Language support for fast and reliable message-based communication in singularity OS. Message-based communication offers the potential benefits of providing stronger specification and cleaner separation between components. Compared with shared-memory interactions, message passing has the potential disadvantages of more expensive data exchange (no direct sharing) and more complicated programming.In this paper we report on the language, verification, and run-time system features that make messages practical as the sole means of communication between processes in the Singularity operating system. We show that using advanced programming language and verification techniques, it is possible to provide and enforce strong system-wide invariants that enable efficient communication and low-overhead software-based process isolation. Furthermore, specifications on communication channels help in detecting programmer mistakes early---namely at compile-time---thereby reducing the difficulty of the message-based programming model.The paper describes our communication invariants, the language and verification features that support them, as well as implementation details of the infrastructure. A number of benchmarks show the competitiveness of this approach.

Question: What is the singularity os?


A socially assistive robot exercise coach for the elderly. We present a socially assistive robot (SAR) system designed to engage elderly users in physical exercise. We discuss the system approach, design methodology, and implementation details, which incorporate insights from psychology research on intrinsic motivation, and we present five clear design principles for SAR-based therapeutic interventions. We then describe the system evaluation, consisting of a multi-session user study with older adults (n = 33), to evaluate the effectiveness of our SAR exercise system and to investigate the role of embodiment by comparing user evaluations of similar physically and virtually embodied coaches. The results validate the system approach and effectiveness at motivating physical exercise in older adults according to a variety of user performance and outcomes measures. The results also show a clear preference by older adults for the physically embodied robot coach over the virtual coach in terms of enjoyableness, helpfulness, and social attraction, among other factors.

Question: What is a socially assistive robot?


Multiple kernel learning, conic duality, and the SMO algorithm. While classical kernel-based classifiers are based on a single kernel, in practice it is often desirable to base classifiers on combinations of multiple kernels. Lanckriet et al. (2004) considered conic combinations of kernel matrices for the support vector machine (SVM), and showed that the optimization of the coefficients of such a combination reduces to a convex optimization problem known as a quadratically-constrained quadratic program (QCQP). Unfortunately, current convex optimization toolboxes can solve this problem only for a small number of kernels and a small number of data points; moreover, the sequential minimal optimization (SMO) techniques that are essential in large-scale implementations of the SVM cannot be applied because the cost function is non-differentiable. We propose a novel dual formulation of the QCQP as a second-order cone programming problem, and show how to exploit the technique of Moreau-Yosida regularization to yield a formulation to which SMO techniques can be applied. We present experimental results that show that our SMO-based algorithm is significantly more efficient than the general-purpose interior point methods available in current optimization toolboxes.

Question: What is the difference between a kernel and a kernel matrix?


Batch Identification Game Model for Invalid Signatures in Wireless Mobile Networks. Secure access is one of the fundamental problems in wireless mobile networks. Digital signature is a widely used technique to protect messages' authenticity and nodes' identities. From the practical perspective, to ensure the quality of services in wireless mobile networks, ideally the process of signature verification should introduce minimum delay. Batch cryptography technique is a powerful tool to reduce verification time. However, most of the existing works focus on designing batch verification algorithms for wireless mobile networks without sufficiently considering the impact of invalid signatures, which can lead to verification failures and performance degradation. In this paper, we propose a Batch Identification Game Model (BIGM) in wireless mobile networks, enabling nodes to find invalid signatures with reasonable delay no matter whether the game scenario is complete information or incomplete information. Specifically, we analyze and prove the existence of Nash Equilibriums (NEs) in both scenarios, to select the dominant algorithm for identifying invalid signatures. To optimize the identification algorithm selection, we propose a self-adaptive auto-match protocol which estimates the strategies and states of attackers based on historical information. Comprehensive simulation results in terms of NE reasonability, algorithm selection accuracy, and identification delay are provided to demonstrate that BIGM can identify invalid signatures more efficiently than existing algorithms.

Question: What is the impact of invalid signatures on wireless mobile networks?


Cognitive Complexity and Attentional Control in the Bilingual Mind. In the analysis and control framework, Bialystok identifies analysis (representation) and control (selective attention) as components of language processing and has shown that one of these, control, develops earlier in bilingual children than in comparable monolinguals. In the theory of cognitive complexity and control (CCC), Zelazo and Frye argue that preschool children lack the conscious representation and executive functioning needed to solve problems based on conflicting rules. The present study investigates whether the bilingual advantage in control can be found in a nonverbal task, the dimensional change card sort, used by Zelazo and Frye to assess CCC. This problem contains misleading information characteristic of high-control tasks but minimal demands for analysis. Sixty preschool children, half of whom were bilingual, were divided into a group of younger ( M 5 4,2) and older ( M 5 5,5) children. All the children were given a test of English proficiency (PPVT-R) and working memory (Visually-Cued Recall Task) to assure comparability of the groups and then administered the dimensional change card sort task and the moving word task. The bilingual children were more advanced than the monolinguals in the solving of experimental problems requiring high levels of control. These results demonstrate the role of attentional control in both these tasks, extends our knowledge about the cognitive development of bilingual children, and provides a means of relating developmental proposals articulated in two different theoretical frameworks, namely, CCC and analysis-control.

Question: What is the difference between analysis and control?


Capturing the Temporal Domain in Echonest Features for Improved Classification Effectiveness. This paper proposes Temporal Echonest Features to harness the information available from the beat-aligned vector sequences of the features provided by The Echo Nest. Rather than aggregating them via simple averaging approaches, the statistics of temporal variations are analyzed and used to represent the audio content. We evaluate the performance on four traditional music genre classification test collections and compare them to state of the art audio descriptors. Experiments reveal, that the exploitation of temporal variability from beat-aligned vector sequences and combinations of different descriptors leads to an improvement of classification accuracy. Comparing the results of Temporal Echonest Features to those of approved conventional audio descriptors used as benchmarks, these approaches perform well, often significantly outperforming their predecessors, and can be effectively used for large scale music genre classification.

Question: What is the beat-aligned vector sequences?


Subjective Reality and Strong Artificial Intelligence. The main prospective aim of modern research related to Artificial Intelligence is the creation of technical systems that implement the idea of Strong Intelligence. According our point of view the path to the development of such systems comes through the research in the field related to perceptions. Here we formulate the model of the perception of external world which may be used for the description of perceptual activity of intelligent beings. We consider a number of issues related to the development of the set of patterns which will be used by the intelligent system when interacting with environment. The key idea of the presented perception model is the idea of subjective reality. The principle of the relativity of perceived world is formulated. It is shown that this principle is the immediate consequence of the idea of subjective reality. In this paper we show how the methodology of subjective reality may be used for the creation of different types of Strong AI systems.

Question: What is the difference between subjective reality and objective reality?


Comparison among three analytical methods for knowledge communities group-decision analysis. Knowledge management can greatly facilitate an organization's learning via strategic insight. Assessing the achievements of knowledge communities (KC) includes both a theoretical basis and practical aspect; however, a cautionary word is in order, because using improper measurements will increase complexity and reduce applicability. Group decision-making, the essence of knowledge communities, lets one considers multi-dimensional problems for the decision-maker, sets priorities for each decision factor, and assesses rankings for all alternatives. The purpose of this study is to establish the objective and measurable patterns to obtain anticipated achievements of KC through conducting a group-decision comparison. The three multiple-criteria decision-making methods we used, simple average weight (SAW), ''Technique for Order Preference by Similarity to an Ideal Solution'' (TOPSIS) and ''VlseKriterijumska Optimizacija I Kompromisno Resenje'' (VIKOR), are based on an aggregating function representing ''closeness to the ideal point''. The TOPSIS and VIKOR methods were used to highlight our innovative idea, academic analysis, and practical appliance value. Simple average weight (SAW) is known to be a common method to get the preliminary outcome. Our study provides a comparison analysis of the above-three methods. An empirical case is illustrated to demonstrate the overall KC achievements, showing their similarities and differences to achieve group decisions. Our results showed that TOPSIS and simple average weight (SAW) had identical rankings overall, but TOPSIS had better distinguishing capability. TOPSIS and VIKOR had almost the same success setting priorities by weight. However, VIKOR produced different rankings than those from TOPSIS and SAW, and VIKOR also made it easy to choose appropriate strategies. Both the TOPSIS and VIKOR methods are suitable for assessing similar problems

Question: What is the difference between the three methods?


Predicting India Volatility Index: An Application of Artificial Neural Network. Forecasting has always been an area of interest for the researchers in various realms of finance especially in the stock market e. g. stock index, return on a stock, etc. Stock market volatility is one such area. Since the inception of implied volatility index (VIX) by the Chicago Board of Options Exchange (CBOE) in 1993, VIX index has generated a lot of interest. This study examines the predicting ability of several technical indicators related to VIX index to forecast the next trading day's volatility. There is a wide set of methods available for forecasting in finance. In this study, Artificial neural network (ANN) modeling technique has been employed to forecast the upwards or downwards movement in next trading day's volatility using India VIX (a volatility index based on the NIFTY Index Option prices) based indicators. The results of the study reveal that ANN models can be real handy in forecasting the downwards movement in VIX. The knowledge about a more probable downwards movement in volatility might be significant value add for the investors and help them in making decisions related to trading.

Question: What is the volatility index?


CellNet: Network Biology Applied to Stem Cell Engineering. Somatic cell reprogramming, directed differentiation of pluripotent stem cells, and direct conversions between differentiated cell lineages represent powerful approaches to engineer cells for research and regenerative medicine. We have developed CellNet, a network biology platform that more accurately assesses the fidelity of cellular engineering than existing methodologies and generates hypotheses for improving cell derivations. Analyzing expression data from 56 published reports, we found that cells derived via directed differentiation more closely resemble their in vivo counterparts than products of direct conversion, as reflected by the establishment of target cell-type gene regulatory networks (GRNs). Furthermore, we discovered that directly converted cells fail to adequately silence expression programs of the starting population and that the establishment of unintended GRNs is common to virtually every cellular engineering paradigm. CellNet provides a platform for quantifying how closely engineered cell populations resemble their target cell type and a rational strategy to guide enhanced cellular engineering.

Question: What is a stem cell?


Optimal and Low-Complexity Algorithms for Dynamic Spectrum Access in Centralized Cognitive Radio Networks with Fading Channels. In this paper, we develop a centralized spectrum sensing and Dynamic Spectrum Access (DSA) scheme for secondary users (SUs) in a Cognitive Radio (CR) network. Assuming that the primary channel occupancy follows a Markovian evolution, the channel sensing problem is modeled as a Partially Observable Markov Decision Process (POMDP). We assume that each SU can sense only one channel at a time by using energy detection, and the sensing outcomes are then reported to a central unit, called the secondary system decision center (SSDC), that determines the channel sensing/accessing policies. We derive both the optimal channel assignment policy for secondary users to sense the primary channels, and the optimal channel access rule. Our proposed optimal sensing and accessing policies alleviate many shortcomings and limitations of existing proposals: (a) ours allows fully utilizing all available primary spectrum white spaces, (b) our model, and thus the proposed solution, exploits the temporal and spatial diversity across different primary channels, and (c) is based on realistic local sensing decisions rather than complete knowledge of primary signalling structure. As an alternative to the high complexity of the optimal channel sensing policy, a suboptimal sensing policy is obtained by using the Hungarian algorithm iteratively, which reduces the complexity of the channel assignment from an exponential to a polynomial order. We also propose a heuristic algorithm that reduces the complexity of the sensing policy further to a linear order. The simulation results show that the proposed algorithms achieve a near-optimal performance with a significant reduction in computational time.

Question: What is the optimal channel access rule?


Mechatronic model of a novel slotless permanent magnet DC-motor with air gap winding design. This paper presents a mechatronic model of a novel slotless permanent magnet DC-motor with air gap winding. Besides technical advantages of this type of motor like high power density, high torque, very low weight and high efficiency, the motor design allows a very precise and efficient modelling with limited effort. A nonlinear model of magnetic field density can be extracted from a detailed nonlinear FE-model build in ANSYS/Maxwell, approximated by Fourier series and then used to model driving torque and back EMF, representing the coupling between electrical and mechanical subsystems. Analytically founded numerical models for driving torque and back EMF will be given. Real geometry of the phase winding is taken into account to improve model accuracy. The electrical subsystem will be described as coupled three phase system, whose parameters can also be extracted from the nonlinear FE-model with high accuracy. Together with a mechanical model of the rotor a MATLAB/Simulink model is build and extended by models of the hall sensors to detect rotor position and commutation logic to control the HEX-Bridge during operation. Finally, results of a complex simulation model, based on the parameters of the prototype of a wheel-hub motor, implementing the new motor design, are getting shown. Simulation results compare very well to measured data. Simulation time is very short due to the efficient approximation of magnetic flux density.

Question: What is the difference between a slotless and a slot motor?


3D-Parallel Coordinates: Visualization for time varying multidimensional data. Parallel coordinates can visualize multidimensional data efficiently, during to its shortage in displaying time-varying data, we present a new method to add time dimension, which can extend parallel coordinates into 3D space(3D-Parallel Coordinates) consisted of attribute, range and time dimension. So that time-varying multidimensional data can be displayed as polygonal line cluster for recording and analyzing. A technique called clipping shade is taken to highlight datasets around the current time, and results reveal that 3D-Parallel Coordinates can effectively analyze attribute's time-varying character.

Question: How to visualize time-varying multidimensional data?


The foreign-language effect: thinking in a foreign tongue reduces decision biases.. Would you make the same decisions in a foreign language as you would in your native tongue? It may be intuitive that people would make the same choices regardless of the language they are using, or that the difficulty of using a foreign language would make decisions less systematic. We discovered, however, that the opposite is true: Using a foreign language reduces decision-making biases. Four experiments show that the framing effect disappears when choices are presented in a foreign tongue. Whereas people were risk averse for gains and risk seeking for losses when choices were presented in their native tongue, they were not influenced by this framing manipulation in a foreign language. Two additional experiments show that using a foreign language reduces loss aversion, increasing the acceptance of both hypothetical and real bets with positive expected value. We propose that these effects arise because a foreign language provides greater cognitive and emotional distance than a native tongue does.

Question: What is the framing effect?


Social media for the emergency manager in disaster planning and response. This practitioner paper outlines some of the benefits for the use of social media, from the perspective of a locallevel or county-level emergency manager (EM). As compared to state and national level emergency management, because local level EMs have limited manpower and resources, social media can positively or negatively impact the effectiveness of communication before, during and after disaster strikes. Outlined in this paper are six key points where local EMs have specific needs that could be addressed by the effective use of social media and, in the opinion of the authors, represent the top issues that EMs face when considering how to leverage Twitter, Facebook, YouTube, Instagram and other social media platforms. The six needs addressed in this paper include: 1) Best practices for general social media use by EMs, 2) Social media use for internal command and control within the EM group, 3) Developing situation awareness by monitoring social media, especially prior to predicable events, 4) Communicating disaster preparedness messages through social media, 5) Using social media for gathering damage assessment information during, or immediately following a crisis, and 6) Leveraging social media volunteer groups. This short paper picks up where the Federal Emergency Management Agency's social media training leaves off and attempts to represent these six needs as use cases for researchers and developers to address in future publications and products.

Question: What are the benefits of social media for emergency managers?


Film Critics : Influencers or Predictors ?. Critics and their reviews pervade many industries and are particularly important in the entertainment industry. Few marketing scholars, however, have considered the relationship between the market performance of entertainment services and the role of critics. The authors do so here. They show empirically that critical reviews correlate with late and cumulative box office receipts but do not have a significant correlation with early box office receipts. Although still far from any definitive conclusion, this finding suggests that critics, at least from an aggregate-level perspective, appear to act more as leading indicators than as opinion leaders.

Question: What is the relationship between critics and box office receipts?


Smurf-based Distributed Denial of Service (DDoS) Attack Amplification in Internet. The Smurf-based distributed denial of service (DDoS) attack is an amplification attack where the attacker uses unprotected intermediate networks to amplify the attack traffic load and direct it to the victim computer. In this paper, we investigate the factors that contribute to the amplification of the smurf attack traffic and understand the relation among the original attack traffic, intermediate unprotected network and the final amplified attack traffic. We also define a new term called attack amplification factor which represents the degree of amplification that original attack traffic undergoes during its transmission towards the victim computer. It is also shown in this paper that utilizing this attack, it is possible for an attacker to just use a dialup modem and an unprotected intermediary network to exhaust even an ultra high speed optical line such as OC-192 of the victim network.

Question: What is the smurf attack?


Portfolio Choice and the Bayesian Kelly Criterion. We derive optimal gambling and investment policies for cases in which the underlying stochastic process has parameter values that are unobserved random variables. For the objective of maximizing logarithmic utility when the underlying stochastic process is a simple random walk in a random environment, we show that a state-dependent control is optimal, which is a generalization of the celebrated Kelly strategy: The optimal strategy is to bet a fraction of current wealth equal to a linear function of the posterior mean increment. To approximate more general stochastic processes, we consider a continuous-time analog involving Brownian motion. To analyze the continuous-time problem, we study the diffusion limit of random walks in a random environment. We prove that they converge weakly to a Kiefer process, or tied-down Brownian sheet. We then find conditions under which the discrete-time process converges to a diffusion, and analyze the resulting process. We analyze in detail the case of the natural conjugate prior, where the success probability has a beta distribution, and show that the resulting limiting diffusion can be viewed as a rescaled Brownian motion. These results allow explicit computation of the optimal control policies for the continuoustime gambling and investment problems without resorting to continuous-time stochastic-control procedures. Moreover they also allow an explicit quantitative evaluation of the financial value of randomness, the financial gain of perfect information and the financial cost of learning in the Bayesian problem.

Question: What is the Bayesian Kelly Criterion?


Diagnose brain tumor through MRI using image processing clustering algorithms such as Fuzzy C Means along with intelligent optimization techniques. Magnetic Resonance Imaging (MRI) is one of the best technologies currently being used for diagnosing brain tumor. Brain tumor is diagnosed at advanced stages with the help of the MRI image. Segmentation is an important process to extract suspicious region from complex medical images. Automatic detection of brain tumor through MRI can provide the valuable outlook and accuracy of earlier brain tumor detection. In this paper an intelligent system is designed to diagnose brain tumor through MRI using image processing clustering algorithms such as Fuzzy C Means along with intelligent optimization tools, such as Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). The detection of tumor is performed in two phases: Preprocessing and Enhancement in the first phase and segmentation and classification in the second phase

Question: How to diagnose brain tumor through MRI using image processing clustering algorithms such as Fuzzy C Means along with intelligent optimization techniques?


An Approach for Schema Extraction of JSON and Extended JSON Document Collections. JSON documents are raising as a common format for data representation due to the increasing popularity of NoSQL document-oriented databases. One of the reasons for such a popularity is their ability to handle large volumes of data at the absence of an explicit data schema. However, schema information is sometimes essential for applications during data retrieval, integration and analysis tasks, for example. Given this context, this paper presents an approach that extracts a schema from a JSON or Extended JSON document collection stored in a NoSQL document-oriented database or other document repository. Aggregation operations are considered in order to obtain a schema for each distinct structure in the collection, and a hierarchical data structure is proposed to group these schemas in order to generate a global schema in JSON Schema format. Experiments conducted on actual datasets, like DBPedia and Foursquare, demonstrate that the accuracy of the generated schemas is equivalent or even superior than related work.

Question: How to extract a schema from a JSON document?


Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Recent studies have demonstrated the usefulness of optical indices from hyperspectral remote sensing in the assessment of vegetation biophysical variables both in forestry and agriculture. Those indices are, however, the combined response to variations of several vegetation and environmental properties, such as Leaf Area Index (LAI), leaf chlorophyll content, canopy shadows, and background soil reflectance. Of particular significance to precision agriculture is chlorophyll content, an indicator of photosynthesis activity, which is related to the nitrogen concentration in green vegetation and serves as a measure of the crop response to nitrogen application. This paper presents a combined modeling and indices-based approach to predicting the crop chlorophyll content from remote sensing data while minimizing LAI (vegetation parameter) influence and underlying soil (background) effects. This combined method has been developed first using simulated data and followed by evaluation in terms of quantitative predictive capability using real hyperspectral airborne data. Simulations consisted of leaf and canopy reflectance modeling with PROSPECT and SAILH radiative transfer models. In this modeling study, we developed an index that integrates advantages of indices minimizing soil background effects and indices that are sensitive to chlorophyll concentration. Simulated data have shown that the proposed index Transformed Chlorophyll Absorption in Reflectance Index/Optimized Soil-Adjusted Vegetation Index (TCARI/OSAVI) is both very sensitive to chlorophyll content variations and very resistant to the variations of LAI and solar zenith angle. It was therefore possible to generate a predictive equation to estimate leaf chlorophyll content from the combined optical index derived from above-canopy reflectance. This relationship was evaluated by application to

Question: What is the difference between a hyperspectral remote sensing and a multispectral remote sensing?


Maximum likelihood linear transformations for HMM-based speech recognition. This paper examines the application of linear transformations for speaker and environmental adaptation in an HMM-based speech recognition system. In particular, transformations that are trained in a maximum likelihood sense on adaptation data are investigated. Other than in the form of a simple bias, strict linear feature-space transformations are inappropriate in this case. Hence, only model-based linear transforms are considered. The paper compares the two possible forms of model-based transforms: (i) unconstrained, where any combination of mean and variance transform may be used, and (ii) constrained, which requires the variance transform to have the same form as the mean transform (sometimes referred to as feature-space transforms). Re-estimation formulae for all appropriate cases of transform are given. This includes a new and e cient \full" variance transform and the extension of the constrained model-space transform from the simple diagonal case to the full or block-diagonal case. The constrained and unconstrained transforms are evaluated in terms of computational cost, recognition time e ciency, and use for speaker adaptive training. The recognition performance of the two model-space transforms on a large vocabulary speech recognition task using incremental adaptation is investigated. In addition, initial experiments using the constrained model-space transform for speaker adaptive training are detailed.

Question: What is the maximum likelihood linear transformation for HMM-based speech recognition?


Does Language Shape Thought?: Mandarin and English Speakers' Conceptions of Time. Does the language you speak affect how you think about the world? This question is taken up in three experiments. English and Mandarin talk about time differently--English predominantly talks about time as if it were horizontal, while Mandarin also commonly describes time as vertical. This difference between the two languages is reflected in the way their speakers think about time. In one study, Mandarin speakers tended to think about time vertically even when they were thinking for English (Mandarin speakers were faster to confirm that March comes earlier than April if they had just seen a vertical array of objects than if they had just seen a horizontal array, and the reverse was true for English speakers). Another study showed that the extent to which Mandarin-English bilinguals think about time vertically is related to how old they were when they first began to learn English. In another experiment native English speakers were taught to talk about time using vertical spatial terms in a way similar to Mandarin. On a subsequent test, this group of English speakers showed the same bias to think about time vertically as was observed with Mandarin speakers. It is concluded that (1) language is a powerful tool in shaping thought about abstract domains and (2) one's native language plays an important role in shaping habitual thought (e.g., how one tends to think about time) but does not entirely determine one's thinking in the strong Whorfian sense.

Question: How does language shape thought?


A model-based approach to security flaw detection of network protocol implementations. A lot of efforts have been devoted to the analysis of network protocol specification for reliability and security properties using formal techniques. However, faults can also be introduced during system implementation; it is indispensable to detect protocol implementation flaws, yet due to the black-box nature of protocol implementation and the unavailability of protocol specification most of the approaches resort to random or manual testing. In this paper we propose a model-based approach for security flaw detection of protocol implementation with a high fault coverage, measurability, and automation. Our approach first synthesizes an abstract behavioral model from a protocol implementation and then uses it to guide the testing process for detecting security and reliability flaws. For protocol specification synthesis we reduce the problem a trace minimization with a finite state machine model and an efficient algorithm is presented for state space reduction. Our method is implemented and applied to real network protocols. Guided by the synthesized model our testing tool reveals a number of unknown reliability and security issues by automatically crashing the implementations of the Microsoft MSN instant messaging (MSNIM) protocol. Analytical comparison between our model-based and prevalent syntax-based flaw detection schemes is also provided with the support of experimental results.

Question: What is a model-based approach to security flaw detection of network protocol implementations?


An Integrated Environment for the Development of Knowledge-Based Recommender Applications. The complexity of product assortments offered by online selling platforms makes the selection of appropriate items a challenging task. Customers can differ significantly in their expertise and level of knowledge regarding such product assortments. Consequently, intelligent recommender systems are required which provide personalized dialogues supporting the customer in the product selection process. In this paper we present the domainindependent, knowledge-based recommender environment CWAdvisor which assists users by guaranteeing the consistency and appropriateness of solutions, by identifying additional selling opportunities, and by providing explanations for solutions. Using examples from different application domains, we show how model-based diagnosis, personalization, and intuitive knowledge acquisition techniques support the effective implementation of customer-oriented sales dialogues. In this context, we report our experiences gained in industrial projects and present an evaluation of successfully deployed recommender applications.

Question: What is the domainindependent, knowledge-based recommender environment CWAdvisor?


Differential Cryptanalysis of Feal and N-Hash. In 1,2] we introduced the notion of diierential cryptanalysis and described its application to DESS11] and several of its variants. In this paper we show the applicability of diierential cryptanalysis to the Feal family of encryption algorithms and to the N-Hash hash function. In addition, we show how to transform diierential cryptanalytic chosen plaintext attacks into known plaintext attacks.

Question: What is the difference between differential cryptanalysis and differential attacks?


Information Feedback, Targeting, and Coordination: An Experimental Study. We experimentally study the role of information targeting and its effect on coordination in a multi-threshold public goods game. To the best of our knowledge, ours is the first to consider this problem. In a lab setting, we consider four treatments, one in which no information is provided and three others that vary in whom we provide the information to: a random sample of subjects; those whose contributions are below the average of their group, and those whose contributions are above the average of their group. We find that random provision of information is no better than providing no information at all. More importantly, the average contributions improve with targeted treatments. Coordination waste is also lower with targeted treatments. The insights from this research may also be relevant to management in contexts such as piracy, teendrinking, among others, where positively or negatively affecting coordination between consumers is of interest.

Question: What is the average contribution in a public goods game?


What If You Can't Trust Your Network Card?. In the last few years, many different attacks against computing platform targeting hardware or low level firmware have been published. Such attacks are generally quite hard to detect and to defend against as they target components that are out of the scope of the operating system and may not have been taken into account in the security policy enforced on the platform. In this paper, we study the case of remote attacks against network adapters. In our case study, we assume that the target adapter is running a flawed firmware that an attacker may subvert remotely by sending packets on the network to the adapter. We study possible detection techniques and their efficiency. We show that, depending on the architecture of the adapter and the interface provided by the NIC to the host operating system, building an efficient detection framework is possible. We explain the choices we made when designing such a framework that we called NAVIS and give details on our proof of concept implementation.

Question: What is a flawed firmware?


Figurative language processing after traumatic brain injury in adults: A preliminary study. Figurative speech (e.g., proverb, irony, metaphor, and idiom) has been reported to be particularly sensitive to measurement of abstract thinking in patients who suffer from impaired abstraction and language abilities. Metaphor processing was investigated with fMRI in adults with moderate to severe post-acute traumatic brain injury (TBI) and healthy age-matched controls using a valence-judgment task. We hypothesized that TBI patients would display decreased activation of the left inferior frontal gyrus (LIFG), which is considered central to semantic memory retrieval and abstract thought, in comparison with healthy controls. We also predicted that decreased activation in TBI individuals would correlate with their behavioral response times. A whole-brain analysis across the two participant groups revealed that patients did not strongly engage frontal and temporal regions related to semantic processing for novel metaphor comprehension, whereas control participants exhibited more intensive and concentrated activation within frontal and temporal areas. A region of interest (ROI) analysis verified that the LIFG was underactivated in TBI patients compared to controls across all conditions. TBI patients' impaired abstraction of novel stimuli may stem from reduced prefrontal control of semantic memory as well as disrupted interconnectivity of prefrontal cortex with other regions.

Question: What is the difference between figurative language processing and abstract thinking?


The integration of the internal and external milieu in the insula during dynamic emotional experiences. Whilst external events trigger emotional responses, interoception (the perception of internal physiological states) is fundamental to core emotional experience. By combining high resolution functional neuroimaging with concurrent physiological recordings, we investigated the neural mechanisms of interoceptive integration during free listening to an emotionally salient audio film. We found that cardiac activity, a key interoceptive signal, was robustly synchronised across participants and centrally represented in the posterior insula. Effective connectivity analysis revealed that the anterior insula, specifically tuned to the emotionally salient moments of the audio stream, serves as an integration hub of interoceptive processing: interoceptive states represented in the posterior insula are integrated with exteroceptive representations by the anterior insula to highlight these emotionally salient moments. Our study for the first time demonstrates the insular hierarchy for interoceptive processing during natural emotional experience. These findings provide an ecologically-valid framework for elucidating the neural underpinnings of emotional deficits in neuropsychiatric disorders.

Question: How does the insula work?


Designing mobile interfaces for novice and low-literacy users. While mobile phones have found broad application in bringing health, financial, and other services to the developing world, usability remains a major hurdle for novice and low-literacy populations. In this article, we take two steps to evaluate and improve the usability of mobile interfaces for such users. First, we offer an ethnographic study of the usability barriers facing 90 low-literacy subjects in India, Kenya, the Philippines, and South Africa. Then, via two studies involving over 70 subjects in India, we quantitatively compare the usability of different points in the mobile design space. In addition to text interfaces such as electronic forms, SMS, and USSD, we consider three text-free interfaces: a spoken dialog system, a graphical interface, and a live operator.  Our results confirm that textual interfaces are unusable by first-time low-literacy users, and error prone for literate but novice users. In the context of healthcare, we find that a live operator is up to ten times more accurate than text-based interfaces, and can also be cost effective in countries such as India. In the context of mobile banking, we find that task completion is highest with a graphical interface, but those who understand the spoken dialog system can use it more quickly due to their comfort and familiarity with speech. We synthesize our findings into a set of design recommendations.

Question: How can we design mobile interfaces for novice and low-literacy users?


Efficient Local Search Heuristics for Packing Irregular Shapes in Two-Dimensional Heterogeneous Bins. In this paper we proposed a local search heuristic and a genetic algorithm to solve the two-dimensional irregular multiple bin-size bin packing problem. The problem consists of placing a set of pieces represented as 2D polygons in rectangular bins with different dimensions such that the total area of bins used is minimized. Most packing algorithms available in the literature for 2D irregular bin packing consider single size bins only. However, for many industries the material can be supplied in a number of standard size sheets, for example, metal, foam, plastic and timber sheets. For this problem, the cut plans must decide the set of standard size stock sheets as well as which pieces to cut from each bin and how to arrange them in order to minimise waste material. Moreover, the literature constrains the orientation of pieces to a single or finite set of angles. This is often an artificial constraint that makes the solution space easier to navigate. In this paper we do not restrict the orientation of the pieces. We show that the local search heuristic and the genetic algorithm can address all of these decisions and obtain good solutions, with the local search performing better. We also discuss the affect of different groups of stock sheet sizes.

Question: How to solve the 2D irregular multiple bin-size bin packing problem?


Data Integration: A Theoretical Perspective. Data integration is the problem of combining data residing at different sources, and providing the user with a unified view of these data. The problem of designing data integration systems is important in current real world applications, and is characterized by a number of issues that are interesting from a theoretical point of view. This document presents on overview of the material to be presented in a tutorial on data integration. The tutorial is focused on some of the theoretical issues that are relevant for data integration. Special attention will be devoted to the following aspects: modeling a data integration application, processing queries in data integration, dealing with inconsistent data sources, and reasoning on queries.

Question: What is the difference between data integration and data warehousing?


Learning Dynamic Guidance for Depth Image Enhancement. The depth images acquired by consumer depth sensors (e.g., Kinect and ToF) usually are of low resolution and insufficient quality. One natural solution is to incorporate with high resolution RGB camera for exploiting their statistical correlation. However, most existing methods are intuitive and limited in characterizing the complex and dynamic dependency between intensity and depth images. To address these limitations, we propose a weighted analysis representation model for guided depth image enhancement, which advances the conventional methods in two aspects: (i) task driven learning and (ii) dynamic guidance. First, we generalize the analysis representation model by including a guided weight function for dependency modeling. And the task-driven learning formulation is introduced to obtain the optimized guidance tailored to specific enhancement task. Second, the depth image is gradually enhanced along with the iterations, and thus the guidance should also be dynamically adjusted to account for the updating of depth image. To this end, stage-wise parameters are learned for dynamic guidance. Experiments on guided depth image upsampling and noisy depth image restoration validate the effectiveness of our method.

Question: How to improve depth image quality?


Single-image SVBRDF capture with a rendering-aware deep network. Texture, highlights, and shading are some of many visual cues that allow humans to perceive material appearance in single pictures. Yet, recovering spatially-varying bi-directional reflectance distribution functions (SVBRDFs) from a single image based on such cues has challenged researchers in computer graphics for decades. We tackle lightweight appearance capture by training a deep neural network to automatically extract and make sense of these visual cues. Once trained, our network is capable of recovering per-pixel normal, diffuse albedo, specular albedo and specular roughness from a single picture of a flat surface lit by a hand-held flash. We achieve this goal by introducing several innovations on training data acquisition and network design. For training, we leverage a large dataset of artist-created, procedural SVBRDFs which we sample and render under multiple lighting directions. We further amplify the data by material mixing to cover a wide diversity of shading effects, which allows our network to work across many material classes. Motivated by the observation that distant regions of a material sample often offer complementary visual cues, we design a network that combines an encoder-decoder convolutional track for local feature extraction with a fully-connected track for global feature extraction and propagation. Many important material effects are view-dependent, and as such ambiguous when observed in a single image. We tackle this challenge by defining the loss as a differentiable SVBRDF similarity metric that compares the renderings of the predicted maps against renderings of the ground truth from several lighting and viewing directions. Combined together, these novel ingredients bring clear improvement over state of the art methods for single-shot capture of spatially varying BRDFs.

Question: What is a single-shot capture of a spatially varying BRDF?


LTE-V for Sidelink 5G V2X Vehicular Communications: A New 5G Technology for Short-Range Vehicle-to-Everything Communications. This article provides an overview of the long-term evolution-vehicle (LTE-V) standard supporting sidelink or vehicle-to-vehicle (V2V) communications using LTE's direct interface named PC5 in LTE. We review the physical layer changes introduced under Release 14 for LTE-V, its communication modes 3 and 4, and the LTE-V evolutions under discussion in Release 15 to support fifth-generation (5G) vehicle-to-everything (V2X) communications and autonomous vehicles' applications. Modes 3 and 4 support direct V2V communications but differ on how they allocate the radio resources. Resources are allocated by the cellular network under mode 3. Mode 4 does not require cellular coverage, and vehicles autonomously select their radio resources using a distributed scheduling scheme supported by congestion control mechanisms. Mode 4 is considered the baseline mode and represents an alternative to 802.11p or dedicated shortrange communications (DSRC). In this context, this article also presents a detailed analysis of the performance of LTE-V sidelink mode 4, and proposes a modification to its distributed scheduling.

Question: What is the difference between LTE-V sidelink mode 3 and mode 4?


Adaptive systems for foreign exchange trading. Foreign exchange markets are notoriously difficult to predict. For many years academics and practitioners alike have tried to build trading models, but history has not been kind to their efforts. Consistently predicting FX markets has seemed like an impossible goal but recent advances in financial research now suggest otherwise. With newly developed computational techniques and newly available data, the development of successful trading models is looking possible. The Centre for Financial Research (CFR) at Cambridge University's Judge Institute of Management has been researching trading techniques in foreign exchange markets for a number of years. Over the last 18 months a joint project with HSBC Global Markets has looked at how the bank's proprietary information on customer order flow and on the customer limit order book can be used to enhance the profitability of technical trading systems in FX markets. Here we give an overview of that research and report our results.

Question: What is the difference between a trading model and a trading system?


Four types of context for automatic spelling correction. This paper presents an investigation on using four types of contextual information for improving the accuracy of automatic correction of single-token non-word misspellings. The task is framed as contextually-informed re-ranking of correction candidates. Immediate local context is captured by word n-grams statistics from a Web-scale language model. The second approach measures how well a candidate correction fits in the semantic fabric of the local lexical neighborhood, using a very large Distributional Semantic Model. In the third approach, recognizing a misspelling as an instance of a recurring word can be useful for reranking. The fourth approach looks at context beyond the text itself. If the approximate topic can be known in advance, spelling correction can be biased towards the topic. Effectiveness of proposed methods is demonstrated with an annotated corpus of 3,000 student essays from international high-stakes English language assessments. The paper also describes an implemented system that achieves high accuracy on this task. RÉSUMÉ. Cet article présente une enquête sur l'utilisation de quatre types d'informations contextuelles pour améliorer la précision de la correction automatique de fautes d'orthographe de mots seuls. La tâche est présentée comme un reclassement contextuellement informé. Le contexte local immédiat, capturé par statistique de mot n-grammes est modélisé à partir d'un modèle de langage à l'échelle du Web. La deuxième méthode consiste à mesurer à quel point une correction s'inscrit dans le tissu sémantique local, en utilisant un très grand modèle sémantique distributionnel. La troisième approche reconnaissant une faute d'orthographe comme une instance d'un mot récurrent peut être utile pour le reclassement. La quatrième approche s'attache au contexte au-delà du texte lui-même. Si le sujet approximatif peut être connu à l'avance, la correction orthographique peut être biaisée par rappor

Question: What is the difference between the four types of context?


Effectiveness of domestic wastewater treatment using microbial fuel cells at ambient and mesophilic temperatures.. Domestic wastewater treatment was examined under two different temperature (23+/-3 degrees C and 30+/-1 degrees C) and flow modes (fed-batch and continuous) using single-chamber air-cathode microbial fuel cells (MFCs). Temperature was an important parameter for treatment efficiency and power generation. The highest power density of 422 mW/m(2) (12.8 W/m(3)) was achieved under continuous flow and mesophilic conditions, at an organic loading rate of 54 g COD/L-d, achieving 25.8% COD removal. Energy recovery was found to depend significantly on the operational conditions (flow mode, temperature, organic loading rate, and HRT) as well as the reactor architecture. The results demonstrate that the main advantages of using temperature-phased, in-series MFC configurations for domestic wastewater treatment are power savings, low solids production, and higher treatment efficiency.

Question: What is the effect of temperature on the efficiency of wastewater treatment?


Tweeties Squabbling: Positive and Negative Results in Applying Argument Mining on Social Media.

Question: What is the positive and negative results in applying argument mining on social media?


Scientific Table Search Using Keyword Queries. Tables are common and important in scientific documents, yet most text-based document search systems do not capture structures and semantics specific to tables. How to bridge different types of mismatch between keywords queries and scientific tables and what influences ranking quality needs to be carefully investigated. This paper considers the structure of tables and gives different emphasis to table components. On the query side, thanks to external knowledge such as knowledge bases and ontologies, key concepts are extracted and used to build structured queries, and target quantity types are identified and used to expand original queries. A probabilistic framework is proposed to incorporate structural and semantic information from both query and table sides. We also construct and release TableArXiv, a high quality dataset with 105 queries and corresponding relevance judgements for scientific table search. Experiments demonstrate significantly higher accuracy overall and at the top of the rankings than several baseline methods.

Question: How to bridge different types of mismatch between keywords queries and scientific tables and what influences ranking quality needs to be carefully investigated.


Instant messaging and the future of language. The writing style commonly used in IMing, texting, and other forms of computer-mediated communication need not spell the end of normative language.

Question: What is the future of language?


Quality Attributes of Web Software Applications. In only four or five years, the world wide web has changed from a static collection of HTML web pages to a dynamic engine that powers e-commerce, collaborative work, and distribution of information and entertainment. These exciting changes have been fueled by many changes in software technology, the software development process, and how software is deployed. Although the word "heterogeneous" is commonly used to describe web software, we might easily forget to notice in how many ways it can be applied. In fact, the synonymous term "diverse" is more general and familiar, and may be more appropriate. Web software applications use diverse types of hardware, they include a diverse collection of types of implementation languages (including traditional programs, HTML, interpreted scripts, and databases), they are composed of software written in diverse languages, and they are built by collections of people with very diverse sets of skills. Although these changes in how web applications are built are interesting and fascinating, one of the most unique aspects of web software applications is in terms of the needs they must satisfy. Web applications have very high requirements for a number of quality attributes. Some of these quality attributes have been important in other (mostly relatively small) segments of the industry, but some of them are relatively new. This paper discusses some of the unique technological aspects of building web software applications, the unique requirements of quality attributes, and how they can be achieved.

Question: What are the quality attributes of web software applications?


Photonic ADC: overcoming the bottleneck of electronic jitter.. Accurate conversion of wideband multi-GHz analog signals into the digital domain has long been a target of analog-to-digital converter (ADC) developers, driven by applications in radar systems, software radio, medical imaging, and communication systems. Aperture jitter has been a major bottleneck on the way towards higher speeds and better accuracy. Photonic ADCs, which perform sampling using ultra-stable optical pulse trains generated by mode-locked lasers, have been investigated for many years as a promising approach to overcome the jitter problem and bring ADC performance to new levels. This work demonstrates that the photonic approach can deliver on its promise by digitizing a 41 GHz signal with 7.0 effective bits using a photonic ADC built from discrete components. This accuracy corresponds to a timing jitter of 15 fs - a 4-5 times improvement over the performance of the best electronic ADCs which exist today. On the way towards an integrated photonic ADC, a silicon photonic chip with core photonic components was fabricated and used to digitize a 10 GHz signal with 3.5 effective bits. In these experiments, two wavelength channels were implemented, providing the overall sampling rate of 2.1 GSa/s. To show that photonic ADCs with larger channel counts are possible, a dual 20-channel silicon filter bank has been demonstrated.

Question: What is the jitter of a photonic ADC?


Combined long short-term memory based network employing wavelet coefficients for MI-EEG recognition. Motor Imagery Electroencephalography (MI-EEG) plays an important role in brain computer interface (BCI) based rehabilitation robot, and its recognition is the key problem. The Discrete Wavelet Transform (DWT) has been applied to extract the time-frequency features of MI-EEG. However, the existing EEG classifiers, such as support vector machine (SVM), linear discriminant analysis (LDA) and BP network, did not make full use of the time sequence information in time-frequency features, the resulting recognition performance were not very ideal. In this paper, a Long Short-Term Memory (LSTM) based recurrent Neural Network (RNN) is integrated with Discrete Wavelet Transform (DWT) to yield a novel recognition method, denoted as DWT-LSTM. DWT is applied to analyze the each channel of MI-EEG and extract its effective wavelet coefficients, representing the time-frequency features. Then a LSTM based RNN is used as a classifier for the patten recognition of observed MI-EEG data. Experiments are conducted on a publicly available dataset, and the 5-fold cross validation experimental results show that DWT-LSTM yields relatively higher classification accuracies compared to the existing approaches. This is helpful for the further research and application of RNN in processing of MI-EEG.

Question: What is the difference between a wavelet and a wavelet transform?


Machine learning classifiers and fMRI: A tutorial overview. Interpreting brain image experiments requires analysis of complex, multivariate data. In recent years, one analysis approach that has grown in popularity is the use of machine learning algorithms to train classifiers to decode stimuli, mental states, behaviours and other variables of interest from fMRI data and thereby show the data contain information about them. In this tutorial overview we review some of the key choices faced in using this approach as well as how to derive statistically significant results, illustrating each point from a case study. Furthermore, we show how, in addition to answering the question of 'is there information about a variable of interest' (pattern discrimination), classifiers can be used to tackle other classes of question, namely 'where is the information' (pattern localization) and 'how is that information encoded' (pattern characterization).

Question: How to interpret fMRI data?


Using vanishing points for camera calibration. In this article a new method for the calibration of a vision system which consists of two (or more) cameras is presented. The proposed method, which uses simple properties of vanishing points, is divided into two steps. In the first step, the intrinsic parameters of each camera, that is, the focal length and the location of the intersection between the optical axis and the image plane, are recovered from a single image of a cube. In the second step, the extrinsic parameters of a pair of cameras, that is, the rotation matrix and the translation vector which describe the rigid motion between the coordinate systems fixed in the two cameras are estimated from an image stereo pair of a suitable planar pattern. Firstly, by matching the corresponding vanishing points in the two images the rotation matrix can be computed, then the translation vector is estimated by means of a simple triangulation. The robustness of the method against noise is discussed, and the conditions for optimal estimation of the rotation matrix are derived. Extensive experimentation shows that the precision that can be achieved with the proposed method is sufficient to efficiently perform machine vision tasks that require camera calibration, like depth from stereo and motion from image sequence.

Question: How to calibrate a camera?


SPICE Models to Analyze Radiated and Conducted Susceptibilities of Shielded Coaxial Cables. This paper presents simulation program with integrated circuit emphasis (SPICE) models for the analyses of the radiated and conducted susceptibilities of shielded coaxial cables. Derived from the transmission-line equations of shielded cables, these models can be directly used for the time-domain and frequency-domain analyses, and connected to nonlinear and time-varying loads with the models already available in SPICE. In the proposed models, the usual discretization of shielded cables is not needed, and both the transfer impedance and admittance have been taken into account. Results obtained with these new models are in a good agreement with those obtained by using other approaches.

Question: What is the difference between the time-domain and frequency-domain analyses?


Introducing Serendipity in a Content-Based Recommender System. Today recommenders are commonly used with various purposes, especially dealing with e-commerce and information filtering tools. Content-based recommenders rely on the concept of similarity between the bought/ searched/ visited item and all the items stored in a repository. It is a common belief that the user is interested in what is similar to what she has already bought/searched/visited. We believe that there are some contexts in which this assumption is wrong: it is the case of acquiring unsearched but still useful items or pieces of information. This is called serendipity. Our purpose is to stimulate users and facilitate these serendipitous encounters to happen. This paper presents the design and implementation of a hybrid recommender system that joins a content-based approach and serendipitous heuristics in order to mitigate the over-specialization problem with surprising suggestions.

Question: What is the purpose of a content-based recommender system?


Fully convolutional network and sparsity-based dictionary learning for liver lesion detection in CT examinations.

Question: How to detect liver lesions in CT examinations?


Mermaid: Integrating Vertex-Centric with Edge-Centric for Real-World Graph Processing. There has been increasing interests in processing large-scale real-world graphs, and recently many graph systems have been proposed. Vertex-centric GAS (Gather-Apply-Scatter) and Edge-centric GAS are two graph computation models being widely adopted, and existing graph analytics systems commonly follow only one computation model, which is not the best choice for real-world graph processing. In fact, vertex degrees in real-world graphs often obey skewed power-law distributions: most vertices have relatively few neighbors while a few have many neighbors. We observe that vertex-centric GAS for high-degree vertices and edge-centric GAS for low-degree vertices is a much better choice for real-world graph processing. In this paper, we present Mermaid, a system for processing large-scale real-world graphs on a single machine. Mermaid skillfully integrates vertex-centric GAS with edge-centric GAS through a novel vertex-mapping mechanism, and supports streamlined graph processing. On a total of 6 practical natural graph processing tasks, we demonstrate that, on average, Mermaid achieves 1.83x better performance than the state-of-the-art graph system on a single machine.

Question: What is the best graph system for real-world graph processing?


Using JFLAP to interact with theorems in automata theory. An automata theory course can be taught in an interactive, hands-on manner using a computer. At Duke we have been using the software tool JFLAP to provide interaction and feedback in CPS 140, our automata theory course. JFLAP is a tool for designing and running nondeterministic versions of finite automata, pushdown automata, and Turing machines. Recently, we have enhanced JFLAP to allow one to study the proofs of several theorems that focus on conversions of languages, from one form to another, such as converting an NFA to a DFA and then to a minimum state DFA. In addition, our enhancements combined with other tools allow one to interactively study LL and LR parsing methods.

Question: How to use JFLAP to interact with theorems in automata theory.


Anonymous Walk Embeddings. The task of representing entire graphs has seen a surge of prominent results, mainly due to learning convolutional neural networks (CNNs) on graphstructured data. While CNNs demonstrate stateof-the-art performance in graph classification task, such methods are supervised and therefore steer away from the original problem of network representation in task-agnostic manner. Here, we coherently propose an approach for embedding entire graphs and show that our feature representations with SVM classifier increase classification accuracy of CNN algorithms and traditional graph kernels. For this we describe a recently discovered graph object, anonymous walk, on which we design task-independent algorithms for learning graph representations in explicit and distributed way. Overall, our work represents a new scalable unsupervised learning of state-of-the-art representations of entire graphs.

Question: What is the task of representing entire graphs?


A Generative Approach to Audio-Visual Person Tracking. This paper focuses on the integration of acoustic and visual information for people tracking. The system presented relies on a probabilistic framework within which information from multiple sources is integrated at an intermediate stage. An advantage of the method proposed is that of using a generative approach which supports easy and robust integration of multi source information by means of sampled projection instead of triangulation. The system described has been developed in the EU funded CHIL Project research activities. Experimental results from the CLEAR evaluation workshop are reported.

Question: What is a generative approach?


Moodle's Ontology Development from UML for Social Learning Network Analysis. The online learning called e-learning is a new learning path that offers to learners to study at their own pace and at the moments that suit them. It is in this perspective that the semantic web has known its emergence in the field of e-learning to offer platforms content more personalized and more adapted to student's and teacher's needs. Since Moodle is the most popular e-learning platform, we propose in this paper to build its OWL ontology by exploring the representative data that we collected from its UML class diagram. The choice of UML class diagram as a basis for data collection for the development of the ontology is justified by the fact that the transition from UML to OWL ontology brings ontology development process closer to the wider software engineering population. The built ontology brings also great benefit in the field of the Social Learning Network Analysis. Because it gives the opportunity to study the behavior of the platform users by giving meaning to their relationships instead of modelling them only as knots and edges.

Question: What is the ontology of moodle?


VNF-FG design and VNF placement for 5G mobile networks. Network function virtualization (NFV) is envisioned as one of the critical technologies in 5th-Generation (5G) mobile networks. This paper investigates the virtual network function forwarding graph (VNF-FG) design and virtual network function (VNF) placement for 5G mobile networks. We first propose a two-step method composed of flow designing and flow combining for generating VNF-FGs according to network service requests. For mapping VNFs in the generated VNF-FG to physical resources, we then modify the hybrid NFV environment with introducing more types of physical nodes and mapping modes for the sake of completeness and practicality, and formulate the VNF placement optimization problem for achieving lower bandwidth consumption and lower maximum link utilization simultaneously. To resolve this problem, four genetic algorithms are proposed on the basis of the frameworks of two existing algorithms (multiple objective genetic algorithm and improved non-dominated sorting genetic algorithm). Simulation results show that Greedy-NSGA-II achieves the best performance among our four algorithms. Compared with three non-genetic algorithms (random, backtracking mapping and service chains deployment with affiliation-aware), Greedy-NSGA-II reduces 97.04%, 87.76% and 88.42% of the average total bandwidth consumption, respectively, and achieves only 13.81%, 25.04% and 25.41% of the average maximum link utilization, respectively. Moreover, using our VNF-FG design method and Greedy-NSGA-II together can also reduce the total bandwidth consumption remarkably.

Question: What is the total bandwidth consumption?


Why Not?. As humans, we have expectations for the results of any action, e.g. we expect at least one student to be returned when we query a university database for student records. When these expectations are not met, traditional database users often explore datasets via a series of slightly altered SQL queries. Yet most database access is via limited interfaces that deprive end users of the ability to alter their query in any way to garner better understanding of the dataset and result set. Users are unable to question why a particular data item is Not in the result set of a given query. In this work, we develop a model for answers to WHY NOT? queries. We show through a user study the usefulness of our answers, and describe two algorithms for finding the manipulation that discarded the data item of interest. Moreover, we work through two different methods for tracing the discarded data item that can be used with either algorithm. Using our algorithms, it is feasible for users to find the manipulation that excluded the data item of interest, and can eliminate the need for exhausting debugging.

Question: Why is this data item not in the result set of this query?


Controlling Robot Morphology From Incomplete Measurements. Mobile robots with complex morphology are essential for traversing rough terrains in Urban Search & Rescue missions. Since teleoperation of the complex morphology causes high cognitive load of the operator, the morphology is controlled autonomously. The autonomous control measures the robot state and surrounding terrain which is usually only partially observable, and thus the data are often incomplete. We marginalize the control over the missing measurements and evaluate an explicit safety condition. If the safety condition is violated, tactile terrain exploration by the body-mounted robotic arm gathers the missing data.

Question: How to control robot morphology from incomplete measurements?


Using Dynamic Programming for Solving Variational Problems in Vision. Variational approaches have been proposed for solving many inverse problems in early vision, such as in the computation of optical flow, shape from shading, and energy-minimizing active contour models. In general however, variational approaches do not guarantee global optimality of the solution, require estimates of higher order derivatives of the discrete data, and do not allow direct and natural enforcement of constraints. In this paper we discuss dynamic programming as a novel approach to solving variational problems in vision. Dynamic programming ensures global optimality of the solution, it is numerically stable, and it allows for hard constraints to be enforced on the behavior of the solution within a natural and straightforward structure. As a specific example of the efficacy of the proposed approach, application of dynamic programming to the energy-minimizing active contours is described. The optimization problem is set up as a discrete multistage decision process and is solved by a " time-delayed " discrete dynamic programming algorithm. A parallel procedure is discussed that can result in savings in computational costs.

Question: What is the difference between a variational approach and a dynamic programming approach?


Plantar fasciitis: evidence-based review of diagnosis and therapy.. Plantar fasciitis causes heel pain in active as well as sedentary adults of all ages. The condition is more likely to occur in persons who are obese or in those who are on their feet most of the day. A diagnosis of plantar fasciitis is based on the patient's history and physical findings. The accuracy of radiologic studies in diagnosing plantar heel pain is unknown. Most interventions used to manage plantar fasciitis have not been studied adequately; however, shoe inserts, stretching exercises, steroid injection, and custom-made night splints may be beneficial. Extracorporeal shock wave therapy may effectively treat runners with chronic heel pain but is ineffective in other patients. Limited evidence suggests that casting or surgery may be beneficial when conservative measures fail.

Question: What is the evidence for the effectiveness of different treatments for plantar fasciitis?


A Relational Approach to Monitoring Complex Systems. Monitoring is an essential part of many program development tools, and plays a central role in debugging, optimization, status reporting, and reconfiguration. Traditional monitoring techniques are inadequate when monitoring complex systems such as multiprocessors or distributed systems. A new approach is described in which a historical database forms the conceptual basis for the information processed by the monitor. This approach permits advances in specifying the low-level data collection, specifying the analysis of the collected data, performing the analysis, and displaying the results. Two prototype implementations demonstrate the feasibility of the approach.

Question: What is a relational approach to monitoring complex systems?


Control optimization for a power-split hybrid vehicle. Toyota hybrid system (THS) is used in the current best-selling hybrid vehicle on the market - the Toyota Prius. This hybrid system contains a power split planetary gear system which combines the benefits of series and parallel hybrid vehicles. This paper first develops a dynamic model to investigate the control strategy of the THS power train. An equivalent consumption minimization strategy (ECMS) is developed which is based on instantaneous optimization concept. The dynamic programming (DP) technique is then utilized to obtain a performance benchmark and insight toward fine-tuning of the ECMS algorithm for better performance

Question: What is a power-split hybrid vehicle?


Integrated transition of coplanar to rectangular waveguides. Usual transitions between planar circuit and rectangular waveguide make use of 3-D complex mounting structures. Such an integration requires costly high precision mechanical alignment, In this paper, a new planar platform is developed in which a coplanar waveguide (CPW) and a rectangular waveguide are fully integrated on the same substrate, and they are interconnected via a simple transition. They can be built with a standard PCB process. Our experiments at 28 GHz show that an effective bandwidth of 7% at 15 dB return loss can easily be achieved. The CPW-to-waveguide transition allows for a complete integration of waveguide components on substrate with active components such as MMIC.

Question: What is a coplanar waveguide?


Saliency Detection via Absorbing Markov Chain. In this paper, we formulate saliency detection via absorbing Markov chain on an image graph model. We jointly consider the appearance divergence and spatial distribution of salient objects and the background. The virtual boundary nodes are chosen as the absorbing nodes in a Markov chain and the absorbed time from each transient node to boundary absorbing nodes is computed. The absorbed time of transient node measures its global similarity with all absorbing nodes, and thus salient objects can be consistently separated from the background when the absorbed time is used as a metric. Since the time from transient node to absorbing nodes relies on the weights on the path and their spatial distance, the background region on the center of image may be salient. We further exploit the equilibrium distribution in an ergodic Markov chain to reduce the absorbed time in the long-range smooth background regions. Extensive experiments on four benchmark datasets demonstrate robustness and efficiency of the proposed method against the state-of-the-art methods.

Question: What is the absorbing Markov chain?


A MapReduce Approach to NoSQL RDF Databases. In recent years, the increased need to house and process large volumes of data has prompted the need for distributed storage and querying systems. The growth of machine-readable RDF triples has prompted both industry and academia to develop new database systems, called "NoSQL," with characteristics that differ from classical databases. Many of these systems compromise ACID properties for increased horizontal scalability and data availability. This thesis concerns the development and evaluation of a NoSQL triplestore. Triplestores are database management systems central to emerging technologies such as the Semantic Web and linked data. A triplestore comprises data storage using the RDF (resource description framework) data model and the execution of queries written in SPARQL. The triplestore developed here exploits an opensource stack comprising, Hadoop, HBase, and Hive. The evaluation spans several benchmarks, including the two most commonly used in triplestore evaluation, the Berlin SPARQL Benchmark, and the DBpedia benchmark, a query workload that operates an RDF representation of Wikipedia. Results reveal that the join algorithm used by the system plays a critical role in dictating query runtimes. Distributed graph databases must carefully optimize queries before generating MapReduce query plans as network traffic for large datasets can become prohibitive if the query is executed naively.

Question: What is the difference between a triplestore and a graph database?


A column store engine for real-time streaming analytics. This paper describes novel aspects of the column store implemented in the MemSQL database engine and describes the design choices made to support real-time streaming workloads. Column stores have traditionally been restricted to data warehouse scenarios where low latency queries are a secondary goal, and where restricting data ingestion to be offline, batched, append-only, or some combination thereof is acceptable. In contrast, the MemSQL column store implementation treats low latency queries and ongoing writes as first class citizens, with a focus on avoiding interference between read, ingest, update, and storage optimization workloads through the use of fragmented snapshot transactions and optimistic storage reordering. This implementation broadens the range of serviceable column store workloads to include those with more stringent demands on query and data latency, such as those backing operational systems used by adtech, financial services, fraud detection and other real-time or data streaming applications.

Question: What is a column store?


Alzheimer disease therapy—moving from amyloid-β to tau. Disease-modifying treatments for Alzheimer disease (AD) have focused mainly on reducing levels of amyloid-β (Aβ) in the brain. Some compounds have achieved this goal, but none has produced clinically meaningful results. Several methodological issues relating to clinical trials of these agents might explain this failure; an additional consideration is that the amyloid cascade hypothesis—which places amyloid plaques at the heart of AD pathogenesis—does not fully integrate a large body of data relevant to the emergence of clinical AD. Importantly, amyloid deposition is not strongly correlated with cognition in multivariate analyses, unlike hyperphosphorylated tau, neurofibrillary tangles, and synaptic and neuronal loss, which are closely associated with memory deficits. Targeting tau pathology, therefore, might be more clinically effective than Aβ-directed therapies. Furthermore, numerous immunization studies in animal models indicate that reduction of intracellular levels of tau and phosphorylated tau is possible, and is associated with improved cognitive performance. Several tau-related vaccines are in advanced preclinical stages and will soon enter clinical trials. In this article, we present a critical analysis of the failure of Aβ-directed therapies, discuss limitations of the amyloid cascade hypothesis, and suggest the potential value of tau-targeted therapy for AD.

Question: What is the amyloid cascade hypothesis?


Haptic feedback for pen computing: directions and strategies. We seek to improve the xperience of using pen computing devices by augmenting them with haptic, tactile feedback displays. We present the design of the haptic display for pen computers, and explore interaction techniques that allow users to feel GUI elements, textures, photographs and other interface elements with a pen. We discuss research directions in haptic displays for pen devices and report results of an early experimental study that evaluated the benefits of tactile feedback in pen computing.

Question: How do you feel about haptic feedback?


Design of a haptic arm exoskeleton for training and rehabilitation. A high-quality haptic interface is typically characterized by low apparent inertia and damping, high structural stiffness, minimal backlash, and absence of mechanical singularities in the workspace. In addition to these specifications, exoskeleton haptic interface design involves consideration of space and weight limitations, workspace requirements, and the kinematic constraints placed on the device by the human arm. These constraints impose conflicting design requirements on the engineer attempting to design an arm exoskeleton. In this paper, the authors present a detailed review of the requirements and constraints that are involved in the design of a high-quality haptic arm exoskeleton. In this context, the design of a five-degree-of-freedom haptic arm exoskeleton for training and rehabilitation in virtual environments is presented. The device is capable of providing kinesthetic feedback to the joints of the lower arm and wrist of the operator, and will be used in future work for robot-assisted rehabilitation and training. Motivation for such applications is based on findings that show robot-assisted physical therapy aids in the rehabilitation process following neurological injuries. As a training tool, the device provides a means to implement flexible, repeatable, and safe training methodologies.

Question: What is the design of a haptic arm exoskeleton?


Applying the Waek Learning Framework to Understand and Improve C4.5. There has long been a chasm between theoretical mod els of machine learning and practical machine learn ing algorithms For instance empirically successful algorithms such as C and backpropagation have not met the criteria of the PAC model and its vari ants Conversely the algorithms suggested by com putational learning theory are usually too limited in various ways to nd wide application The theoreti cal status of decision tree learning algorithms is a case in point while it has been proven that C and all reasonable variants of it fails to meet the PAC model criteria other recently proposed decision tree algo rithms that do have non trivial performance guaran tees unfortunately require membership queries Two recent developments have narrowed this gap between theory and practice not for the PAC model but for the related model known as weak learning or boosting First an algorithm called Adaboost was proposed that meets the formal criteria of the boosting model and is also competitive in practice Second the basic algorithms underlying the popular C and CART programs have also very recently been shown to meet the formal criteria of the boosting model Thus it seems plausible that the weak learning frame work may provide a setting for interaction between formal analysis and machine learning practice that is lacking in other theoretical models Our aim in this paper is to push this interaction further in light of these recent developments In par ticular we perform experiments suggested by the for mal results for Adaboost and C within the weak learning framework We concentrate on two particu larly intriguing issues First the theoretical boosting results for top down decision tree algorithms such as C suggest that a new splitting criterion may result in trees that are smaller and more accurate than those obtained using the usual information gain We con rm this suggestion experimentally Second a super cial int

Question: What is the difference between weak learning and boosting?


A service orchestration architecture for Fog-enabled infrastructures. The development of Fog Computing technology is crucial to address the challenges to come with the mass adoption of Internet Of Things technology, where the generation of data tends to grow at an unprecedented pace. The technology brings computing power to the surrounds of devices, to offer local processing, filtering, storage and analysis of data and control over actuators. Orchestration is a requirement of Fog Computing technology to deliver services, based on the composition of microservices. It must take into consideration the heterogeneity of the IoT environment and device's capabilities and constraints. This heterogeneity requires a different approach for orchestration, be it regarding infrastructure management, node selection and/or service placement. Orchestrations shall be manually or automatically started through event triggers. Also, the Orchestrator must be flexible enough to work in a centralized or distributed fashion. Orchestration is still a hot topic and can be seen in different areas, especially in the Service Oriented Architectures, hardware virtualization, in the Cloud, and in Network Virtualization Function. However, the architecture of these solutions is not enough to handle Fog Requirements, specially Fog's heterogeneity, and dynamics. In this paper, we propose an architecture for Orchestration for the Fog Computing environment. We developed a prototype to prof some concepts. We discuss in this paper the implementation, and the tools chose, and their roles. We end the paper with a discussion on performance indicators and future direction on the evaluation of non-functional aspects of the Architecture.

Question: What is the fog computing architecture?


The human amygdala and the emotional evaluation of sensory stimuli. A wealth of animal data implicates the amygdala in aspects of emotional processing. In recent years, functional neuroimaging and neuropsychological studies have begun to refine our understanding of the functions of the amygdala in humans. This literature offers insights into the types of stimuli that engage the amygdala and the functional consequences that result from this engagement. Specific conclusions and hypotheses include: (1) the amygdala activates during exposure to aversive stimuli from multiple sensory modalities; (2) the amygdala responds to positively valenced stimuli, but these responses are less consistent than those induced by aversive stimuli; (3) amygdala responses are modulated by the arousal level, hedonic strength or current motivational value of stimuli; (4) amygdala responses are subject to rapid habituation; (5) the temporal characteristics of amygdala responses vary across stimulus categories and subject populations; (6) emotionally valenced stimuli need not reach conscious awareness to engage amygdala processing; (7) conscious hedonic appraisals do not require amygdala activation; (8) activation of the amygdala is associated with modulation of motor readiness, autonomic functions, and cognitive processes including attention and memory; (9) amygdala activations do not conform to traditional models of the lateralization of emotion; and (10) the extent and laterality of amygdala activations are related to factors including psychiatric status, gender and personality. The strengths and weakness of these hypotheses and conclusions are discussed with reference to the animal literature.

Question: What is the emotional evaluation of sensory stimuli?


Detecting concept change in dynamic data streams. In this research we present a novel approach to the concept change detection problem. Change detection is a fundamental issue with data stream mining as classification models generated need to be updated when significant changes in the underlying data distribution occur. A number of change detection approaches have been proposed but they all suffer from limitations with respect to one or more key performance factors such as high computational complexity, poor sensitivity to gradual change, or the opposite problem of high false positive rate. Our approach uses reservoir sampling to build a sequential change detection model that offers statistically sound guarantees on false positive and false negative rates but has much smaller computational complexity than the ADWIN concept drift detector. Extensive experimentation on a wide variety of datasets reveals that the scheme also has a smaller false detection rate while maintaining a competitive true detection rate to ADWIN.

Question: What is a sequential change detection model?


Quotidian resilience: Exploring mechanisms that drive resilience from a perspective of everyday stress and coping. Resilience is often associated with extreme trauma or overcoming extraordinary odds. This way of thinking about resilience leaves most of the ontogenetic picture a mystery. In the following review we put forth the Everyday Stress Resilience Hypothesis where resilience is analyzed from a systems perspective and seen as a process of regulating everyday life stressors. Successful regulation accumulates into regulatory resilience which emerges during early development from successful coping with the inherent stress in typical interactions. These quotidian stressful events lead to activation of behavioral and physiologic systems. Stress that is effectively resolved in the short run and with reiteration over the long-term increases children's as well as adults' capacity to cope with more intense stressors. Infants, however, lack the regulatory capacities to take on this task by themselves. Therefore, through communicative and regulatory processes during infant-adult interactions, we demonstrate that the roots of regulatory resilience originate in infants' relationship with their caregiver and that maternal sensitivity can help or hinder the growth of resilience.

Question: What is quotidian resilience?


Sentiment Analysis using Deep Convolutional Neural Networks with Distant Supervision Master Thesis. This thesis addresses the problem of predicting message-level sentiments of English micro-blog messages from Twitter. Convolutional neural networks (CNN) have shown great promise in the task of sentiment classification. Here we expand the CNN proposed by [31, 32] and perform an in-depth analysis to deepen the understanding of these systems. In a first step we compare the performance of different architectures with focus on deeper architectures. We show how different choices of hyper-parameters impact the results and present the benefits and challenges of using deeper architectures. Second, we introduce the procedure for achieving a high quality initialization of the CNN and show the massive impact it has on its predictive capabilities. This procedure consists of creating custom word embeddings followed by pre-training the network using 90M tweets where the polarity of a tweet is inferred by noisy labels. Third, we present an in-depth analysis of the supervised phase. We show the impact of the pre-training on the supervised phase. We show how the choice of the optimization algorithm and the step size impact the variance of the score during the supervised phase. The analysis is concluded by an inspection of the errors made by the system. This gives us insights into the inner workings of the CNN and hints as to what is needed for further improvement. The CNN presented in this thesis is part of an ensemble which ranked 1st in the SemEval-2016 competition, Task 4a [22]. The task consists of classifying a given tweet as either positive, negative or neutral. This further demonstrates the potential these systems have in the field of sentiment analysis.

Question: What is the canadian military?


Structural Data De-anonymization: Quantification, Practice, and Implications. In this paper, we study the quantification, practice, and implications of structural data (e.g., social data, mobility traces) De-Anonymization (DA). First, we address several open problems in structural data DA by quantifying perfect and (1-ε)-perfect structural data DA}, where ε is the error tolerated by a DA scheme. To the best of our knowledge, this is the first work on quantifying structural data DA under a general data model, which closes the gap between structural data DA practice and theory. Second, we conduct the first large-scale study on the de-anonymizability of 26 real world structural datasets, including Social Networks (SNs), Collaborations Networks, Communication Networks, Autonomous Systems, and Peer-to-Peer networks. We also quantitatively show the conditions for perfect and (1-ε)-perfect DA of the 26 datasets. Third, following our quantification, we design a practical and novel single-phase cold start Optimization based DA} (ODA) algorithm. Experimental analysis of ODA shows that about 77.7% - 83.3% of the users in Gowalla (.2M users and 1M edges) and 86.9% - 95.5% of the users in Google+ (4.7M users and 90.8M edges) are de-anonymizable in different scenarios, which implies optimization based DA is implementable and powerful in practice. Finally, we discuss the implications of our DA quantification and ODA and provide some general suggestions for future secure data publishing.

Question: What is the de-anonymizability of the 26 real world structural datasets?


The German Traffic Sign Recognition Benchmark: A multi-class classification competition. The "German Traffic Sign Recognition Benchmark" is a multi-category classification competition held at IJCNN 2011. Automatic recognition of traffic signs is required in advanced driver assistance systems and constitutes a challenging real-world computer vision and pattern recognition problem. A comprehensive, lifelike dataset of more than 50,000 traffic sign images has been collected. It reflects the strong variations in visual appearance of signs due to distance, illumination, weather conditions, partial occlusions, and rotations. The images are complemented by several precomputed feature sets to allow for applying machine learning algorithms without background knowledge in image processing. The dataset comprises 43 classes with unbalanced class frequencies. Participants have to classify two test sets of more than 12,500 images each. Here, the results on the first of these sets, which was used in the first evaluation stage of the two-fold challenge, are reported. The methods employed by the participants who achieved the best results are briefly described and compared to human traffic sign recognition performance and baseline results.

Question: What is the German Traffic Sign Recognition Benchmark?


A Review of Heuristic Global Optimization Based Artificial Neural Network Training Approahes. Received Nov 5, 2016 Revised Jan 9, 2017 Accepted Feb 18, 2017 Artificial Neural Networks have earned popularity in recent years because of their ability to approximate nonlinear functions. Training a neural network involves minimizing the mean square error between the target and network output. The error surface is nonconvex and highly multimodal. Finding the minimum of a multimodal function is a NP complete problem and cannot be solved completely. Thus application of heuristic global optimization algorithms that computes a good global minimum to neural network training is of interest. This paper reviews the various heuristic global optimization algorithms used for training feedforward neural networks and recurrent neural networks. The training algorithms are compared in terms of the learning rate, convergence speed and accuracy of the output produced by the neural network. The paper concludes by suggesting directions for novel ANN training algorithms based on recent advances in global optimization. Keyword:

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


Grounded Theory. Grounded theory was originally expounded by Barney Glaser and Anselm Strauss in their 1967 book The Discovery of Grounded Theory (Glaser and Strauss 1967). Reacting against what they saw as the dominance of hypothetico-deductive, theory-testing approaches, Glaser and Strauss proposed grounded theory as a way of building theory systematically using data obtained from social research. Since its first appearance, grounded theory has gone on to become 'currently the most widely used and popular qualitative research method across a wide range of disciplines and subject areas' (Bryant and Charmaz 2007: 1).

Question: What is grounded theory?