Query suggestion for E-commerce sites. Query suggestion module is an integral part of every search engine. It helps search engine users narrow or broaden their searches. Published work on query suggestion methods has mainly focused on the web domain. But, the module is also popular in the domain of e-commerce for product search. In this paper, we discuss query suggestion and its methodologies in the context of e-commerce search engines. We show that dynamic inventory combined with long and sparse tail of query distribution poses unique challenges to build a query suggestion method for an e-commerce marketplace. We compare and contrast the design of a query suggestion system for web search engines and e-commerce search engines. Further, we discuss interesting measures to quantify the effectiveness of our query suggestion methodologies. We also describe the learning gained from exposing our query suggestion module to a vibrant community of millions of users.

Question: Query suggestion for e-commerce sites


Learning to map between ontologies on the semantic web. Ontologies play a prominent role on the Semantic Web. They make possible the widespread publication of machine understandable data, opening myriad opportunities for automated information processing. However, because of the Semantic Web's distributed nature, data on it will inevitably come from many different ontologies. Information processing across ontologies is not possible without knowing the semantic mappings between their elements. Manually finding such mappings is tedious, error-prone, and clearly not possible at the Web scale. Hence, the development of tools to assist in the ontology mapping process is crucial to the success of the Semantic Web.We describe glue, a system that employs machine learning techniques to find such mappings. Given two ontologies, for each concept in one ontology glue finds the most similar concept in the other ontology. We give well-founded probabilistic definitions to several practical similarity measures, and show that glue can work with all of them. This is in contrast to most existing approaches, which deal with a single similarity measure. Another key feature of glue is that it uses multiple learning strategies, each of which exploits a different type of information either in the data instances or in the taxonomic structure of the ontologies. To further improve matching accuracy, we extend glue to incorporate commonsense knowledge and domain constraints into the matching process. For this purpose, we show that relaxation labeling, a well-known constraint optimization technique used in computer vision and other fields, can be adapted to work efficiently in our context. Our approach is thus distinguished in that it works with a variety of well-defined similarity notions and that it efficiently incorporates multiple types of knowledge. We describe a set of experiments on several real-world domains, and show that glue proposes highly accurate semantic mappings.

Question: What is the difference between ontologies and ontology mapping?


Latent Variable PixelCNNs for Natural Image Modeling. We study probabilistic models of natural images and extend the autoregressive family of PixelCNN models by incorporating latent variables. Subsequently, we describe two new generative image models that exploit different image transformations as latent variables: a quantized grayscale view of the image or a multi-resolution image pyramid. The proposed models tackle two known shortcomings of existing PixelCNN models: 1) their tendency to focus on low-level image details, while largely ignoring high-level image information, such as object shapes, and 2) their computationally costly procedure for image sampling. We experimentally demonstrate benefits of our LatentPixelCNN models, in particular showing that they produce much more realistically looking image samples than previous stateof-the-art probabilistic models.

Question: What is a probabilistic model of natural images?


Non-Intrusive Load Monitoring Using Prior Models of General Appliance Types. Non-intrusive appliance load monitoring is the process of disaggregating a household's total electricity consumption into its contributing appliances. In this paper we propose an approach by which individual appliances can be iteratively separated from an aggregate load. Unlike existing approaches, our approach does not require training data to be collected by sub-metering individual appliances, nor does it assume complete knowledge of the appliances present in the household. Instead, we propose an approach in which prior models of general appliance types are tuned to specific appliance instances using only signatures extracted from the aggregate load. The tuned appliance models are then used to estimate each appliance's load, which is subsequently subtracted from the aggregate load. This process is applied iteratively until all appliances for which prior behaviour models are known have been disaggregated. This paper summarises the contribution described in full in [9].

Question: How do you disaggregate an aggregate load?


Applying connectionist modal logics to distributed knowledge representation problems. Neural-Symbolic Systems concern the integration of the symbolic and connectionist paradigms of Artificial Intelligence. Distributed knowledge representation is traditionally seen under a symbolic perspective. In this paper, we show how neural networks can represent distributed symbolic knowledge, acting as multi-agent systems with learning capability (a key feature of neural networks). We apply the framework of Connectionist Modal Logics to well-known testbeds for distributed knowledge representation formalisms, namely the muddy children and the wise men puzzles. Finally, we sketch a full solution to these problems by extending our approach to deal with knowledge evolution over time.

Question: What is the muddy children puzzle?


Virtual machine placement strategies in cloud computing. The prominent technology that drives the industry now-a-days is cloud computing. The growth of cloud computing has resulted in the setup of large number of data centers around the world. The data centers consume more power making it source for the carbon dioxide emission and a major contributor to greenhouse effect. This led to the deployment of virtualization. Infrastructure as a Service is one of the important services offered by cloud computing that allows virtualization and hardware to get virtualized by creating many instances of Virtual Machine (VM) on a single Physical Machine (PM) and helps in improving utilization of resources. VM consolidation includes method of choosing the more appropriate algorithm for migration of VM's and placement of VMs to the most suitable host. VM placement is a part of VM migration. The effective placement of VM is aimed to improve performance, resource utilization and reduce the energy consumption in data centers without SLA violation. This paper aims to focus on various VM placement schemes.

Question: What is virtualization?


Methods of combining multiple classifiers and their applications to handwriting recognition. Method of combining the classification powers of several classifiers is regarded as a general problem in various application areas of pattern recognition, and a systematic investigation has been made. Possible solutions to the problem can be divided into three categories according to the levels of information available from the various classifiers. Four approaches are proposed based on different methodologies for solving this problem. One is suitable for combining individual classifiers such as Bayesian, k-NN and various distance classifiers. The other three could be used for combining any kind of individual classifiers. On applying these methods to combine several classifiers for recognizing totally unconstrained handwritten numerals, the experimental results show that the performance of individual classifiers could be improved significantly. For example, on the U.S. zipcode database, the result of 98.9% recognition with 0.90% substitution and 0.2% rejection can be obtained, as well as a high reliability with 95% recognition, 0% substitution and 5% rejection. These results compared favorably to other research p u p s in Europe, Asia, and North America.

Question: What is the difference between the first and second approaches?


The Overview of Research on Microgrid Protection Development. In general, the microgrid can operate in both grid-connection mode and islanded mode that challenges the traditional over-current protection scheme in the distribution network. The novel research and development of protection, including both the microgrid and distribution network are comprehensively analyzed in this paper, which mainly focuses on two aspects. The first one analyses the impact on the network protection from the fault current feature while microgrid-connected. Secondly, several innovative protection schemes for network and microgrid are discussed. It is essential to improve traditional protection schemes by using communication techniques. In order to protect the microgrid in islanded mode, two innovative voltage protection schemes are discussed. However, the differential current protection scheme is possibly still the most effective solution for both network and microgrid.

Question: What is the overview of research on microgrid protection development?


Learning to Make Predictions on Graphs with Autoencoders. We examine two fundamental tasks associated with graph representation learning: link prediction and semi-supervised node classification. We present a novel autoencoder architecture capable of learning a joint representation of both local graph structure and available node features for the multi-task learning of link prediction and node classification. Our autoencoder architecture is efficiently trained end-to-end in a single learning stage to simultaneously perform link prediction and node classification, whereas previous related methods require multiple training steps that are difficult to optimize. We provide a comprehensive empirical evaluation of our models on nine benchmark graph-structured datasets and demonstrate significant improvement over related methods for graph representation learning. Reference code and data are available at https://github.com/vuptran/graph-representation-learning.

Question: How to make predictions on graphs with autoencoders?


Detecting Encrypted Traffic: A Machine Learning Approach. Detecting encrypted traffic is increasingly important for deep packet inspection (DPI) to improve the performance of intrusion detection systems. We propose a machine learning approach with several randomness tests to achieve high accuracy detection of encrypted traffic while requiring low overhead incurred by the detection procedure. To demonstrate how effective the proposed approach is, the performance of four classification methods (Näıve Bayesian, Support Vector Machine, CART and AdaBoost) are explored. Our recommendation is to use CART which is not only capable of achieving an accuracy of 99.9% but also up to about 2.9 times more efficient than the second best candidate (Näıve Bayesian).

Question: Detecting encrypted traffic


New support vector algorithms with parametric insensitive/margin model. In this paper, a modification of v-support vector machines (v-SVM) for regression and classification is described, and the use of a parametric insensitive/margin model with an arbitrary shape is demonstrated. This can be useful in many cases, especially when the noise is heteroscedastic, that is, the noise strongly depends on the input value x. Like the previous v-SVM, the proposed support vector algorithms have the advantage of using the parameter 0 < or = v < or = 1 for controlling the number of support vectors. To be more precise, v is an upper bound on the fraction of training errors and a lower bound on the fraction of support vectors. The algorithms are analyzed theoretically and experimentally.

Question: What is a support vector machine?


Effective training of convolutional neural networks for face-based gender and age prediction. Convolutional Neural Networks (CNNs) have been proven very effective for human demographics estimation by a number of recent studies. However, the proposed solutions significantly vary in different aspects leaving many open questions on how to choose an optimal CNN architecture and which training strategy to use. In this work, we shed light on some of these questions improving the existing CNN-based approaches for gender and age prediction and providing practical hints for future studies. In particular, we analyse four important factors of the CNN training for gender recognition and age estimation: (1) the target age encoding and loss function, (2) the CNN depth, (3) the need for pretraining, and (4) the training strategy: mono-task or multi-task. As a result, we design the state-of-the-art gender recognition and age estimation models according to three popular benchmarks: LFW, MORPH-II and FGNET. Moreover, our best model won the ChaLearn Apparent Age Estimation Challenge 2016 significantly outperforming the solutions of other participants.

Question: How to train a convolutional neural network for face-based gender and age prediction?


Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning.. Sensors in everyday devices, such as our phones, wearables, and computers, leave a stream of digital traces. Personal sensing refers to collecting and analyzing data from sensors embedded in the context of daily life with the aim of identifying human behaviors, thoughts, feelings, and traits. This article provides a critical review of personal sensing research related to mental health, focused principally on smartphones, but also including studies of wearables, social media, and computers. We provide a layered, hierarchical model for translating raw sensor data into markers of behaviors and states related to mental health. Also discussed are research methods as well as challenges, including privacy and problems of dimensionality. Although personal sensing is still in its infancy, it holds great promise as a method for conducting mental health research and as a clinical tool for monitoring at-risk populations and providing the foundation for the next generation of mobile health (or mHealth) interventions.

Question: What is the difference between mental health and mental illness?


Didn't you see my message?: predicting attentiveness to mobile instant messages. Mobile instant messaging (e.g., via SMS or WhatsApp) often goes along with an expectation of high attentiveness, i.e., that the receiver will notice and read the message within a few minutes. Hence, existing instant messaging services for mobile phones share indicators of availability, such as the last time the user has been online. However, in this paper we not only provide evidence that these cues create social pressure, but that they are also weak predictors of attentiveness. As remedy, we propose to share a machine-computed prediction of whether the user will view a message within the next few minutes or not. For two weeks, we collected behavioral data from 24 users of mobile instant messaging services. By the means of machine-learning techniques, we identified that simple features extracted from the phone, such as the user's interaction with the notification center, the screen activity, the proximity sensor, and the ringer mode, are strong predictors of how quickly the user will attend to the messages. With seven automatically selected features our model predicts whether a phone user will view a message within a few minutes with 70.6% accuracy and a precision for fast attendance of 81.2%

Question: How to predict attentiveness to mobile instant messages?


Integration of it governance and security risk management: A systematic literature review. GRC is an umbrella acronym covering the three disciplines of governance, risk management and compliance. In this context, IT GRC is the subset of GRC dealing with IT aspects of GRC. The main challenge of GRC is to have an approach as integrated as possible of the three domains. The objective of our paper is to study one facet of IT GRC: the links and integration between IT governance and risk management that we consider today as the least integrated. To do so, the method followed in this paper is a systematic literature review, in order to identify the existing research works in this field. The resulting contribution of the paper is a set of recommendations established for practitioners and for researchers on how better deal with the integration between IT governance and risk management.

Question: What is the difference between IT governance and IT risk management?


The Double Dance of Agency : a socio-theoretic account of how machines and humans interact. The nature of the relationship between information technology (IT) and organizations has been a long-standing debate in the Information Systems literature. Does IT shape organizations, or do people in organisations control how IT is used? To formulate the question a little differently: does agency (the capacity to make a difference) lie predominantly with machines (computer systems) or humans (organisational actors)? Many proposals for a middle way between the extremes of technological and social determinism have been put advanced; in recent years researchers oriented towards social theories have focused on structuration theory and (lately) actor network theory. These two theories, however, adopt different and incompatible views of agency. Thus, structuration theory sees agency as exclusively a property of humans, whereas the principle of general symmetry in actor network theory implies that machines may also be agents. Drawing on critiques of both structuration theory and actor network theory, this paper develops a theoretical account of the interaction between human and machine agency: the double dance of agency. The account seeks to contribute to theorisation of the relationship between technology and organisation by recognizing both the different character of human and machine agency, and the emergent properties of their interplay.

Question: What is the difference between structuration theory and actor network theory?


The Dual-State Theory of Prefrontal Cortex Dopamine Function with Relevance to Catechol-<ce:italic>O</ce:italic>-Methyltransferase Genotypes and Schizophrenia. There is now general consensus that at least some of the cognitive deficits in schizophrenia are related to dysfunctions in the prefrontal cortex (PFC) dopamine (DA) system. At the cellular and synaptic level, the effects of DA in PFC via D1- and D2-class receptors are highly complex, often apparently opposing, and hence difficult to understand with regard to their functional implications. Biophysically realistic computational models have provided valuable insights into how the effects of DA on PFC neurons and synaptic currents as measured in vitro link up to the neural network and cognitive levels. They suggest the existence of two discrete dynamical regimes, a D1-dominated state characterized by a high energy barrier among different network patterns that favors robust online maintenance of information and a D2-dominated state characterized by a low energy barrier that is beneficial for flexible and fast switching among representational states. These predictions are consistent with a variety of electrophysiological, neuroimaging, and behavioral results in humans and nonhuman species. Moreover, these biophysically based models predict that imbalanced D1:D2 receptor activation causing extremely low or extremely high energy barriers among activity states could lead to the emergence of cognitive, positive, and negative symptoms observed in schizophrenia. Thus, combined experimental and computational approaches hold the promise of allowing a detailed mechanistic understanding of how DA alters information processing in normal and pathological conditions, thereby potentially providing new routes for the development of pharmacological treatments for schizophrenia.

Question: What is the dual state theory of prefrontal cortex dopamine function?


Big data, smart cities and city planning. I define big data with respect to its size but pay particular attention to the fact that the data I am referring to is urban data, that is, data for cities that are invariably tagged to space and time. I argue that this sort of data are largely being streamed from sensors, and this represents a sea change in the kinds of data that we have about what happens where and when in cities. I describe how the growth of big data is shifting the emphasis from longer term strategic planning to short-term thinking about how cities function and can be managed, although with the possibility that over much longer periods of time, this kind of big data will become a source for information about every time horizon. By way of conclusion, I illustrate the need for new theory and analysis with respect to 6 months of smart travel card data of individual trips on Greater London's public transport systems.

Question: What is the size of the canadian military?


Combining multiple feature selection methods for stock prediction: Union, intersection, and multi-intersection approaches.

Question: How to combine multiple feature selection methods for stock prediction?


Relational active learning for link-based classification. Many information tasks involve objects that are explicitly or implicitly connected in a network (or graph), such as webpages connected by hyperlinks or people linked by "friendships" in a social network. Research on link-based classification (LBC) has shown how to leverage these connections to improve classification accuracy. Unfortunately, acquiring a sufficient number of labeled examples to enable accurate learning for LBC can often be expensive or impractical. In response, some recent work has proposed the use of active learning, where the LBC method can intelligently select a limited set of additional labels to acquire, so as to reduce the overall cost of learning a model with sufficient accuracy. This work, however, has produced conflicting results and has not considered recent progress for LBC inference and semi-supervised learning. In this paper, we evaluate multiple prior methods for active learning and demonstrate that none consistently improve upon random guessing. We then introduce two new methods that both seek to improve active learning by leveraging the link structure to identify nodes to acquire that are more representative of the underlying data. We show that both approaches have some merit, but that one method, by proactively acquiring nodes so as to produce a more representative distribution of known labels, often leads to significant accuracy increases with minimal computational cost.

Question: What is the difference between active learning and semi-supervised learning?


Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation. Predicting personalized sequential behavior is a key task for recommender systems. In order to predict user actions such as the next product to purchase, movie to watch, or place to visit, it is essential to take into account both long-term user preferences and sequential patterns (i.e., short-term dynamics). Matrix Factorization and Markov Chain methods have emerged as two separate but powerful paradigms for modeling the two respectively. Combining these ideas has led to unified methods that accommodate long-and short-term dynamics simultaneously by modeling pairwise user-item and item-item interactions. In spite of the success of such methods for tackling dense data, they are challenged by sparsity issues, which are prevalent in real-world datasets. In recent years, similarity-based methods have been proposed for (sequentially-unaware) item recommendation with promising results on sparse datasets. In this paper, we propose to fuse such methods with Markov Chains to make personalized sequential recommendations. We evaluate our method, Fossil, on a variety of large, real-world datasets. We show quantitatively that Fossil outperforms alternative algorithms, especially on sparse datasets, and qualitatively that it captures personalized dynamics and is able to make meaningful recommendations.

Question: What is the difference between matrix factorization and markov chain?


Efficient parallel skyline processing using hyperplane projections. The skyline of a set of multi-dimensional points (tuples) consists of those points for which no clearly better point exists in the given set, using component-wise comparison on domains of interest. Skyline queries, i.e., queries that involve computation of a skyline, can be computationally expensive, so it is natural to consider parallelized approaches which make good use of multiple processors. We approach this problem by using hyperplane projections to obtain useful partitions of the data set for parallel processing. These partitions not only ensure small local skyline sets, but enable efficient merging of results as well. Our experiments show that our method consistently outperforms similar approaches for parallel skyline computation, regardless of data distribution, and provides insights on the impacts of different optimization strategies.

Question: What is the efficient parallel skyline processing using hyperplane projections?


WiMo: location-based emotion tagging. In this paper we introduce WiMo, a location-based social networking tool that enables users to share and store their emotional feelings about places. WiMo creates a mobile social network based on common interests and enables users to share not just information but also opinions, experiences and passions. The application uses a geo-emotional tagging system running on a GPS enabled mobile phone to ascribe emotions to places. In this paper we describe the development of an emotion tagging interface and present a case study scenario to situate the application in a real world setting. We then describe work-in-progress on the development of the WiMo prototype interface, and the interaction process for users. We further outline the features of the system and finally discuss next steps in the development of the application.

Question: What is WiMo?


Slip-aware Model Predictive optimal control for Path following. Traditional control and planning algorithms for wheeled mobile robots (WMR) either totally ignore or make simplifying assumptions about the effects of wheel slip on the motion. While this approach works reasonably well in practice on benign terrain, it fails very quickly when the WMR is deployed in terrain that induces significant wheel slip. We contribute a novel control framework that predictively corrects for the wheel slip to effectively minimize path following errors. Our framework, the Receding Horizon Model Predictive Path Follower (RHMPPF), specifically addresses the problem of path following in challenging environments where the wheel slip substantially affects the vehicle mobility. We formulate the solution to the problem as an optimal controller that utilizes a slip-aware model predictive component to effectively correct the controls generated by a strictly geometric pure-pursuit path follower. We present extensive experimental validation of our approach using a simulated 6-wheel skid-steered robot in a high-fidelity data-driven simulator, and on a real 4-wheel skid-steered robot. Our results show substantial improvement in the path following performance in both simulation and real world experiments.

Question: What is the receding horizon model predictive path follower?


Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks. Factorization Machines (FMs) are a supervised learning approach that enhances the linear regression model by incorporating the second-order feature interactions. Despite effectiveness, FM can be hindered by its modelling of all feature interactions with the same weight, as not all feature interactions are equally useful and predictive. For example, the interactions with useless features may even introduce noises and adversely degrade the performance. In this work, we improve FM by discriminating the importance of different feature interactions. We propose a novel model named Attentional Factorization Machine (AFM), which learns the importance of each feature interaction from data via a neural attention network. Extensive experiments on two real-world datasets demonstrate the effectiveness of AFM. Empirically, it is shown on regression task AFM betters FM with a 8.6% relative improvement, and consistently outperforms the state-of-the-art deep learning methods Wide&Deep [Cheng et al., 2016] and DeepCross [Shan et al., 2016] with a much simpler structure and fewer model parameters. Our implementation of AFM is publicly available at: https://github. com/hexiangnan/attentional factorization machine

Question: Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks


Dissociation Following Traumatic Stress Etiology and Treatment. We postulate that the cascade ''Freeze-Flight-Fight-Fright-Flag-Faint'' is a coherent sequence of six fear responses that escalate as a function of defense possibilities and proximity to danger during life-threat. The actual sequence of trauma-related response dispositions acted out in an extremely dangerous situation therefore depends on the appraisal of the threat by the organism in relation to her/his own power to act (e.g., age and gender) as well as the perceived characteristics of threat and perpetrator. These reaction patterns provide optimal adaption for particular stages of imminence. Subsequent to the traumatic threats, portions of the experience may be replayed. The actual individual cascade of defense stages a survivor has gone through during the traumatic event will repeat itself every time the fear network, which has evolved peritraumatically, is activated again (i.e., through internal or external triggers or, e.g., during exposure therapy).When a parasympathetically dominated ''shutdown'' was the prominent peri-traumatic response during the traumatic incident, comparable dissociative responses may dominate responding to subsequently experienced threat and may also reappear when the traumatic memory is reactivated. Repeated experience of traumatic stress forms a fear network that can become pathologically detached from contextual cues such as time and location of the danger, a condition which manifests itself as posttraumatic stress disorder (PTSD). Intrusions, for example, can therefore be understood as repetitive displays of fragments of the event, which would then, depending on the dominant physiological response during the threat, elicit a corresponding combination of hyperarousal and dissociation. We suggest that trauma treatment must therefore differentiate between patients on two dimensions: those with peritraumatic sympathetic activation versus those who went down the whole defense

Question: What is the difference between PTSD and dissociation?


A Simple Compact Reconfigurable Slot Antenna With a Very Wide Tuning Range. A simple and compact slot antenna with a very wide tuning range is proposed. A 25 mm (roughly equal to λH/8, where λH corresponds to the highest frequency of the tuning range) open slot is etched at the edge of the ground. To achieve the tunability, only two lumped elements, namely, a PIN diode and a varactor diode are used in the structure. By switching the PIN diode placed at the open end of the slot, the slot antenna can resonate as a standard slot (when the switch is on) or a half slot (when the switch is off). Continuous tuning over a wide frequency range in those two modes can be achieved by adjusting the reverse bias (giving different capacitances) of the varactor diode loaded in the slot. Through optimal design, the tuning bands of the two modes are stitched together to form a very wide tuning range. The fabricated prototype has a tuning frequency range from 0.42 GHz to 1.48 GHz with Sll better than -10 dB, giving a frequency ratio (fR = fu/fL) of 3.52:1. The measured full spherical radiation patterns show consistent radiation characteristics of the proposed antenna within the whole tuning range.

Question: What is a slot antenna?


Tapped-inductor buck converter for high-step-down DC-DC conversion. The narrow duty cycle in the buck converter limits its application for high-step-down dc-dc conversion. With a simple structure, the tapped-inductor buck converter shows promise for extending the duty cycle. However, the leakage inductance causes a huge turn-off voltage spike across the top switch. Also, the gate drive for the top switch is not simple due to its floating source connection. This paper solves all these problems by modifying the tapped-inductor structure. A simple lossless clamp circuit can effectively clamp the switch turn-off voltage spike and totally recover the leakage energy. Experimental results for 12V-to-1.5V and 48V-to-6V dc-dc conversions show significant improvements in efficiency.

Question: What is the purpose of the buck converter?


Real time head pose estimation with random regression forests. Fast and reliable algorithms for estimating the head pose are essential for many applications and higher-level face analysis tasks. We address the problem of head pose estimation from depth data, which can be captured using the ever more affordable 3D sensing technologies available today. To achieve robustness, we formulate pose estimation as a regression problem. While detecting specific face parts like the nose is sensitive to occlusions, learning the regression on rather generic surface patches requires enormous amount of training data in order to achieve accurate estimates. We propose to use random regression forests for the task at hand, given their capability to handle large training datasets. Moreover, we synthesize a great amount of annotated training data using a statistical model of the human face. In our experiments, we show that our approach can handle real data presenting large pose changes, partial occlusions, and facial expressions, even though it is trained only on synthetic neutral face data. We have thoroughly evaluated our system on a publicly available database on which we achieve state-of-the-art performance without having to resort to the graphics card.

Question: What is the state of the art in head pose estimation?


Deep Neural Networks in Machine Translation: An Overview. Deep neural networks (DNNs) are widely used in machine translation (MT). This article gives an overview of DNN applications in various aspects of MT.

Question: What is a deep neural network?


An Edge Detection Algorithm for Online Image Analysis. Online image analysis is used in a wide variety of applications. Edge detection is a fundamental tool used to obtain features of objects as a prerequisite step to object segmentation. This paper presents a simple and relatively fast online edge detection algorithm based on second derivative. The proposed edge detector is less sensitive to noise and may be applied on color, gray and binary images without preprocessing requirements. The merits of the algorithm are demonstrated by comparison with Canny's and Sobel's edge detectors.

Question: What is an online image analysis?


A Study of Cardiovascular Disease Prediction Models Using Discriminant Analysis. Cardiovascular disease is a condition of interest to clinicians around the world and is one of the chief causes of death. In Korea, cardiovascular disease is the second largest cause of death following cancer, and is classified as one of the four major diseases, making an early diagnosis of cardiovascular disease essential for proper and timely treatment. Although the risk of cardiovascular disease is recognized in Korea, there is a lack of quality studies regarding the disease. In the present study, a prediction model was developed which allows for the calculation of the risk of cardiovascular disease among Korean patients. The prediction model for cardiovascular disease was developed using a discriminant analysis based on the data set of Korean National Health and Nutrition Examinations Survey V (KNHANES V).

Question: What is the risk of cardiovascular disease?


Knowledge Engineering and Knowledge Management. Recently, experts and practitioners in language resources have started recognizing the benefits of the linked data (LD) paradigm for the representation and exploitation of linguistic data on the Web. The adoption of the LD principles is leading to an emerging ecosystem of multilingual open resources that conform to the Linguistic Linked Open Data Cloud, in which datasets of linguistic data are interconnected and represented following common vocabularies, which facilitates linguistic information discovery, integration and access. In order to contribute to this initiative, this paper summarizes several key aspects of the representation of linguistic information as linked data from a practical perspective. The main goal of this document is to provide the basic ideas and tools for migrating language resources (lexicons, corpora, etc.) as LD on the Web and to develop some useful NLP tasks with them (e.g., word sense disambiguation). Such material was the basis of a tutorial imparted at the EKAW'14 conference, which is also reported in the paper.

Question: What is the difference between a lexicon and a dictionary?


Sparse Probabilistic Principal Component Analysis. Principal component analysis (PCA) is a popular dimensionality reduction algorithm. However, it is not easy to interpret which of the original features are important based on the principal components. Recent methods improve interpretability by sparsifying PCA through adding an L1 regularizer. In this paper, we introduce a probabilistic formulation for sparse PCA. By presenting sparse PCA as a probabilistic Bayesian formulation, we gain the benefit of automatic model selection. We examine three different priors for achieving sparsification: (1) a two-level hierarchical prior equivalent to a Laplacian distribution and consequently to an L1 regularization, (2) an inverse-Gaussian prior, and (3) a Jeffrey's prior. We learn these models by applying variational inference. Our experiments verify that indeed our sparse probabilistic model results in a sparse PCA solution.

Question: What is a Laplacian distribution?


Design and Validation of Computer Protocols. This report aims at giving a short overview on rank function analysis of authentication protocols concentrating on the results of Schneider in [4] and Heather in [2]. Therefore it shows a standard form of protocol implementation in CSP, shows how authentication properties can be captured as trace specifications, gives basic definitions necessary to reason about the unbounded set of messages, reformulates the central rank function theorem and restates it proof. Additionally it gives hints on applying this approach to verify secrecy protocols as well, a short overview on tool support available and some hints on interesting properties of rank functions. This report will use notations introduced in earlier reports [11-16] throughout and only reintroduce notations where they are considered vital.

Question: What is the rank function of a protocol?


Wearable/disposable sweat-based glucose monitoring device with multistage transdermal drug delivery module. Electrochemical analysis of sweat using soft bioelectronics on human skin provides a new route for noninvasive glucose monitoring without painful blood collection. However, sweat-based glucose sensing still faces many challenges, such as difficulty in sweat collection, activity variation of glucose oxidase due to lactic acid secretion and ambient temperature changes, and delamination of the enzyme when exposed to mechanical friction and skin deformation. Precise point-of-care therapy in response to the measured glucose levels is still very challenging. We present a wearable/disposable sweat-based glucose monitoring device integrated with a feedback transdermal drug delivery module. Careful multilayer patch design and miniaturization of sensors increase the efficiency of the sweat collection and sensing process. Multimodal glucose sensing, as well as its real-time correction based on pH, temperature, and humidity measurements, maximizes the accuracy of the sensing. The minimal layout design of the same sensors also enables a strip-type disposable device. Drugs for the feedback transdermal therapy are loaded on two different temperature-responsive phase change nanoparticles. These nanoparticles are embedded in hyaluronic acid hydrogel microneedles, which are additionally coated with phase change materials. This enables multistage, spatially patterned, and precisely controlled drug release in response to the patient's glucose level. The system provides a novel closed-loop solution for the noninvasive sweat-based management of diabetes mellitus.

Question: What is the difference between a wearable and a disposable?


A Comprehensive Framework for Evaluation in Design Science Research. Evaluation is a central and essential activity in conducting rigorous Design Science Research (DSR), yet there is surprisingly little guidance about designing the DSR evaluation activity beyond suggesting possible methods that could be used for evaluation. This paper extends the notable exception of the existing framework of Pries-Heje et al [11] to address this problem. The paper proposes an extended DSR evaluation framework together with a DSR evaluation design method that can guide DSR researchers in choosing an appropriate strategy for evaluation of the design artifacts and design theories that form the output from DSR. The extended DSR evaluation framework asks the DSR researcher to consider (as input to the choice of the DSR evaluation strategy) contextual factors of goals, conditions, and constraints on the DSR evaluation, e.g. the type and level of desired rigor, the type of artifact, the need to support formative development of the designed artifacts, the properties of the artifact to be evaluated, and the constraints on resources available, such as time, labor, facilities, expertise, and access to research subjects. The framework and method support matching these in the first instance to one or more DSR evaluation strategies, including the choice of ex ante (prior to artifact construction) versus ex post evaluation (after artifact construction) and naturalistic (e.g., field setting) versus artificial evaluation (e.g., laboratory setting). Based on the recommended evaluation strategy(ies), guidance is provided concerning what methodologies might be appropriate within the chosen strategy(ies).

Question: What is a comprehensive framework for evaluation in design science research?


Confusion Matrix. A confusion matrix (Kohavi and Provost, 1998) contains information about actual and predicted classifications done by a classification system. Performance of such systems is commonly evaluated using the data in the matrix. The following table shows the confusion matrix for a two class classifier. The entries in the confusion matrix have the following meaning in the context of our study: ● a is the number of correct predictions that an instance is negative, ● b is the number of incorrect predictions that an instance is positive, ● c is the number of incorrect of predictions that an instance negative, and ● d is the number of correct predictions that an instance is positive. Predicted Negative Positive Actual Negative a b Positive c d

Question: What is a confusion matrix?


Privacy attacks in social media using photo tagging networks: a case study with Facebook. Social-networking users unknowingly reveal certain kinds of personal information that malicious attackers could profit from to perpetrate significant privacy breaches. This paper quantitatively demonstrates how the simple act of tagging pictures on the social-networking site of Facebook could reveal private user attributes that are extremely sensitive. Our results suggest that photo tags can be used to help predicting some, but not all, of the analyzed attributes. We believe our analysis make users aware of significant breaches of their privacy and could inform the design of new privacy-preserving ways of tagging pictures on social-networking sites.

Question: What is the privacy of facebook?


Convolutional neural networks for small-footprint keyword spotting. We explore using Convolutional Neural Networks (CNNs) for a small-footprint keyword spotting (KWS) task. CNNs are attractive for KWS since they have been shown to outperform DNNs with far fewer parameters. We consider two different applications in our work, one where we limit the number of multiplications of the KWS system, and another where we limit the number of parameters. We present new CNN architectures to address the constraints of each applications. We find that the CNN architectures offer between a 27-44% relative improvement in false reject rate compared to a DNN, while fitting into the constraints of each application.

Question: What is a convolutional neural network?


Syntax-aware Neural Machine Translation Using CCG. Neural machine translation (NMT) models are able to partially learn syntactic information from sequential lexical information. Still, some complex syntactic phenomena such as prepositional phrase attachment are poorly modeled. This work aims to answer two questions: 1) Does explicitly modeling source or target language syntax help NMT? 2) Is tight integration of words and syntax better than multitask training? We introduce syntactic information in the form of CCG supertags either in the source as an extra feature in the embedding, or in the target, by interleaving the target supertags with the word sequence. Our results on WMT data show that explicitly modeling syntax improves machine translation quality for English↔German, a high-resource pair, and for English↔Romanian, a lowresource pair and also several syntactic phenomena including prepositional phrase attachment. Furthermore, a tight coupling of words and syntax improves translation quality more than multitask training.

Question: What is the difference between a neural machine translation model and a statistical machine translation model?


Static detection of cross-site scripting vulnerabilities. Web applications support many of our daily activities, but they often have security problems, and their accessibility makes them easy to exploit. In cross-site scripting (XSS), an attacker exploits the trust a web client (browser) has for a trusted server and executes injected script on the browser with the server's privileges. In 2006, XSS constituted the largest class of newly reported vulnerabilities making it the most prevalent class of attacks today. Web applications have XSS vulnerabilities because the validation they perform on untrusted input does not suffice to prevent that input from invoking a browser's JavaScript interpreter, and this validation is particularly difficult to get right if it must admit some HTML mark-up. Most existing approaches to finding XSS vulnerabilities are taint-based and assume input validation functions to be adequate, so they either miss real vulnerabilities or report many false positives.  This paper presents a static analysis for finding XSS vulnerabilities that directly addresses weak or absent input validation. Our approach combines work on tainted information flow with string analysis. Proper input validation is difficult largely because of the many ways to invoke the JavaScript interpreter; we face the same obstacle checking for vulnerabilities statically, and we address it by formalizing a policy based on the W3C recommendation, the Firefox source code, and online tutorials about closed-source browsers. We provide effective checking algorithms based on our policy. We implement our approach and provide an extensive evaluation that finds both known and unknown vulnerabilities in real-world web applications.

Question: What is the largest class of newly reported vulnerabilities?


Multi-Antenna Wireless Legitimate Surveillance Systems: Design and Performance Analysis. To improve national security, government agencies have long been committed to enforcing powerful surveillance measures on suspicious individuals or communications. In this paper, we consider a wireless legitimate surveillance system, where a full-duplex multi-antenna legitimate monitor aims to eavesdrop on a dubious communication link between a suspicious pair via proactive jamming. Assuming that the legitimate monitor can successfully overhear the suspicious information only when its achievable data rate is no smaller than that of the suspicious receiver, the key objective is to maximize the eavesdropping non-outage probability by joint design of the jamming power, receive and transmit beamformers at the legitimate monitor. Depending on the number of receive/transmit antennas implemented, i.e., single-input single-output, single-input multiple-output, multiple-input single-output, and multiple-input multiple-output (MIMO), four different scenarios are investigated. For each scenario, the optimal jamming power is derived in a closed form and efficient algorithms are obtained for the optimal transmit/receive beamforming vectors. Moreover, low-complexity suboptimal beamforming schemes are proposed for the MIMO case. Our analytical findings demonstrate that by exploiting multiple antennas at the legitimate monitor, the eavesdropping non-outage probability can be significantly improved compared with the single-antenna case. In addition, the proposed suboptimal transmit zero-forcing scheme yields similar performance as the optimal scheme.

Question: How many antennas does the legitimate monitor need to eavesdrop on a suspicious communication link?


Fashion-focused creative commons social dataset. In this work, we present a fashion-focused Creative Commons dataset, which is designed to contain a mix of general images as well as a large component of images that are focused on fashion (i.e., relevant to particular clothing items or fashion accessories). The dataset contains 4810 images and related metadata. Furthermore, a ground truth on image's tags is presented. Ground truth generation for large-scale datasets is a necessary but expensive task. Traditional expert based approaches have become an expensive and non-scalable solution. For this reason, we turn to crowdsourcing techniques in order to collect ground truth labels; in particular we make use of the commercial crowdsourcing platform, Amazon Mechanical Turk (AMT). Two different groups of annotators (i.e., trusted annotators known to the authors and crowdsourcing workers on AMT) participated in the ground truth creation. Annotation agreement between the two groups is analyzed. Applications of the dataset in different contexts are discussed. This dataset contributes to research areas such as crowdsourcing for multimedia, multimedia content analysis, and design of systems that can elicit fashion preferences from users.

Question: What is the fashion-focused creative commons social dataset?


Techniques for Data Hiding. by International Business Machines Corporation. Copying in printed form for private use is permitted without payment of royalty provided that (1) each reproduction is done without alteration and (2) the Journal reference and IBM copyright notice are included on the first page. The title and abstract, but no other portions, of this paper may be copied or distributed royalty free without further permission by computer-based and other information service systems. Permission to republish any other portion of this paper must be obtained from the Editor. Data hiding, a form of steganography, embeds data into digital media for the purpose of identification, annotation, and copyright. Several constraints affect this process: the quantity of data to be hidden, the need for invariance of these data under conditions where a " host " signal is subject to distortions, e.g., lossy compression, and the degree to which the data must be immune to interception, modification, or removal by a third party. We explore both traditional and novel techniques for addressing the data-hiding process and evaluate these techniques in light of three applications: copyright protection, tamper-proofing, and augmentation data embedding. igital representation of media facilitates access and potentially improves the portability, efficiency , and accuracy of the information presented. Undesirable effects of facile data access include an increased opportunity for violation of copyright and tampering with or modification of content. The motivation for this work includes the provision of protection of intellectual property rights, an indication of content manipulation, and a means of annotation. Data hiding represents a class of processes used to embed data, such as copyright information, into various forms of media such as image, audio, or text with a minimum amount of perceivable degradation to the " host " signal; i.e., the embedded data should be invisible and inaudible to

Question: What is the difference between steganography and data hiding?


Development and Validation of a Surgical Workload Measure: The Surgery Task Load Index (SURG-TLX). The purpose of the present study was to develop and validate a multidimensional, surgery-specific workload measure (the SURG-TLX), and to determine its utility in providing diagnostic information about the impact of various sources of stress on the perceived demands of trained surgical operators. As a wide range of stressors have been identified for surgeons in the operating room, the current approach of considering stress as a unidimensional construct may not only limit the degree to which underlying mechanisms may be understood but also the degree to which training interventions may be successfully matched to particular sources of stress. The dimensions of the SURG-TLX were based on two current multidimensional workload measures and developed via focus group discussion. The six dimensions were defined as mental demands, physical demands, temporal demands, task complexity, situational stress, and distractions. Thirty novices were trained on the Fundamentals of Laparoscopic Surgery (FLS) peg transfer task and then completed the task under various conditions designed to manipulate the degree and source of stress experienced: task novelty, physical fatigue, time pressure, evaluation apprehension, multitasking, and distraction. The results were supportive of the discriminant sensitivity of the SURG-TLX to different sources of stress. The sub-factors loaded on the relevant stressors as hypothesized, although the evaluation pressure manipulation was not strong enough to cause a significant rise in situational stress. The present study provides support for the validity of the SURG-TLX instrument and also highlights the importance of considering how different stressors may load surgeons. Implications for categorizing the difficulty of certain procedures, the implementation of new technology in the operating room (man–machine interface issues), and the targeting of stress trai

Question: How many novices are needed to validate a surgical workload measure?


Motivational Leadership: Tips From the Business World.. It is an important task for leadership to identify the motivating factors for employees and motivate them to fulfill their individual and organizational goals. Although there are several motivational factors (extrinsic and intrinsic), intrinsic motivational factors such as autonomy, mastery, and purpose are more important for deeper lasting job satisfaction and higher performance. In this article, the authors discuss how an understanding of these factors that influence motivation has the potential to transform an organization.

Question: What are the motivating factors for employees?


Facial Expression Analysis. The face is one of the most powerful channels of nonverbal communication. Facial expression provides cues about emotion, intention, alertness, pain, personality, regulates interpersonal behavior, and communicates psychiatric and biomedical status among other functions. Within the past 15 years, there has been increasing interest in automated facial expression analysis within the computer vision and machine learning communities. This chapter reviews fundamental approaches to facial measurement by behavioral scientists and current efforts in automated facial expression recognition. We consider challenges, review databases available to the research community, approaches to feature detection, tracking, and representation, and both supervised and unsupervised learning. keywords : Facial expression analysis, Action unit recognition, Active Appearance Models, temporal clustering.

Question: What is the difference between facial expression analysis and facial expression recognition?


Bluetooth positioning using RSSI and triangulation methods. Location based services are the hottest applications on mobile devices nowadays and the growth is continuing. Indoor wireless positioning is the key technology to enable location based services to work well indoors, where GPS normally could not work. Bluetooth has been widely used in mobile devices like phone, PAD etc. therefore Bluetooth based indoor positioning has great market potential. Radio Signal Strength (RSS) is a key parameter for wireless positioning. New Bluetooth standard (since version 2.1) enables RSS to be discovered without time consuming pre-connection. In this research, general wireless positioning technologies are firstly analysed. Then RSS based Bluetooth positioning using the new feature is studied. The mathematical model is established to analyse the relation between RSS and the distance between two Bluetooth devices. Three distance-based algorithms are used for Bluetooth positioning: Least Square Estimation, Three-border and Centroid Method. Comparison results are analysed and the ways to improve the positioning accuracy are discussed.

Question: What is RSSI?


Grace: safe multithreaded programming for C/C++. The shift from single to multiple core architectures means that programmers must write concurrent, multithreaded programs in order to increase application performance. Unfortunately, multithreaded applications are susceptible to numerous errors, including deadlocks, race conditions, atomicity violations, and order violations. These errors are notoriously difficult for programmers to debug.  This paper presents Grace, a software-only runtime system that eliminates concurrency errors for a class of multithreaded programs: those based on fork-join parallelism. By turning threads into processes, leveraging virtual memory protection, and imposing a sequential commit protocol, Grace provides programmers with the appearance of deterministic, sequential execution, while taking advantage of available processing cores to run code concurrently and efficiently. Experimental results demonstrate Grace's effectiveness: with modest code changes across a suite of computationally-intensive benchmarks (1-16 lines), Grace can achieve high scalability and performance while preventing concurrency errors.

Question: What is a fork-join parallelism?


Animal-assisted therapy with children suffering from insecure attachment due to abuse and neglect: a method to lower the risk of intergenerational transmission of abuse?. Children suffering from insecure attachment due to severe abuse and/or neglect are often characterized by internal working models which, although perhaps adaptive within the original family situation, are inappropriate and maladaptive in other relationships and situations. Such children have a higher probability than the general population of becoming abusing or neglecting parents. Besides the usual goals of psychotherapy, an overall goal is to stop the cycle of abuse in which abused children may grow up to be abusing parents. Therapy with these children is complicated by their distrust in adults as well as difficulties in symbolization due to trauma during the preverbal stage. Animal-Assisted Therapy (AAT) provides avenues for circumventing these difficulties, as well as providing additional tools for reaching the inner world of the client. This article gives a brief background of the connection between insecure attachment and intergenerational transmission of abuse and neglect as well as a brief overview of the principles of AAT in a play therapy setting. A rationale for the use of AAT as a unique therapy technique for children having suffered from abuse and neglect is followed by a number of clinical examples illustrating AAT.

Question: What is animal-assisted therapy?


Inflammation and atherosclerosis.. Atherosclerosis, formerly considered a bland lipid storage disease, actually involves an ongoing inflammatory response. Recent advances in basic science have established a fundamental role for inflammation in mediating all stages of this disease from initiation through progression and, ultimately, the thrombotic complications of atherosclerosis. These new findings provide important links between risk factors and the mechanisms of atherogenesis. Clinical studies have shown that this emerging biology of inflammation in atherosclerosis applies directly to human patients. Elevation in markers of inflammation predicts outcomes of patients with acute coronary syndromes, independently of myocardial damage. In addition, low-grade chronic inflammation, as indicated by levels of the inflammatory marker C-reactive protein, prospectively defines risk of atherosclerotic complications, thus adding to prognostic information provided by traditional risk factors. Moreover, certain treatments that reduce coronary risk also limit inflammation. In the case of lipid lowering with statins, this anti-inflammatory effect does not appear to correlate with reduction in low-density lipoprotein levels. These new insights into inflammation in atherosclerosis not only increase our understanding of this disease, but also have practical clinical applications in risk stratification and targeting of therapy for this scourge of growing worldwide importance.

Question: What is the role of inflammation in atherosclerosis?


Revisiting Traffic Anomaly Detection Using Software Defined Networking. Despite their exponential growth, home and small office/home office networks continue to be poorly managed. Consequently, security of hosts in most home networks is easily compromised and these hosts are in turn used for largescale malicious activities without the home users' knowledge. We argue that the advent of Software Defined Networking (SDN) provides a unique opportunity to effectively detect and contain network security problems in home and home office networks. We show how four prominent traffic anomaly detection algorithms can be implemented in an SDN context using Openflow compliant switches and NOX as a controller. Our experiments indicate that these algorithms are significantly more accurate in identifying malicious activities in the home networks as compared to the ISP. Furthermore, the efficiency analysis of our SDN implementations on a programmable home network router indicates that the anomaly detectors can operate at line rates without introducing any performance penalties for the home network traffic.

Question: What is the difference between a home network and an office network?


Probabilistic in-network caching for information-centric networks. In-network caching necessitates the transformation of centralised operations of traditional, overlay caching techniques to a decentralised and uncoordinated environment. Given that caching capacity in routers is relatively small in comparison to the amount of forwarded content, a key aspect is balanced distribution of content among the available caches. In this paper, we are concerned with decentralised, real-time distribution of content in router caches. Our goal is to reduce caching redundancy and in turn, make more efficient utilisation of available cache resources along a delivery path.  Our in-network caching scheme, called ProbCache, approximates the caching capability of a path and caches contents probabilistically in order to: i) leave caching space for other flows sharing (part of) the same path, and ii) fairly multiplex contents of different flows among caches of a shared path.  We compare our algorithm against universal caching and against schemes proposed in the past for Web-Caching architectures, such as Leave Copy Down (LCD). Our results show reduction of up to 20% in server hits, and up to 10% in the number of hops required to hit cached contents, but, most importantly, reduction of cache-evictions by an order of magnitude in comparison to universal caching.

Question: What is the difference between probabilistic and universal caching?


A Survey of MAC Protocols for Mission-Critical Applications in Wireless Sensor Networks. Wireless Sensor Networks (WSNs) are generally designed to support applications in long-term deployments, and thus WSN protocols are primarily designed to be energy efficient. However, the research community has recently explored new WSN applications such as industrial process automation. These mission-critical applications demand not only energy efficient operation but also strict data transport performance. In particular, data must be transported to a sink in a timely and reliable fashion. Both WSN's data transport performance and energy consumption pattern are mainly defined by the employed medium access control (MAC) protocol. Therefore, this survey paper explores to what extent existing MAC protocols for WSNs can serve mission-critical applications. The reviewed protocols are classified according to data transport performance and suitability for mission-critical applications. The survey reveals that the existing solutions have a number of limitations and only a few recently developed MAC protocols are suitable for this application domain.

Question: What is the difference between a MAC protocol and a MAC protocol?


YAGO: A Large Ontology from Wikipedia and WordNet. This article presents YAGO, a large ontology with high coverage and precision. YAGO has been automatically derived from Wikipedia and WordNet. It comprises entities and relations, and currently contains more than 1.7 million entities and 15 million facts. These include the taxonomic Is-A hierarchy as well as semantic relations between entities. The facts for YAGO have been extracted from the category system and the infoboxes of Wikipedia and have been combined with taxonomic relations from WordNet. Type Ontologies Information extraction K checking techniques help us keep YAGO's precision at 95%—as proven by an extensive evaluation study. YAGO is based on a clean logical model with a decidable consistency. Furthermore, it allows representing n-ary relations in a natural way while maintaining compatibility with RDFS. A powerful query model s data

Question: What is the ontology of wikipedia?


DENSER Cities: A System for Dense Efficient Reconstructions of Cities. This paper is about the efficient generation of dense, colored models of city-scale environments from range data and in particular, stereo cameras. Better maps make for better understanding; better understanding leads to better robots, but this comes at a cost. The computational and memory requirements of large dense models can be prohibitive. We provide the theory and the system needed to create cityscale dense reconstructions. To do so, we apply a regularizer over a compressed 3D data structure while dealing with the complex boundary conditions this induces during the data-fusion stage. We show that only with these considerations can we swiftly create neat, large, "well behaved" reconstructions. We evaluate our system using the KITTI dataset and provide statistics for the metric errors in all surfaces created compared to those measured with 3D laser. Our regularizer reduces the median error by 40% in 3.4 km of dense reconstructions with a median accuracy of 6 cm. For subjective analysis, we provide a qualitative review of 6.1 km of our dense reconstructions in an attached video. These are the largest dense reconstructions from a single passive camera we are aware of in the literature. Video: https://youtu.be/FRmF7mH86EQ

Question: How large is the canadian military?


Diagnostically relevant facial gestalt information from ordinary photos. Craniofacial characteristics are highly informative for clinical geneticists when diagnosing genetic diseases. As a first step towards the high-throughput diagnosis of ultra-rare developmental diseases we introduce an automatic approach that implements recent developments in computer vision. This algorithm extracts phenotypic information from ordinary non-clinical photographs and, using machine learning, models human facial dysmorphisms in a multidimensional 'Clinical Face Phenotype Space'. The space locates patients in the context of known syndromes and thereby facilitates the generation of diagnostic hypotheses. Consequently, the approach will aid clinicians by greatly narrowing (by 27.6-fold) the search space of potential diagnoses for patients with suspected developmental disorders. Furthermore, this Clinical Face Phenotype Space allows the clustering of patients by phenotype even when no known syndrome diagnosis exists, thereby aiding disease identification. We demonstrate that this approach provides a novel method for inferring causative genetic variants from clinical sequencing data through functional genetic pathway comparisons.DOI: http://dx.doi.org/10.7554/eLife.02020.001.

Question: What is the difference between a clinical face phenotype space and a clinical face phenotype map?


Automating metadata generation: the simple indexing interface. In this paper, we focus on the development of a framework for automatic metadata generation. The first step towards this framework is the definition of an Application Programmer Interface (API), which we call the Simple Indexing Interface (SII). The second step is the definition of a framework for implementation of the SII. Both steps are presented in some detail in this paper. We also report on empirical evaluation of the metadata that the SII and supporting framework generated in a real-life context.

Question: What is the simple indexing interface?


Compact second harmonic-suppressed bandstop and bandpass filters using open stubs. Integration of bandstop filters with the bandstop or bandpass filter are presented in this paper. By replacing the series quarter-wavelength connecting lines of conventional open-stub bandpass/bandstop filters with the equivalent T-shaped lines, one could have compact open-stub bandstop/bandpass filters with second harmonic suppression. Transmission-line model calculation is used to derive the design equations of the equivalent T-shaped lines. Experiments have also been done to validate the design concept. Compared with the conventional open-stub bandpass/bandstop filters, over 30-dB improvement of the second harmonic suppression and 28.6% size reduction are achieved

Question: What is compact second harmonic-suppressed bandstop and bandpass filters using open stubs?


Performance Engineering for Microservices: Research Challenges and Directions. Microservices complement approaches like DevOps and continuous delivery in terms of software architecture. Along with this architectural style, several important deployment technologies, such as container-based virtualization and container orchestration solutions, have emerged. These technologies allow to efficiently exploit cloud platforms, providing a high degree of scalability, availability, and portability for microservices.  Despite the obvious importance of a sufficient level of performance, there is still a lack of performance engineering approaches explicitly taking into account the particularities of microservices. In this paper, we argue why new solutions to performance engineering for microservices are needed. Furthermore, we identify open issues and outline possible research directions with regard to performance-aware testing, monitoring, and modeling of microservices.

Question: What is the difference between microservices and containers?


Motion Mode Recognition for Indoor Pedestrian Navigation Using Portable Devices. While indoor portable navigation using portable devices is becoming increasingly required, it faces many challenges in obtaining accurate positioning performance. One of the methods to improve navigation results is to recognize the mode of motion of the user carrying the portable device containing low-cost microelectromechanical systems (MEMS) motion sensors. Pattern recognition methodology has been employed to detect a group of motion modes that are common indoors, namely, walking, stationary, going up/down stairs, standing on an escalator, walking on an escalator, standing on a moving walkway, and walking on a moving walkway. The performance of the motion mode recognition module was examined on different types of mobile computing devices, including various brands of smartphones, tablets, smartwatches, and smartglasses, and the results obtained showed the capability of enhancing positioning performance. The module does not require satellite or wireless positioning signals, and depends only on MEMS sensors.

Question: How to recognize the mode of motion of a user carrying a portable device?


Electronic Game Play and School Performance of Adolescents in Southern Thailand. Increasing exposure of children and adolescents to electronic media is a worldwide phenomenon, including in Thailand. To date, few studies examine electronic game play in Thai adolescents. Our research describes the prevalence of electronic game play and examines associations between the time spent engaged in electronic game play and school performance of adolescents in Hat-Yai municipality. A cross-sectional study was conducted among 1,492 adolescents from four secondary schools and one commercial college from January through March 2007, using questionnaires for collecting information about demographic data, school grades, and electronic game play behaviors. The prevalence of electronic game play was 75% in boys and 59% in girls. Twenty-two percent of boys and 8.7% of girls played electronic games every day with more than 2 hours per session. The two most common places of game play were at game shops (71%), followed by at their own home (70%). Using linear regression analysis, the "low user or less than 2 hours per session" game players and females were less likely to have school grades below 3.00 with adjusted odds ratios of 0.44 (95% CI 0.25-0.80, p = 0.004) and 0.49 (95% CI 0.30-0.76, p = 0.005) respectively. This study finds that excessive playing of electronic games is associated with school grades below 3.00.

Question: What is the prevalence of electronic game play in Thai adolescents?


MovieGEN : A Movie Recommendation System. In this paper we introduce MovieGEN, an expert system for movie recommendation. We implement the system using machine learning and cluster analysis based on a hybrid recommendation approach. The system takes in the users' personal information and predicts their movie preferences using well-trained support vector machine (SVM) models. Based on the SVM prediction it selects movies from the dataset, clusters the movies and generates questions to the users. Based on the users' answers, it refines its movie set and it finally recommends movies for the users. In the process we traverse the parameter space, thus enabling the customizability of the system.

Question: What is a movie recommendation system?


IT capability and organizational performance: the roles of business process agility and environmental factors. School of Business Administration, Southwestern University of Finance and Economics, Sichuan, P. R. of China; Business School, Shantou University, Guangdong, P. R. of China; Information Technology Management, School of Business, University at Albany, Albany, NY, U.S.A; Department of Management, Hong Kong University of Science and Technology, Hong Kong, P.R. China; Department of Finance and Decision Sciences, School of Business, Hong Kong Baptist University, Hong Kong, P.R. China

Question: What is the role of business process agility in IT capability and organizational performance?


Interactive example-based urban layout synthesis. We present an interactive system for synthesizing urban layouts by example. Our method simultaneously performs both a structure-based synthesis and an image-based synthesis to generate a complete urban layout with a plausible street network and with aerial-view imagery. Our approach uses the structure and image data of real-world urban areas and a synthesis algorithm to provide several high-level operations to easily and interactively generate complex layouts by example. The user can create new urban layouts by a sequence of operations such as join, expand, and blend without being concerned about low-level structural details. Further, the ability to blend example urban layout fragments provides a powerful way to generate new synthetic content. We demonstrate our system by creating urban layouts using example fragments from several real-world cities, each ranging from hundreds to thousands of city blocks and parcels.

Question: How to create a new urban layout by example?


The Faces of Engagement: Automatic Recognition of Student Engagementfrom Facial Expressions. Student engagement is a key concept in contemporary education, where it is valued as a goal in its own right. In this paper we explore approaches for automatic recognition of engagement from students' facial expressions. We studied whether human observers can reliably judge engagement from the face; analyzed the signals observers use to make these judgments; and automated the process using machine learning. We found that human observers reliably agree when discriminating low versus high degrees of engagement (Cohen's κ = 0.96). When fine discrimination is required (four distinct levels) the reliability decreases, but is still quite high ( κ = 0.56). Furthermore, we found that engagement labels of 10-second video clips can be reliably predicted from the average labels of their constituent frames (Pearson r=0.85), suggesting that static expressions contain the bulk of the information used by observers. We used machine learning to develop automatic engagement detectors and found that for binary classification (e.g., high engagement versus low engagement), automated engagement detectors perform with comparable accuracy to humans. Finally, we show that both human and automatic engagement judgments correlate with task performance. In our experiment, student post-test performance was predicted with comparable accuracy from engagement labels ( r=0.47) as from pre-test scores ( r=0.44).

Question: How can we automatically recognize student engagement?


SQLrand: Preventing SQL Injection Attacks. We present a practical protection mechanism against SQL injection attacks. Such attacks target databases that are accessible through a web frontend, and take advantage of flaws in the input validation logic of Web components such as CGI scripts. We apply the concept of instruction-set randomization to SQL, creating instances of the language that are unpredictable to the attacker. Queries injected by the attacker will be caught and terminated by the database parser. We show how to use this technique with the MySQL database using an intermediary proxy that translates the random SQL to its standard language. Our mechanism imposes negligible performance overhead to query processing and can be easily retrofitted to existing systems.

Question: How to prevent SQL injection attacks?


Future Internet: The Internet of Things Architecture, Possible Applications and Key Challenges. The Internet is continuously changing and evolving. The main communication form of present Internet is human-human. The Internet of Things (IoT) can be considered as the future evaluation of the Internet that realizes machine-to-machine (M2M) learning. Thus, IoT provides connectivity for everyone and everything. The IoT embeds some intelligence in Internet-connected objects to communicate, exchange information, take decisions, invoke actions and provide amazing services. This paper addresses the existing development trends, the generic architecture of IoT, its distinguishing features and possible future applications. This paper also forecast the key challenges associated with the development of IoT. The IoT is getting increasing popularity for academia, industry as well as government that has the potential to bring significant personal, professional and economic benefits.

Question: What is the future of the internet?


Chronos : Serverless Multi-User Chat Over NDN. Multi-user applications are commonly implemented using a centralized server. This paper presents a new design for multi-user chat applications (Chronos) that works in a distributed, serverless fashion over Named Data Networking. In Chronos, all participants share their views by exchanging the cryptographic digests of the chat room data set. A newly generated message causes a change of the digest at the message originator, which leads to retrieving the new data by all other participants in an efficient way and resynchronization of chat room views. Chronos does not have a single point of failure and eliminates traffic concentration problem of server-based implementations. We use simulations to evaluate and compare Chronos with a traditional serverbased chat room implementation. Our results demonstrate Chronos' robustness and efficiency in data dissemination. Chronos' approach of replacing centralized servers by distributed data synchronization can be applied to a variety of distributed applications to simplify design and ease deployment.

Question: What is a serverless multi-user chat application?


Discussions on control loop design in average current mode control [PWM DC/DC power convertors]. Large control output ripple in ACMC (average current mode control) PWM DC/DC converters exposes challenges in accurate modeling and design. In this paper, superposition models are utilized to perform control design analysis for ACMC PWM DC/DC converters. The ACMC DC/DC PWM converter system is converted to a cascade control structure and a step-by-step control design algorithm is then proposed. The control design algorithms differ from existing, conventional algorithms because they carefully compensate for newly discovered feed forward influence in the model.

Question: How to design a control loop for a DC/DC converter?


A Twitter Tale of Three Hurricanes: Harvey, Irma, and Maria. People increasingly use microblogging platforms such as Twitter during natural disasters and emergencies. Research studies have revealed the usefulness of the data available on Twitter for several disaster response tasks. However, making sense of social media data is a challenging task due to several reasons such as limitations of available tools to analyze high-volume and high-velocity data streams. This work presents an extensive multidimensional analysis of textual and multimedia content from millions of tweets shared on Twitter during the three disaster events. Specifically, we employ various Artificial Intelligence techniques from Natural Language Processing and Computer Vision fields, which exploit different machine learning algorithms to process the data generated during the disaster events. Our study reveals the distributions of various types of useful information that can inform crisis managers and responders as well as facilitate the development of future automated systems for disaster management.

Question: What is the distribution of useful information?


Computational Thinking … and Doing. A t our most recent CiSE meeting, one of the board members approached me during a short break and asked: " Why do you do this? you seem to have better things to do. " seeking clarification , i came to understand that he Was Wondering Why a computer scientist such as myself would want to be on the editorial board of a publication that isn't necessarily focused on the " tradition " of computer science. then and there, i realized that a computer sci-entist's role is something worth explaining and understanding. much has been made recently of computational thinking, a term coined by Jeanette Wing at the us national science foundation. 1 While i have my doubts about the term's longevity, 2 it struck me as an excellent starting point for explaining what a computer scientist is and does, especially in the CiSE context. computer scientists are an increasingly important part of interdisciplinary—or multidisciplinary, a term i prefer—problem solving. We help others understand the merits of bringing the right methods (algorithms), architecture/design (software engineering), and programming tools (languages and systems) and techniques (visualization) to science and engineering applications. so, as i see it, computer scientists bring a panoply of options to the table that enhance multi disciplinary research. if you will, we're about computational doing—and doing it right. as the former chez thiruvathukal in scientific programming , i like to view computer science's various ideas as the masala found in my (and possibly your) favorite curries. this brings me back to why i'm here. i've always had an eye to applying computer science to other disciplines, scientific and otherwise. in the past few months, i've begun working on two new (funded) projects—one addresses healthcare (modeling health issues in social networks using agent-based simulation) and the other addresses digital humanities (developing collaborative tools for tex-tual studies). in b

Question: What is computational thinking?


A First Look at a Telepresence System with Room-Sized Real-Time 3 D Capture and Life-Sized Tracked Display Wall. This paper provides a first look at a telepresence system offering room-sized, fully dynamic real-time 3D scene capture and continuous-viewpoint head-tracked display on a life-sized tiled display wall. The system is an expansion of a previous system, based an array of commodity depth sensors. We describe adjustments and improvements made to camera calibration, sensor data processing, data merger, rendering, and display, as required to scale the earlier system to room-sized.

Question: What is a telepresence system?


A method to infer emotions from facial Action Units. We present a robust method to map detected facial Action Units (AUs) to six basic emotions. Automatic AU recognition is prone to errors due to illumination, tracking failures and occlusions. Hence, traditional rule based methods to map AUs to emotions are very sensitive to false positives and misses among the AUs. In our method, a set of chosen AUs are mapped to the six basic emotions using a learned statistical relationship and a suitable matching technique. Relationships between the AUs and emotions are captured as template strings comprising the most discriminative AUs for each emotion. The template strings are computed using a concept called discriminative power. The Longest Common Subsequence (LCS) distance, an approach for approximate string matching, is applied to calculate the closeness of a test string of AUs with the template strings, and hence infer the underlying emotions. LCS is found to be efficient in handling practical issues like erroneous AU detection and helps to reduce false predictions. The proposed method is tested with various databases like CK+, ISL, FACS, JAFFE, MindReading and many real-world video frames. We compare our performance with rule based techniques, and show clear improvement on both benchmark databases and real-world datasets.

Question: How to infer emotions from facial Action Units?


A controlled clinical comparison of attention performance in children with ADHD in a virtual reality classroom compared to standard neuropsychological methods.. In this initial pilot study, a controlled clinical comparison was made of attention perforance in children with attention deficit-hyperactivity disorder (ADHD) in a virtual reality (VR) classroom. Ten boys diagnosed with ADHD and ten normal control boys participated in the study. Groups did not significantly differ in mean age, grade level, ethnicity, or handedness. No participants reported simulator sickness following VR exposure. Children with ADHD exhibited more omission errors, commission errors, and overall body movement than normal control children in the VR classroom. Children with ADHD were more impacted by distraction in the VR classroom. VR classroom measures were correlated with traditional ADHD assessment tools and the flatscreen CPT. Of note, the small sample size incorporated in each group and higher WISC-III scores of normal controls might have some bearing on the overall interpretation of results. These data suggested that the Virtual Classroom had good potential for controlled performance assessment within an ecologically valid environment and appeared to parse out significant effects due to the presence of distraction stimuli.

Question: What is the difference between a controlled clinical comparison and a pilot study?


Making working memory work: a meta-analysis of executive-control and working memory training in older adults.. This meta-analysis examined the effects of process-based executive-function and working memory training (49 articles, 61 independent samples) in older adults (> 60 years). The interventions resulted in significant effects on performance on the trained task and near-transfer tasks; significant results were obtained for the net pretest-to-posttest gain relative to active and passive control groups and for the net effect at posttest relative to active and passive control groups. Far-transfer effects were smaller than near-transfer effects but were significant for the net pretest-to-posttest gain relative to passive control groups and for the net gain at posttest relative to both active and passive control groups. We detected marginally significant differences in training-induced improvements between working memory and executive-function training, but no differences between the training-induced improvements observed in older adults and younger adults, between the benefits associated with adaptive and nonadaptive training, or between the effects in active and passive control conditions. Gains did not vary with total training time.

Question: What is the difference between working memory and executive function?


A typology for visualizing uncertainty. Information analysts, especially those working in the field of intelligence analysis, must assess not only the information presented to them, but the confidence they have in that information. Visual representations of information are challenged to incorporate a notion of confidence or certainty because the factors that influence the certainty or uncertainty of information vary with the type of information and the type of decisions being made. Visualization researchers have no model or framework for describing uncertainty as it relates to intelligence analysis, thus no consistent basis for constructing visualizations of uncertainty. This paper presents a typology describing the aspects of uncertainty related to intelligence analysis, drawing on frameworks for uncertainty representation in scientific computing.

Question: What is a typology for visualizing uncertainty?


Coordinated Scheduling of Residential Distributed Energy Resources to Optimize Smart Home Energy Services. We describe algorithmic enhancements to a decision-support tool that residential consumers can utilize to optimize their acquisition of electrical energy services. The decision-support tool optimizes energy services provision by enabling end users to first assign values to desired energy services, and then scheduling their available distributed energy resources (DER) to maximize net benefits. We chose particle swarm optimization (PSO) to solve the corresponding optimization problem because of its straightforward implementation and demonstrated ability to generate near-optimal schedules within manageable computation times. We improve the basic formulation of cooperative PSO by introducing stochastic repulsion among the particles. The improved DER schedules are then used to investigate the potential consumer value added by coordinated DER scheduling. This is computed by comparing the end-user costs obtained with the enhanced algorithm simultaneously scheduling all DER, against the costs when each DER schedule is solved separately. This comparison enables the end users to determine whether their mix of energy service needs, available DER and electricity tariff arrangements might warrant solving the more complex coordinated scheduling problem, or instead, decomposing the problem into multiple simpler optimizations.

Question: How to schedule residential distributed energy resources?


A System for Video Surveillance and Monitoring. The Robotics Institute at Carnegie Mellon University (CMU) and the Sarnoff Corporation are developing a system for autonomous Video Surveillance and Monitoring. The technical objective is to use multiple, cooperative video sensors to provide continuous coverage of people and vehicles in cluttered environments. This paper presents an overview of the system and significant results achieved to date.

Question: What is the name of the system?


An aspect representation for object manipulation based on convolutional neural networks. We propose an intelligent visuomotor system that interacts with the environment and memorizes the consequences of actions. As more memories are recorded and more interactions are observed, the agent becomes more capable of predicting the consequences of actions and is, thus, better at planning sequences of actions to solve tasks. In previous work, we introduced the aspect transition graph (ATG) which represents how actions lead from one observation to another using a directed multi-graph. In this work, we propose a novel aspect representation based on hierarchical CNN features, learned with convolutional neural networks, that supports manipulation and captures the essential affordances of an object based on RGB-D images. In a traditional planning system, robots are given a pre-defined set of actions that take the robot from one symbolic state to another. However symbolic states often lack the flexibility to generalize across similar situations. Our proposed representation is grounded in the robot's observations and lies in a continuous space that allows the robot to handle similar unseen situations. The hierarchical CNN features within a representation also allow the robot to act precisely with respect to the spatial location of individual features. We evaluate the robustness of this representation using the Washington RGB-D Objects Dataset and show that it achieves state of the art results for instance pose estimation. We then test this representation in conjunction with an ATG on a drill grasping task on Robonaut-2. We show that given grasp, drag, and turn demonstrations on the drill, the robot is capable of planning sequences of learned actions to compensate for reachability constraints.

Question: What is the canadian military?


Persuasive messages on information system acceptance: A theoretical extension of elaboration likelihood model and social influence theory. Firms invest millions of dollars in the introduction of new information systems for long-term benefit. If employees are not willing to accept a new information system, such investments may be wasted. Employee acceptance of a new information system is in part determined by external influences. However, previous research has neglected the paths of persuasive strategies and external social influences on information system acceptance. Linkages between persuasive strategies and external social influences are also scarce. By integrating social influence theory and an elaboration likelihood model, this study explores the influence of persuasive messages (source credibility and argument quality) on social influence, affective response and cognitive response. This study also investigates the interrelationships among affective response, cognitive response and behavior intention. Furthermore, the moderating roles of social influences on the impact of affective response and cognitive response on behavior intention are identified. 2012 Elsevier Ltd. All rights reserved.

Question: How can we persuade people to accept a new information system?


BabelNet: The automatic construction, evaluation and application of a wide-coverage multilingual semantic network. a r t i c l e i n f o a b s t r a c t We present an automatic approach to the construction of BabelNet, a very large, wide-coverage multilingual semantic network. Key to our approach is the integration of lexicographic and encyclopedic knowledge from WordNet and Wikipedia. In addition, Machine Translation is applied to enrich the resource with lexical information for all languages. We first conduct in vitro experiments on new and existing gold-standard datasets to show the high quality and coverage of BabelNet. We then show that our lexical resource can be used successfully to perform both monolingual and cross-lingual Word Sense Disambiguation: thanks to its wide lexical coverage and novel semantic relations, we are able to achieve state-of the-art results on three different SemEval evaluation tasks.

Question: What is the size of the babelnet?


Cue-based feeding in the NICU: using the infant's communication as a guide.. Although studies have shown cue-based feeding can lead to earlier achievement of full oral feeding, the successful implementation of cue-based feeding has been constrained by the volume-driven culture, which has existed for many years in the NIC U. This culture was built on the notion that a "better" nurse is one who could "get more in," and infants who are "poor feeders" are ones who "can't take enough." The infant who feeds faster is often viewed as more skilled in this task-oriented approach. The feeding relationship and the infant's communication about the experience of feeding may not be nurtured. This article will explain the central role of the preterm infant's communication in successful cue-based feeding. When the infant is perceived as having meaningful behavior (i.e., communicative intent), the focus changes from a volume-driven to a co-regulated approach, through which the infant guides the caregiver. This is cue-based feeding.

Question: What is cue-based feeding?