Detecting malicious tweets in trending topics using a statistical analysis of language. 0957-4174/$ see front matter 2012 Elsevier Ltd. A http://dx.doi.org/10.1016/j.eswa.2012.12.015 ⇑ Corresponding author. E-mail addresses: juaner@lsi.uned.es (J. MartinezAraujo). Twitter spam detection is a recent area of research in which most previous works had focused on the identification of malicious user accounts and honeypot-based approaches. However, in this paper we present a methodology based on two new aspects: the detection of spam tweets in isolation and without previous information of the user; and the application of a statistical analysis of language to detect spam in trending topics. Trending topics capture the emerging Internet trends and topics of discussion that are in everybody's lips. This growing microblogging phenomenon therefore allows spammers to disseminate malicious tweets quickly and massively. In this paper we present the first work that tries to detect spam tweets in real time using language as the primary tool. We first collected and labeled a large dataset with 34 K trending topics and 20 million tweets. Then, we have proposed a reduced set of features hardly manipulated by spammers. In addition, we have developed a machine learning system with some orthogonal features that can be combined with other sets of features with the aim of analyzing emergent characteristics of spam in social networks. We have also conducted an extensive evaluation process that has allowed us to show how our system is able to obtain an F-measure at the same level as the best state-ofthe-art systems based on the detection of spam accounts. Thus, our system can be applied to Twitter spam detection in trending topics in real time due mainly to the analysis of tweets instead of user accounts. 2012 Elsevier Ltd. All rights reserved.

Question: What is the difference between a statistical analysis of language and a statistical analysis of language?


Concepts for dynamic obstacle avoidance and their extended application in underground navigation. An effective approach to dynamic obstacle avoidance for mobile robots is described in this paper. The main concepts in this approach include local Polar Object Chart (POC) for defining possible ways out, obstacle definition, detour mode, anchor for preventing getting lost, steering delimiting and velocity delimiting. The presented concepts and methods were tested and verified both by simulations and experiments on a developed prototype industrial Automated Guided Vehicle (AGV). This paper also presents the extended application of some of the concepts in underground navigation. © 2002 Elsevier Science B.V. All rights reserved.

Question: What is the difference between a robot and an AGV?


A Novel Phase-Shift Control of Semibridgeless Active Rectifier for Wireless Power Transfer. A novel phase-shift control of a semibridgeless active rectifier (S-BAR) is investigated in order to utilize the S-BAR in wireless energy transfer applications. The standard receiver-side rectifier topology is developed by replacing rectifier lower diodes with synchronous switches controlled by a phase-shifted PWM signal. Theoretical and simulation results show that with the proposed control technique, the output quantities can be regulated without communication between the receiver and transmitter. To confirm the performance of the proposed converter and control, experimental results are provided using 8-, 15-, and 23-cm air gap coreless transformer which has dimension of 76 cm × 76 cm, with 120-V input and the output power range of 0 to 1kW with a maximum efficiency of 94.4%.

Question: How to control a semibridgeless active rectifier?


Behaviour Analysis of Multilayer Perceptrons with Multiple Hidden Neurons and Hidden Layers. —The terms " Neural Network " (NN) and " Artificial Neural Network " (ANN) usually refer to a Multilayer Perceptron Network. It process the records one at a time, and "learn" by comparing their prediction of the record with the known actual record. The problem of model selection is considerably important for acquiring higher levels of generalization capability in supervised learning. This paper discussed behavioral analysis of different number of hidden layers and different number of hidden neurons. It's very difficult to select number of hidden layers and hidden neurons. There are different methods like Akaike's Information Criterion, Inverse test method and some traditional methods are used to find Neural Network architecture. What to do while neural network is not getting train or errors are not getting reduced. To reduce Neural Network errors, what we have to do with Neural Network architecture. These types of techniques are discussed and also discussed experiment and result. To solve different problems a neural network should be trained to perform correct classification..

Question: What is the difference between a neural network and a multilayer perceptron?


Overconfidence and Trading Volume. Theoretical models predict that overconfident investors will trade more than rational investors. We directly test this hypothesis by correlating individual overconfidence scores with several measures of trading volume of individual investors. Approximately 3,000 online broker investors were asked to answer an internet questionnaire which was designed to measure various facets of overconfidence (miscalibration, volatility estimates, better than average effect). The measures of trading volume were calculated by the trades of 215 individual investors who answered the questionnaire. We find that investors who think that they are above average in terms of investment skills or past performance (but who did not have above average performance in the past) trade more. Measures of miscalibration are, contrary to theory, unrelated to measures of trading volume. This result is striking as theoretical models that incorporate overconfident investors mainly motivate this assumption by the calibration literature and model overconfidence as underestimation of the variance of signals. In connection with other recent findings, we conclude that the usual way of motivating and modeling overconfidence which is mainly based on the calibration literature has to be treated with caution. Moreover, our way of empirically evaluating behavioral finance models the correlation of economic and psychological variables and the combination of psychometric measures of judgment biases (such as overconfidence scores) and field data seems to be a promising way to better understand which psychological phenomena actually drive economic behavior.

Question: What is the correlation between overconfidence and trading volume?


A power-efficient 4-2 Adder Compressor topology. The addition is the most used arithmetic operation in Digital Signal Processing (DSP) algorithms, such as filters, transforms and predictions. These algorithms are increasingly present in audio and video processing of battery-powered mobile devices having, therefore, energy constraints. In the context of addition operation, the efficient 4-2 adder compressor is capable to performs four additions simultaneously. This higher order of parallelism reduces the critical path and the internal glitching, thus reducing mainly the dynamic power dissipation. This work proposes two CMOS+ gate-based topologies to further reduce the power, area and delay of the 4-2 adder compressor. The proposed CMOS+ 4-2 adder compressor circuits topologies were implemented with Cadence Virtuoso tool at 45 nm technology and simulated in both electric and layout levels. The results show that a proper choice of gates in 4-2 adder compressor realization can reduce the power, delay and area about 22.41%, 32.45% and 7.4%, respectively, when compared with the literature.

Question: What is the power consumption of a 4-2 adder compressor?


Deep Metric and Hash-Code Learning for Content-Based Retrieval of Remote Sensing Images. The growing volume of Remote Sensing (RS) image archives demands for feature learning techniques and hashing functions which can: (1) accurately represent the semantics in the RS images; and (2) have quasi real-time performance during retrieval. This paper aims to address both challenges at the same time, by learning a semantic-based metric space for content based RS image retrieval while simultaneously producing binary hash codes for an efficient archive search. This double goal is achieved by training a deep network using a combination of different loss functions which, on the one hand, aim at clustering semantically similar samples (i.e., images), and, on the other hand, encourage the network to produce final activation values (i.e., descriptors) that can be easily binarized. Moreover, since RS annotated training images are too few to train a deep network from scratch, we propose to split the image representation problem in two different phases. In the first we use a general-purpose, pre-trained network to produce an intermediate representation, and in the second we train our hashing network using a relatively small set of training images. Experiments on two aerial benchmark archives show that the proposed method outperforms previous state-of-the-art hashing approaches by up to 5.4% using the same number of hash bits per image.

Question: How to train a deep network for content-based retrieval of remote sensing images?


Today ' s State-Owned Enterprises of China : Are They Dying Dinosaurs or Dynamic Dynamos ?. NOTE: The authors thank Bianca Bain for her assistance on an earlier draft of the paper and the University of Macao for providing financial support. Summary This paper raises the question and provides empirical evidence regarding the status of the evolution of the state-owned enterprises (SOEs) in China today. In this study, we compare the SOEs to domestic private-owned enterprises (POEs) and foreign-controlled businesses (FCBs) in the context of their organizational cultures. While a new ownership form, many of the POEs, evolved from former collectives that reflect the traditional values of Chinese business. Conversely, the FCBs are much more indicative of the large global MNCs. Therefore, we look at the SOEs in the context of these two reference points. We conclude that the SOEs of today have substantially transformed to approximate a configuration desired by the Chinese government when it began the SOE transformation a couple of decades ago to make them globally competitive. The SOEs of today appear to be appropriately described as China's economic dynamic dynamo for the future.

Question: What is the difference between a state-owned enterprise and a private-owned enterprise?


The characteristics of persistent sexual offenders: a meta-analysis of recidivism studies.. A meta-analysis of 82 recidivism studies (1,620 findings from 29,450 sexual offenders) identified deviant sexual preferences and antisocial orientation as the major predictors of sexual recidivism for both adult and adolescent sexual offenders. Antisocial orientation was the major predictor of violent recidivism and general (any) recidivism. The review also identified some dynamic risk factors that have the potential of being useful treatment targets (e.g., sexual preoccupations, general self-regulation problems). Many of the variables commonly addressed in sex offender treatment programs (e.g., psychological distress, denial of sex crime, victim empathy, stated motivation for treatment) had little or no relationship with sexual or violent recidivism.

Question: What are the characteristics of persistent sexual offenders?


InverseRenderNet: Learning single image inverse rendering. We show how to train a fully convolutional neural network to perform inverse rendering from a single, uncontrolled image. The network takes an RGB image as input, regresses albedo and normal maps from which we compute lighting coefficients. Our network is trained using large uncontrolled image collections without ground truth. By incorporating a differentiable renderer, our network can learn from self-supervision. Since the problem is ill-posed we introduce additional supervision: 1. We learn a statistical natural illumination prior, 2. Our key insight is to perform offline multiview stereo (MVS) on images containing rich illumination variation. From the MVS pose and depth maps, we can cross project between overlapping views such that Siamese training can be used to ensure consistent estimation of photometric invariants. MVS depth also provides direct coarse supervision for normal map estimation. We believe this is the first attempt to use MVS supervision for learning inverse rendering.

Question: How to train a neural network to perform inverse rendering from a single image?


Exploiting Redundancy in Question Answering. Our goal is to automatically answer brief factual questions of the form ``When was the Battle of Hastings?'' or ``Who wrote The Wind in the Willows?''. Since the answer to nearly any such question can now be found somewhere on the Web, the problem reduces to finding potential answers in large volumes of data and validating their accuracy. We apply a method for arbitrary passage retrieval to the first half of the problem and demonstrate that answer redundancy can be used to address the second half. The success of our approach depends on the idea that the volume of available Web data is large enough to supply the answer to most factual questions multiple times and in multiple contexts. A query is generated from a question and this query is used to select short passages that may contain the answer from a large collection of Web data. These passages are analyzed to identify candidate answers. The frequency of these candidates within the passages is used to ``vote'' for the most likely answer. The approach is experimentally tested on questions taken from the TREC-9 question-answering test collection. As an additional demonstration, the approach is extended to answer multiple choice trivia questions of the form typically asked in trivia quizzes and television game shows.

Question: What is the name of the first large-scale Canadian peacekeeping mission?


High resolution maps from wide angle sonar.

Question: What is a wide angle sonar?


What makes a chair a chair?. Many object classes are primarily defined by their functions. However, this fact has been left largely unexploited by visual object categorization or detection systems. We propose a method to learn an affordance detector. It identifies locations in the 3d space which "support" the particular function. Our novel approach "imagines" an actor performing an action typical for the target object class, instead of relying purely on the visual object appearance. So, function is handled as a cue complementary to appearance, rather than being a consideration after appearance-based detection. Experimental results are given for the functional category "sitting". Such affordance is tested on a 3d representation of the scene, as can be realistically obtained through SfM or depth cameras. In contrast to appearance-based object detectors, affordance detection requires only very few training examples and generalizes very well to other sittable objects like benches or sofas when trained on a few chairs.

Question: What is the function of a chair?


Discovery of inference rules for question-answering. One of the main challenges in question-answering is the potential mismatch between the expressions in questions and the expressions in texts. While humans appear to use inference rules such as "X writes Y" implies "X is the author of Y" in answering questions, such rules are generally unavailable to question-answering systems due to the inherent difficulty in constructing them. In this paper, we present an unsupervised algorithm for discovering inference rules from text. Our algorithm is based on an extended version of Harris' Distributional Hypothesis, which states that words that occurred in the same contexts tend to be similar. Instead of using this hypothesis on words, we apply it to paths in the dependency trees of a parsed corpus. Essentially, if two paths tend to link the same set of words, we hypothesize that their meanings are similar. We use examples to show that our system discovers many inference rules easily missed by humans.

Question: What is the discovery of inference rules for question-answering?


Graphical Figure Classification Using Data Fusion for Integrating Text and Image Features. This paper describes a multimodal (image + text) learning approach for automatically identifying three graphical figure types commonly found in biomedical literature, namely, diagrams, statistical figures and flow charts. The goal is to improve retrieval of figures from biomedical journal articles. In this article, we describe a data fusion approach to combine information from both text and image sources, believed to contain complementary information. Text information about the image is extracted from the figure caption. The data fusion process includes a hybrid of evolutionary algorithm (EA) and Binary Particle Swarm Optimization (BPSO) called method applied to find an optimal subset of extracted image features. Chi-square statistic and information gain metric are used to select the optimal subset of extracted text features, which along with image features are input to Multi-Layer Perceptron Neural Network classifiers, whose outputs are characterized as fuzzy sets to determine the final classification result. Evaluation performed on 1707 figure images extracted from a test subset of Biome Central® journals extracted from U.S. National Library of Medicine's PubMed Central ® repository yielded classification accuracy as high as 96.1%.

Question: How to classify graphical figures?


Cloud Log Forensics Metadata Analysis. The increase in the quantity and questionable quality of the forensic information retrieved from the current virtualized data cloud system architectures has made it extremely difficult for law enforcement to resolve criminal activities within these logical domains. This paper poses the question of what kind of information is desired from virtual machine (VM) hosted operating systems (OS) investigated by a cloud forensic examiner. The authors gives an overview of the information that exists on current VM OS by looking at it's kernel hypervisor logs and discusses the shortcomings. An examination of the role that the VM kernel hypervisor logs provide as OS metadata in cloud investigations is also presented.

Question: What kind of information is desired from virtual machine hosted operating systems investigated by a cloud forensic examiner?


A multivariate time series clustering approach for crime trends prediction. In recent past, there is an increased interest in time series clustering research, particularly for finding useful similar trends in multivariate time series in various applied areas such as environmental research, finance, and crime. Clustering multivariate time series has potential for analyzing large volume of crime data at different time points as law enforcement agencies are interested in finding crime trends of various police administration units such as states, districts and police stations so that future occurrences of similar incidents can be overcome. Most of the traditional time series clustering algorithms deals with only univariate time series data and for clustering high dimensional data, it has to be transformed into single dimension using a dimension reduction technique. The conventional time series clustering techniques do not provide desired results for crime data set, since crime data is high dimensional and consists of various crime types with different weight age. In this paper, a novel approach based on dynamic time wrapping and parametric Minkowski model has been proposed to find similar crime trends among various crime sequences of different crime locations and subsequently use this information for future crime trends prediction. Analysis on Indian crime records show that the proposed technique generally outperforms the existing techniques in clustering of such multivariate time series data.

Question: What is the purpose of this paper?


A Low Cost Eeg Based Bci Prosthetic Using Motor Imagery. Brain Computer Interfaces (BCI) provide the opportunity to control external devices using the brain ElectroEncephaloGram (EEG) signals. In this paper we propose two software framework in order to control a 5 degree of freedom robotic and prosthetic hand. Results are presented where an Emotiv Cognitive Suite (i.e. the 1 st framework) combined with an embedded software system (i.e. an open source Arduino board) is able to control the hand through character input associated with the taught actions of the suite. This system provides evidence of the feasibility of brain signals being a viable approach to controlling the chosen prosthetic. Results are then presented in the second framework. This latter one allowed for the training and classification of EEG signals for motor imagery tasks. When analysing the system, clear visual representations of the performance and accuracy are presented in the results using a confusion matrix, accuracy measurement and a feedback bar signifying signal strength. Experiments with various acquisition datasets were carried out and with a critical evaluation of the results given. Finally depending on the classification of the brain signal a Python script outputs the driving command to the Arduino to control the prosthetic. The proposed architecture performs overall good results for the design and implementation of economically convenient BCI and prosthesis.

Question: What is the difference between a BCI and a prosthetic?


Recall of childhood trauma: a prospective study of women's memories of child sexual abuse.. One hundred twenty-nine women with previously documented histories of sexual victimization in childhood were interviewed and asked detailed questions about their abuse histories to answer the question "Do people actually forget traumatic events such as child sexual abuse, and if so, how common is such forgetting?" A large proportion of the women (38%) did not recall the abuse that had been reported 17 years earlier. Women who were younger at the time of the abuse and those who were molested by someone they knew were more likely to have no recall of the abuse. The implications for research and practice are discussed. Long periods with no memory of abuse should not be regarded as evidence that the abuse did not occur.

Question: Do people actually forget traumatic events such as child sexual abuse?


Improved Collision Attack on Hash Function MD5. In this paper, we present a fast attack algorithm to find two-block collision of hash function MD5. The algorithm is based on the two-block collision differential path of MD5 that was presented by Wang et al. in the Conference EUROCRYPT 2005. We found that the derived conditions for the desired collision differential path were not sufficient to guarantee the path to hold and that some conditions could be modified to enlarge the collision set. By using technique of small range searching and omitting the computing steps to check the characteristics in the attack algorithm, we can speed up the attack of MD5 efficiently. Compared with the Advanced Message Modification technique presented by Wang et al., the small range searching technique can correct 4 more conditions for the first iteration differential and 3 more conditions for the second iteration differential, thus improving the probability and the complexity to find collisions. The whole attack on the MD5 can be accomplished within 5 hours using a PC with Pentium4 1.70GHz CPU.

Question: How to find a two-block collision of hash function MD5?


Aromas of rosemary and lavender essential oils differentially affect cognition and mood in healthy adults.. This study was designed to assess the olfactory impact of the essential oils of lavender (Lavandula angustifolia) and rosemary (Rosmarlnus officinalis) on cognitive performance and mood in healthy volunteers. One hundred and forty-four participants were randomly assigned to one of three independent groups, and subsequently performed the Cognitive Drug Research (CDR) computerized cognitive assessment battery in a cubicle containing either one of the two odors or no odor (control). Visual analogue mood questionnaires were completed prior to exposure to the odor, and subsequently after completion of the test battery. The participants were deceived as to the genuine aim of the study until the completion of testing to prevent expectancy effects from possibly influencing the data. The outcome variables from the nine tasks that constitute the CDR core battery feed into six factors that represent different aspects of cognitive functioning. Analysis of performance revealed that lavender produced a significant decrement in performance of working memory, and impaired reaction times for both memory and attention based tasks compared to controls. In contrast, rosemary produced a significant enhancement of performance for overall quality of memory and secondary memory factors, but also produced an impairment of speed of memory compared to controls. With regard to mood, comparisons of the change in ratings from baseline to post-test revealed that following the completion of the cognitive assessment battery, both the control and lavender groups were significantly less alert than the rosemary condition; however, the control group was significantly less content than both rosemary and lavender conditions. These findings indicate that the olfactory properties of these essential oils can produce objective effects on cognitive performance, as well as subjective effects on mood.

Question: What is the difference between lavender and rosemary?


Question: What is compressive sensing?


Quantum Field Theory in Curved Spacetime. We review the mathematically rigorous formulation of the quantum theory of a linear field propagating in a globally hyperbolic spacetime. This formulation is accomplished via the algebraic approach, which, in essence, simultaneously admits all states in all possible (unitarily inequivalent) Hilbert space constructions. The physically nonsingular states are restricted by the requirement that their two-point function satisfy the Hadamard condition, which insures that the ultra-violet behavior of the state be similar to that of the vacuum state in Minkowski spacetime, and that the expected stress-energy tensor in the state be finite. We briefly review the Unruh and Hawking effects from the perspective of the theoretical framework adopted here. A brief discussion also is given of several open issues and questions in quantum field theory in curved spacetime regarding the treatment of "backreaction", the validity of some version of the "averaged null energy condition", and the formulation and properties of quantum field theory in causality violating

Question: What is the difference between the quantum field theory in curved spacetime and the quantum field theory in Minkowski spacetime?


Enhancing scholarly use of digital libraries: A comparative survey and review of bibliographic metadata ontologies. The HathiTrust Research Center (HTRC) is engaged in the development of tools that will give scholars the ability to analyze the HathiTrust digital library's 14 million volume corpus. A cornerstone of the HTRC's digital infrastructure is the workset -- a kind of scholar-built research collection intended for use with the HTRC's analytics platform. Because more than 66% of the digital corpus is subject to copyright restrictions, scholarly users remain dependent upon the descriptive accounts provided by traditional metadata records in order to identify and gather together bibliographic resources for analysis. This paper compares the MADSRDF/MODSRDF, Bibframe, schema.org, BIBO, and FaBiO ontologies by assessing their suitability for employment by the HTRC to meet scholars' needs. These include distinguishing among multiple versions of the same work; representing the complex historical and physical relationships among those versions; and identifying and providing access to finer grained bibliographic entities, e.g., poems, chapters, sections, and even smaller segments of content.

Question: What is the difference between the MADSRDF/MODSRDF, Bibframe, schema.org, BIBO, and FaBiO ontologies?


Learning Multilingual Word Representations using a Bag-of-Words Autoencoder. Recent work on learning multilingual word representations usually relies on the use of word-level alignements (e.g. infered with the help of GIZA++) between translated sentences, in order to align the word embeddings in different languages. In this workshop paper, we investigate an autoencoder model for learning multilingual word representations that does without such word-level alignements. The autoencoder is trained to reconstruct the bag-of-word representation of given sentence from an encoded representation extracted from its translation. We evaluate our approach on a multilingual document classification task, where labeled data is available only for one language (e.g. English) while classification must be performed in a different language (e.g. French). In our experiments, we observe that our method compares favorably with a previously proposed method that exploits word-level alignments to learn word representations.

Question: How to learn multilingual word representations without word-level alignements?


Jointly Embedding Entities and Text with Distant Supervision. Learning representations for knowledge base entities and concepts is becoming increasingly important for NLP applications. However, recent entity embedding methods have relied on structured resources that are expensive to create for new domains and corpora. We present a distantly-supervised method for jointly learning embeddings of entities and text from an unnanotated corpus, using only a list of mappings between entities and surface forms. We learn embeddings from open-domain and biomedical corpora, and compare against prior methods that rely on human-annotated text or large knowledge graph structure. Our embeddings capture entity similarity and relatedness better than prior work, both in existing biomedical datasets and a new Wikipedia-based dataset that we release to the community. Results on analogy completion and entity sense disambiguation indicate that entities and words capture complementary information that can be effectively combined for downstream use.

Question: How do we learn embeddings for entities and text?


Classification accuracy is not enough. We argue that an evaluation of system behavior at the level of the music is required to usefully address the fundamental problems of music genre recognition (MGR), and indeed other tasks of music information retrieval, such as autotagging. A recent review of works in MGR since 1995 shows that most (82 %) measure the capacity of a system to recognize genre by its classification accuracy. After reviewing evaluation in MGR, we show that neither classification accuracy, nor recall and precision, nor confusion tables, necessarily reflect the capacity of a system to recognize genre in musical signals. Hence, such figures of merit cannot be used to reliably rank, promote or discount the genre recognition performance of MGR systems if genre recognition (rather than identification by irrelevant confounding factors) is the objective. This motivates the development of a richer experimental toolbox for evaluating any system designed to intelligently extract information from music signals.

Question: What is the difference between classification accuracy and recall and precision?


Separate Hydrolysis and Fermentation of Pretreated Spruce Josefin Axelsson.

Question: What is the difference between hydrolysis and fermentation?


The Word Tree, an Interactive Visual Concordance. We introduce the Word Tree, a new visualization and information-retrieval technique aimed at text documents. A Word Tree is a graphical version of the traditional "keyword-in-context" method, and enables rapid querying and exploration of bodies of text. In this paper we describe the design of the technique, along with some of the technical issues that arise in its implementation. In addition, we discuss the results of several months of public deployment of word trees on Many Eyes, which provides a window onto the ways in which users obtain value from the visualization.

Question: What is the word tree?


Nonparametric variable importance using an augmented neural network with multi-task learning. In predictive modeling applications, it is often of interest to determine the relative contribution of subsets of features in explaining the variability of an outcome. It is useful to consider this variable importance as a function of the unknown, underlying data-generating mechanism rather than the specific predictive algorithm used to fit the data. In this paper, we connect these ideas in nonparametric variable importance to machine learning, and provide a method for efficient estimation of variable importance when building a predictive model using a neural network. We show how a single augmented neural network with multi-task learning simultaneously estimates the importance of many feature subsets, improving on previous procedures for estimating importance. We demonstrate on simulated data that our method is both accurate and computationally efficient, and apply our method to both a study of heart disease and for predicting mortality in ICU patients.

Question: How to estimate variable importance in a neural network?


Relationship between Variants of One-Class Nearest Neighbors and Creating Their Accurate Ensembles. In one-class classification problems, only the data for the target class is available, whereas the data for the non-target class may be completely absent. In this paper, we study one-class nearest neighbor (OCNN) classifiers and their different variants. We present a theoretical analysis to show the relationships among different variants of OCNN that may use different neighbors or thresholds to identify unseen examples of the non-target class. We also present a method based on inter-quartile range for optimizing parameters used in OCNN in the absence of non-target data during training. Then, we propose two ensemble approaches based on random subspace and random projection methods to create accurate OCNN ensembles. We tested the proposed methods on 15 benchmark and real world domain-specific datasets and show that random-projection ensembles of OCNN perform best.

Question: What is the relationship between variants of one-class nearest neighbors and creating their accurate ensembles?


VCDB: A Large-Scale Database for Partial Copy Detection in Videos. The task of partial copy detection in videos aims at finding if one or more segments of a query video have (transformed) copies in a large dataset. Since collecting and annotating large datasets of real partial copies are extremely time-consuming, previous video copy detection research used either small-scale datasets or large datasets with simulated partial copies by imposing several pre-defined transformations (e.g., photometric or geometric changes). While the simulated datasets were useful for research, it is unknown how well the techniques developed on such data work on real copies, which are often too complex to be simulated. In this paper, we introduce a large-scale video copy database (VCDB) with over 100,000 Web videos, containing more than 9,000 copied segment pairs found through careful manual annotation. We further benchmark a baseline system on VCDB, which has demonstrated state-of-the-art results in recent copy detection research. Our evaluation suggests that existing techniques—which have shown near-perfect results on the simulated benchmarks—are far from satisfactory in detecting complex real copies. We believe that the release of VCDB will largely advance the research around this challenging problem.

Question: What is the largest database of partial copies of videos?


PhaseNet: A Deep Convolutional Neural Network for Two-Dimensional Phase Unwrapping. Phase unwrapping is a crucial signal processing problem in several applications that aims to restore original phase from the wrapped phase. In this letter, we propose a novel framework for unwrapping the phase using deep fully convolutional neural network termed as PhaseNet. We reformulate the problem definition of directly obtaining continuous original phase as obtaining the wrap-count (integer jump of 2 $\pi$) at each pixel by semantic segmentation and this is accomplished through a suitable deep learning framework. The proposed architecture consists of an encoder network, a corresponding decoder network followed by a pixel-wise classification layer. The relationship between the absolute phase and the wrap-count is leveraged in generating abundant simulated data of several random shapes. This deliberates the network on learning continuity in wrapped phase maps rather than specific patterns in the training data. We compare the proposed framework with the widely adapted quality-guided phase unwrapping algorithm and also with the well-known MATLAB's unwrap function for varying noise levels. The proposed framework is found to be robust to noise and computationally fast. The results obtained highlight that deep convolutional neural network can indeed be effectively applied for phase unwrapping, and the proposed framework will hopefully pave the way for the development of a new set of deep learning based phase unwrapping methods.

Question: What is a deep convolutional neural network?


Gain enhancement of dual-band antenna using square loop FSS. This paper presents a high gain dual-band microstrip antenna. The antenna operates in 9.25 and 11 GHz (X-band). High gain of antenna is achieved by employing a FSS as superstrate which is formed by the 5 × 5 array of square loops. The square loop FSS resonance is varied and its influence on the antenna behavior is analyzed. Analyze of the simulated antenna gain and return loss are shown. The gain improvement depends on the resonance of the FSS superstrate. The higher gain obtained is 6 dB at 11 GHz while maintaining a good matching level for the first resonance.

Question: How to design a dual band antenna?


Ultrawideband All-Metal Flared-Notch Array Radiator. Simulations and measurements are presented for an all-metal flared-notch array element in both single and dual-polarization configurations. The ultrawideband radiator exhibits an operational bandwidth of 12:1 for broadside scan and 8:1 bandwidth at a 45-degree scan in all planes, maintaining active VSWR<;2. The feed consists of a direct coax-to-slot-line transition that mounts directly into the base of the radiator. The all-metal flared-notches are machined from common metal stock and fed via SMA coaxial connectors. No soldering is required for any part of the design-including the feed-and assembly is simple and modular. The array parts are machined using a high-precision wire-EDM cutting technology, ensuring that measurements (in the 700 MHz-9 GHz range) are repeatable and give close agreement with theory, even through multiple assembly cycles of the modular construction system. This paper presents results for a 32-element linear array of horizontal elements and also an 8 × 8 planar array of dual-polarized elements, comparing measurements with full-wave simulations of the complete finite array structures.

Question: What is the difference between a radiator and a reflector?


Applying Reinforcement Learning to Blackjack Using Q-Learning. Blackjack is a popular card game played in many casinos. The objective of the game is to win money by obtaining a point total higher than the dealer's without exceeding 21. Determining an optimal blackjack strategy proves to be a difficult challenge due to the stochastic nature of the game. This presents an interesting opportunity for machine learning algorithms. Supervised learning techniques may provide a viable solution, but do not take advantage of the inherent reward structure of the game. Reinforcement learning algorithms generally perform well in stochastic environments, and could utilize blackjack's reward structure. This paper explores reinforcement learning as a means of approximating an optimal blackjack strategy using the Q-learning algorithm.

Question: What is reinforcement learning?


Eigenvector-centrality - a node-centrality?. Networks of social relations can be represented by graphs and socioor adjacency-matrices and their structure can be analyzed using different concepts, one of them called centrality. We will provide a new formalization of a "node-centrality" which leads to some properties a measure of centrality has to satisfy. These properties allow to test given measures, for example measures based on degree, closeness, betweenness or Bonacich's eigenvector-centrality. It turns out that it depends on normalization whether eigenvector-centrality does satisfy the expected properties or not. © 2000 Elsevier Science B.V. All rights reserved.

Question: What is a node-centrality?


A comparison of the performance of latent Dirichlet allocation and the Dirichlet multinomial mixture model on short text. The expansion of the World Wide Web and the increasing popularity of microblogging websites such as Twitter and Facebook has created massive stores of textual data that is short in length. Although traditional topic models have proven to work successfully on collections of long texts such as books and news articles, they tend to produce results that are less coherent when applied to short text, such as status messages and product reviews. Over the last few decades it has become of greater relevance to analyse short texts due to the realisation that such bodies of text could potentially hold useful information. Latent Dirichlet allocation (LDA) is one of the most popular topic models and it makes the generative assumption that each document contains multiple topics in varying proportions, which is a sensible assumption about long text. On the other hand, the Dirichlet multinomial mixture model (GSDMM), a seemingly less popular topic model, assumes that a document can only belong to a single topic, which seems to be a more appropriate assumption for short text. The objective of this paper is to investigate the hypothesis that GSDMM will outperform LDA on short text, using topic coherence and stability as performance measures.

Question: What is the difference between LDA and GSDMM?


Separation of Fine Granular Mixtures in S-Plate-Type Electrostatic Separators. Plate-type electrostatic separators are commonly used for the selective sorting of conductive from nonconductive constituents of a granular mixture. This paper addresses the more delicate issue of granular materials that are characterized by finite nonzero resistivities. The study was conducted with three materials having similar granule size range and specific weight but slightly different electrical properties. Two sets of experiments were performed for evaluating the effect of electrode geometry on the behavior of each of the materials. These results were then employed for predicting the outcome of the electrostatic separation of binary mixtures of good and poor conductors. A third set of experiments validated the predictions and pointed out a couple of nonelectric factors that might deteriorate the performance of a plate-type electrostatic separator in an industrial environment.

Question: What is the effect of electrode geometry on the behavior of each of the materials?


Implementing a Personal Health Record Cloud Platform Using Ciphertext-Policy Attribute-Based Encryption. Work on designing and implementing a patient-centric, personal health record cloud platform based on open-source Indivo X system. We adopt cipher text-policy attribute-based encryption to provide privacy protection and fine-grained access control.

Question: What is a personal health record cloud platform?


Visual Place Recognition: A Survey. Visual place recognition is a challenging problem due to the vast range of ways in which the appearance of real-world places can vary. In recent years, improvements in visual sensing capabilities, an ever-increasing focus on long-term mobile robot autonomy, and the ability to draw on state-of-the-art research in other disciplines-particularly recognition in computer vision and animal navigation in neuroscience-have all contributed to significant advances in visual place recognition systems. This paper presents a survey of the visual place recognition research landscape. We start by introducing the concepts behind place recognition-the role of place recognition in the animal kingdom, how a "place" is defined in a robotics context, and the major components of a place recognition system. Long-term robot operations have revealed that changing appearance can be a significant factor in visual place recognition failure; therefore, we discuss how place recognition solutions can implicitly or explicitly account for appearance change within the environment. Finally, we close with a discussion on the future of visual place recognition, in particular with respect to the rapid advances being made in the related fields of deep learning, semantic scene understanding, and video description.

Question: What is the role of place recognition in the animal kingdom?


Gradient-based learning algorithms for recurrent networks and their computational complexity.

Question: What is the computational complexity of gradient-based learning algorithms for recurrent networks?


The design and implementation of microdrivers. Device drivers commonly execute in the kernel to achieve high performance and easy access to kernel services. However, this comes at the price of decreased reliability and increased programming difficulty. Driver programmers are unable to use user-mode development tools and must instead use cumbersome kernel tools. Faults in kernel drivers can cause the entire operating system to crash. User-mode drivers have long been seen as a solution to this problem, but suffer from either poor performance or new interfaces that require a rewrite of existing drivers.  This paper introduces the Microdrivers architecture that achieves high performance and compatibility by leaving critical path code in the kernel and moving the rest of the driver code to a user-mode process. This allows data-handling operations critical to I/O performance to run at full speed, while management operations such as initialization and configuration run at reduced speed in user-level. To achieve compatibility, we present DriverSlicer, a tool that splits existing kernel drivers into a kernel-level component and a user-level component using a small number of programmer annotations. Experiments show that as much as 65% of driver code can be removed from the kernel without affecting common-case performance, and that only 1-6 percent of the code requires annotations.

Question: What is the microdrivers architecture?


Teaching Syntax by Adversarial Distraction. Existing entailment datasets mainly pose problems which can be answered without attention to grammar or word order. Learning syntax requires comparing examples where different grammar and word order change the desired classification. We introduce several datasets based on synthetic transformations of natural entailment examples in SNLI or FEVER, to teach aspects of grammar and word order. We show that without retraining, popular entailment models are unaware that these syntactic differences change meaning. With retraining, some but not all popular entailment models can learn to compare the syntax properly.

Question: What is entailment?


Trends in augmented reality tracking, interaction and display: A review of ten years of ISMAR. Although Augmented Reality technology was first developed over forty years ago, there has been little survey work giving an overview of recent research in the field. This paper reviews the ten-year development of the work presented at the ISMAR conference and its predecessors with a particular focus on tracking, interaction and display research. It provides a roadmap for future augmented reality research which will be of great value to this relatively young field, and also for helping researchers decide which topics should be explored when they are beginning their own studies in the area.

Question: What is the ten-year development of the work presented at the ISMAR conference?


Example-Based Super-Resolution. Image-based models for computer graphics lack resolution independence: they cannot be zoomed much beyond the pixel resolution they were sampled at without a degradation of quality. Interpolating images usually results in a blurring of edges and image details. We describe image interpolation algorithms which use a database of training images to create plausible high-frequency details in zoomed images. Image pre-processing steps allow the use of image detail from regions of the training images which may look quite di erent from the image to be processed. These methods preserve ne details, such as edges, generate believable textures, and can give good results even after zooming multiple octaves. This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonpro t educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Information Technology Center America; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Information Technology Center America. All rights reserved. Copyright c Mitsubishi Electric Information Technology Center America, 2001 201 Broadway, Cambridge, Massachusetts 02139 1. First printing, TR2001-30, August, 2001. Egon Pasztor's present address: MIT Media Lab 20 Ames St. Cambridge, MA 02139 Example-based Super-resolution William T. Freeman, Thouis R. Jones, Egon C. Pasztor MitsubishiElectricResearchLaboratories(MERL) 201Broadway Cambridge,MA 02139 (a) (b) Figure 1: An object modelledby traditionalpolygon techniques may lack someof the richnessof real-world objects,but behaves properlyunderzooming,(b).

Question: How to create a super-resolution image?


Mental Stress Assessment of ECG Signal using Statistical Analysis of Bio-Orthogonal Wavelet Coefficients. It is observed that the stress level is function of various statistical parameters like standard deviation, entropy, energy,, mean, Covariance and power of the ECG signals of two states i.e. normal state of mind and stressed state of mind. Further, it is observed that the features extracted are directly from the ECG in frequency domain using db4 wavelet. However, db4 introduces some error on account of db4 wavelet shape. This error in turn amplifies while measuring the features as mentioned above. In order to reduce this error, we propose a Bior 3.9 wavelet family to decompose the ECG signal. The decomposed ECG signal is now analyzed statistically to extract the above feature. Further, in the existing work, all the ECGs are taken by using the 12 leads method. This factor also adds some undue stress to the person under scanner. And this is time consuming too. Therefore, to reduce this complexity, we propose to analyze all above features by using a two lead ECG. Further, a back propagation neural network is trained using the above features as input neurons in order to classify the stress level.

Question: How to reduce the error in the features extracted using db4 wavelet?


Development of an exoskeleton haptic interface for virtual task training. An exoskeleton haptic interface is developed for functional training in virtual environments. A composite control scheme enables a variety of tasks to be implemented, and a "Qt" graphics library is used to generate the virtual environment for the haptic interface at the hand and graphical user interfaces for input and telemetry. Inter-process communications convert telemetry from the exoskeleton into motion commands for objects in the virtual environment. A second haptic interface at the upper arm is used to control the elbow orbit self-motion of the arm during tasks. Preliminary results are reviewed for a wall-painting task in which the virtual wall stiffness and viscosity are generated using an admittance controller.

Question: What is an exoskeleton haptic interface?


A cognitive developmental approach to morality: investigating the psychopath. Various social animal species have been noted to inhibit aggressive attacks when a conspecific displays submission cues. Blair (1993) has suggested that humans possess a functionally similar mechanism which mediates the suppression of aggression in the context of distress cues. He has suggested that this mechanism is a prerequisite for the development of the moral/conventional distinction; the consistently observed distinction in subject's judgments between moral and conventional transgressions. Psychopaths may lack this violence inhibitor. A causal model is developed showing how the lack of this mechanism would explain the core behavioural symptoms associated with the psychopathic disorder. A prediction of such a causal model would be that psychopaths should fail to make the moral/conventional distinction. This prediction was confirmed. The implication of this finding for other theories of morality is discussed.

Question: What is the difference between a psychopath and a sociopath?


The Impact of Experimental Setup in Prepaid Churn Prediction for Mobile Telecommunications: What to Predict, for Whom and Does the Customer Experience Matter?. Prepaid customers in mobile telecommunications are not bound by a contract and can therefore change operators ('churn') at their convenience and without notification. This makes the task of predicting prepaid churn both challenging and financially rewarding. This paper presents an explorative, real world study of prepaid churn modeling by varying the experimental setup on three dimensions: data, outcome definition and population sample. Firstly, we add Customer Experience Management (CEM) data to data traditionally used for prepaid churn prediction. Secondly, we vary the outcome definition of prepaid churn. Thirdly, we alter the sample of the customers included, based on their inactivity period at the time of recording. While adding CEM parameters did not influence the predictability of churn, one variation on the sample and especially a particular change in the outcome definition had a substantial

Question: What is the impact of experimental setup in prepaid churn prediction for mobile telecommunications?


Bayesian Personalized Ranking with Multi-Channel User Feedback. Pairwise learning-to-rank algorithms have been shown to allow recommender systems to leverage unary user feedback. We propose Multi-feedback Bayesian Personalized Ranking (MF-BPR), a pairwise method that exploits different types of feedback with an extended sampling method. The feedback types are drawn from different "channels", in which users interact with items (e.g., clicks, likes, listens, follows, and purchases). We build on the insight that different kinds of feedback, e.g., a click versus a like, reflect different levels of commitment or preference. Our approach differs from previous work in that it exploits multiple sources of feedback simultaneously during the training process. The novelty of MF-BPR is an extended sampling method that equates feedback sources with "levels" that reflect the expected contribution of the signal. We demonstrate the effectiveness of our approach with a series of experiments carried out on three datasets containing multiple types of feedback. Our experimental results demonstrate that with a right sampling method, MF-BPR outperforms BPR in terms of accuracy. We find that the advantage of MF-BPR lies in its ability to leverage level information when sampling negative items.

Question: What is the difference between a click and a like?


Blind Trust Online : Experimental Evidence from Baseball Cards. Before reaping transaction cost savings from the Internet, online sellers must credibly communicate the quality of their goods or their reliability of delivery. We use baseball cards as an example to address (1) whether consumers fully understand the risk of online trading, (2) the extent to which consumers employ seller reputation and professional grading to alleviate the risk they are exposed to, and (3) the impact of online trading on the retail market and supporting industries. An experiment is carried out to obtain actual baseball cards from both online and retail markets whose quality is then professionally graded and compared to the prices paid by online buyers for goods with similar claims. Our findings indicate that some uninformed online buyers are misled by non-credible claims of quality; they pay higher prices but do not receive better quality and in fact are defrauded more often. Although a combination of professional grading and reputation mechanisms appear to have the potential to solve the problem, they have only had limited success so far. Data on structural changes in the baseball card industry suggest the problems stemming from buyer misconception may have far-reaching consequences, extending beyond the immediate losses of defrauded consumers to influencing producer and supply decisions. Our findings also suggest that empirical studies using online price data should be careful of what assumptions they are making of online users and what behaviors of buyers and sellers are likely to drive the price data. Department of Economics, University of Maryland, 3115 G Tydings, College Park, MD 20742. Contact Email: jin@econ.umd.edu. We thank Seth Sanders, John List, John Shea, Dan Vincent, David Lucking-Reiley, Larry Ausubel, Peter Cramton, V. Joseph Hotz, Jimmy Chan, Vincent Crawford, attendants in Johns Hopkins UMD conference and participants of UCLA,UCSD and FTC seminars for constructive adv

Question: What is the difference between online and retail markets?


Christopher Alexander's fifteen properties: Toward developing evaluation metrics for security visualizations. The objective of security data visualizations is to help cyber analysts to perceive trends and patterns, and gain insights into security data. Sound and systematic evaluations of security data visualizations are rarely performed, in part due to the lack of proper quantitative and qualitative measures. In this paper, we present a novel evaluation approach for security visualization based on Christopher Alexander's fifteen properties of living structures. Alexander's fifteen properties are derived from various visual patterns that appear in nature. Each property represents the guidelines for good design. We believe that using these fundamental properties have the potential for building a more robust evaluation. Each property offers essential qualities that enable better analytical reasoning. We demonstrate how to use Alexander's properties to evaluate security related visualizations. We derive a set of visualization-specific properties for evaluation, developed based on Alexander's original properties.

Question: How to evaluate security visualizations?


Relationship between smartphone addiction and physical activity in Chinese international students in Korea. BACKGROUND AND AIMS Excessive usage of smartphones may induce social problems, such as depression and impairment of social and emotional functioning. Moreover, its usage can impede physical activity, but the relationship between smartphone addiction and physical activity is obscure. Therefore, we examined the relationship and the impact of excessive smartphone use on physical activity.   METHODS This study collected data through the structured questionnaire consisting of general characteristics, the number and hours of smartphone usage, and the Smartphone Addiction Proneness Scale (SAPS) from 110 Chinese international students in Korea. The body composition and physical activity, such as the total daily number of steps and consumed calories, were measured.   RESULTS In this study, high-risk smartphone users showed less physical activity, such as the total number of steps taken and the average consumed calories per day. Moreover, their body composition, such as muscle mass and fat mass, was significantly different. Among these factors, the hours of smartphone use revealed the proportional relationship with smartphone addiction (β = 0.209, p = 0.026), while the average number of walking steps per day showed a significant reverse proportional tendency in participants with smartphone addiction (β = -0.883, p < 0.001).   CONCLUSIONS Participants with smartphone addiction were less likely to walk for each day. Namely, smartphone addiction may negatively influence physical health by reducing the amount of physical activity, such as walking, resulting in an increase of fat mass and a decrease of muscle mass associated with adverse health consequences.

Question: What is the relationship between smartphone addiction and physical activity?


Generative Adversarial Imitation Learning. The first needs a lot of data and suffers from covariate shift. It's a supervised learning method when we fit the state-action pair mapping. The latter finds a cost function under which the expert is uniquely optimal. Compounding error is not a problem here, but IRL is super data inefficiend and requires RL in the inner loop. Since we only interested in the final policy, why do we learn the cost function? So, GAIL was born.

Question: What is the difference between GAIL and IRL?


The most effective exercise for strengthening the supraspinatus muscle: evaluation by magnetic resonance imaging.. BACKGROUND Electromyography has been used to determine the best exercise for strengthening the supraspinatus muscle, but conflicting results have been reported. Magnetic resonance imaging T2 relaxation time appears to be more accurate in determining muscle activation.   PURPOSE To determine the best exercises for strengthening the supraspinatus muscle.   STUDY DESIGN Criterion standard.   METHODS Six male volunteers performed three exercises: the empty can, the full can, and horizontal abduction. Immediately before and after each exercise, magnetic resonance imaging examinations were performed and changes in relaxation time for the subscapularis, supraspinatus, infraspinatus, teres minor, and deltoid muscles were recorded.   RESULTS The supraspinatus muscle had the greatest change among the studied muscles in relaxation time for the empty can (10.5 ms) and full can (10.5 ms) exercises. After the horizontal abduction exercise the change in relaxation time for the supraspinatus muscle (3.6 ms) was significantly smaller than that for the posterior deltoid muscle (11.5 ms) and not significantly different from that of the other muscles studied.   CONCLUSION The empty can and full can exercises were most effective in activating the supraspinatus muscle.

Question: What is the best exercise for strengthening the supraspinatus muscle?


The price of power: power seeking and backlash against female politicians.. Two experimental studies examined the effect of power-seeking intentions on backlash toward women in political office. It was hypothesized that a female politician's career progress may be hindered by the belief that she seeks power, as this desire may violate prescribed communal expectations for women and thereby elicit interpersonal penalties. Results suggested that voting preferences for female candidates were negatively influenced by her power-seeking intentions (actual or perceived) but that preferences for male candidates were unaffected by power-seeking intentions. These differential reactions were partly explained by the perceived lack of communality implied by women's power-seeking intentions, resulting in lower perceived competence and feelings of moral outrage. The presence of moral-emotional reactions suggests that backlash arises from the violation of communal prescriptions rather than normative deviations more generally. These findings illuminate one potential source of gender bias in politics.

Question: What is the price of power?


See-through-wall imaging using ultra wideband short-pulse radar system. See-through-wall imaging radar is a unique application of ultra wideband communication that can provide soldiers and law enforcement officers with an enhanced situation awareness. We have developed an ultra-wideband high-resolution short pulse imaging radar system operating around 10 GHz, where two essential considerations are addressed: the effect of penetrating the walls; the pulse fidelity through the UWB components and antennas of the radar. Modeled and measured wall parameters, and the effect of antenna types on signal fidelity are discussed in detail.

Question: What is the effect of penetrating the walls?


Segmentation of Glioma Tumors in Brain Using Deep Convolutional Neural Network. Detection of brain tumor using a segmentation based approach is critical in cases, where survival of a subject depends on an accurate and timely clinical diagnosis. Gliomas are the most commonly found tumors having irregular shape and ambiguous boundaries, making them one of the hardest tumors to detect. The automation of brain tumor segmentation remains a challenging problem mainly due to significant variations in its structure. An automated brain tumor segmentation algorithm using deep convolutional neural network (DCNN) is presented in this paper. A patch based approach along with an inception module is used for training the deep network by extracting two co-centric patches of different sizes from the input images. Recent developments in deep neural networks such as drop-out, batch normalization, non-linear activation and inception module are used to build a new ILinear nexus architecture. The module overcomes the over-fitting problem arising due to scarcity of data using drop-out regularizer. Images are normalized and bias field corrected in the pre-processing step and then extracted patches are passed through a DCNN, which assigns an output label to the central pixel of each patch. Morphological operators are used for post-processing to remove small false positives around the edges. A two-phase weighted training method is introduced and evaluated using BRATS 2013 and BRATS 2015 datasets, where it improves the performance parameters of state-of-the-art techniques under similar settings.

Question: How to segment brain tumors using deep convolutional neural network?


Problems of Reliability and Validity in Ethnographic Research. Although problems of reliability and validity have been explored thoroughly by experimenters and other quantitative researchers, their treatment by ethnographers has been sporadic and haphazard This article analyzes these constructs as defined and addressed by ethnographers. Issues of reliability and validity in ethnographic design are compared to their counterparts in experimental design. Threats to the credibility of ethnographic research are summarized and categorized from field study methodology. Strategies intended to enhance credibility are incorporated throughout the investigative process: study design, data collection, data analysis, and presentation of findings. Common approaches to resolving various categories of contamination are illustrated from the current literature in educational ethnography.

Question: What is the difference between reliability and validity?


Linear Model Selection by Cross-Validation. JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. American Statistical Association is collaborating with JSTOR to digitize, preserve and extend access to Journal of the American Statistical Association. We consider the problem of selecting a model having the best predictive ability among a class of linear models. The popular leave-one-out cross-validation method, which is asymptotically equivalent to many other model selection methods such as the Akaike information criterion (AIC), the Cp, and the bootstrap, is asymptotically inconsistent in the sense that the probability of selecting the model with the best predictive ability does not converge to 1 as the total number of observations n-s o. We show that the inconsistency of the leave-one-out cross-validation can be rectified by using a leave-n,-out cross-validation with nv, the number of observations reserved for validation, satisfying no/n-1 I as n s* xoo. This is a somewhat shocking discovery, because ne/n-* 1 is totally opposite to the popular leave-one-out recipe in cross-validation. Motivations, justifications, and discussions of some practical aspects of the use of the leave-n,-out cross-validation method are provided, and results from a simulation study are presented.

Question: What is the difference between the leave-one-out cross-validation and the leave-n,-out cross-validation?


Digital Ordering System for Restaurant Using Android. Nowadays web services technology is widely used to integrate heterogeneous systems and develop new applications. Here an application of integration of hotel management systems by web services technology is presented. Digital Hotel Management integrates lots of systems of hotel industry such as Ordering System Kitchen Order Ticket (KOT), Billing System, Customer Relationship Management system (CRM) together. This integration solution can add or expand hotel software system in any size of hotel chains environment. This system increases quality and speed of service. This system also increases attraction of place for large range of customers. Implementing this system gives a cost-efficient opportunity to give your customers a personalized service experience where they are in control choosing what they want, when they want it – from dining to ordering to payment and feedback. We are implementing this system using android application for Tablet PC's. The front end will be developed using JAVA Android and the backend will work on MySQL database. Index TermsDFD: Data Flow Diagram. DOSRUA: Digital Ordering System for Restaurant Using KOT: Kitchen Order Ticket Android UML: Unified Modeling Language.

Question: What is the name of the android application?


An Integrated Experimental Environment for Distributed Systems and Networks. Three experimental environments traditionally support network and distributed systems research: network emulators, network simulators, and live networks. The continued use of multiple approaches highlights both the value and inadequacy of each. Netbed, a descendant of Emulab, provides an experimentation facility that integrates these approaches, allowing researchers to configure and access networks composed of emulated, simulated, and wide-area nodes and links. Netbed's primary goals are ease of use, control, and realism, achieved through consistent use of virtualization and abstraction.By providing operating system-like services, such as resource allocation and scheduling, and by virtualizing heterogeneous resources, Netbed acts as a virtual machine for network experimentation. This paper presents Netbed's overall design and implementation and demonstrates its ability to improve experimental automation and efficiency. These, in turn, lead to new methods of experimentation, including automated parameter-space studies within emulation and straightforward comparisons of simulated, emulated, and wide-area scenarios.

Question: What is the name of the canadian military?


High Efficient Interleaved Multi-channel dc/dc Converter Dedicated to Mobile Applications. In the transportation field, the empty weight of the vehicle has to be as light as possible in order to have the highest energetic efficiency. For onboard storage electrical systems, DC/DC converters are usually used to control the power flows in the vehicle. This paper focuses on two different DC/DC converter applications: high current/low voltage on the one hand and low current/high voltage on the other hand. For all power mobile applications, a standard dc/dc converter is not realizable due to mass and volume constraints. As the inductor is usually the biggest and heaviest component in the system, it is important to develop a new structure allowing the reduction of its size and weight. The chosen technology is based on interleaved channels operating in discontinuous mode, also using reverse conduction of power MOSFET transistors. For both of the cases described above, a prototype has been built composed of 8 and 4 channels respectively. The first converter is used for power assistance (250 A/18 V) using the MOS reverse conduction property. In this particular case, efficiency is up to 96.3 %. The second is developed for a mobile solar application (3.8 A/270 V). Efficiencies are up to 98.4 %

Question: What is the size of the inductor in the dc/dc converter?


Scholarly use of social media and altmetrics: A review of the literature. Social media has become integrated into the fabric of the scholarly communication system in fundamental ways: principally through scholarly use of social media platforms and the promotion of new indicators on the basis of interactions with these platforms. Research and scholarship in this area has accelerated since the coining and subsequent advocacy for altmetrics—that is, research indicators based on social media activity. This review provides an extensive account of the state-of-the art in both scholarly use of social media and altmetrics. The review consists of two main parts: the first examines the use of social media in academia, examining the various functions these platforms have in the scholarly communication process and the factors that affect this use. The second part reviews empirical studies of altmetrics, discussing the various interpretations of altmetrics, data collection and methodological limitations, and differences according to platform. The review ends with a critical discussion of the implications of this transformation in the scholarly communication system.

Question: What is the scholarly use of social media?


GRAPHITE : Polyhedral Analyses and Optimizations for GCC. We present a plan to add loop nest optimizations in GCC based on polyhedral representations of loop nests. We advocate a static analysis approach based on a hierarchy of interchangeable abstractions with solvers that range from the exact solvers such as OMEGA, to faster but less precise solvers based on more coarse abstractions. The intermediate representation GRAPHITE1 (GIMPLE Represented as Polyhedra with Interchangeable Envelopes), built on GIMPLE and the natural loops, hosts the high level loop transformations. We base this presentation on the WRaP-IT project developed in the Alchemy group at INRIA Futurs and Paris-Sud University, on the PIPS compiler developed at École des mines de Paris, and on a joint work with several members of the static analysis and polyhedral compilation community in France. The main goal of this project is to bring more high level loop optimizations to GCC: loop fusion, tiling, strip mining, etc. Thanks to the 1This work was partially supported by ACI/APRON. WRaP-IT experience, we know that the polyhedral analyzes and transformations are affordable in a production compiler. A second goal of this project is to experiment with compile time reduction versus attainable precision when replacing operations on polyhedra with faster operations on more abstract domains. However, the use of a too coarse representation for computing might also result in an over approximated solution that cannot be used in subsequent computations. There exists a trade off between speed of the computation and the attainable precision that has not yet been analyzed for real world programs.

Question: What is the polyhedral analyzes and optimizations for GCC?


Explainable Prediction of Medical Codes from Clinical Text. Clinical notes are text documents that are created by clinicians for each patient encounter. They are typically accompanied by medical codes, which describe the diagnosis and treatment. Annotating these codes is labor intensive and error prone; furthermore, the connection between the codes and the text is not annotated, obscuring the reasons and details behind specific diagnoses and treatments. We present an attentional convolutional network that predicts medical codes from clinical text. Our method aggregates information across the document using a convolutional neural network, and uses an attention mechanism to select the most relevant segments for each of the thousands of possible codes. The method is accurate, achieving precision@8 of 0.71 and a Micro-F1 of 0.54, which are both better than the prior state of the art. Furthermore, through an interpretability evaluation by a physician, we show that the attention mechanism identifies meaningful explanations for each code assignment.

Question: Explainable Prediction of Medical Codes from Clinical Text.


Static analysis of cable-driven manipulators with non-negligible cable mass. This paper addresses the static analysis of cable-driven robotic manipulators with non-negligible cable mass. An approach to computing the static displacement of a homogeneous elastic cable is presented. The resulting cable-displacement expression is used to solve the inverse kinematics of general cable-driven robotic manipulators. In addition, the sag-induced stiffness of the cables is derived. Finally, two sample robotic manipulators with dimensions and system parameters similar to a large scale cable-driven manipulator currently under development are analyzed. The results show that cable sag can have a significant effect on both the inverse kinematics and stiffness of such manipulators.

Question: How large is the cable-driven manipulator?


Low power LVDS transmitter with low common mode variation for 1GB/s-per pin operation. This paper presents the design of a low voltage differential signalling (LVDS) transmitter intended using a LCD panel interface and is capable of transmitting 1GB/s per pin data rate. The transmitter is fully LVDS standard compatible which is achieved by employing a common mode feedback circuit to meet all Ac and DC specifications over PVT variation, specifically due to the implementation of feedback circuit, the common mode variation is made very less quiescent current of 5.5mA. Further, the implementation incorporates the idea of sharing of bias blocks among different transmitters in full chip resulting in less active area. The transmitter is implemented in 3.3V, 0.35/spl mu/ CMOS technology. In this implementation the active area (excluding ESD) per transmitter is 0.039 mm square. The initial silicon data (output offset voltage of 1.20V and differential voltage of 320mV) at room temperature shows the consistency of the simulation results.

Question: What is the common mode variation of a transmitter?


Learning Structured Output Representation using Deep Conditional Generative Models. Supervised deep learning has been successfully applied to many recognition problems. Although it can approximate a complex many-to-one function well when a large amount of training data is provided, it is still challenging to model complex structured output representations that effectively perform probabilistic inference and make diverse predictions. In this work, we develop a deep conditional generative model for structured output prediction using Gaussian latent variables. The model is trained efficiently in the framework of stochastic gradient variational Bayes, and allows for fast prediction using stochastic feed-forward inference. In addition, we provide novel strategies to build robust structured prediction algorithms, such as input noise-injection and multi-scale prediction objective at training. In experiments, we demonstrate the effectiveness of our proposed algorithm in comparison to the deterministic deep neural network counterparts in generating diverse but realistic structured output predictions using stochastic inference. Furthermore, the proposed training methods are complimentary, which leads to strong pixel-level object segmentation and semantic labeling performance on Caltech-UCSD Birds 200 and the subset of Labeled Faces in the Wild dataset.

Question: What is the difference between a deep conditional generative model and a deep neural network?


Chapter 2 INTRUSION DETECTION : A SURVEY. This chapter provides the overview of the state of the art in intrusion detection research. Intrusion detection systems are software and/or hardware components that monitor computer systems and analyze events occurring in them for signs of intrusions. Due to widespread diversity and complexity of computer infrastructures, it is difficult to provide a completely secure computer system. Therefore, there are numerous security systems and intrusion detection systems that address different aspects of computer security. This chapter first provides taxonomy of computer intrusions, along with brief descriptions of major computer attack categories. Second, a common architecture of intrusion detection systems and their basic characteristics are presented. Third, taxonomy of intrusion detection systems based on five criteria (information source, analysis strategy, time aspects, architecture, response) is given. Finally, intrusion detection systems are classified according to each of these categories and the most representative research prototypes are briefly described.

Question: What is the difference between a computer and a computer system?


A Low-Profile Wideband Substrate-Integrated Waveguide Cavity-Backed E-Shaped Patch Antenna for the Q-LINKPAN Applications. A low-profile substrate-integrated waveguide cavity-backed E-shaped patch antenna is proposed for the Q-LINKPAN application in this paper. In order to expand the operating bandwidth, a co-planar waveguide (CPW) is used to feed the proposed antenna with a metallized via for generating one more resonant mode. In addition, a substrate-integrated cavity is employed to suppress the surface wave and improve the radiation efficiency. A differential feeding network is used in the design to improve the symmetry of the E-plane radiation pattern and the H-plane cross polarization. A  $2 \times 2$  prototype is designed, fabricated, and measured for a demonstration. The measured results show that the prototype has a 10 dB impedance bandwidth of 34.4%, a gain of around 12.5 dBi with a narrow E-plane radiation beam within 37.5–46 GHz for long distance applications, and a gain of around 8 dBi with a broad E-plane radiation beam within 47–53 GHz for short distance applications. The proposed technique can be used to develop compact planar antenna for meeting both the short- and long-rang communication requirements of the emerging Q-LINKPAN wireless system.

Question: What is a low-profile wideband substrate-integrated waveguide cavity-backed E-shaped patch antenna?


Node Representation Learning for Multiple Networks: The Case of Graph Alignment. Recent advances in representation learning produce node embeddings that may be used successfully in many downstream tasks (e.g., link prediction), but do not generally extend beyond a single network. Motivated by the prevalence of multi-network problems, such as graph alignment, similarity, and transfer learning, we introduce an elegant and principled node embedding formulation, Cross-network Matrix Factorization or xNetMF, to effectively and efficiently learn embeddings from similarities based on the nodes' structural and attribute identities (if available), which are comparable across networks. Leveraging the Nyström method for low-rank approximation, we achieve significant reduction in space and time compared to the naïve approach of directly factorizing the node similarity matrix, with xNetMF running up to 30× faster than existing representation learning methods. We study its effectiveness on the task of network alignment, and introduce REGAL (REpresentationbased Graph ALignment), the first method to use node representation learning for this task. Extensive experiments on a variety of challenging real-world networks with and without attributes show that REGAL outperforms existing network alignment methods by 20 to 30% in accuracy on average, uses attributes up to 40× faster, and scales to networks with millions of nodes each.

Question: What is the first large-scale Canadian peacekeeping mission?


Character-based feature extraction with LSTM networks for POS-tagging task. In this paper we describe a work in progress on designing the continuous vector space word representations able to map unseen data adequately. We propose a LSTM-based feature extraction layer that reads in a sequence of characters corresponding to a word and outputs a single fixed-length real-valued vector. We then test our model on a POS tagging task on four typologically different languages. The results of the experiments suggest that the model can offer a solution to the out-of-vocabulary words problem, as in a comparable setting its OOV accuracy improves over that of a state of the art tagger.

Question: What is a LSTM?


Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. We develop a face recognition algorithm which is insensitive to large variation in lighting direction and facial expression. Taking a pattern classification approach, we consider each pixel in an image as a coordinate in a high-dimensional space. We take advantage of the observation that the images of a particular face, under varying illumination but fixed pose, lie in a 3D linear subspace of the high dimensional image space—if the face is a Lambertian surface without shadowing. However, since faces are not truly Lambertian surfaces and do indeed produce self-shadowing, images will deviate from this linear subspace. Rather than explicitly modeling this deviation, we linearly project the image into a subspace in a manner which discounts those regions of the face with large deviation. Our projection method is based on Fisher's Linear Discriminant and produces well separated classes in a low-dimensional subspace, even under severe variation in lighting and facial expressions. The Eigenface technique, another method based on linearly projecting the image space to a low dimensional subspace, has similar computational requirements. Yet, extensive experimental results demonstrate that the proposed "Fisherface" method has error rates that are lower than those of the Eigenface technique for tests on the Harvard and Yale Face Databases. Index Terms —Appearance-based vision, face recognition, illumination invariance, Fisher's linear discriminant. —————————— ✦ ——————————

Question: What is the difference between eigenfaces and fisherfaces?


Fixed budget quantized kernel least-mean-square algorithm. We present a quantization-based kernel least mean square (QKLMS) algorithm with a fixed memory budget. In order to deal with the growing support inherent in online kernel methods, the proposed method utilizes a growing and pruning combined technique and defines a criterion, significance, based on weighted statistical contribution of a data center. This method doesn't need any apriori information and its computational complexity is acceptable, linear with the center number. As we show theoretically and experimentally, the introduced algorithm successfully quantifies the least 'significant' datum and preserves the most important ones resulting in less system error.

Question: What is a quantization-based kernel least-mean-square algorithm?


Tri-state median filter for image denoising. In this work, a novel nonlinear filter, called tri-state median (TSM) filter, is proposed for preserving image details while effectively suppressing impulse noise. We incorporate the standard median (SM) filter and the center weighted median (CWM) filter into a noise detection framework to determine whether a pixel is corrupted, before applying filtering unconditionally. Extensive simulation results demonstrate that the proposed filter consistently outperforms other median filters by balancing the tradeoff between noise reduction and detail preservation.

Question: What is a tri-state median filter?


Fleet-sizing for multi-depot and periodic vehicle routing problems using a modular heuristic algorithm. In this paper, we address the problem of determining the optimal fleet size for three vehicle routing problems, i.e., multi-depot VRP, periodic VRP and multidepot periodic VRP. In each of these problems, we consider three kinds of constraints that are often found in reality, i.e., vehicle capacity, route duration and budget constraints. To tackle the problems, we propose a new Modular Heuristic Algorithm (MHA) whose exploration and exploitation strategies enable the algorithm to produce promising results. Extensive computational experiments show that MHA performs impressively well, in terms of solution quality and computational time, for the three problem classes.

Question: What is the optimal fleet size for a multi-depot and periodic vehicle routing problem?


Facial Landmark Detection with Tweaked Convolutional Neural Networks. This paper concerns the problem of facial landmark detection. We provide a unique new analysis of the features produced at intermediate layers of a convolutional neural network (CNN) trained to regress facial landmark coordinates. This analysis shows that while being processed by the CNN, face images can be partitioned in an unsupervised manner into subsets containing faces in similar poses (i.e., 3D views) and facial properties (e.g., presence or absence of eye-wear). Based on this finding, we describe a novel CNN architecture, specialized to regress the facial landmark coordinates of faces in specific poses and appearances. To address the shortage of training data, particularly in extreme profile poses, we additionally present data augmentation techniques designed to provide sufficient training examples for each of these specialized sub-networks. The proposed Tweaked CNN (TCNN) architecture is shown to outperform existing landmark detection methods in an extensive battery of tests on the AFW, ALFW, and 300W benchmarks. Finally, to promote reproducibility of our results, we make code and trained models publicly available through our project webpage.

Question: How to detect facial landmarks?


A near-optimal algorithm for computing the entropy of a stream. We describe a simple algorithm for approximating the empirical entropy of a stream of m values in a single pass, using O(ε-2 log(Δ-1) log m) words of space. Our algorithm is based upon a novel extension of a method introduced by Alon, Matias, and Szegedy [1]. We show a space lower bound of Ω(ε-2 / log(ε-1)), meaning that our algorithm is near-optimal in terms of its dependency on ε. This improves over previous work on this problem [8, 13, 17, 5]. We show that generalizing to kth order entropy requires close to linear space for all k ≥ 1, and give additive approximations using our algorithm. Lastly, we show how to compute a multiplicative approximation to the entropy of a random walk on an undirected graph.

Question: What is the space complexity of computing the entropy of a stream?


Fused Matrix Factorization with Geographical and Social Influence in Location-Based Social Networks. Recently, location-based social networks (LBSNs), such as Gowalla, Foursquare, Facebook, and Brightkite, etc., have attracted millions of users to share their social friendship and their locations via check-ins. The available check-in information makes it possible to mine users' preference on locations and to provide favorite recommendations. Personalized Point-of-interest (POI) recommendation is a significant task in LBSNs since it can help targeted users explore their surroundings as well as help third-party developers to provide personalized services. To solve this task, matrix factorization is a promising tool due to its success in recommender systems. However, previously proposed matrix factorization (MF) methods do not explore geographical influence, e.g., multi-center check-in property, which yields suboptimal solutions for the recommendation. In this paper, to the best of our knowledge, we are the first to fuse MF with geographical and social influence for POI recommendation in LBSNs. We first capture the geographical influence via modeling the probability of a user's check-in on a location as a Multi-center Gaussian Model (MGM). Next, we include social information and fuse the geographical influence into a generalized matrix factorization framework. Our solution to POI recommendation is efficient and scales linearly with the number of observations. Finally, we conduct thorough experiments on a large-scale real-world LBSNs dataset and demonstrate that the fused matrix factorization framework with MGM utilizes the distance information sufficiently and outperforms other state-of-the-art methods significantly.

Question: What is the first large-scale Canadian peacekeeping mission?


ORTHONORMAL BASES OF COMPACTLY SUPPORTED WAVELETS. We construct orthonormal bases of compactly supported wavelets, with arbitrarily high regularity. The order of regularity increases linearly with the support width. We start by reviewing the concept of multiresolution analysis as well as several algorithms in vision decomposition and reconstruction. The construction then follows from a synthesis of these different approaches.

Question: How to construct orthonormal bases of compactly supported wavelets?


MLH-IDS: A Multi-Level Hybrid Intrusion Detection Method. With the growth of networked computers and associated applications, intrusion detection has become essential to keeping networks secure. A number of intrusion detection methods have been developed for protecting computers and networks using conventional statistical methods as well as data mining methods. Data mining methods for misuse and anomaly-based intrusion detection, usually encompass supervised, unsupervised and outlier methods. It is necessary that the capabilities of intrusion detection methods be updated with the creation of new attacks. This paper proposes a multi-level hybrid intrusion detection method that uses a combination of supervised, unsupervised and outlierbased methods for improving the efficiency of detection of new and old attacks. The method is evaluated with a captured real-time flow and packet dataset called the Tezpur University intrusion detection system (TUIDS) dataset, a distributed denial of service dataset, and the benchmark intrusion dataset called the knowledge discovery and data mining Cup 1999 dataset and the new version of KDD (NSL-KDD) dataset. Experimental results are compared with existing multi-level intrusion detection methods and other classifiers. The performance of our method is very good.

Question: What is a multi-level hybrid intrusion detection method?


Robust Model Predictive Control : A Survey. This paper gives an overview of robustness in Model Predictive Control (MPC). After reviewing the basic concepts of MPC, we survey the uncertainty descriptions considered in the MPC literature, and the techniques proposed for robust constraint handling, stability, and performance. The key concept of "closedloop prediction" is discussed at length. The paper concludes with some comments on future research directions.

Question: What is robust model predictive control?


Sociable driving agents to maintain driver's attention in autonomous driving. Recently, many studies have been conducted on increasing the automation level in cars to achieve safer and more efficient transportation. The increased automation level creates room for the drivers to shift their attention to non-driving related activities. However, there are cases that cannot be handled by automation where a driver should take over the control. This pilot study investigates a paradigm for keeping the drivers' situation-awareness active during autonomous driving by utilizing a social robot system, NAMIDA. NAMIDA is an interface consisting of three sociable driving agents that can interact with the driver through eye-gaze behaviors. We analyzed the effectiveness of NAMIDA on maintaining the drivers' attention to the road, by evaluating the response time of the drivers to a critical situation on the road. An experiment consisting of a take over scenario was conducted in a dynamic driving simulator. The results showed that existence of NAMIDA significantly reduced the response time of the drivers. However, surprisingly, NAMIDA without eye-gaze behaviors was more effective in reducing the response time than NAMIDA with eye-gaze behaviors. Additionally, the results revealed better subjective impressions for NAMIDA with eye-gaze behaviors behaviors.

Question: What is a sociable driving agent?


Continental Airlines Flies High with Real-Time Business Intelligence. Real-time business intelligence (BI) is taking Continental Airlines to new heights. Powered by a real-time data warehouse, the company has dramatically changed all aspects of its business. Continental's president and COO, Larry Kellner, describes the impact of real-time BI in the following way: "Real-time BI is critical to the accomplishment of our business strategy and has created significant business benefits." In fact, Continental has realized more than $500 million in cost savings and revenue generation over the past six years from its BI initiatives, producing an ROI of more than 1,000 percent.

Question: What is the ROI of real-time BI?