Superfaces: A Super-Resolution Model for 3D Faces. Face recognition based on the analysis of 3D scans has been an active research subject over the last few years. However, the impact of the resolution of 3D scans on the recognition process has not been addressed explicitly yet being of primal importance after the introduction of a new generation of low cost 4D scanning devices. These devices are capable of combined depth/rgb acquisition over time with a low resolution compared to the 3D scanners typically used in 3D face recognition benchmarks. In this paper, we define a super-resolution model for 3D faces by which a sequence of low-resolution 3D scans can be processed to extract a higher resolution 3D face model, namely the superface model. The proposed solution relies on the Scaled ICP procedure to align the low-resolution 3D models with each other and estimate the value of the high-resolution 3D model based on the statistics of values of the lowresolution scans in corresponding points. The approach is validated on a data set that includes, for each subject, one sequence of low-resolution 3D face scans and one ground-truth high-resolution 3D face model acquired through a high-resolution 3D scanner. In this way, results of the super-resolution process are evaluated qualitatively and quantitatively by measuring the error between the superface and the ground-truth.

Question: How to extract a high resolution 3D face model from a low resolution 3D face model?


Intonation issues in HMM-based speech synthesis for Vietnamese. In an HMM-based Text-To-Speech system, contextual features, including phonetic and prosodic factors have a significant influence to the spectrum, F0 and duration of the synthetic voice. This paper proposes prosodic features aiming at improving the naturalness of an HMM-based TTS system (VTed) for a tonal language, Vietnamese. The ToBI (Tones and Break Indices) features are used to learn two crucial prosodic cues i.e. intonation (boundary tones) and pause (break indices), concurrently with another set of features. The result of MOS test showed that the general quality of synthetic voice is rather good, 1.21 point lower than the natural voice. About 55% of the voice trained with ToBI boundary tone feature are perceived as similar to the voice trained without this feature, while a 10% difference in favour of the voice trained without this ToBI feature is observed. This may be linked with F0 contour lowering or raising regardless of lexical tones. This brought two main problems in the synthetic voice: discontinuity in spectrum and F0 or unexpected voice quality. This paper then concluded the need of much more work on intonation modeling that should take into account the Vietnamese tones. A new prosody model can be designed, which may consider the ToBI model, with respect to lexical tones and the syntactic structure of Vietnamese. Index Terms — Text-to-speech (TTS), speech synthesis, tonal language, Vietnamese, HMM-based speech synthesis, intonation, ToBI

Question: What is the prosody model for Vietnamese?


Road-marking Analysis for Autonomous Vehicle Guidance. Driving an automobile autonomously on rural roads requires knowledge about the geometry of the road. Furthermore, knowledge about the meaning of each lane of the road is needed in order to decide which lane should be taken and if the vehicle can do a lane change. This paper addresses the problem of extracting additional information about lanes. The information is extracted from the types of road-markings. The type of lane border markings is estimated in order to find out if a lane change is allowed. Arrows, which are painted on the road, are extracted and classified in order to determine the meaning of a lane such as a turn off lane.

Question: How to extract information about the road markings?


OPTIMAL PATHS FOR A CAR THAT GOES BOTH FORWARDS AND BACKWARDS. The path taken by a car with a given minimum turning radius has a lower bound on its radius of curvature at each point, but the path has cusps if the car shifts into or out of reverse gear. What is the shortest such path a car can travel between two points if its starting and ending directions are specified? One need consider only paths with at most 2 cusps or reversals. We give a set of paths which is sufficient in the sense that it always contains a shortest path and small in the sense that there are at most 68, but usually many fewer paths in the set for any pair of endpoints and directions. We give these paths by explicit formula. Calculating the length of each of these paths and selecting the (not necessarily unique) path with smallest length yields a simple algorithm for a shortest path in each case. These optimal paths or geodesies may be described as follows: If C is an arc of a circle of the minimal turning radius and S is a line segment, then it is sufficient to consider only certain paths of the form CCSCC where arcs and segments fit smoothly, one or more of the arcs or segments may vanish, and where reversals, or equivalently cusps, between arcs or segments are allowed. This contrasts with the case when cusps are not allowed, where Dubins (1957) has shown that paths of the form CCC and CSC suffice.

Question: What is the shortest path between two points?


SimICS/Sun4m: A Virtual Workstation. System level simulators allow computer architects and system software designers to recreate an accurate and complete replica of the program behavior of a target system, regardless of the availability, existence, or instrumentation support of such a system. Applications include evaluation of architectural design alternatives as well as software engineering tasks such as traditional debugging and performance tuning. We present an implementation of a simulator acting as a virtual workstation fully compatible with the sun4m architecture from Sun Microsystems. Built using the system-level SPARC V8 simulator SimICS, SimICS/sun4m models one or more SPARC V8 processors, supports user-developed modules for data cache and instruction cache simulation and execution profiling of all code, and provides a symbolic and performance debugging environment for operating systems. SimICS/sun4m can boot unmodified operating systems, including Linux 2.0.30 and Solaris 2.6, directly from snapshots of disk partitions. To support essentially arbitrary code, we implemented binary-compatible simulators for several devices, including SCSI, console, interrupt, timers, EEPROM, and Ethernet. The Ethernet simulation hooks into the host and allows the virtual workstation to appear on the local network with full services available ( NFS, NIS, rsh, etc). Ethernet and console traffic can be recorded for future playback. The performance of SimICS/sun4m is sufficient to run realistic workloads, such as the database benchmark TPC-D, scaling factor 1/100, or an interactive network application such as Mozilla. The slowdown in relation to native hardware is in the range of 25 to 75 (measured using SPECint95). We also demonstrate some applications, including modeling an 8-processor sun4m version (which does not exist), modeling future memory hierarchies, and debugging an operating system.

Question: What is a virtual workstation?


Implementing Fault-Tolerant Services Using the State Machine Approach: A Tutorial. The state machine approach is a general method for implementing fault-tolerant services in distributed systems. This paper reviews the approach and describes protocols for two different failure models—Byzantine and fail stop. Systems reconfiguration techniques for removing faulty components and integrating repaired components are also discussed.

Question: What is the state machine approach?


New Maximum Power Point Tracker Using Sliding-Mode Observer for Estimation of Solar Array Current in the Grid-Connected Photovoltaic System. A new maximum power point tracker (MPPT) for a grid-connected photovoltaic system without solar array current sensor is proposed. The solar array current information is obtained from the sliding-mode observer and fed into the MPPT to generate the reference voltage. The parameter values such as capacitances can be changed up to 50% from their nominal values, and the linear observer cannot estimate the correct state values under the parameter variations and noisy environments. The structure of a sliding-mode observer is simple, but it shows the robust tracking property against modeling uncertainties and parameter variations. In this paper, the sliding-mode observer for the solar array current has been proposed to compensate for the parameter variations. The mathematical modeling and the experimental results verify the validity of the proposed method

Question: How to estimate the solar array current in a grid-connected photovoltaic system without a solar array current sensor?


Development of a Biomimetic Hand Exotendon Device (BiomHED) for Restoration of Functional Hand Movement Post-Stroke. Significant functional impairment of the hand is common among stroke survivors and restoration of hand function should be prioritized during post-stroke rehabilitation. The goal of this study was to develop a novel biomimetic device to assist patients in producing complex hand movements with a limited number of actuators. The Biomimetic Hand Exoskeleton Device (BiomHED) is actuated by exotendons that mimic the geometry of the major tendons of the hand. Ten unimpaired subjects and four chronic stroke survivors participated in experiments that tested the efficacy of the system. The exotendons reproduced distinct spatial joint coordination patterns similar to their target muscle-tendon units for both subject groups. In stroke survivors, the exotendon-produced joint angular displacements were smaller, but not significantly different, than those of unimpaired subjects (\mbi p = 0.15-0.84). Even with limited use of the BiomHED, the kinematic workspace of the index finger increased by 63%-1014% in stroke survivors. The device improved the kinematics of the tip-pinch task in stroke survivors and resulted in a significant reduction in the fingertip-thumb tip distance ( 17.9 ±15.3 mm). This device is expected to enable effective "task-oriented" training of the hand post-stroke.

Question: What is the goal of this study?


NLOS Identification in Range-Based Source Localization: Statistical Approach. Least squares estimation is a widely-used technique for range-based source localization, which obtains the most probable position of mobile station. These methods cannot provide desirable accuracy in the case with a non line of sight (NLOS) path between mobile station and base stations. To circumvent this drawback, many algorithms have been proposed to identify and mitigate this error; however, they have a large run-time overhead. On the other hand, new positioning systems utilize a large set of base stations, and a practical algorithm should be fast enough to deal with them. In this paper, we propose a novel algorithm based on subspace method to identify and eliminate the NLOS error. Simulation studies show that our algorithm is faster and more accurate compared with other conventional methods, especially in the large-scale cases.

Question: How to identify and eliminate the NLOS error?


Etiology of the Psychopathic Serial Killer : An Analysis of Antisocial Personality Disorder , Psychopathy , and Serial Killer Personality and Crime Scene Characteristics. The purpose of this article is tomake the distinction between antisocial personality disorder and psychopathy, discuss possible etiologies of psychopathy, and analyze the crimes, personality characteristics, and historical aspects of psychopathic serial killers. The research indicates that both environmental and biological factors affect the development of psychopathy. Several different serial killers were compared to assess the similarities and differences between their histories, crimes, and personalities. Though there were marked differences between their crimes, startling historical and personality similarities were clearly identified. Based on these findings, the validity and reliability of offender profiling is also discussed. [Brief Treatment and Crisis Intervention 7:151–160 (2007)]

Question: What is the difference between antisocial personality disorder and psychopathy?


Multiresolution EO/IR target tracking and identification. Simultaneous target tracking and identification through feature association, attribute matching, or blob analysis is dependent on spatio-temporal measurements. Improved track maintenance should be achievable by maintaining coarse sensor resolutions on maneuvering targets and utilizing finer sensor resolutions to resolve closely-spaced targets. There are inherent optimal resolutions for sensors and restricted altitudes that constrain operational performance that a sensor manager must optimize for both wide-area surveillance and precision tracking. The advent of better optics, coordinated sensor management, and fusion strategies provide an opportunity to enhance simultaneous tracking and identification algorithms. We investigate utilizing electro-optical (EO) and infrared (IR) sensors operating at various resolutions to optimize target tracking and identification. We use a target-dense maneuvering scenario to highlight the performance gains with the multiresolution EO/IR data association (MEIDA) algorithm in tracking crossing targets.

Question: What is the optimal resolution for an EO/IR sensor?


Actuator fault detection and isolation system for an hexacopter. The problem of detection and isolation of actuator faults in hexacopter vehicles is address in this article. The aim is to detect and isolate possible faults which can occur on the vehicle actuation system (i.e. rotors and propellers). The dynamic nonlinear model of an hexarotor vehicle is first derived to build a model-based diagnosis system. Then a Thau observer is developed to estimate the states of the hexarotor and, on the usage of this information, to generate the needed set of residuals. The developed fault detection and isolation system is finally tested in a high fidelity hexacopter simulator in which different fault situations are considered.

Question: How to detect and isolate actuator faults in hexacopter vehicles?


Comparing ability and self-report trait emotional intelligence , fluid intelligence , and personality traits in career decision. This study investigated the role of ability and trait emotional intelligence (EI), fluid intelligence, and personality traits in career decision-making self-efficacy, career indecision and indecisiveness. The Advanced Progressive Matrices, Big Five Questionnaire, Mayer-Salovey-Caruso Emotional Intelligence Test, Bar-On Emotional Intelligence Inventory, Trait Emotional Intelligence Questionnaire, Career Decision SelfEfficacy Scale: Short Form, Career Decision-Making Difficulties Questionnaire, and Indecisiveness Scale were administered to 194 Italian high school students. These results highlighted the potentially important role of self-reported EI in career decisions. 2014 Elsevier Ltd. All rights reserved.

Question: What is the role of self-reported EI in career decision-making?


The See-Through System: A VANET-enabled assistant for overtaking maneuvers. The use of wireless technology based on Vehicular Ad hoc Networks (VANETs) for information exchange can influence the drivers' behavior towards improving driving performance and reducing road accidents. This information can even be more relevant if it is presented as a video stream. In this paper we propose a system that relies on VANET and video-streaming technology: the See-Through System (STS). The system enhances driver's visibility and supports the driver's overtaking decision in challenging situations, such as overtaking a vision-obstructing vehicle. The use of the See-Through System provides the driver with an additional tool for determining if traffic conditions permit starting an overtaking maneuver thus reducing the risk of overtaking. We tested the See-Through System on an experimental vehicle on the road as well as in the context of a driving simulator for real world environment. Results are promising, since the use of the 802.11p standard wireless communication protocol allows a vehicle-to-vehicle connection without significant delay and the totality of the participants regarded the information provided by the STS as useful.

Question: How does the see-through system work?


Identifying extremism in social media with multi-view context-aware subset optimization. Cyber threat intelligence and security informatics play critical roles in the identification of the principal influencers and threats associated with criminal activity and extremism in web-based communities. This paper presents an effective, dynamic learning framework utilizing domain specific context information to design a novel classification model that can robustly identify malicious social media posts (e.g. enrollment propaganda for extremist groups) with expressions of extremism or criminal intent. Research towards automated identification of extreme online posts and their associated key suspects and threats faces numerous challenges, 1) Online data, particularly social media data, originated from numerous independent and heterogeneous sources are largely unstructured. 2) The tactics, techniques, and procedures (TTPs) of criminal activity and extremism are constantly evolving. 3) There are limited ground truth data to support the development of effective classification technologies. In this paper, we present a human-machine collaborative, semi-supervised learning system that can efficiently and effectively identify malicious social media posts in presence of these challenges. Our system and framework develops an initial classifier from limited annotated data and in an interactive manner evolves dynamically into a sophisticated model using shortlisted relevant samples, identified via a graph-based optimization method, solvable by maximum flow algorithm. This same method also may be used to refine the classifier as TTPs evolve. Under this framework, the classifier performance converges faster using roughly 1–2 orders of magnitude fewer annotated samples, as compared to fully supervised solutions, resulting in a reasonably acceptable accuracy of nearly 80%. We validate our framework using a large collection of English and non-English flagged words extracted from three web-bas

Question: What is the difference between a human and a machine?


Characterizing Task Completion Latencies in Fog Computing. Fog computing, which distributes computing resources to multiple locations between the Internet of Things (IoT) devices and the cloud, is attracting considerable attention from academia and industry. Yet, despite the excitement about the potential of fog computing, few comprehensive quantitative characteristics of the properties of fog computing architectures have been conducted. In this paper we examine the properties of task completion latencies in fog computing. First, we present the results of our empirical benchmarking-based study of task completion latencies. The study covered a range of settings, and uniquely considered both traditional and serverless fog computing execution points. It demonstrated the range of execution point characteristics in different locations and the relative stability of latency characteristics for a given location. It also highlighted properties of serverless execution that are not incorporated in existing fog computing algorithms. Second, we present a framework we developed for co-optimizing task completion quality and latency, which was inspired by the insights of our empirical study. We describe fog computing task assignment problems we formulated under this framework, and present the algorithms we developed for solving them.

Question: What is the fog computing architecture?


Robust Mapping and Localization in Indoor Environments Using Sonar Data. In this paper we describe a new technique for the creation of featurebased stochastic maps using standard Polaroid sonar sensors. The fundamental contributions of our proposal are: (1) a perceptual grouping process that permits the robust identification and localization of environmental features, such as straight segments and corners, from the sparse and noisy sonar data; (2) a map joining technique that allows the system to build a sequence of independent limited-size stochastic maps and join them in a globally consistent way; (3) a robust mechanism to determine which features in a stochastic map correspond to the same environment feature, allowing the system to update the stochastic map accordingly, and perform tasks such as revisiting and loop closing. We demonstrate the practicality of this approach by building a geometric map of a medium size, real indoor environment, with several people moving around the robot. Maps built from laser data for the same experiment are provided for comparison. KEY WORDS—map building, local maps, data association, sonar sensors, Hough transform

Question: How to build a map of an indoor environment?


Modular Forms of Weight One. The first three sections are preliminary. The fourth section contains the statement of the main theorem and some corollaries. The proof occupies sections 5 through 9. The idea is as follows: one begins by constructing, for each prime number `, a representation of Gal(Q/Q) in characteristic ` (see §6); we then show that the image of Gal(Q/Q) is "small" under each of these representations, which permits us to lift this to characteristic 0 and obtain the complex representation we seek. The "smallness" in question itself results from an increase of the average of the eigenvalues of the Hecke operators (Rankin, §5). Section 9 contains an estimate of the coefficients of the modular forms of weight one. We note that we have used an essential point (§6, Thm. 6.1) of some results demonstrated by one of us (P. Deligne), but of which no complete proof has been published; this proof should appear in SGA 5, so we ask that the reader accept this for now.

Question: What is the modular forms of weight one?


Self-organization patterns in wasp and open source communities. In this paper, we conducted a comparative study of how social organization takes place in a wasp colony and OSS developer communities. Both these systems display similar global organization patterns, such as hierarchies and clear labor divisions. As our analysis shows, both systems also define interacting agent networks with similar common features that reflect limited information sharing among agents. As far as we know, this is the first research study analyzing the patterns and functional significance of these systems' weighted-interaction networks. By illuminating the extent to which self-organization is responsible for patterns such as hierarchical structure, we can gain insight into the origins of organization in OSS communities.

Question: What is the difference between a wasp colony and an open source community?


Software project management with GAs. A Project Scheduling Problem consists in deciding who does what during the software project lifetime. This is a capital issue in the practice of software engineering, since the total budget and human resources involved must be managed optimally in order to end in a successful project. In short, companies are principally concerned with reducing the duration and cost of projects, and these two goals are in conflict with each other. In this work we tackle the problem by using genetic algorithms (GAs) to solve many different software project scenarios. Thanks to our newly developed instance generator we can perform structured studies on the influence the most important problem attributes have on the solutions. Our conclusions show that GAs are quite flexible and accurate for this application, and an important tool for automatic project management. 2007 Elsevier Inc. All rights reserved.

Question: What is a software project?


Cascaded Random Forest for Fast Object Detection. A Random Forest consists of several independent decision trees arranged in a forest. A majority vote over all trees leads to the final decision. In this paper we propose a Random Forest framework which incorporates a cascade structure consisting of several stages together with a bootstrap approach. By introducing the cascade, 99% of the test images can be rejected by the first and second stage with minimal computational effort leading to a massively speeded-up detection framework. Three different cascade voting strategies are implemented and evaluated. Additionally, the training and classification speed-up is analyzed. Several experiments on public available datasets for pedestrian detection, lateral car detection and unconstrained face detection demonstrate the benefit of our contribution.

Question: What is a random forest?


SVM Soft Margin Classifiers: Linear Programming versus Quadratic Programming. Support vector machine (SVM) soft margin classifiers are important learning algorithms for classification problems. They can be stated as convex optimization problems and are suitable for a large data setting. Linear programming SVM classifiers are especially efficient for very large size samples. But little is known about their convergence, compared with the well-understood quadratic programming SVM classifier. In this article, we point out the difficulty and provide an error analysis. Our analysis shows that the convergence behavior of the linear programming SVM is almost the same as that of the quadratic programming SVM. This is implemented by setting a stepping-stone between the linear programming SVM and the classical 1-norm soft margin classifier. An upper bound for the misclassification error is presented for general probability distributions. Explicit learning rates are derived for deterministic and weakly separable distributions, and for distributions satisfying some Tsybakov noise condition.

Question: What is the difference between linear programming and quadratic programming?


Refining Geometry from Depth Sensors using IR Shading Images. We propose a method to refine geometry of 3D meshes from a consumer level depth camera, e.g. Kinect, by exploiting shading cues captured from an infrared (IR) camera. A major benefit to using an IR camera instead of an RGB camera is that the IR images captured are narrow band images that filter out most undesired ambient light, which makes our system robust against natural indoor illumination. Moreover, for many natural objects with colorful textures in the visible spectrum, the subjects appear to have a uniform albedo in the IR spectrum. Based on our analyses on the IR projector light of the Kinect, we define a near light source IR shading model that describes the captured intensity as a function of surface normals, albedo, lighting direction, and distance between light source and surface points. To resolve the ambiguity in our model between the normals and distances, we utilize an initial 3D mesh from the Kinect fusion and multi-view information to reliably estimate surface details that were not captured and reconstructed by the Kinect fusion. Our approach directly operates on the mesh model for geometry refinement. We ran experiments on our algorithm for geometries captured by both the Kinect I and Kinect II, as the depth acquisition in Kinect I is based on a structured-light technique and that of the Kinect II is based on a time-of-flight technology. The effectiveness of our approach is demonstrated through several challenging real-world examples. We have also performed a user study to evaluate the quality of the mesh models before and after our refinements.

Question: What is the difference between the Kinect I and Kinect II?


SDN Multi-Domain Orchestration and Control: Challenges and innovative future directions. This paper proposes a hierarchical Software-Defined Networking (SDN) Multi-Domain Orchestration and Control architectural framework to address complex multi-tier governance domain deployments by Carriers, Service Providers (SP), Cloud Service Providers (CSP), large Enterprises, and Network Functions Virtualization (NFV) Infrastructure/Platform as a Service (NFVIaaS/NFVPaaS) providers. The proposed framework allows for simpler networking control and orchestration, more agile development processes, accelerated pace of innovation, and increased speed of deployment, while improving scalability and configuration automation as compared to existing frameworks.

Question: What is SDN?


The role of satellites in 5G. The next generation of mobile radio communication systems - so called 5G - will provide some major changes to those generations to date. The ability to cope with huge increases in data traffic at reduced latencies and improved quality of user experience together with major reduction in energy usage are big challenges. In addition future systems will need to embody connections to billions of objects - the so called Internet of Things (IoT) which raise new challenges. Visions of 5G are now available from regions across the World and research is ongoing towards new standards. The consensus is a flatter architecture that adds a dense network of small cells operating in the millimetre wave bands and which are adaptable and software controlled. But what place for satellites in such a vision? The paper examines several potential roles for satellite including coverage extension, content distribution, providing resilience, improved spectrum utilisation and integrated signalling systems.

Question: What is the role of satellites in 5G?


Early Detection of Sudden Pedestrian Crossing for Safe Driving During Summer Nights. Sudden pedestrian crossing (SPC) is the major reason for pedestrian-vehicle crashes. In this paper, we focus on detecting SPCs at night for supporting an advanced driver assistance system using a far-infrared (FIR) camera mounted on the front roof of a vehicle. Although the thermal temperature of the road is similar to or higher than that of the pedestrians during summer nights, many previous researches have focused on pedestrian detection during the winter, spring, or autumn seasons. However, our research concentrates on SPC during the hot summer season because the number of collisions between pedestrians and vehicles in Korea is higher at that time than during the other seasons. For real-time processing, we first decide the optimal levels of image scaling and search area. We then use our proposed method for detecting virtual reference lines that are associated with road segmentation without using color information and change these lines according to the turning direction of the vehicle. Pedestrian detection is conducted using a cascade random forest with low-dimensional Haar-like features and oriented center-symmetric local binary patterns. The SPC is predicted based on the likelihood and the spatiotemporal features of the pedestrians, such as their overlapping ratio with virtual reference lines, as well as the direction and magnitude of each pedestrian's movement. The proposed algorithm was successfully applied to various pedestrian data sets captured by an FIR camera, and the results show that its SPC detection performance is better than those of other methods.

Question: How to detect sudden pedestrian crossing?


A Statistical Nonparametric Approach of Face Recognition: Combination of Eigenface & Modified k-Means Clustering. Keywords: Eigenface, face Recognition, k-means clustering, principle component analysis (PCA)

Question: What is the difference between eigenface and k-means clustering?


Using Ground Reaction Forces from Gait Analysis: Body Mass as a Weak Biometric. Ground reaction forces generated during normal walking have recently been used to identify and/or classify individuals based upon the pattern of the forces observed over time. One feature that can be extracted from vertical ground reaction forces is body mass. This single feature has identifying power comparable to other studies that use multiple and more complex features. This study contributes to understanding the role of body mass in identification by (1) quantifying the accuracy and precision with which body mass can be obtained using vertical ground reaction forces, (2) quantifying the distribution of body mass across a population larger than has previously been studied in relation to gait analysis, and (3) quantifying the expected identification capabilities of systems using body mass as a weak biometric. Our results show that body mass can be measured in a fraction of a second with less than a 1 kilogram standard deviation of error.

Question: How accurate is body mass as a biometric?


A high-performance silicon-on-insulator MEMS gyroscope operating at atmospheric pressure. This paper presents a new, high-performance silicon-on-insulator (SOI) MEMS gyroscope with decoupled oscillation modes. The gyroscope tructure allows it to achieve matched-resonance-frequencies, large drive-mode oscillation amplitude, high sense-mode quality factor, and low echanical cross-talk. The gyroscope is fabricated through the commercially available SOIMUMPS process of MEMSCAP Inc. The fabricated yroscope has minimum capacitive sense gaps of 2.6 m and a structural silicon thickness of 25 m, and it fits into a chip area smaller than mm × 3 mm. The fabricated gyroscope is hybrid connected to a CMOS capacitive interface ASIC chip, which is fabricated in a standard 0.6 m MOS process. The characterization of the hybrid-connected gyroscope demonstrates a low measured noise-equivalent rate of 90◦/h/Hz1/2 at tmospheric pressure, eliminating the need for a vacuum package for a number of applications. R2-non-linearity of the gyroscope is measured to e better than 0.02%. The gyroscope has a low quadrature signal of 70◦/s and a short-term bias stability of 1.5◦/s. The angular rate sensitivity of the yroscope is 100 V/(◦/s) at atmospheric pressure, which improves 24 times to 2.4 mV/(◦/s) at vacuum. The noise-equivalent rate of the gyroscope t 20 mTorr vacuum is measured to be 35◦/h/Hz1/2, which can be improved further by reducing the electromechanical noise. 2006 Elsevier B.V. All rights reserved.

Question: What is the noise-equivalent rate of a gyroscope?


Development of a helical climbing modular snake robot. This research aims to study the grasping torque profile of the helical climbing robot on the cylindrical pole with constant radius. The modular snake robot is formed into a helical shape which can be described by various parameters such as the helical pitch angle, the radius and the pitch distance. The torque in each axis of rotation is affected by the helical pitch angle parameter. Five grasping configurations with different helical pitch angle were tested on the 18 degree of freedoms, 7 modules wheeled snake robot. The experimental result showed that the torque around yaw axis transferred to the pitch axis when the helical pitch angle was increasing. The profile of torque magnitude along the robot's body resulted in parabolic shape due to the unbalanced grasping force of the discrete points of contact between the robot and the pole.

Question: What is the helical pitch angle?


Speed measurement algorithm for low speed permanent magnet synchronous motor based on overlapped measurement regions. In order to improve the speed measurement performance of low speed PMSM drives equipped with absolute optical encoders, this paper proposes a speed measurement method based on overlapped measurement regions. The proposed method replaces the long measurement region of the conventional backward differentiation method with several short ones which are overlapped at most regions. In one speed measurement process, the results obtained from the short regions are averaged to improve precision. Then the relationship between precision, time delay and region length, overlap times, are analyzed using expectation and variance as performance index. Based on the analysis, an optimal design method of the measurement region is derived. Experimental researches are carried out on a low speed permanent magnet synchronous motor equipped with a 13 bit absolute encoder. The results prove the validity and effectiveness of the proposed method.

Question: How to measure the speed of a low speed PMSM?


Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences. We provide two fundamental results on the population (infinite-sample) likelihood function of Gaussian mixture models with M ≥ 3 components. Our first main result shows that the population likelihood function has bad local maxima even in the special case of equally-weighted mixtures of well-separated and spherical Gaussians. We prove that the log-likelihood value of these bad local maxima can be arbitrarily worse than that of any global optimum, thereby resolving an open question of Srebro [21]. Our second main result shows that the EM algorithm (or a first-order variant of it) with random initialization will converge to bad critical points with probability at least 1 − e. We further establish that a first-order variant of EM will not converge to strict saddle points almost surely, indicating that the poor performance of the first-order method can be attributed to the existence of bad local maxima rather than bad saddle points. Overall, our results highlight the necessity of careful initialization when using the EM algorithm in practice, even when applied in highly favorable settings.

Question: What is the difference between a local maximum and a local minimum?


The comparability of measurements of attitudes toward immigration in the European Social Survey : exact versus approximate measurement equivalence. International survey datasets are analyzed with increasing frequency to investigate and compare attitudes toward immigration and to examine the contextual factors that shape these attitudes. However, international comparisons of abstract, psychological constructs require the measurements to be equivalent–i.e., they should measure the same concept on the same measurement scale. Traditional approaches to assessing measurement equivalence quite often lead to the conclusion that measurements are cross-nationally incomparable but have been criticized for being overly strict. In the current study, we present an alternative Bayesian approach that assesses whether measurements are approximately (rather than exactly) equivalent. This approach allows small variations in measurement parameters across groups. Taking a multiple group confirmatory factor analysis framework as a starting point, this study applies approximate and exact equivalence tests to the anti-immigration attitudes scale that was implemented in the European Social Survey (ESS). Measurement equivalence is tested across the full set of 271,220 individuals in 35 ESS countries over six rounds. The results of the exact and the approximate approaches are quite different. Approximate scalar measurement equivalence is established in all ESS rounds, thus allowing researchers to meaningfully compare these mean scores and their relationships with other theoretical constructs of interest. The exact approach, however, eventually proves to be overly strict and leads to the conclusion that measurements are incomparable for a large number of countries and time points. DOI: https://doi.org/10.1093/poq/nfv008 Posted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: https://doi.org/10.5167/uzh-110767 Published Version Originally published at: Davidov, Eldad; C

Question: What is the difference between exact and approximate equivalence?


Tuning metaheuristics: A data mining based approach for particle swarm optimization. The paper is concerned with practices for tuning the parameters of metaheuristics. Settings such as, e.g., the cooling factor in simulated annealing, may greatly affect a metaheuristic's efficiency as well as effectiveness in solving a given decision problem. However, procedures for organizing parameter calibration are scarce and commonly limited to particular metaheuristics. We argue that the parameter selection task can appropriately be addressed by means of a data mining based approach. In particular, a hybrid system is devised, which employs regression models to learn suitable parameter values from past moves of a metaheuristic in an online fashion. In order to identify a suitable regression method and, more generally, to demonstrate the feasibility of the proposed approach, a case study of particle swarm optimization is conducted. Empirical results suggest that characteristics of the decision problem as well as search history data indeed embody information that allows suitable parameter values to be determined, and that this type of information can successfully be extracted by means of nonlinear regression models. 2011 Elsevier Ltd. All rights reserved.

Question: What is a metaheuristic?


The 3D linear inverted pendulum mode: a simple modeling for a biped walking pattern generation. F or D walkingcontr ol of a biped robot we analyze the dynamics of a three dimensional inverted pendu lum in which motion is constr aine d to move along an arbitr arily de ned plane This analysis leads us a sim ple line ar dynamics the Thr ee Dimensional Linear In verte d Pendulum Mode D LIPM Geometric nature of trajectories under the D LIPM and a method for walking pattern gener ationare discusse d A simula tion r esult of a walking contr ol using a d o f biped robot model is also shown

Question: What is the geometric nature of trajectories under the D LIPM?


Vehicle location finder using Global position system and Global System for Mobile. Each year, the number of stolen vehicle is on the rise. Usually, to prevent theft, a physical type countermeasure is used such as padlock, disk break lock and other more which is a preventive action but it is not enough safe. The objective of this study is to create a controllable system that can display the location of a vehicle using Global position system (GPS) to pin point the location and Global System for Mobile (GSM) as a mean for communicating with the vehicle for ease of finding after a theft attempt. The system is made to test the accuracy of the location that is send to the user when the vehicle is in motion and stationary in the city and suburb. The system is made by combining a micro controller with GPS and GSM, then comparing it with other similar device available in the market like Garmin and a reference website to find the radius of error. The study of proposed device begins by studying IEEE journal about alternative product and the vehicle itself. The hardware and program development is done by research and trial and error as the controller do not interact with both module at the same time, after successfully programming both module, it is combined into a single program with addition of interrupt program. The experiment is done in three set of tests so that the system accuracy can be determine when stationary and in motion on vehicle, output controlling is the test to determine if the controller can be made into anti-theft system. The result of the test concludes that the system can provide standard GPS coordinate when requested via Short Message Service (SMS). The system can also be used to control an actuator.

Question: How to find the location of a vehicle using Global position system and Global System for Mobile?


Adoption and use of Java generics. Support for generic programming was added to the Java language in 2004, representing perhaps the most significant change to one of the most widely used programming languages today. Researchers and language designers anticipated this addition would relieve many long-standing problems plaguing developers, but surprisingly, no one has yet measured how generics have been adopted and used in practice. In this paper, we report on the first empirical investigation into how Java generics have been integrated into open source software by automatically mining the history of 40 popular open source Java programs, traversing more than 650 million lines of code in the process. We evaluate five hypotheses and research questions about how Java developers use generics. For example, our results suggest that generics sometimes reduce the number of type casts and that generics are usually adopted by a single champion in a project, rather than all committers. We also offer insights into why some features may be adopted sooner and others features may be held back.

Question: How many lines of code are in the java language?


A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification. Convolutional Neural Networks (CNNs) have recently achieved remarkably strong performance on sentence classification tasks (Kim, 2014; Kalchbrenner et al., 2014). However, these models require practitioners to specify the exact model architecture and accompanying hyperparameters, e.g., the choice of filter region size, regularization parameters, and so on. It is currently unknown how sensitive model performance is to changes in these configurations for the task of sentence classification. We thus conduct a sensitivity analysis of one-layer CNNs to explore the effect of architecture components on model performance; our aim is to distinguish between important and comparatively inconsequential design decisions for sentence classification. We focus on one-layer CNNs (to the exclusion of more complex models) due to their comparative simplicity and strong empirical performance (Kim, 2014). We derive practical advice from our extensive empirical results for those interested in getting the most out of CNNs for sentence classification. One important observation borne out by our experimental results is that researchers should report performance variances, as these can be substantial due to stochastic initialization and inference.

Question: What is the sensitivity of the performance of a convolutional neural network for sentence classification?


Texture Segmentation Based Video Compression Using Convolutional Neural Networks. There has been a growing interest in using different approaches to improve the coding efficiency of modern video codec in recent years as demand for web-based video consumption increases. In this paper, we propose a model-based approach that uses texture analysis/synthesis to reconstruct blocks in texture regions of a video to achieve potential coding gains using the AV1 codec developed by the Alliance for Open Media (AOM). The proposed method uses convolutional neural networks to extract texture regions in a frame, which are then reconstructed using a global motion model. Our preliminary results show an increase in coding efficiency while maintaining satisfactory visual quality. Introduction With the increasing amount of videos being created and consumed, better video compression tools are needed to provide fast transmission and high visual quality. Modern video coding standards utilize spatial and temporal redundancy in the videos to achieve high coding efficiency and high visual quality with motion compensation techniques and 2-D orthogonal transforms. However, efficient exploitation of statistical dependencies measured by a mean squared error (MSE) does not always produce the best psychovisual result, and may require higher data rate to preserve detail information in the video. Recent advancement in GPU computing has enabled the analysis of large scale data using deep learning method. Deep learning techniques have shown promising performance in many applications such as object detection, natural language process, and synthetic images generation [1, 2, 3, 4]. Several methods have been developed for video applications to improve coding efficiency using deep learning. In [5], sample adaptive offset (SAO) is replaced by a CNN-based in-loop filter (IFCNN) to improve the coding efficiency in HEVC. By learning the predicted residue between the quantized reconstructed frames obtained after

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


Cache'n DASH: Efficient Caching for DASH. HTTP-based video streaming services have been dominating the global IP traffic over the last few years. Caching of video content reduces the load on the content servers. In the case of Dynamic Adaptive Streaming over HTTP (DASH), for every video the server needs to host multiple representations of the same video file. These individual representations are further broken down into smaller segments. Hence, for each video the server needs to host thousands of segments out of which, the client downloads a subset of the segments. Also, depending on the network conditions, the adaptation scheme used at the client-end might request a different set of video segments (varying in bitrate) for the same video. The caching of DASH videos presents unique challenges. In order to optimize the cache hits and minimize the misses for DASH video streaming services we propose an Adaptation Aware Cache (AAC) framework to determine the segments that are to be prefetched and retained in the cache. In the current scheme, we use bandwidth estimates at the cache server and the knowledge of the rate adaptation scheme used by the client to estimate the next segment requests, thus improving the prefetching at the cache.

Question: How to cache DASH videos?


Fingerprint image enhancement using GWT and DMF. Fingerprint image enhancement is an essential preprocessing step in fingerprint recognition applications. In this paper we introduce an approach that extracts simultaneously orientation and frequency of local ridge in the fingerprint image by Gabor wavelet filter bank and use them in Gabor Filtering of image. Furthermore, we describes a robust approach to fingerprint image enhancement, which is based on integration of Gabor Filters and Directional Median Filter(DMF). In fact, Gaussian-distributed noises are reduced effectively by Gabor Filters and impulse noises by DMF. the proposed DMF not only can finish its original tasks, it can also join broken fingerprint ridges, fill out the holes of fingerprint images, smooth irregular ridges as well as remove some annoying small artifacts between ridges. Experimental results show our method to be superior to those described in the literature.

Question: How to enhance fingerprint image?


The EM Side-Channel(s). We present results of a systematic investigation of leakage of compromising information via electromagnetic (EM) emanations from CMOS devices. These emanations are shown to consist of a multiplicity of signals, each leaking somewhat different information about the underlying computation. We show that not only can EM emanations be used to attack cryptographic devices where the power side–channel is unavailable, they can even be used to break power analysis countermeasures.

Question: What is the EM side-channel?


Image and video abstraction by multi-scale anisotropic Kuwahara filtering. The anisotropic Kuwahara filter is an edge-preserving filter that is especially useful for creating stylized abstractions from images or videos. It is based on a generalization of the Kuwahara filter that is adapted to the local structure of image features. In this work, two limitations of the anisotropic Kuwahara filter are addressed. First, it is shown that by adding thresholding to the weighting term computation of the sectors, artifacts are avoided and smooth results in noise-corrupted regions are achieved. Second, a multi-scale computation scheme is proposed that simultaneously propagates local orientation estimates and filtering results up a low-pass filtered pyramid. This allows for a strong abstraction effect and avoids artifacts in large low-contrast regions. The propagation is controlled by the local variances and anisotropies that are derived during the computation without extra overhead, resulting in a highly efficient scheme that is particularly suitable for real-time processing on a GPU.

Question: What is the anisotropic Kuwahara filter?


Curvature-based regularization for surface approximation. We propose an energy-based framework for approximating surfaces from a cloud of point measurements corrupted by noise and outliers. Our energy assigns a tangent plane to each (noisy) data point by minimizing the squared distances to the points and the irregularity of the surface implicitly defined by the tangent planes. In order to avoid the well-known "shrinking" bias associated with first-order surface regularization, we choose a robust smoothing term that approximates curvature of the underlying surface. In contrast to a number of recent publications estimating curvature using discrete (e.g. binary) labellings with triple-cliques we use higher-dimensional labels that allows modeling curvature with only pair-wise interactions. Hence, many standard optimization algorithms (e.g. message passing, graph cut, etc) can minimize the proposed curvature-based regularization functional. The accuracy of our approach for representing curvature is demonstrated by theoretical and empirical results on synthetic and real data sets from multiview reconstruction and stereo.

Question: How to model curvature?


Improving Document Clustering for Short Texts by Long Documents via a Dirichlet Multinomial Allocation Model. Document clustering for short texts has received considerable interest. Traditional document clustering approaches are designed for long documents and perform poorly for short texts due to the their sparseness representation. To better understand short texts, we observe that words that appear in long documents can enrich short text context and improve the clustering performance for short texts. In this paper, we propose a novel model, namely DDMAfs, which 1) improves the clustering performance of short texts by sharing structural knowledge of long documents to short texts; 2) automatically identifies the number of clusters; 3) separates discriminative words from irrelevant words for long documents to obtain high quality structural knowledge. Our experiments indicate that the DDMAfs model performs well on the synthetic dataset and real datasets. Comparisons between the DDMAfs model and state-of-the-art short text clustering approaches show that the DDMAfs model is effective.

Question: How to improve document clustering for short texts?


Learning user interaction models for predicting web search result preferences. Evaluating user preferences of web search results is crucial for search engine development, deployment, and maintenance. We present a real-world study of modeling the behavior of web search users to predict web search result preferences. Accurate modeling and interpretation of user behavior has important applications to ranking, click spam detection, web search personalization, and other tasks. Our key insight to improving robustness of interpreting implicit feedback is to model query-dependent deviations from the expected "noisy" user behavior. We show that our model of clickthrough interpretation improves prediction accuracy over state-of-the-art clickthrough methods. We generalize our approach to model user behavior beyond clickthrough, which results in higher preference prediction accuracy than models based on clickthrough information alone. We report results of a large-scale experimental evaluation that show substantial improvements over published implicit feedback interpretation methods.

Question: How to predict web search result preferences?


Learning Semantic Deformation Flows with 3D Convolutional Networks. Shape deformation requires expert user manipulation even when the object under consideration is in a high fidelity format such as a 3D mesh. It becomes even more complicated if the data is represented as a point set or a depth scan with significant self occlusions. We introduce an end-to-end solution to this tedious process using a volumetric Convolutional Neural Network (CNN) that learns deformation flows in 3D. Our network architectures take the voxelized representation of the shape and a semantic deformation intention (e.g., make more sporty) as input and generate a deformation flow at the output. We show that such deformation flows can be trivially applied to the input shape, resulting in a novel deformed version of the input without losing detail information. Our experiments show that the CNN approach achieves comparable results with state of the art methods when applied to CAD models. When applied to single frame depth scans, and partial/noisy CAD models we achieve ∼60% less error compared to the state-of-the-art.

Question: How to make a 3D mesh more sporty?


Analysis, synthesis, and perception of voice quality variations among female and male talkers.. Voice quality variations include a set of voicing sound source modifications ranging from laryngealized to normal to breathy phonation. Analysis of reiterant imitations of two sentences by ten female and six male talkers has shown that the potential acoustic cues to this type of voice quality variation include: (1) increases to the relative amplitude of the fundamental frequency component as open quotient increases; (2) increases to the amount of aspiration noise that replaces higher frequency harmonics as the arytenoids become more separated; (3) increases to lower formant bandwidths; and (4) introduction of extra pole zeros in the vocal-tract transfer function associated with tracheal coupling. Perceptual validation of the relative importance of these cues for signaling a breathy voice quality has been accomplished using a new voicing source model for synthesis of more natural male and female voices. The new formant synthesizer, KLSYN88, is fully documented here. Results of the perception study indicate that, contrary to previous research which emphasizes the importance of increased amplitude of the fundamental component, aspiration noise is perceptually most important. Without its presence, increases to the fundamental component may induce the sensation of nasality in a high-pitched voice. Further results of the acoustic analysis include the observations that: (1) over the course of a sentence, the acoustic manifestations of breathiness vary considerably--tending to increase for unstressed syllables, in utterance-final syllables, and at the margins of voiceless consonants; (2) on average, females are more breathy than males, but there are very large differences between subjects within each gender; (3) many utterances appear to end in a "breathy-laryngealized" type of vibration; and (4) diplophonic irregularities in the timing of glottal periods occur frequently, especi

Question: What is the difference between a male and female voice?


Emotion regulation for frustrating driving contexts. Driving is a challenging task because of the physical, attentional, and emotional demands. When drivers become frustrated by events their negative emotional state can escalate dangerously. This study examines behavioral and attitudinal effects of cognitively reframing frustrating events. Participants (N = 36) were asked to navigate a challenging driving course that included frustrating events such as long lights and being cut-off. Drivers were randomly assigned to three conditions. After encountering a frustrating event, drivers in a reappraisal-down condition heard voice prompts that reappraised the event in an effort to deflate negative reactions. Drivers in the second group, reappraisal-up, heard voice prompts that brought attention to the negative actions of vehicles and pedestrians. Drivers in a silent condition drove without hearing any voice prompts. Participants in the reappraisal-down condition had better driving behavior and reported less negative emotions than participants in the other conditions.

Question: How can we improve our driving?


A Rank Minimization Heuristic with Application to Minimum Order System Approximation. Several problems arising in control system analysis and design, such as reduced order controller synthesis, involve minimizing the rank of a matrix variable subject to linear matrix inequality (LMI) constraints. Except in some special cases, solving this rank minimization problem (globally) is very difficult. One simple and surprisingly effective heuristic, applicable when the matrix variable is symmetric and positive semidefinite, is to minimize its trace in place of its rank. This results in a semidefinite program (SDP) which can be efficiently solved. In this paper we describe a generalization of the trace heuristic that applies to general nonsymmetric, even non-square, matrices, and reduces to the trace heuristic when the matrix is positive semidefinite. The heuristic is to replace the (nonconvex) rank objective with the sum of the singular values of the matrix, which is the dual of the spectral norm. We show that this problem can be reduced to an SDP, hence efficiently solved. To motivate the heuristic, we show that the dual spectral norm is the convex envelope of the rank on the set of matrices with norm less than one. We demonstrate the method on the problem of minimum order system approximation.

Question: What is the convex envelope of the rank?


Cross-lingual Argumentation Mining: Machine Translation (and a bit of Projection) is All You Need!. Argumentation mining (AM) requires the identification of complex discourse structures and has lately been applied with success monolingually. In this work, we show that the existing resources are, however, not adequate for assessing cross-lingual AM, due to their heterogeneity or lack of complexity. We therefore create suitable parallel corpora by (human and machine) translating a popular AM dataset consisting of persuasive student essays into German, French, Spanish, and Chinese. We then compare (i) annotation projection and (ii) bilingual word embeddings based direct transfer strategies for cross-lingual AM, finding that the former performs considerably better and almost eliminates the loss from cross-lingual transfer. Moreover, we find that annotation projection works equally well when using either costly human or cheap machine translations. Our code and data are available at http://github.com/UKPLab/ coling2018-xling_argument_mining.

Question: What is the difference between monolingual and cross-lingual argumentation mining?


Biomimetic design through natural language analysis to facilitate cross-domain information retrieval. Biomimetic, or biologically inspired, design uses analogous biological phenomena to develop solutions for engineering problems. Several instances of biomimetic design result from personal observations of biological phenomena. However, many engineers' knowledge of biology may be limited, thus reducing the potential of biologically inspired solutions. Our approach to biomimetic design takes advantage of the large amount of biological knowledge already available in books, journals, and so forth, by performing keyword searches on these existing natural-language sources. Because of the ambiguity and imprecision of natural language, challenges inherent to natural language processing were encountered. One challenge of retrieving relevant cross-domain information involves differences in domain vocabularies, or lexicons. A keyword meaningful to biologists may not occur to engineers. For an example problem that involved cleaning, that is, removing dirt, a biochemist suggested the keyword "defend." Defend is not an obvious keyword to most engineers for this problem, nor are the words defend and "clean0remove" directly related within lexical references. However, previous work showed that biological phenomena retrieved by the keyword defend provided useful stimuli and produced successful concepts for the clean0remove problem. In this paper, we describe a method to systematically bridge the disparate biology and engineering domains using natural language analysis. For the clean0remove example, we were able to algorithmically generate several biologically meaningful keywords, including defend, that are not obviously related to the engineering problem. We developed a method to organize and rank the set of biologically meaningful keywords identified, and confirmed that we could achieve similar results for two other examples in encapsulation and microassembly. Although we specificall

Question: How large is the canadian military?


The impact of design management and process management on quality : an empirical investigation. Ž . Design management and process management are two important elements of total quality management TQM implementation. They are drastically different in their targets of improvement, visibility, and techniques. In this paper, we establish a framework for identifying the synergistic linkages of design and process management to the operational quality Ž . Ž . outcomes during the manufacturing process internal quality and upon the field usage of the products external quality . Through a study of quality practices in 418 manufacturing plants from multiple industries, we empirically demonstrate that both design and process management efforts have an equal positive impact on internal quality outcomes such as scrap, rework, defects, performance, and external quality outcomes such as complaints, warranty, litigation, market share. A detailed contingency analysis shows that the proposed model of synergies between design and process management holds true for large and small firms; for firms with different levels of TQM experience; and in different industries with varying levels of competition, logistical complexity of production, or production process characteristics. Finally, the results also suggest that organizational learning enables mature TQM firms to implement both design and process efforts more rigorously and their synergy helps these firms to attain better quality outcomes. These findings indicate that, to attain superior quality outcomes, firms need to balance their design and process management efforts and persevere with long-term Ž . implementation of these efforts. Because the study spans all of the manufacturing sectors SIC 20 through 39 , these conclusions should help firms in any industry revisit their priorities in terms of the relative efforts in design management and process management. q 2000 Elsevier Science B.V. All rights reserved.

Question: What is the impact of design management and process management on quality?


Ensemble Classifiers for Network Intrusion Detection System. Two of the major challenges in designing anomaly intrusion detection are to maximize detection accuracy and to minimize false alarm rate. In addressing this issue, this paper proposes an ensemble of one-class classifiers where each adopts different learning paradigms. The techniques deployed in this ensemble model are; Linear Genetic Programming (LGP), Adaptive Neural Fuzzy Inference System (ANFIS) and Random Forest (RF). The strengths from the individual models were evaluated and ensemble rule was formulated. Prior to classification, a 2-tier feature selection process was performed to expedite the detection process. Empirical results show an improvement in detection accuracy for all classes of network traffic; Normal, Probe, DoS, U2R and R2L. Random Forest, which is an ensemble learning technique that generates many classification trees and aggregates the individual result was also able to address imbalance dataset problem that many of machine learning techniques fail to sufficiently address it.

Question: What is the difference between ensemble and ensemble learning?


Pseudo test collections for training and tuning microblog rankers. Recent years have witnessed a persistent interest in generating pseudo test collections, both for training and evaluation purposes. We describe a method for generating queries and relevance judgments for microblog search in an unsupervised way. Our starting point is this intuition: tweets with a hashtag are relevant to the topic covered by the hashtag and hence to a suitable query derived from the hashtag. Our baseline method selects all commonly used hashtags, and all associated tweets as relevance judgments; we then generate a query from these tweets. Next, we generate a timestamp for each query, allowing us to use temporal information in the training process. We then enrich the generation process with knowledge derived from an editorial test collection for microblog search.  We use our pseudo test collections in two ways. First, we tune parameters of a variety of well known retrieval methods on them. Correlations with parameter sweeps on an editorial test collection are high on average, with a large variance over retrieval algorithms. Second, we use the pseudo test collections as training sets in a learning to rank scenario. Performance close to training on an editorial test collection is achieved in many cases. Our results demonstrate the utility of tuning and training microblog search algorithms on automatically generated training material.

Question: What is a pseudo test collection for training and tuning microblog rankers?


Diagnosis Code Prediction from Electronic Health Records as Multilabel Text Classification : A Survey. This article presents a survey on diagnosis code prediction from various information in Electronic Health Records (EHR): both unstructured free text and structured data. Particularly, our interests are in casting the problem as text classification with multiple sources and using neural network based models. We will first present previous work in this area and describe some simple baseline models for the relevant tasks.

Question: What is the diagnosis code prediction from electronic health records?


Predicting User Intentions: Comparing the Technology Acceptance Model with the Theory of Planned Behavior.

Question: What is the theory of planned behavior?


Map Matching with Inverse Reinforcement Learning. We study map-matching, the problem of estimating the route that is traveled by a vehicle, where the points observed with the Global Positioning System are available. A state-of-the-art approach for this problem is a Hidden Markov Model (HMM). We propose a particular transition probability between latent road segments by the use of the number of turns in addition to the travel distance between the latent road segments. We use inverse reinforcement learning to estimate the importance of the number of turns relative to the travel distance. This estimated importance is incorporated in the transition probability of the HMM. We show, through numerical experiments, that the error of map-matching can be reduced substantially with the proposed transition probability.

Question: How to estimate the importance of the number of turns relative to the travel distance?


Qualitative clinical evaluation of scapular dysfunction: a reliability study.. The purpose of this study was to determine the intrarater and interrater reliability of a clinical evaluation system for scapular dysfunction. No commonly accepted terminology presently exists for describing the abnormal dynamic scapular movement patterns that are commonly associated with shoulder injury. A method of observation was devised for clinical evaluation of scapular dysfunction. Blinded evaluators (2 physicians and 2 physical therapists) were familiarized with the evaluation method of scapular movement patterns before viewing a videotape of 26 subjects with and without scapular dysfunction. Each evaluator was asked to categorize the predominant scapular movement pattern observed during bilateral humeral scaption and abduction motions. Reliability was assessed by a kappa coefficient. Intertester reliability (kappa = 0.4) was found to be slightly lower than intratester reliability (kappa = 0.5). These results indicate that, with refinement, this qualitative evaluation method may allow clinicians to standardize the categorization of dynamic scapular dysfunction patterns.

Question: What is the purpose of this study?


Text and Data Mining Techniques in Adverse Drug Reaction Detection. We review data mining and related computer science techniques that have been studied in the area of drug safety to identify signals of adverse drug reactions from different data sources, such as spontaneous reporting databases, electronic health records, and medical literature. Development of such techniques has become more crucial for public heath, especially with the growth of data repositories that include either reports of adverse drug reactions, which require fast processing for discovering signals of adverse reactions, or data sources that may contain such signals but require data or text mining techniques to discover them. In order to highlight the importance of contributions made by computer scientists in this area so far, we categorize and review the existing approaches, and most importantly, we identify areas where more research should be undertaken.

Question: What is the importance of computer scientists in the area of drug safety?


Establishing rigour in qualitative research: the decision trail.. The aim of this paper is to show the way in which the decision trail of a qualitative research process can be maintained. It is argued that the trustworthiness (rigour) of a study may be established if the reader is able to audit the events, influences and actions of the researcher. The actual study containing the recording of this decision trail aimed to express the concerns of older patients who were admitted to the acute care sector. The study took place in two care of the elderly wards in a 1000-bed National Health Service hospital in the UK, in 1991. Eventually, 14 patients were interviewed, each on several occasions, and their concerns are expressed in themes, namely: routine geriatric style of care, depersonalization, care deprivation and geriatric segregation. I describe the preparations that were undertaken before patient interviews could commence. The literature recording the process of the interviewer's experience as data in qualitative research is scarce. I show the researcher's participation in making the data as part of an existential phenomenological research process. Existential phenomenology relies on recording influences while generating data such as significant literature, media reports, my value position and journal data.

Question: How can the decision trail of a qualitative research process be maintained?


Power divider in ridge gap waveguide technology. A power divider in ridge gap waveguide technology has been designed to work at 15GHz. Measurements and simulations are provided. Besides, a broad numerical study of the characteristic impedance of a ridge gap waveguide is presented.

Question: What is the characteristic impedance of a ridge gap waveguide?


Digital Beamforming on Receive: Techniques and Optimization Strategies for High-Resolution Wide-Swath SAR Imaging. Synthetic Aperture Radar (SAR) is a well-proven imaging technique for remote sensing of the Earth. However, conventional SAR systems are not capable of fulfilling the increasing demands for improved spatial resolution and wider swath coverage. To overcome these inherent limitations, several innovative techniques have been suggested which employ multiple receive-apertures to gather additional information along the synthetic aperture. These digital beamforming (DBF) on receive techniques are reviewed with particular emphasis on the multi-aperture signal processing in azimuth and a multi-aperture reconstruction algorithm is presented that allows for the unambiguous recovery of the Doppler spectrum. The impact of Doppler aliasing is investigated and an analytic expression for the residual azimuth ambiguities is derived. Further, the influence of the processing on the signal-to-noise ratio (SNR) is analyzed, resulting in a pulse repetition frequency (PRF) dependent factor describing the SNR scaling of the multi-aperture beamforming network. The focus is then turned to a complete high-resolution wide-swath SAR system design example which demonstrates the intricate connection between multi-aperture azimuth processing and the system architecture. In this regard, alternative processing approaches are compared with the multi-aperture reconstruction algorithm. In a next step, optimization strategies are discussed as pattern tapering, prebeamshaping-on-receive, and modified processing algorithms. In this context, the analytic expressions for both the residual ambiguities and the SNR scaling factor are generalized to cascaded beamforming networks. The suggested techniques can moreover be extended in many ways. Examples discussed are a combination with ScanSAR burst mode operation and the transfer to multistatic sparse array configurations.

Question: What is the difference between a radar and a sonar?


A Reconfigurable Antenna With Frequency and Polarization Agility. A new antenna with both frequency and polarization reconfigurability is presented. The antenna consists of a square microstrip patch with a single probe feed located along the diagonal line. The center of each edge of the patch is connected to a shorting post via a p-i-n diode for polarization switching and two varactor diodes for frequency tuning. By switching between the different states of the p-i-n diodes, the proposed antenna can produce radiation patterns with horizontal, vertical, or 45° linear polarization. By varying the dc bias voltage, the operating frequency of each polarization of the antenna can be independently tuned. The frequency tuning range is from 1.35 to 2.25 GHz (|S11| <; -10 dB) for either horizontal or vertical polarization and from 1.35 to 1.9 GHz for the 45° linear polarization. Measured results on frequency tuning ranges and radiation patterns agree well with numerical simulations.

Question: What is a reconfigurable antenna?


Modeling and animating eye blinks. Facial animation often falls short in conveying the nuances present in the facial dynamics of humans. In this article, we investigate the subtleties of the spatial and temporal aspects of eye blinks. Conventional methods for eye blink animation generally employ temporally and spatially symmetric sequences; however, naturally occurring blinks in humans show a pronounced asymmetry on both dimensions. We present an analysis of naturally occurring blinks that was performed by tracking data from high-speed video using active appearance models. Based on this analysis, we generate a set of key-frame parameters that closely match naturally occurring blinks. We compare the perceived naturalness of blinks that are animated based on real data to those created using textbook animation curves. The eye blinks are animated on two characters, a photorealistic model and a cartoon model, to determine the influence of character style. We find that the animated blinks generated from the human data model with fully closing eyelids are consistently perceived as more natural than those created using the various types of blink dynamics proposed in animation textbooks.

Question: How do you model eye blinks?


Survey of multi-objective optimization methods for engineering. Asurveyofcurrentcontinuousnonlinearmultiobjective optimization (MOO) concepts and methods is presented. It consolidates and relates seemingly different terminology and methods. The methods are divided into three major categories: methods with a priori articulation of preferences, methods with a posteriori articulation of preferences, and methods with no articulation of preferences. Genetic algorithms are surveyed as well. Commentary is provided on three fronts, concerning the advantages and pitfalls of individual methods, the different classes of methods, and the field of MOO as a whole. The Characteristics of the most significant methods are summarized. Conclusions are drawn that reflect often-neglected ideas and applicability to engineering problems. It is found that no single approach is superior. Rather, the selection of a specific method depends on the type of information that is provided in the problem, the user's preferences, the solution requirements, and the availability of software.

Question: What is a survey of multi-objective optimization methods for engineering?


Path-space manipulation of physically-based light transport. Industry-quality content creation relies on tools for lighting artists to quickly prototype, iterate, and refine final renders. As industry-leading studios quickly adopt physically-based rendering (PBR) across their art generation pipelines, many existing tools have become unsuitable as they address only simple effects without considering underlying PBR concepts and constraints. We present a novel light transport manipulation technique that operates directly on path-space solutions of the rendering equation. We expose intuitive direct and indirect manipulation approaches to edit complex effects such as (multi-refracted) caustics, diffuse and glossy indirect bounces, and direct/indirect shadows. With our sketch- and object-space selection, all built atop a parameterized regular expression engine, artists can search and isolate shading effects to inspect and edit. We classify and filter paths on the fly and visualize the selected transport phenomena. We survey artists who used our tool to manipulate complex phenomena on both static and animated scenes.

Question: How to manipulate physically-based light transport?


Analysis of user keyword similarity in online social networks. How do two people become friends? What role does homophily play in bringing two people closer to help them forge friendship? Is the similarity between two friends different from the similarity between any two people? How does the similarity between a friend of a friend compare to similarity between direct friends? In this work, our goal is to answer these questions. We study the relationship between semantic similarity of user profile entries and the social network topology. A user profile in an on-line social network is characterized by its profile entries. The entries are termed as user keywords. We develop a model to relate keywords based on their semantic relationship and define similarity functions to quantify the similarity between a pair of users. First, we present a 'forest model' to categorize keywords across multiple categorization trees and define the notion of distance between keywords. Second, we use the keyword distance to define similarity functions between a pair of users. Third, we analyze a set of Facebook data according to the model to determine the effect of homophily in on-line social networks. Based on our evaluations, we conclude that direct friends are more similar than any other user pair. However, the more striking observation is that except for direct friends, similarities between users are approximately equal, irrespective of the topological distance between them.

Question: What is the forest model?


Unsupervised prediction of citation influences. Publication repositories contain an abundance of information about the evolution of scientific research areas. We address the problem of creating a visualization of a research area that describes the flow of topics between papers, quantifies the impact that papers have on each other, and helps to identify key contributions. To this end, we devise a probabilistic topic model that explains the generation of documents; the model incorporates the aspects of topical innovation and topical inheritance via citations. We evaluate the model's ability to predict the strength of influence of citations against manually rated citations.

Question: How to predict citation influences?


The Design and Implementation of the Clouds Distributed Operating System. Clouds is a native operating system for a distributed environment. The Clouds operating system is built on top of a kernel called Ra. Ra ís a second generation kernel derived from our experience with the first version of the Clouds operating system. Rø is a minimal, flexible kernel that provides a framework for implementing a variety of distributed operating systems. This paper presents the Clouds paradigm and a brief overview of its first implementation. We then present the details of the Rø kernel, the rationale for its design, and the system services that constitute the Clouds operating system. This work was funded by NSF grant CCR-8619886. @ Computing Systems, Vol. 3 . No. I . Winter 1990 11

Question: What is the first version of the clouds operating system?


Augmented Virtual Environments (AVE): Dynamic Fusion of Imagery and 3D Models. An Augmented Virtual Environment (AVE) fuses dynamic imagery with 3D models. The AVE provides a unique approach to visualize and comprehend multiple streams of temporal data or images. Models are used as a 3D substrate for the visualization of temporal imagery, providing improved comprehension of scene activities. The core elements of AVE systems include model construction, sensor tracking, real-time video/image acquisition, and dynamic texture projection for 3D visualization. This paper focuses on the integration of these components and the results that illustrate the utility and benefits of the resulting augmented virtual environment.

Question: What is the difference between a virtual environment and an augmented virtual environment?


SimRank: a measure of structural-context similarity. The problem of measuring "similarity" of objects arises in many applications, and many domain-specific measures have been developed, e.g., matching text across documents or computing overlap among item-sets. We propose a complementary approach, applicable in any domain with object-to-object relationships, that measures similarity of the structural context in which objects occur, based on their relationships with other objects. Effectively, we compute a measure that says "two objects are similar if they are related to similar objects:" This general similarity measure, called SimRank, is based on a simple and intuitive graph-theoretic model. For a given domain, SimRank can be combined with other domain-specific similarity measures. We suggest techniques for efficient computation of SimRank scores, and provide experimental results on two application domains showing the computational feasibility and effectiveness of our approach.

Question: What is the simrank measure?


RCS Reduction of Patch Array Antenna by Electromagnetic Band-Gap Structure. In this letter, electromagnetic band-gap (EBG) structure is used to reduce the radar cross section (RCS) of the patch array antenna. The proposition of this method is based on the high impedance characteristic of the mushroom-like EBG structure. The basic patch array antenna is designed with a central frequency of 5.0 GHz while replacing the substrate of the array with the mushroom-like EBG structure. The frequency band in which RCS of the patch array antenna reduced significantly can be adjusted by parameters of the EBG. The backward RCS of the patch array antenna with EBG can be reduced as much as 10 dB compared to that of the conventional array antenna, and the degradation of the antenna performance is not significant.

Question: What is the radar cross section?


Overconfidence in IT Investment Decisions: Why Knowledge can be a Boon and Bane at the same Time. Despite their strategic relevance in organizations, information technology (IT) investments still result in alarmingly high failure rates. As IT investments are such a delicate high-risk/high-reward matter, it is crucial for organizations to avoid flawed IT investment decision-making. Previous research in consumer and organizational decision-making shows that a decision's accuracy is often influenced by decisionmakers' overconfidence and that the magnitude of overconfidence strongly depends on decision-makers' certainty of their knowledge. Drawing on these strands of research, our findings from a field survey (N=166) show that IT managers' decisions in IT outsourcing are indeed affected by overconfidence. However, an in-depth investigation of three types of knowledge, namely experienced, objective and subjective knowledge, reveals that different types of knowledge can have contrasting effects on overconfidence and thus on the quality of IT outsourcing decisions. Knowledge can be a boon and bane at the same time. Implications for research and practice are discussed.

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


An autonomous flyer photographer. In this paper we explore the combination of a latest generation mobile device and a micro quadrotor platform to perform indoor autonomous navigation for the purpose of autonomous photography. We use the Yellowstone tablet from Google's Tango project [1], equipped with onboard, fully integrated sensing platform and with significant computational capability. To the best of our knowledge we are the first to exploit the Google's Tango tablet as source of pose estimate to control the quadrotor's motion. Using the tablet's onboard camera the system is able to detect people and generate a desired pose that the quadrotor will have to reach in order to take a well framed picture of the detected subject. The experimental results and live video demonstrate the capabilities of the autonomous flying robot photographer using the system described throughout this manuscript.

Question: What is the name of the quadrotor platform?


Pareto Optimal Security Resource Allocation for Internet of Things. In many Internet of Thing (IoT) application domains security is a critical requirement, because malicious parties can undermine the effectiveness of IoT-based systems by compromising single components and/or communication channels. Thus, a security infrastructure is needed to ensure the proper functioning of such systems even under attack. However, it is also critical that security be at a reasonable resource and energy cost. In this article, we focus on the problem of efficiently and effectively securing IoT networks by carefully allocating security resources in the network area. In particular, given a set of security resources R and a set of attacks to be faced A, our method chooses the subset of R that best addresses the attacks in A, and the set of locations where to place them, that ensure the security coverage of all IoT devices at minimum cost and energy consumption. We model our problem according to game theory and provide a Pareto-optimal solution in which the cost of the security infrastructure, its energy consumption, and the probability of a successful attack are minimized. Our experimental evaluation shows that our technique improves the system robustness in terms of packet delivery rate for different network topologies. Furthermore, we also provide a method for handling the computation of the resource allocation plan for large-scale networks scenarios, where the optimization problem may require an unreasonable amount of time to be solved. We show how our proposed method drastically reduces the computing time, while providing a reasonable approximation of the optimal solution.

Question: What is the pareto optimal solution for the security resource allocation problem?


A Nonlinear Unified State-Space Model for Ship Maneuvering and Control in a Seaway. This article presents a unified state-space model for ship maneuvering, station-keeping, and control in a seaway. The frequency-dependent potential and viscous damping terms, which in classic theory results in a convolution integral not suited for real-time simulation, is compactly represented by using a state-space formulation. The separation of the vessel model into a low-frequency model (represented by zero-frequency added mass and damping) and a wave-frequency model (represented by motion transfer functions or RAOs), which is commonly used for simulation, is hence made superfluous.

Question: What is a seaway?


Conditional Generative Adversarial Nets. Generative Adversarial Nets [8] were recently introduced as a novel way to train generative models. In this work we introduce the conditional version of generative adversarial nets, which can be constructed by simply feeding the data, y, we wish to condition on to both the generator and discriminator. We show that this model can generate MNIST digits conditioned on class labels. We also illustrate how this model could be used to learn a multi-modal model, and provide preliminary examples of an application to image tagging in which we demonstrate how this approach can generate descriptive tags which are not part of training labels.

Question: How to generate MNIST digits conditioned on class labels?


Template-Based Information Extraction without the Templates. Standard algorithms for template-based information extraction (IE) require predefined template schemas, and often labeled data, to learn to extract their slot fillers (e.g., an embassy is the Target of a Bombing template). This paper describes an approach to template-based IE that removes this requirement and performs extraction without knowing the template structure in advance. Our algorithm instead learns the template structure automatically from raw text, inducing template schemas as sets of linked events (e.g., bombings include detonate, set off, and destroy events) associated with semantic roles. We also solve the standard IE task, using the induced syntactic patterns to extract role fillers from specific documents. We evaluate on the MUC-4 terrorism dataset and show that we induce template structure very similar to handcreated gold structure, and we extract role fillers with an F1 score of .40, approaching the performance of algorithms that require full knowledge of the templates.

Question: How to extract role fillers from specific documents?


Using Knowledge Representation and Reasoning Tools in the Design of Robots. The paper describes the authors' experience in using knowledge representation and reasoning tools in the design of robots. The focus is on the systematic construction of models of the robot's capabilities and its domain at different resolutions, and on establishing a clear relationship between the models at the different resolutions.

Question: What is the relationship between knowledge representation and reasoning tools and the design of robots?


A foundation for representing and querying moving objects. Spatio-temporal databases deal with geometries changing over time. The goal of our work is to provide a DBMS data model and query language capable of handling such time-dependent geometries, including those changing continuously that describe moving objects. Two fundamental abstractions are moving point and moving region, describing objects for which only the time-dependent position, or position and extent, respectively, are of interest. We propose to present such time-dependent geometries as attribute data types with suitable operations, that is, to provide an abstract data type extension to a DBMS data model and query language. This paper presents a design of such a system of abstract data types. It turns out that besides the main types of interest, moving point and moving region, a relatively large number of auxiliary data types are needed. For example, one needs a line type to represent the projection of a moving point into the plane, or a "moving real" to represent the time-dependent distance of two points. It then becomes crucial to achieve (i) orthogonality in the design of the system, i.e., type constructors can be applied unifomly; (ii) genericity and consistency of operations, i.e., operations range over as many types as possible and behave consistently; and (iii) closure and consistency between structure and operations of nontemporal and related temporal types. Satisfying these goal leads to a simple and expressive system of abstract data types that may be integrated into a query language to yield a powerful language for querying spatio-temporal data, including moving objects. The paper formally defines the types and operations, offers detailed insight into the considerations that went into the design, and exemplifies the use of the abstract data types using SQL. The paper offers a precise and conceptually clean foundation for implementing a spatio-temporal DBMS extension.

Question: What is the size of the canadian military?


Entropic Brain-computer Interfaces - Using fNIRS and EEG to Measure Attentional States in a Bayesian Framework. Implicit Brain-Computer Interfaces (BCI) adapt system settings subtly based on real time measures of brain activation without the user's explicit awareness. For example, measures of the user's cognitive workload might drive a system that alters the timing of notifications in order to minimize user interruption. Here, we consider new avenues for implicit BCI based on recent discoveries in cognitive neuroscience and conduct a series of experiments using BCI's principal non-invasive brain sensors, fNIRS and EEG. We show how Bayesian and systems neuroscience formulations explain the difference in performance of machine learning algorithms trained on brain data in different conditions. These new formulations posit that the brain aims to minimize its long-term surprisal of sensory data and organizes its calculations on two anti-correlated networks. We consider how to use real-time input that portrays a user along these dimensions in designing Bidirectional BCIs, which are Implicit BCIs that aim to optimize the user's state by modulating computer output based on feedback from a brain monitor. We introduce Entropic Brain-Computer Interfacing as a type of Bidirectional BCI which uses physiological measurements of information theoretical dimensions of the user's state to evaluate the digital flow of information to the user's brain, tweaking this output in a feedback loop to the user's benefit.

Question: What is the difference between a bidirectional brain computer interface and an implicit brain computer interface?