NBP (network bandwidth predictor) for rapid network performance prediction. NBP is a new system that employs a neural network based approach for network bandwidth forecasting. This system is designed to integrate with most advanced technologies. It employs the NWS (network weather service) monitoring subsystem to measure the network traffic, and provides an improved, more accurate performance prediction than that of NWS, especially with applications with a network usage pattern. The NBP system has been tested on real time data collected by NWS monitoring subsystem and on trace files. Experimental results confirm that NBP has an improved prediction.

Question: What is the difference between NBP and NWS?


Intentional behaviour in dog-human communication: an experimental analysis of "showing" behaviour in the dog. Despite earlier scepticism there is now evidence for simple forms of intentional and functionally referential communication in many animal species. Here we investigate whether dogs engage in functional referential communication with their owners. "Showing" is defined as a communicative action consisting of both a directional component related to an external target and an attention-getting component that directs the attention of the perceiver to the informer or sender. In our experimental situation dogs witness the hiding of a piece of food (or a favourite toy) which they cannot get access to. We asked whether dogs would engage in "showing" in the presence of their owner. To control for the motivational effects of both the owner and the food on the dogs' behaviour, control observations were also staged where only the food (or the toy) or the owner was present. Dogs' gazing frequency at both the food (toy) and the owner was greater when only one of these was present. In other words, dogs looked more frequently at their owner when the food (toy) was present, and they looked more at the location of the food (toy) when the owner was present. When both the food (toy) and the owner were present a new behaviour, "gaze alternation", emerged which was defined as changing the direction of the gaze from the location of the food (toy) to looking at the owner (or vice versa) within 2 s. Vocalisations that occurred in this phase were always associated with gazing at the owner or the location of the food. This behaviour, which was specific to this situation, has also been described in chimpanzees, a gorilla and humans, and has often been interpreted as a form of functionally referential communication. Based on our observations we argue that dogs might be able to engage in functionally referential communication with their owner, and their behaviour could be described as a fo

Question: What is the difference between a dog and a chimpanzee?


Automated Fingerprint Identification and Imaging Systems. More than a century has passed since Alphonse Bertillon first conceived and then industriously practiced the idea of using body measurements for solving crimes [1]. Just as his idea was gaining popularity, it faded into relative obscurity by a far more significant and pract ic l discovery of the uniqueness of the human fingerprints 1. Soon after this discovery, many major law enforcement departments embraced the idea of first "booking" the fingerprints of criminals, so tha t their records are readily available and later using leftover fingerprint smudges (latent s), the identity of criminals can be determined. These agencies sponsored a rigorous study of fingerprints, developed s cientific 1In 1893, the Home Ministry Office, UK, accepted that no two ind ividuals have the same fingerprints.

Question: What is the difference between a fingerprint and a palm print?


Millimeter-Wave MultiBeam Aperture-Coupled Magnetoelectric Dipole Array With Planar Substrate Integrated Beamforming Network for 5G Applications. A modified topology of the 2-D multibeam antenna array fed by a passive beamforming network (BFN) is proposed by introducing two sets of vertical interconnections into the conventional array configuration. Different from the traditional design, the new array structure can be integrated into multilayered planar substrates conveniently, which has advantages of low loss characteristics, ease of realization, and low fabrication cost for millimeter-wave (mmWave) applications. A  $4\times4$  multibeam antenna array that can generate 16 beams is then designed, fabricated, and measured in order to demonstrate the correctness of the proposed topology. As the crucial components of the design, two kinds of aperture-coupled magnetoelectric dipole antenna elements with multilayered feed structures and three types of vertical interconnections consisting of the vertical substrate integrated waveguides are investigated, respectively. Wide bandwidth of 16.4%, stable radiation beams, and gain up to 14.7 dBi are achieved by the fabricated prototype. The proposed array configuration provides a new mean to implement the relatively large size 2-D multibeam antenna arrays with planar passive BFNs, which would be attractive for future mmWave wireless systems used for 5G communications and other applications.

Question: What is the difference between the traditional and the proposed array configuration?


Enhancing bank security system using Face Recognition, Iris Scanner and Palm Vein Technology. The objective of this paper is to design a bank locker security system which is using Face Recognition, Iris Scanner and Palm Vein Technology (PVR) for securing valuable belongings. A face recognition system is a system which identifies and authenticates the image of the authorized user by using MATLAB software. The images of a person entering a unrestricted zone are taken by the camera and software compares the image with an existing database of valid users. Iris Recognition system uses generous characteristics present in human body. This technology is employed for biometric authentication in ATM's, Immigration & border control, public safety, hospitality and tourism etc. This paper serves techniques so that capability of palm vein recognition system can be improved using modified vascular pattern thinning algorithm. Palm Vein Recognition (PVR) is a technology which recognizes palm vein pattern of an individual and matches it with the data stored in database for authentication. This is a very reliable technique and has a very good accuracy and is considered as the most secure and better technique for security purposes.

Question: What is the objective of this paper?


MICA - A new generation of versatile instruments in robotic surgery. Robotic surgery systems are highly complex and expensive pieces of equipment. Demands for lower cost of care can be met if these systems are employable in a flexible manner for a large variety of procedures. To protect the initial investment the capabilities of a robotic system need to be expandable as new tasks arise.

Question: What is a robotic surgery system?


Mobile Robot Path Planning with Randomly Moving Obstacles and Goal. This article presents the dynamic path planning for a mobile robot to track a randomly moving goal with avoidance of multiple randomly moving obstacles. The main feature of the developed scheme is its capability of dealing with the situation that the paths of both the goal and the obstacles are unknown a priori to the mobile robot. A new mathematical approach that is based on the concepts of 3-D geometry is proposed to generate the path of the mobile robot. The mobile robot decides its path in real time to avoid the randomly moving obstacles and to track the randomly moving goal. The developed scheme results in faster decision-making for successful goal tracking. 3-D simulations using MATLAB validate the developed scheme.

Question: How to generate a path for a mobile robot?


Electromagnetic Vortex Imaging Using Uniform Concentric Circular Arrays. Electromagnetic vortex imaging based on orbital angular momentum (OAM) modulation has the ability of azimuthal resolution without relative motion, which needs various OAM modes. However, the main-lobe directions of different OAM beams differ from each other so that the echo energy is limited. In this letter, several concentric circular arrays are employed to adjust the directivity, each array generating a beam carrying special OAM mode. With this scheme, the target can be illuminated by main lobes of all OAM beams simultaneously. Our proposal can benefit the image reconstruction in noisy environment. Moreover, this scheme can successfully reduce the artifacts caused by high sidelobes at the cost of reduction in azimuthal resolution. Simulation results demonstrate the effectiveness of this scheme.

Question: How to reduce the artifacts caused by high sidelobes at the cost of reduction in azimuthal resolution?


10.6 THz figure-of-merit phase-change RF switches with embedded micro-heater. We report on GeTe-based phase-change RF switches with embedded micro-heater for thermal switching. With heater parasitics reduced, GeTe RF switches show onstate resistance of 0.05 ohm*mm and off-state capacitance of 0.3 pF/mm. The RF switch figure-of-merit is estimated to be 10.6 THz, which is about 15 times better than state-of-the-art silicon-on-insulator switches. With on-state resistance of 1 ohm and off-state capacitance of 15 fF, RF insertion loss was measured at <;0.2 dB, and isolation was >25 dB at 20 GHz, respectively. RF power handling was >5.6 W for both onand off-state of GeTe.

Question: What is the figure-of-merit of a phase-change RF switch?


Information theory-based shot cut/fade detection and video summarization. New methods for detecting shot boundaries in video sequences and for extracting key frames using metrics based on information theory are proposed. The method for shot boundary detection relies on the mutual information (MI) and the joint entropy (JE) between the frames. It can detect cuts, fade-ins and fade-outs. The detection technique was tested on the TRECVID2003 video test set having different types of shots and containing significant object and camera motion inside the shots. It is demonstrated that the method detects both fades and abrupt cuts with high accuracy. The information theory measure provides us with better results because it exploits the inter-frame information in a more compact way than frame subtraction. It was also successfully compared to other methods published in literature. The method for key frame extraction uses MI as well. We show that it captures satisfactorily the visual content of the shot.

Question: What is the information theory based shot cut/fade detection and video summarization?


Industrial Applications of Pulsed Power Technology. A review of mainly the past two years is undertaken of the industrial applications of pulsed power. Repetitively operated pulsed power generators with a moderate peak power have been developed for industrial applications. These generators are reliable and have low maintenance. Development of the pulsed power generators helps promote industrial applications of pulsed power for such things as food processing, medical treatment, water treatment, exhaust gas treatment, ozone generation, engine ignition, ion implantation and others. Here, industrial applications of pulsed power are classified by application for biological effects, for pulsed streamer discharges in gases, for pulsed discharges in liquid or liquid- mixture, and for material processing.

Question: What are the industrial applications of pulsed power?


C 1-Almost Periodic Solutions of BAM Neural Networks with Time-Varying Delays on Time Scales. On a new type of almost periodic time scales, a class of BAM neural networks is considered. By employing a fixed point theorem and differential inequality techniques, some sufficient conditions ensuring the existence and global exponential stability of C1-almost periodic solutions for this class of networks with time-varying delays are established. Two examples are given to show the effectiveness of the proposed method and results.

Question: What is a neural network?


Factors Affecting the Adoption of Electronic Word-of-Mouth in the Tourism Industry. This paper proposes a theoretical framework to explain the factors that influence consumer adoption of electronic word-of-mouth (eWOM) in the decision to purchase travel-related services. Adoption of eWOM is posited to be influenced by the perceived usefulness of eWOM. The perceived usefulness of eWOM, in turn, is expected to be influenced by the nature of eWOM characterized by message basis, positivity, valence, elaborateness, and timeliness; eWOM quantity; source credibility in the form of message forum type, reviewer identity disclosure, expertise, and reputation; and consumers' prior knowledge of the services being considered. Managerial implications of the framework are also discussed.

Question: What is the theoretical framework for the adoption of electronic word-of-mouth in the tourism industry?


Big data analytics on traditional HPC infrastructure using two-level storage. Data-intensive computing has become one of the major workloads on traditional high-performance computing (HPC) clusters. Currently, deploying data-intensive computing software framework on HPC clusters still faces performance and scalability issues. In this paper, we develop a new two-level storage system by integrating Tachyon, an in-memory file system with OrangeFS, a parallel file system. We model the I/O throughputs of four storage structures: HDFS, OrangeFS, Tachyon and two-level storage. We conduct computational experiments to characterize I/O throughput behavior of two-level storage and compare its performance to that of HDFS and OrangeFS, using TeraSort benchmark. Theoretical models and experimental tests both show that the two-level storage system can increase the aggregate I/O throughputs. This work lays a solid foundation for future work in designing and building HPC systems that can provide a better support on I/O intensive workloads with preserving existing computing resources.

Question: What is the aggregate I/O throughput of two-level storage?


THE RELATIONSHIPS BETWEEN IT FLEXIBILITY, IT-BUSINESS STRATEGIC ALIGNMENT, AND IT CAPABILITY. What seems to still be the main concern for managers in the corporate world across the globe is ITbusiness strategic alignment. This study seeks to address the research problem about the lack of alignment between IT and business strategies. Upon reviewing various literature on this subject, it was found that IT flexibility is one of the most vital factors that help sustain strategic alignment. The researcher upon having a detailed discussion on the possible areas associated with the present body of knowledge has discovered gaps in the studies that have been undertaken on strategic alignment and IT flexibility. This is because IT capability in relation to IT flexibility and strategic alignment has been ignored in the previous studies. As a result, this research proposes a relationship between IT flexibility (i.e., modularity, connectivity and compatibility), IT capability, and strategic alignment.

Question: What is the relationship between IT flexibility and IT capability?


EEG Signal Classification Using Wavelet Feature Extraction and Neural Networks. Decision support systems have been utilised since 1960, providing physicians with fast and accurate means towards more accurate diagnoses and increased tolerance when handling missing or incomplete data. This paper describes the application of neural network models for classification of electroencephalogram (EEG) signals. Decision making was performed in two stages: initially, a feature extraction scheme using the wavelet transform (WT) has been applied and then a learning-based algorithm classifier performed the classification. The performance of the neural model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed scheme has potential in classifying the EEG signals

Question: What is the wavelet transform?


Attention, please! A Critical Review of Neural Attention Models in Natural Language Processing. Attention is an increasingly popular mechanism used in a wide range of neural architectures. Because of the fast-paced advances in this domain, a systematic overview of attention is still missing. In this article, we define a unified model for attention architectures for natural language processing, with a focus on architectures designed to work with vector representation of the textual data. We discuss the dimensions along which proposals differ, the possible uses of attention, and chart the major research activities and open challenges in the area.

Question: What is a unified model for attention architectures in natural language processing?


Vehicle License Plate Recognition Based on Extremal Regions and Restricted Boltzmann Machines. This paper presents a vehicle license plate recognition method based on character-specific extremal regions (ERs) and hybrid discriminative restricted Boltzmann machines (HDRBMs). First, coarse license plate detection (LPD) is performed by top-hat transformation, vertical edge detection, morphological operations, and various validations. Then, character-specific ERs are extracted as character regions in license plate candidates. Followed by suitable selection of ERs, the segmentation of characters and coarse-to-fine LPD are achieved simultaneously. Finally, an offline trained pattern classifier of HDRBM is applied to recognize the characters. The proposed method is robust to illumination changes and weather conditions during 24 h or one day. Experimental results on thorough data sets are reported to demonstrate the effectiveness of the proposed approach in complex traffic environments.

Question: What is the license plate recognition method based on character-specific extremal regions and restricted Boltzmann machines?


(Invited) Nanoimprinted perovskite for optoelectronics. Organic-inorganic hybrid perovskites have recently emerged as promising materials for optoelectronics. Here we show successful patterning of hybrid perovskite into nanostructures with cost-effective nanoimprint technology. Photodetectors are fabricated on nanoimprinted perovskite with improved responsivity. Nanoimprinted perovskite metasurface forms with significantly enhanced photoluminescence. Lasing is expected on nanoimprinted perovskite with optimized cavity design and process.

Question: What is the cost of nanoimprinting perovskite?


Attention Strategies for Multi-Source Sequence-to-Sequence Learning. Modeling attention in neural multi-source sequence-to-sequence learning remains a relatively unexplored area, despite its usefulness in tasks that incorporate multiple source languages or modalities. We propose two novel approaches to combine the outputs of attention mechanisms over each source sequence, flat and hierarchical. We compare the proposed methods with existing techniques and present results of systematic evaluation of those methods on the WMT16 Multimodal Translation and Automatic Post-editing tasks. We show that the proposed methods achieve competitive results on both tasks.

Question: Attention Strategies for Multi-Source Sequence-to-Sequence Learning.


Reducing Buyer Search Costs : Implications for Electronic Marketplaces. Information systems can serve as intermediaries between the buyers and the sellers in a market, creating an "electronic marketplace" that lowers the buyers' cost to acquire information about seller prices and product offerings. As a result, electronic marketplaces reduce the inefficiencies caused by buyer search costs, in the process reducing the ability of sellers to extract monopolistic profits while increasing the ability of markets to optimally allocate productive resources. This article models the role of buyer search costs in markets with differentiated product offerings. The impact of reducing these search costs is analyzed in the context of an electronic marketplace, and the allocational efficiencies such a reduction can bring to a differentiated market are formalized. The resulting implications for the incentives of buyers, sellers and independent intermediaries to invest in electronic marketplaces are explored. Finally, the possibility to separate price information from product attribute information is introduced, and the implications of designing markets promoting competition along each of these dimensions are discussed. Copyright © 1997 by The Institute of Management Sciences

Question: How can electronic marketplaces reduce buyer search costs?


Data-Driven Morphological Analysis and Disambiguation for Morphologically Rich Languages and Universal Dependencies. Parsing texts into universal dependencies (UD) in realistic scenarios requires infrastructure for morphological analysis and disambiguation (MA&D) of typologically different languages as a first tier. MA&D is particularly challenging in morphologically rich languages (MRLs), where the ambiguous space-delimited tokens ought to be disambiguated with respect to their constituent morphemes. Here we present a novel, language-agnostic, framework for MA&D, based on a transition system with two variants, word-based and morpheme-based, and a dedicated transition to mitigate the biases of variable-length morpheme sequences. Our experiments on a Modern Hebrew case study outperform the state of the art, and we show that the morpheme-based MD consistently outperforms our word-based variant. We further illustrate the utility and multilingual coverage of our framework by morphologically analyzing and disambiguating the large set of languages in the UD treebanks.

Question: What is the state of the art for morphological analysis and disambiguation?


Document dewarping via text-line based optimization. This paper presents a new document image dewarping method that removes geometric distortions in camera-captured document images. The proposed method does not directly use the text-line which has been the most widely used feature for the document dewarping. Instead, we use the discrete representation of text-lines and text-blocks which are the sets of connected components. Also, we model the geometric distortions caused by page curl and perspective view as the generalized cylindrical surfaces and camera rotation respectively. With these distortion models and the discrete representation of the features, we design a cost function whose minimization yields the parameters of the distortion model. In the cost function, we encode the properties of the pages such as text-block alignment, linespacing, and the straightness of text-lines. By describing the text features using the sets of discrete points, the cost function can be easily defined and efficiently solved by Levenberg–Marquadt algorithm. Experiments show that the proposed method works well for the various layouts and curved surfaces, and compares favorably with the conventional methods on the standard dataset. & 2015 Elsevier Ltd. All rights reserved.

Question: What is the cost function?