The place for ubiquitous computing in schools: lessons learned from a school-based intervention for youth physical activity. With rising concerns about obesity and sedentary lifestyles in youth, there has been an increasing interest in understanding how pervasive and ubiquitous computing technologies can catalyze positive health behaviors in children and teens. School-based interventions seem like a natural choice, and ubiquitous computing technologies hold much promise for these interventions. Yet the literature contains little guidance for how to approach school-based ubicomp deployments. Grounded in our analysis of a large-scale US school-based intervention for promoting youth physical activity, we present an approach to the design and evaluation of school-based ubicomp that treats the school as a social institution. We show how the school regulates students' daily lives, drawing from work in the sociology of schools to create a framing for planning, executing and analyzing school-based ubicomp deployments. These insights will assist other researchers and designers engaging in deployments of ubiquitous computing systems in settings with established institutional structures.

Question: How can ubiquitous computing be used to promote physical activity in schools?


Aircraft angle of attack and air speed detection by redundant strip pressure sensors. Air speed and angle of attack are fundamental parameters in the control of flying bodies. Conventional detection techniques use sensors that may protrude outside the aircraft, be too bulky for installation on small UAVs, or be excessively intrusive. We propose a novel readout methodology where flight parameters are inferred from redundant pressure readings made by capacitive strip sensors directly applied on the airfoil skin. Redundancy helps lower the accuracy requirements on the individual sensors. A strategy for combining sensor data is presented with an error propagation analysis. The latter enables foreseeing the precision by which the flight parameters can be detected. The methodology has been validated by fluid dynamic simulation and a sample case is illustrated.

Question: How to detect air speed and angle of attack from pressure sensors?


Privacy-Preserving Attribute-Based Access Control Model for XML-Based Electronic Health Record System. Cloud-based electronic health record (EHR) systems enable medical documents to be exchanged between medical institutions; this is expected to contribute to improvements in various medical services in the future. However, as the system architecture becomes more complicated, cloud-based EHR systems may introduce additional security threats when compared with the existing singular systems. Thus, patients may experience exposure of private data that they do not wish to disclose. In order to protect the privacy of patients, many approaches have been proposed to provide access control to patient documents when providing health services. However, most current systems do not support fine-grained access control or take into account additional security factors such as encryption and digital signatures. In this paper, we propose a cloud-based EHR model that performs attribute-based access control using extensible access control markup language. Our EHR model, focused on security, performs partial encryption and uses electronic signatures when a patient's document is sent to a document requester. We use XML encryption and XML digital signature technology. Our proposed model works efficiently by sending only the necessary information to the requesters who are authorized to treat the patient in question.

Question: What is the privacy-preserving attribute-based access control model for XML-based electronic health record system?


Distance estimation with 2 . 5 D anchors and its application to robot navigation. Estimating the distance of a target object from a single image is a challenging task since a large variation in the object appearance makes the regression of the distance difficult. In this paper, to tackle such the challenge, we propose 2.5D anchors which provide the candidate of distances based on a perspective camera model. This candidate is expected to relax the difficulty of the regression model since only the residual from the candidate distance needs to be taken into account. We show the effectiveness of the regression with our proposed anchors, by comparing with ordinary regression methods and state-of-the-art 3D object detection methods, through Pascal 3D+ TV monitor and KITTI car experiments. In addition, we also show an example of practical uses of our proposed method in a real-time system, robot navigation, by integrating with ROS-based simultaneous localization and mapping.

Question: How to estimate the distance of a target object from a single image?


Impact of Smartphone Position on Sensor Values and Context Discovery. With near-ubiquitous availability of smartphones equipped with a wide variety of sensors, research in building contextaware services has been growing. However, despite a large number of services proposed and developed as research prototypes, the number of truly context-aware applications available in smartphone applications market is quite limited. A major barrier for the large-scale proliferation of context aware applications is poor accuracy. This paper addresses one of the key reasons for this poor accuracy, which is the impact of smartphone positions. Users carry their smartphones in different positions such as holding in their hand, keeping inside their pants or jacket pocket, keeping in their purse, and so on. This paper addresses the issue of poor application accuracy due to varying smartphone positions. It first shows that smartphone positions significantly affect the values of the sensor data being collected by a context aware application, and this in turn has a significant impact on the accuracy of the application. Next, it describes the design and prototype development of a smartphone position discovery service that accurately detects a smartphone position. This service is based on the sensor data collected from carefully chosen sensors and runs orthogonal to any other context aware application or service. Finally, the paper demonstrates that the accuracy of an existing context aware service or application is significantly enhanced when run in conjunction with the proposed smartphone position discovery service.

Question: How to improve the accuracy of context aware applications?


The generalized distributive law. In this semitutorial paper we discuss a general message passing algorithm, which we call the generalized distributive law (GDL). The GDL is a synthesis of the work of many authors in the information theory, digital communications, signal processing, statistics, and artificial intelligence communities. It includes as special cases the Baum–Welch algorithm, the fast Fourier transform (FFT) on any finite Abelian group, the Gallager–Tanner–Wiberg decoding algorithm, Viterbi's algorithm, the BCJR algorithm, Pearl's "belief propagation" algorithm, the Shafer–Shenoy probability propagation algorithm, and the turbo decoding algorithm. Although this algorithm is guaranteed to give exact answers only in certain cases (the "junction tree" condition), unfortunately not including the cases of GTW with cycles or turbo decoding, there is much experimental evidence, and a few theorems, suggesting that it often works approximately even when it is not supposed to.

Question: What is the generalized distributive law?


Suggestibility of the child witness: a historical review and synthesis.. The field of children's testimony is in turmoil, but a resolution to seemingly intractable debates now appears attainable. In this review, we place the current disagreement in historical context and describe psychological and legal views of child witnesses held by scholars since the turn of the 20th century. Although there has been consistent interest in children's suggestibility over the past century, the past 15 years have been the most active in terms of the number of published studies and novel theorizing about the causal mechanisms that underpin the observed findings. A synthesis of this research posits three "families" of factors--cognitive, social, and biological--that must be considered if one is to understand seemingly contradictory interpretations of the findings. We conclude that there are reliable age differences in suggestibility but that even very young children are capable of recalling much that is forensically relevant. Findings are discussed in terms of the role of expert witnesses.

Question: What is the role of expert witnesses?


Analysis of users and non-users of smartphone applications. Purpose: Smartphones facilitate the potential adoption of new mobile applications. The purpose of this research is to study users and non-users of three selected mobile applications, and find out what really drives the intention to use these applications across users and non-users. Design/methodology/approach: The authors measured actual usage of mobile applications in a panel study of 579 Finnish smartphone users, using in-device measurements as an objective way to identify users and non-users. A web-based survey was used in collecting data to test an extended TAM model in explaining intention to use. Findings: Perceived technological barriers negatively affect behavioural control, reflecting people's assessment of themselves being capable of using the services without trouble. Behavioural control is directly linked to perceived usefulness (except for games) and perceived enjoyment, as hypothesized. Perceived enjoyment and usefulness were generically found to explain intention to use applications for both users and for non-users. Research limitations/implications: With regards to the impact of social norms, the study finds that further research needs to be done in exploring its impact more thoroughly. The dataset of the research, consisting purely of male-dominated, young smartphone users, make the generalization of results difficult. Practical implications: There are differences regarding what drives the usage of different kinds of mobile applications. In this study, map applications and mobile Internet, are driven by more utilitarian motivations, whereas games are more hedonic. It is also clear that not everybody are using applications facilitated by smartphones, and therefore the presented approach of studying users and non-users separately provides a new approach to analyze adoption on a practical level. Originality/value: This research proves that models like TAM should not treat mobile services as a

Question: What is the difference between a smartphone and a mobile phone?


Reconfigurable quarter-mode SIW antenna employing a fluidically switchable via. A new method to fluidically switch through via posts is proposed for the first time. The reconfigurable via post is used toward designing a switchable QMSIW antenna. The switching method is based on filling/emptying a non-plated via hole with/from a liquid metal alloy. A QMSIW antenna is designed to initially work at ~3.2 GHz. Connecting the fluidically switchable via post at the corner of the QMSIW antenna shifts up the operating frequency. This translates into a switching range of 3.2-4.7 GHz. The Polydimethylsiloxane (PDMS) structures including the micro-channels are bonded to the QMSIW circuit board using a unique fabrication technique.

Question: How to design a switchable QMSIW antenna?


Combi-Tor: Track-to-Track Association Framework for Automotive Sensor Fusion. The data association algorithm plays the vital role of forming an appropriate and valid set of tracks from the available tracks at the fusion center, which are delivered by different sensor's local tracking systems. The architecture of the data association module has to be designed taking into account the fusion strategy of the sensor fusion system, the granularity and the quality of the data provided by the sensors. The current generation environment perception sensors used for automotive sensor fusion are capable of providing estimated kinematic and as well as non-kinematic information on the observed targets. This paper focuses on integrating the kinematic and non-kinematic information in a track-to-track association (T2TA) procedure. A scalable framework called Combi-Tor is introduced here that is designed to calculate the association decision using likelihood ratio tests based on the available kinematic and non-kinematic information on the targets, which are tracked and classified by different sensors. The calculation of the association decision includes the uncertainty in the sensor's local tracking and classification modules. The required sufficient statistical derivations are discussed. The performance of this T2TA framework and the traditional T2TA scheme considering only the kinematic information are evaluated using Monte-Carlo simulation. The initial results obtained using the real world sensor data is presented.

Question: What is the track-to-track association framework for automotive sensor fusion?


The Bad Boys of Cyberspace: Deviant Behavior in a Multimedia Chat Community. A wide variety of deviant behavior may arise as the population of an online multimedia community increases. That behavior can span the range from simple mischievous antics to more serious expressions of psychopathology, including depression, sociopathy, narcissism, dissociation, and borderline dynamics. In some cases the deviant behavior may be a process of pathological acting out—in others, a healthy attempt to work through. Several factors must be taken into consideration when explaining online deviance, such as social/cultural issues, the technical infrastructure of the environment, transference reactions, and the effects of the ambiguous, anonymous, and fantasy-driven atmosphere of cyberspace life. In what we may consider an "online community psychology," intervention strategies for deviant behavior can be explored along three dimensions: preventative versus remedial, user versus superuser based, and automated versus interpersonal.

Question: What is the difference between preventative and remedial?


Air-Gap Convection in Rotating Electrical Machines. This paper reviews the convective heat transfer within the air gap of both cylindrical and disk geometry rotating electrical machines, including worked examples relevant to fractional horsepower electrical machines. Thermal analysis of electrical machines is important because torque density is limited by maximum temperature. Knowledge of surface convective heat transfer coefficients is necessary for accurate thermal modeling, for example, using lumped parameter models. There exists a wide body of relevant literature, but much of it has traditionally been in other application areas, dominated by mechanical engineers, such as gas turbine design. Particular attention is therefore given to the explanation of the relevant nondimensional parameters and to the presentation of measured convective heat transfer correlations for a wide variety of situations from laminar to turbulent flow at small and large gap sizes for both radial-flux and axial-flux electrical machines.

Question: What is the difference between a radial-flux and an axial-flux electrical machine?


Object Segmentation by Long Term Analysis of Point Trajectories. Unsupervised learning requires a grouping step that defines which data belong together. A natural way of grouping in images is the segmentation of objects or parts of objects. While pure bottom-up segmentation from static cues is well known to be ambiguous at the object level, the story changes as soon as objects move. In this paper, we present a method that uses long term point trajectories based on dense optical flow. Defining pair-wise distances between these trajectories allows to cluster them, which results in temporally consistent segmentations of moving objects in a video shot. In contrast to multi-body factorization, points and even whole objects may appear or disappear during the shot. We provide a benchmark dataset and an evaluation method for this so far uncovered setting.

Question: How to segment moving objects in a video?


Community detection algorithms: a comparative analysis.. Uncovering the community structure exhibited by real networks is a crucial step toward an understanding of complex systems that goes beyond the local organization of their constituents. Many algorithms have been proposed so far, but none of them has been subjected to strict tests to evaluate their performance. Most of the sporadic tests performed so far involved small networks with known community structure and/or artificial graphs with a simplified structure, which is very uncommon in real systems. Here we test several methods against a recently introduced class of benchmark graphs, with heterogeneous distributions of degree and community size. The methods are also tested against the benchmark by Girvan and Newman [Proc. Natl. Acad. Sci. U.S.A. 99, 7821 (2002)] and on random graphs. As a result of our analysis, three recent algorithms introduced by Rosvall and Bergstrom [Proc. Natl. Acad. Sci. U.S.A. 104, 7327 (2007); Proc. Natl. Acad. Sci. U.S.A. 105, 1118 (2008)], Blondel [J. Stat. Mech.: Theory Exp. (2008), P10008], and Ronhovde and Nussinov [Phys. Rev. E 80, 016109 (2009)] have an excellent performance, with the additional advantage of low computational complexity, which enables one to analyze large systems.

Question: What is the community structure of the benchmark graphs?


TopicShop: enhanced support for evaluating and organizing collections of Web sites. TopicShop is an interface that helps users evaluate and organize collections of web sites. The main interface components are site profiles, which contain information that helps users select high-quality items, and a work area, which offers thumbnail images, annotation, and lightweight grouping techniques to help users organize selected sites. The two components are linked to allow task integration. Previous work [2] demonstrated that subjects who used TopicShop were able to select significantly more highquality sites, in less time and with less effort. We report here on studies that confirm and extend these results. We also show that TopicShop subjects spent just half the time organizing sites, yet still created more groups and more annotations, and agreed more in how they grouped sites. Finally, TopicShop subjects tightly integrated the tasks of evaluating and organizing sites. INTRODUCTION In previous work [2], we motivated an important task for web users – gathering, evaluating, and organizing information resources for a given topic. Current web tools do not support this task well; specifically, they do not make it easy to evaluate collections of web sites to select the best ones or to organize sites for future reuse and sharing. Users have to browse and view sites one after another until they are satisfied they have a good set, or, more likely, they get tired and give up. Browsing a web site is an expensive operation, both in time and cognitive effort. And bookmarks, the most common form of keeping track of web sites, are a fairly primitive organizational technique. We designed and implemented the TopicShop system to provide comprehensive, integrated support for this task. TopicShop aids users in finding a set of relevant sites, in narrowing down the set into a smaller set of high quality sites, and in organizing sites for future use. TopicShop has evolved through a number of des

Question: How does TopicShop help users evaluate and organize collections of web sites?


"Exascale Computing and Big Data: The Next Frontier,". For scientific and engineering computing, exascale (10 operations per second) is the next proxy in the long trajectory of exponential performance increases that has continued for over half a century. Similarly, large-scale data preservation and sustainability within and across disciplines, metadata creation and multidisciplinary fusion, and digital privacy and security define the frontiers of big data. Solving the myriad technical, political and economic challenges of exascale computing will require coordinated planning across government, industry and academia, commitment to data sharing and sustainability, collaborative research and development, and recognition that both international competition and collaboration will be necessary.

Question: What is exascale computing?


Single-Stage Single-Switch Four-Output Resonant LED Driver With High Power Factor and Passive Current Balancing. A resonant single-stage single-switch four-output LED driver with high power factor and passive current balancing is proposed. By controlling one output current, the other output currents of four-output LED driver can be controlled via passive current balancing, which makes its control simple. When magnetizing inductor current operates in critical conduction mode, unity power factor is achieved. The proposed LED driver uses only one active switch and one magnetic component, thus it benefits from low cost, small volume, and light weight. Moreover, high-efficiency performance is achieved due to single-stage power conversion and soft-switching characteristics. The characteristics of the proposed LED driver are studied in this paper and experimental results of two 110-W four-output isolated LED drivers are provided to verify the studied results.

Question: What is the purpose of the proposed LED driver?


Filtering of Video using Guided Filter. Image processing is one of the most important areas of signal processing. Image processing is applicable for the improvement of pictorial improvement for human perception and autonomous machine application also the efficient storage and transmission. In this paper guided filter is proposed which can be applicable to both images as well as to video. This guided filter has many advantages over the previous version of filter like mean filter, median filter, sobel filter, bilateral filter, etc. Among of those bilateral filter is more popular. But this bilateral filter has certain limitations like gradient reversal effect this introduces false edges in the image. Proposed guided filter can smooth image/video and also preserved the edge that is information at the edges of image/video but this filter has better behaviors near edges. Also the fast implementation of guided filter is possible. This filter also has algorithm implementation regardless of kernel size. Guided filter gives better output than that of bilateral filter after filtering this bilateral filter gives output like cartoon this also a problem with this. The guided filter is both effective and efficient in many applications of computer vision and computer graphics, etc. Filtering techniques are used in image and video processing applications for various technologies.

Question: What is the difference between bilateral filter and guided filter?


Bayes Optimality in Linear Discriminant Analysis. We present an algorithm that provides the one-dimensional subspace, where the Bayes error is minimized for the C class problem with homoscedastic Gaussian distributions. Our main result shows that the set of possible one-dimensional spaces v, for which the order of the projected class means is identical, defines a convex region with associated convex Bayes error function g(v). This allows for the minimization of the error function using standard convex optimization algorithms. Our algorithm is then extended to the minimization of the Bayes error in the more general case of heteroscedastic distributions. This is done by means of an appropriate kernel mapping function. This result is further extended to obtain the d dimensional solution for any given d by iteratively applying our algorithm to the null space of the (d - l)-dimensional solution. We also show how this result can be used to improve upon the outcomes provided by existing algorithms and derive a low-computational cost, linear approximation. Extensive experimental validations are provided to demonstrate the use of these algorithms in classification, data analysis and visualization.

Question: What is the Bayes error?


Real-time computer vision with OpenCV. Mobile computer-vision technology will soon become as ubiquitous as touch interfaces.

Question: What is computer vision?


Scheduling and locking in multiprocessor real-time operating systems. BJÖRN B. BRANDENBURG: Scheduling and Locking in Multiprocessor Real-Time Operating Systems (Under the direction of James H. Anderson) With the widespread adoption of multicore architectures, multiprocessors are now a standard deployment platform for (soft) real-time applications. This dissertation addresses two questions fundamental to the design of multicore-ready real-time operating systems: (1) Which scheduling policies offer the greatest flexibility in satisfying temporal constraints; and (2) which locking algorithms should be used to avoid unpredictable delays? With regard to Question 1, LITMUSRT, a real-time extension of the Linux kernel, is presented and its design is discussed in detail. Notably, LITMUSRT implements link-based scheduling, a novel approach to controlling blocking due to non-preemptive sections. Each implemented scheduler (22 configurations in total) is evaluated under consideration of overheads on a 24-core Intel Xeon platform. The experiments show that partitioned earliest-deadline first (EDF) scheduling is generally preferable in a hard real-time setting, whereas global and clustered EDF scheduling are effective in a soft real-time setting. With regard to Question 2, real-time locking protocols are required to ensure that the maximum delay due priority inversion can be bounded a priori. Several spinlockand semaphore-based multiprocessor real-time locking protocols for mutual exclusion (mutex), reader-writer (RW) exclusion, and k-exclusion are proposed and analyzed. A new category of RW locks suited to worst-case analysis, termed phase-fair locks, is proposed and three efficient phase-fair spinlock implementations are provided (one with few atomic operations, one with low space requirements, and one with constant RMR complexity). Maximum priority-inversion blocking is proposed as a natural complexity measure for semaphore protocols. It is shown that there are two classes o

Question: What is the difference between a soft real-time and a hard real-time operating system?


Inverse procedural modeling of 3D models for virtual worlds. This course presents a collection of state-of-the-art approaches for modeling and editing of 3D models for virtual worlds, simulations, and entertainment, in addition to real-world applications. The first contribution of this course is a coherent review of inverse procedural modeling (IPM) (i.e., proceduralization of provided 3D content). We describe different formulations of the problem as well as solutions based on those formulations. We show that although the IPM framework seems under-constrained, the state-of-the-art solutions actually use simple analogies to convert the problem into a set of fundamental computer science problems, which are then solved by corresponding algorithms or optimizations. The second contribution includes a description and categorization of results and applications of the IPM frameworks. Moreover, a substantial part of the course is devoted to summarizing different domain IPM frameworks for practical content generation in modeling and animation.

Question: What is the difference between procedural modeling and inverse procedural modeling?


VADI: GPU Virtualization for an Automotive Platform. Modern vehicles are evolving with more electronic components than ever before (In this paper, "vehicle" means "automotive vehicle." It is also equal to "car.") One notable example is graphical processing unit (GPU), which is a key component to implement a digital cluster. To implement the digital cluster that displays all the meters (such as speed and fuel gauge) together with infotainment services (such as navigator and browser), the GPU needs to be virtualized; however, GPU virtualization for the digital cluster has not been addressed yet. This paper presents a Virtualized Automotive DIsplay (VADI) system to virtualize a GPU and its attached display device. VADI manages two execution domains: one for the automotive control software and the other for the in-vehicle infotainment (IVI) software. Through GPU virtualization, VADI provides GPU rendering to both execution domains, and it simultaneously displays their images on a digital cluster. In addition, VADI isolates GPU from the IVI software in order to protect it from potential failures of the IVI software. We implement VADI with Vivante GC2000 GPU and perform experiments to ensure requirements of International Standard Organization (ISO) safety standards. The results show that VADI guarantees 30 frames per second (fps), which is the minimum frame rate for digital cluster mandated by ISO safety standards even with the failure of the IVI software. It also achieves 60 fps in a synthetic workload.

Question: What is the minimum frame rate for digital cluster mandated by ISO safety standards?


Towards a truly mobile auditory brain-computer interface: exploring the P300 to take away.. In a previous study we presented a low-cost, small, and wireless 14-channel EEG system suitable for field recordings (Debener et al., 2012, psychophysiology). In the present follow-up study we investigated whether a single-trial P300 response can be reliably measured with this system, while subjects freely walk outdoors. Twenty healthy participants performed a three-class auditory oddball task, which included rare target and non-target distractor stimuli presented with equal probabilities of 16%. Data were recorded in a seated (control condition) and in a walking condition, both of which were realized outdoors. A significantly larger P300 event-related potential amplitude was evident for targets compared to distractors (p<.001), but no significant interaction with recording condition emerged. P300 single-trial analysis was performed with regularized stepwise linear discriminant analysis and revealed above chance-level classification accuracies for most participants (19 out of 20 for the seated, 16 out of 20 for the walking condition), with mean classification accuracies of 71% (seated) and 64% (walking). Moreover, the resulting information transfer rates for the seated and walking conditions were comparable to a recently published laboratory auditory brain-computer interface (BCI) study. This leads us to conclude that a truly mobile auditory BCI system is feasible.

Question: How many participants were in the study?


AlGaN/GaN current aperture vertical electron transistors. Describes AlGaN/GaN current aperture vertical electron transistor (CAVET) structures. A CAVET consists of a source region separated from a drain region by an insulating layer containing a narrow aperture which is filled with conducting material. A device mesa is formed by reactive ion etching, and source contacts are deposited on either side of the aperture. The drain metal contacts the n-doped region below the aperture. Electrons flow from the source contacts through the aperture into the n-type base region and are collected at the drain. A Schottky gate, located directly above the aperture, is used to modulate the current passing through the aperture. In a CAVET, because the virtual drain (or pinched off region) is located underneath the gate, charge does not accumulate at the gate edge, so no large fields near the gate edge are present. Instead, our simulations show that the high field region in a CAVET is buried in the bulk. The CAVET therefore has the potential to support large source-drain voltages, since surface related breakdown is eliminated.

Question: What is the difference between a CAVET and a CAVET?


A Deep Learning Approach to Human Activity Recognition Based on Single Accelerometer. In this paper, we propose an acceleration-based human activity recognition method using popular deep architecture, Convolution Neural Network (CNN). In particular, we construct a CNN model and modify the convolution kernel to adapt the characteristics of tri-axial acceleration signals. Also, for comparison, we use some widely used methods to accomplish the recognition task on the same dataset. The large dataset we constructed consists of 31688 samples from eight typical activities. The experiment results show that the CNN works well, which can reach an average accuracy of 93.8% without any feature extraction methods.

Question: What is a deep learning approach to human activity recognition based on single accelerometer?


Multilingual representations for low resource speech recognition and keyword search. This paper examines the impact of multilingual (ML) acoustic representations on Automatic Speech Recognition (ASR) and keyword search (KWS) for low resource languages in the context of the OpenKWS15 evaluation of the IARPA Babel program. The task is to develop Swahili ASR and KWS systems within two weeks using as little as 3 hours of transcribed data. Multilingual acoustic representations proved to be crucial for building these systems under strict time constraints. The paper discusses several key insights on how these representations are derived and used. First, we present a data sampling strategy that can speed up the training of multilingual representations without appreciable loss in ASR performance. Second, we show that fusion of diverse multilingual representations developed at different LORELEI sites yields substantial ASR and KWS gains. Speaker adaptation and data augmentation of these representations improves both ASR and KWS performance (up to 8.7% relative). Third, incorporating un-transcribed data through semi-supervised learning, improves WER and KWS performance. Finally, we show that these multilingual representations significantly improve ASR and KWS performance (relative 9% for WER and 5% for MTWV) even when forty hours of transcribed audio in the target language is available. Multilingual representations significantly contributed to the LORELEI KWS systems winning the OpenKWS15 evaluation.

Question: What is the impact of multilingual acoustic representations on low resource speech recognition and keyword search?


Statistical Methods for Speech Recognition. As known, to finish this book, you may not need to get it at once in a day. Doing the activities along the day may make you feel so bored. If you try to force reading, you may prefer to do other entertaining activities. But, one of concepts we want you to have this book is that it will not make you feel bored. Feeling bored when reading will be only unless you don't like the book. statistical methods for speech recognition really offers what everybody wants.

Question: What is the book about?


Feature recognition and shape design in sneakers. Article history: Available online xxxx

Question: How to design a sneaker?


Population-based norms for the Mini-Mental State Examination by age and educational level.. OBJECTIVE To report the distribution of Mini-Mental State Examination (MMSE) scores by age and educational level.   DESIGN National Institute of Mental Health Epidemiologic Catchment Area Program surveys conducted between 1980 and 1984.   SETTING Community populations in New Haven, Conn; Baltimore, Md; St Louis, Mo; Durham, NC; and Los Angeles, Calif.   PARTICIPANTS A total of 18,056 adult participants selected by probability sampling within census tracts and households.   MAIN OUTCOME MEASURES Summary scores for the MMSE are given in the form of mean, median, and percentile distributions specific for age and educational level.   RESULTS The MMSE scores were related to both age and educational level. There was an inverse relationship between MMSE scores and age, ranging from a median of 29 for those 18 to 24 years of age, to 25 for individuals 80 years of age and older. The median MMSE score was 29 for individuals with at least 9 years of schooling, 26 for those with 5 to 8 years of schooling, and 22 for those with 0 to 4 years of schooling.   CONCLUSIONS Cognitive performance as measured by the MMSE varies within the population by age and education. The cause of this variation has yet to be determined. Mini-Mental State Examination scores should be used to identify current cognitive difficulties and not to make formal diagnoses. The results presented should prove to be useful to clinicians who wish to compare an individual patient's MMSE scores with a population reference group and to researchers making plans for new studies in which cognitive status is a variable of interest.

Question: What is the mini mental state examination?


Breaking Mifare DESFire MF3ICD40: Power Analysis and Templates in the Real World. With the advent of side-channel analysis, implementations of mathematically secure ciphers face a new threat: by exploiting the physical characteristics of a device, adversaries are able to break algorithms such as AES or Triple-DES (3DES), for which no efficient analytical or brute-force attacks exist. In this paper, we demonstrate practical, noninvasive side-channel attacks on the Mifare DESFire MF3ICD40 contactless smartcard, a 3DES-based alternative to the cryptanalytically weak Mifare Classic [9, 25]. We detail on how to recover the complete 112-bit secret key of the employed 3DES algorithm, using non-invasive power analysis and template attacks. Our methods can be put into practice at a low cost with standard equipment, thus posing a severe threat to many real-world applications that employ the DESFire MF3ICD40 smartcard.

Question: How to break DESFire MF3ICD40?


Emotion Enhances Learning via Norepinephrine Regulation of AMPA-Receptor Trafficking. Emotion enhances our ability to form vivid memories of even trivial events. Norepinephrine (NE), a neuromodulator released during emotional arousal, plays a central role in the emotional regulation of memory. However, the underlying molecular mechanism remains elusive. Toward this aim, we have examined the role of NE in contextual memory formation and in the synaptic delivery of GluR1-containing alpha-amino-3-hydroxy-5-methyl-4-isoxazoleproprionic acid (AMPA)-type glutamate receptors during long-term potentiation (LTP), a candidate synaptic mechanism for learning. We found that NE, as well as emotional stress, induces phosphorylation of GluR1 at sites critical for its synaptic delivery. Phosphorylation at these sites is necessary and sufficient to lower the threshold for GluR1 synaptic incorporation during LTP. In behavioral experiments, NE can lower the threshold for memory formation in wild-type mice but not in mice carrying mutations in the GluR1 phosphorylation sites. Our results indicate that NE-driven phosphorylation of GluR1 facilitates the synaptic delivery of GluR1-containing AMPARs, lowering the threshold for LTP, thereby providing a molecular mechanism for how emotion enhances learning and memory.

Question: How does NE regulate the synaptic delivery of GluR1-containing AMPA receptors?


Modeling and analysis of electric field and electrostatic adhesion force generated by interdigital electrodes for wall climbing robots. A model is presented for the analysis of the electric field and electrostatic adhesion force produced by interdigital electrodes. Assuming that the potential varies linearly with distance in inter-electrode gaps, the potential distribution on the electrode plane is obtained by taking the first-order Taylor series approximation. The expressions of electric field components are then derived by solving the Laplace equation for the electrical potential in each subregion. The electrostatic adhesion force is calculated using the Maxwell stress tensor formulation. The dynamic properties of the electric field and electrostatic adhesion force are assessed by evaluating the transient response of the field and force under a step in applied voltages. To verify the model developed, an experimental study is carried out in conjunction with the theoretical analysis to evaluate the adhesion performance of an electrode panel on a glass pane. A double tracked wall climbing robot is designed and tested on various wall surfaces. The limit of the approximation method of the inter-electrode potential is discussed. It is found that vacuum suction force is involved in the adhesion. The influence of this vacuum suction force on electrostatic adhesion is also discussed. The results of this work would provide support for theoretical guidelines and system optimization for the electrostatic adhesion technology applied to wall climbing robots.

Question: How to model the electric field and electrostatic adhesion force generated by interdigital electrodes for wall climbing robots?


Seven V's of Big Data understanding Big Data to extract value. Big Data has shown lot of potential in real world industry and research community. We support the power and potential of it in solving real world problems. However, it is imperative to understand Big Data through the lens of 7 V's. 7th V as `Value' is desired output for industry challenges and issues. We provide a brief survey study of 7 Vs of Big Data in order to understand Big Data and extract Value concept in general. Finally we conclude by showing our vision of improved healthcare, a product of Big Data Utilization, as a future work for researchers and students, while moving forward.

Question: What is the value of big data?


Constrained Two-dimensional Cutting Stock Problems a Best-first Branch-and-bound Algorithm Constrained Two-dimensional Cutting Stock Problems a Best-first Branch-and-bound Algorithm. In this work, we develop a new version of the algorithm proposed in 17] for solving exactly some variants of (un)weighted constrained two-dimensional cutting stock problems. We introduce one-dimensional bounded knapsacks in order to obtain an improved initial lower bound for limiting initially the size of the search space, an improved upper bound at each internal node and a symmetry detection for neglecting some useless duplicate patterns. A new eecient data structure of the set representing the best subprob-lems is used to implement the algorithms over the BOB library. The performance of our algorithms is evaluated on some problem instances of the literature and on other randomly generated hard problem instances (a total of 27 problem instances). Les Probl emes de D ecoupe 2-Dimensions Contraints Un Algorithme de Branch-and-Bound meilleur d'abord R esum e : Dans ce travail, nous concevons une nouvelle version de l'algorithme pro-pos e dans 17] pour r esoudre de mani ere exacte des probl emes de d ecoupe en 2-dimensions contraints avec ou sans pond eration. Nous introduisons des sacs a dos unidimension-nels contraints aan d'obtenir une meilleure borne inf erieure initiale pour limiter la taille de l'espace de recherche, une meilleure borne sup erieure pour chaque nnud interne et une d etection de sym etries pour supprimer des conngurations identiques. Une nouvelle structure de donn ees eecace pour g erer les meilleurs sous-probl emes a et e employ ee pour l'impl ementation des algorithmes sur la biblioth eque BOB. La performance de nos algo-rithmes est evalu ee sur 27 instances de probl emes extraites pour certaines de la litt erature et d'autres diiciles, engendr ees al eatoirement.

Question: What is the best-first branch-and-bound algorithm for constrained two-dimensional cutting stock problems?


Curcumin: From ancient medicine to current clinical trials. Curcumin is the active ingredient in the traditional herbal remedy and dietary spice turmeric (Curcuma longa). Curcumin has a surprisingly wide range of beneficial properties, including anti-inflammatory, antioxidant, chemopreventive and chemotherapeutic activity. The pleiotropic activities of curcumin derive from its complex chemistry as well as its ability to influence multiple signaling pathways, including survival pathways such as those regulated by NF-κB, Akt, and growth factors; cytoprotective pathways dependent on Nrf2; and metastatic and angiogenic pathways. Curcumin is a free radical scavenger and hydrogen donor, and exhibits both pro- and antioxidant activity. It also binds metals, particularly iron and copper, and can function as an iron chelator. Curcumin is remarkably non-toxic and exhibits limited bioavailability. Curcumin exhibits great promise as a therapeutic agent, and is currently in human clinical trials for a variety of conditions, including multiple myeloma, pancreatic cancer, myelodysplastic syndromes, colon cancer, psoriasis and Alzheimer's disease.

Question: What is the chemical name of curcumin?


The Google file system. We have designed and implemented the Google File System, a scalable distributed file system for large distributed data-intensive applications. It provides fault tolerance while running on inexpensive commodity hardware, and it delivers high aggregate performance to a large number of clients. While sharing many of the same goals as previous distributed file systems, our design has been driven by observations of our application workloads and technological environment, both current and anticipated, that reflect a marked departure from some earlier file system assumptions. This has led us to reexamine traditional choices and explore radically different design points. The file system has successfully met our storage needs. It is widely deployed within Google as the storage platform for the generation and processing of data used by our service as well as research and development efforts that require large data sets. The largest cluster to date provides hundreds of terabytes of storage across thousands of disks on over a thousand machines, and it is concurrently accessed by hundreds of clients. In this paper, we present file system interface extensions designed to support distributed applications, discuss many aspects of our design, and report measurements from both micro-benchmarks and real world use.

Question: What is the google file system?


Question: What is the difference between VQA and QA?


Cultural psychology: a once and future discipline?. Now, we come to offer you the right catalogues of book to open. cultural psychology a once and future discipline is one of the literary work in this world in suitable to be reading material. That's not only this book gives reference, but also it will show you the amazing benefits of reading a book. Developing your countless minds is needed; moreover you are kind of people with great curiosity. So, the book is very appropriate for you.

Question: What is the right catalogues of book to open?


Cross-lingual Semantic Generalization for the Detection of Metaphor. In this work, we describe a supervised cross-lingual methodology for detecting novel and conventionalized metaphors that derives generalized semantic patterns from a collection of metaphor annotations. For this purpose, we model each metaphor annotation as an abstract tuple – (source, target, relation, metaphoricity) – that packages a metaphoricity judgement with a relational grounding of the source and target lexical units in text. From these annotations, we derive a set of semantic patterns using a three-step process. First, we employ several generalized representations of the target using a variety of WordNet information and representative domain terms. Then, we generalize relations using a rule-based, pseudo-semantic role labeling. Finally, we generalize the source by partitioning a semantic hierarchy (defined by the target and the relation) into metaphoric and non-metaphoric regions so as to optimally account for the evidence in the annotated data. Experiments show that by varying the generality of the source, target, and relation representations in our derived patterns, we are able to significantly extend the impact of our annotations, detecting metaphors in a variety of domains at an F-measure of between 0.88 and 0.92 for English, Spanish, Russian, and Farsi. This generalization process both enhances our ability to jointly detect novel and conventionalized metaphors and enables us to transfer the knowledge encoded in metaphoricity annotations to novel languages.

Question: What is the cross-lingual semantic generalization for the detection of metaphor?


An empirical approach to the experience of architectural space in virtual reality — exploring relations between features and affective appraisals of rectangular indoor spaces. In the presented exploratory study, quantitative relations between the experience of architectural spaces and physical properties were investigated using virtual reality (VR) simulations. Geometric properties from a component-based description of rectangular rooms were tested on their suitability as predictor variables for experiential qualities. In a virtual-reality-based perceptual experiment, qualities of 16 vacant rectangular interiors were rated in eight principal categories by 16 participants using the semantic differential scaling technique. The simulated scenes were automatically generated by means of a custom made utility that also provided for the component-based room descriptions. The data analysis revealed strong correlations between several scene features and the averaged rated experience. For example, a preference for ratios near to the golden section could be observed for spatial proportions, which are not directly perceivable. Altogether, a set of five widely independent factors (openness, two room proportions, room area, and balustrade height) turned out to be most effective for describing the observed variance in the attributed experiential qualities. The combination of realistic virtual reality simulations and psychophysical data raising methods proved to be an effective means for basic architectural research. Several quantitative relations between physical properties and the emotional experience of architectural space could be demonstrated. D 2004 Elsevier B.V. All rights reserved.

Question: What is the golden section?


Scheduling in the Dark ( Improved Result ). We considered non-clairvoyant multiprocessor scheduling of jobs with arbitrary arrival times and changing execution characteristics. The problem has been studied extensively when either the jobs all arrive at time zero, or when all the jobs are fully parallelizable, or when the scheduler has considerable knowledge about the jobs. This paper considers for the first time this problem without any of these three restrictions yet when our algorithm is given more resources than the adversary. We provide new upper and lower bound techniques applicable in this more difficult scenario. The results are of both theoretical and practical interest. In our model, a job can arrive at any arbitrary time and its execution characteristics can change through the life of the job from being anywhere from fully parallelizable to completely sequential. We assume that the scheduler has no knowledge about the jobs except for knowing when a job arrives and knowing when it completes. (This is why we say that the scheduler is completely in the dark.) Given all this, we prove that the scheduler algorithm Equi-partition, though simple, performs within a constant factor as well as the optimal scheduler as long as it is given at least twice as many processors. More over, we prove that if none of the jobs are "strictly" fully parallelizable, then Equi-partition performs competitively with no extra processors. Author is supported by NSERC Canada.

Question: What is the name of the algorithm?


Evaluate the Feasibility of Using Frontal SSVEP to Implement an SSVEP-Based BCI in Young, Elderly and ALS Groups. This paper studies the amplitude-frequency characteristic of frontal steady-state visual evoked potential (SSVEP) and its feasibility as a control signal for brain computer interface (BCI). SSVEPs induced by different stimulation frequencies, from 13 ~ 31 Hz in 2 Hz steps, were measured in eight young subjects, eight elders and seven ALS patients. Each subject was requested to participate in a calibration study and an application study. The calibration study was designed to find the amplitude-frequency characteristics of SSVEPs recorded from Oz and Fpz positions, while the application study was designed to test the feasibility of using frontal SSVEP to control a two-command SSVEP-based BCI. The SSVEP amplitude was detected by an epoch-average process which enables artifact-contaminated epochs can be removed. The seven ALS patients were severely impaired, and four patients, who were incapable of completing our BCI task, were excluded from calculation of BCI performance. The averaged accuracies, command transfer intervals and information transfer rates in operating frontal SSVEP-based BCI were 96.1%, 3.43 s/command, and 14.42 bits/min in young subjects; 91.8%, 6.22 s/command, and 6.16 bits/min in elders; 81.2%, 12.14 s/command, and 1.51 bits/min in ALS patients, respectively. The frontal SSVEP could be an alternative choice to design SSVEP-based BCI.

Question: How large is the canadian military?


ActiveCDN: Cloud Computing Meets Content Delivery Networks. Content delivery networks play a crucial role in today's Internet. They serve a large portion of the multimedia on the Internet and solve problems of scalability and indirectly network congestion (at a price). However, most content delivery networks rely on a statically deployed configuration of nodes and network topology that makes it hard to grow and scale dynamically. We present ActiveCDN, a novel CDN architecture that allows a content publisher to dynamically scale their content delivery services using network virtualization and cloud computing techniques.

Question: What is a content delivery network?


Human simulations of vocabulary learning. The work reported here experimentally investigates a striking generalization about vocabulary acquisition: Noun learning is superior to verb learning in the earliest moments of child language development. The dominant explanation of this phenomenon in the literature invokes differing conceptual requirements for items in these lexical categories: Verbs are cognitively more complex than nouns and so their acquisition must await certain mental developments in the infant. In the present work, we investigate an alternative hypothesis; namely, that it is the information requirements of verb learning, not the conceptual requirements, that crucially determine the acquisition order. Efficient verb learning requires access to structural features of the exposure language and thus cannot take place until a scaffolding of noun knowledge enables the acquisition of clause-level syntax. More generally, we experimentally investigate the hypothesis that vocabulary acquisition takes place via an incremental constraint-satisfaction procedure that bootstraps itself into successively more sophisticated linguistic representations which, in turn, enable new kinds of vocabulary learning. If the experimental subjects were young children, it would be difficult to distinguish between this information-centered hypothesis and the conceptual change hypothesis. Therefore the experimental "learners" are adults. The items to be "acquired" in the experiments were the 24 most frequent nouns and 24 most frequent verbs from a sample of maternal speech to 18-24-month-old infants. The various experiments ask about the kinds of information that will support identification of these words as they occur in mother-to-child discourse. Both the proportion correctly identified and the type of word that is identifiable changes significantly as a function of information type. We discuss these results as consistent with the incremental construction of a highly lexicalized gr

Question: What is the difference between nouns and verbs?


KD-tree acceleration structures for a GPU raytracer. Modern graphics hardware architectures excel at compute-intensive tasks such as ray-triangle intersection, making them attractive target platforms for raytracing. To date, most GPU-based raytracers have relied upon uniform grid acceleration structures. In contrast, the kd-tree has gained widespread use in CPU-based raytracers and is regarded as the best general-purpose acceleration structure. We demonstrate two kd-tree traversal algorithms suitable for GPU implementation and integrate them into a streaming raytracer. We show that for scenes with many objects at different scales, our kd-tree algorithms are up to 8 times faster than a uniform grid. In addition, we identify load balancing and input data recirculation as two fundamental sources of inefficiency when raytracing on current graphics hardware.

Question: What is a kd-tree?


The Unreasonable Effectiveness of Deep Features as a Perceptual Metric. While it is nearly effortless for humans to quickly assess the perceptual similarity between two images, the underlying processes are thought to be quite complex. Despite this, the most widely used perceptual metrics today, such as PSNR and SSIM, are simple, shallow functions, and fail to account for many nuances of human perception. Recently, the deep learning community has found that features of the VGG network trained on ImageNet classification has been remarkably useful as a training loss for image synthesis. But how perceptual are these so-called "perceptual losses"? What elements are critical for their success? To answer these questions, we introduce a new dataset of human perceptual similarity judgments. We systematically evaluate deep features across different architectures and tasks and compare them with classic metrics. We find that deep features outperform all previous metrics by large margins on our dataset. More surprisingly, this result is not restricted to ImageNet-trained VGG features, but holds across different deep architectures and levels of supervision (supervised, self-supervised, or even unsupervised). Our results suggest that perceptual similarity is an emergent property shared across deep visual representations.

Question: What is the difference between perceptual similarity and perceptual similarity?


LTSG: Latent Topical Skip-Gram for Mutually Improving Topic Model and Vector Representations. Topic models have been widely used in discovering latent topics which are shared across documents in text mining. Vector representations, word embeddings and topic embeddings, map words and topics into a low-dimensional and dense real-value vector space, which have obtained high performance in NLP tasks. However, most of the existing models assume the results trained by one of them are perfect correct and used as prior knowledge for improving the other model. Some other models use the information trained from external large corpus to help improving smaller corpus. In this paper, we aim to build such an algorithm framework that makes topic models and vector representations mutually improve each other within the same corpus. An EM-style algorithm framework is employed to iteratively optimize both topic model and vector representations. Experimental results show that our model outperforms state-of-the-art methods on various NLP tasks.

Question: What is the EM-style algorithm framework?


Word Spotting and Recognition with Embedded Attributes. This paper addresses the problems of word spotting and word recognition on images. In word spotting, the goal is to find all instances of a query word in a dataset of images. In recognition, the goal is to recognize the content of the word image, usually aided by a dictionary or lexicon. We describe an approach in which both word images and text strings are embedded in a common vectorial subspace. This is achieved by a combination of label embedding and attributes learning, and a common subspace regression. In this subspace, images and strings that represent the same word are close together, allowing one to cast recognition and retrieval tasks as a nearest neighbor problem. Contrary to most other existing methods, our representation has a fixed length, is low dimensional, and is very fast to compute and, especially, to compare. We test our approach on four public datasets of both handwritten documents and natural images showing results comparable or better than the state-of-the-art on spotting and recognition tasks.

Question: What is the nearest neighbor problem?


ℓ<ce:inf loc=post>1</ce:inf>-penalized linear mixed-effects models for high dimensional data with application to BCI. Recently, a novel statistical model has been proposed to estimate population effects and individual variability between subgroups simultaneously, by extending Lasso methods. We will for the first time apply this so-called ℓ(1)-penalized linear regression mixed-effects model for a large scale real world problem: we study a large set of brain computer interface data and through the novel estimator are able to obtain a subject-independent classifier that compares favorably with prior zero-training algorithms. This unifying model inherently compensates shifts in the input space attributed to the individuality of a subject. In particular we are now for the first time able to differentiate within-subject and between-subject variability. Thus a deeper understanding both of the underlying statistical and physiological structures of the data is gained.

Question: What is the difference between a zero-training algorithm and a ℓ(1)-penalized linear mixed-effects model?


Learning Semantically Coherent and Reusable Kernels in Convolution Neural Nets for Sentence Classification. The purpose of this work is to empirically study desirable properties such as semantic coherence, attention mechanism and kernel reusability in Convolution Neural Networks (CNNs) for learning sentence classification tasks. We propose to learn semantically coherent kernels using clustering scheme combined with Word2Vec representation and domain knowledge such as SentiWordNet. We also suggest a technique to visualize attention mechanism of CNNs. These ideas are useful for decision explanation purpose. Reusable property enables kernels learned on one problem to be used in another problem. This helps in efficient learning as only a few additional domain specific kernels may have to be learned. Experimental results demonstrate the usefulness of our approach. The performance of the proposed approach, which uses semantic and re-usability properties, is close to that of the state-of-the-art approaches on many real-world datasets.

Question: What is the purpose of this work?


An Optimal Power Scheduling Method for Demand Response in Home Energy Management System. With the development of smart grid, residents have the opportunity to schedule their power usage in the home by themselves for the purpose of reducing electricity expense and alleviating the power peak-to-average ratio (PAR). In this paper, we first introduce a general architecture of energy management system (EMS) in a home area network (HAN) based on the smart grid and then propose an efficient scheduling method for home power usage. The home gateway (HG) receives the demand response (DR) information indicating the real-time electricity price that is transferred to an energy management controller (EMC). With the DR, the EMC achieves an optimal power scheduling scheme that can be delivered to each electric appliance by the HG. Accordingly, all appliances in the home operate automatically in the most cost-effective way. When only the real-time pricing (RTP) model is adopted, there is the possibility that most appliances would operate during the time with the lowest electricity price, and this may damage the entire electricity system due to the high PAR. In our research, we combine RTP with the inclining block rate (IBR) model. By adopting this combined pricing model, our proposed power scheduling method would effectively reduce both the electricity cost and PAR, thereby, strengthening the stability of the entire electricity system. Because these kinds of optimization problems are usually nonlinear, we use a genetic algorithm to solve this problem.

Question: What is the optimal power scheduling method for demand response in home energy management system?


High-performance complex event processing over streams. In this paper, we present the design, implementation, and evaluation of a system that executes complex event queries over real-time streams of RFID readings encoded as events. These complex event queries filter and correlate events to match specific patterns, and transform the relevant events into new composite events for the use of external monitoring applications. Stream-based execution of these queries enables time-critical actions to be taken in environments such as supply chain management, surveillance and facility management, healthcare, etc. We first propose a complex event language that significantly extends existing event languages to meet the needs of a range of RFID-enabled monitoring applications. We then describe a query plan-based approach to efficiently implementing this language. Our approach uses native operators to efficiently handle query-defined sequences, which are a key component of complex event processing, and pipeline such sequences to subsequent operators that are built by leveraging relational techniques. We also develop a large suite of optimization techniques to address challenges such as large sliding windows and intermediate result sizes. We demonstrate the effectiveness of our approach through a detailed performance analysis of our prototype implementation under a range of data and query workloads as well as through a comparison to a state-of-the-art stream processor.

Question: What is a complex event language?


An Efficient Representation for Filtrations of Simplicial Complexes. A filtration over a simplicial complex K is an ordering of the simplices of K such that all prefixes in the ordering are subcomplexes of K. Filtrations are at the core of Persistent Homology, a major tool in Topological Data Analysis. To represent the filtration of a simplicial complex, the entire filtration can be appended to any data structure that explicitly stores all the simplices of the complex such as the Hasse diagram or the recently introduced Simplex Tree [Algorithmica'14]. However, with the popularity of various computational methods that need to handle simplicial complexes, and with the rapidly increasing size of the complexes, the task of finding a compact data structure that can still support efficient queries is of great interest.  This direction has been recently pursued for the case of maintaining simplicial complexes. For instance, Boissonnat et al. [Algorithmica'17] considered storing the simplices that are maximal with respect to inclusion and Attali et al. [IJCGA'12] considered storing the simplices that block the expansion of the complex. Nevertheless, so far there has been no data structure that compactly stores the filtration of a simplicial complex, while also allowing the efficient implementation of basic operations on the complex.  In this article, we propose a new data structure called the Critical Simplex Diagram (CSD), which is a variant of the Simplex Array List [Algorithmica'17]. Our data structure allows one to store in a compact way the filtration of a simplicial complex and allows for the efficient implementation of a large range of basic operations. Moreover, we prove that our data structure is essentially optimal with respect to the requisite storage space. Finally, we show that the CSD representation admits fast construction algorithms for Flag complexes and relaxed Delaunay complexes.

Question: What is the optimal data structure for storing a filtration of a simplicial complex?


Spectrum Sensing Methods and Dynamic Spectrum Sharing in Cognitive Radio Networks: A Survey. Dynamic spectrum access and cognitive radio (CR) are emerging technologies to utilize the scarce frequency spectrum in an efficient and opportunistic manner. They have been receiving tremendous amount of well-deserved attention in the literature. One of the challenging problems in dynamic spectrum access for cognitive radios is to sense the spectrum in wide band regime of heterogeneous networks to identify the spectrum opportunities. Our main goal in this paper is to present the state-of-the-art research results in signal processing techniques for spectrum sensing in CR network to make automatic and wise decision by unlicensed secondary users to adapt their transmission parameters according to their operating RF environment and respect the primary users. Specifically, we present dynamic spectrum sharing models for spectrum access and signal processing techniques for spectrum sensing by categorizing them into five basic groups. We also present the comparison of different signal processing techniques from different perspectives including networking metrics for the evaluation of the methods e.g. how accurate and how fast can sense spectrum utilization given the various methods and how this will affect networking functionality, and pros and cons of all the methods in the context of networking systems. We also present the status, research challenges and perspectives of signal processing/detection techniques for spectrum sensing in cognitive radio networks.

Question: What is the difference between spectrum sensing and spectrum sharing?


Common mode noise modeling and analysis of dual boost PFC circuit. To achieve high efficiency PFC front stage in switching mode power supply (SMPS), dual boost PFC (DBPFC) topology shows superior characteristics compared with traditional boost PFC, but it by nature brings higher EMI noise, especially common mode (CM) noise. This paper deals with the modeling and analysis of DBPFC CM noise based on and compared with boost PFC, noise propagation equivalent circuits of both topologies are deduced, and theoretical analysis illustrates the difference. Experiments are performed to validate the EMI model and analysis.

Question: What is the difference between a boost PFC and a dual boost PFC?


Neural Task Programming: Learning to Generalize Across Hierarchical Tasks. In this work, we propose a novel robot learning framework called Neural Task Programming (NTP), which bridges the idea of few-shot learning from demonstration and neural program induction. NTP takes as input a task specification (e.g., video demonstration of a task) and recursively decomposes it into finer sub-task specifications. These specifications are fed to a hierarchical neural program, where bottom-level programs are callable subroutines that interact with the environment. We validate our method in three robot manipulation tasks. NTP achieves strong generalization across sequential tasks that exhibit hierarchal and compositional structures. The experimental results show that NTP learns to generalize well towards unseen tasks with increasing lengths, variable topologies, and changing objectives.stanfordvl.github.io/ntp/.

Question: What is a neural task programming?


Moving from syntactic to semantic organizations using JXML2OWL. Today's enterprises face critical needs in integrating disparate information spread over several data sources inside and even outside the organization. Most organizations already rely on XML standard to define their data models. Unfortunately, even when using XML to represent data, problems arise when it is necessary to integrate different data sources. Emerging Semantic Web technologies, such as ontologies, RDF, RDFS, and OWL, can play an important role in the semantic definition and integration of data. The purpose of our study is to present a framework to assist organizations to move from a syntactic data infrastructure defined in XML to a semantic data infrastructure using OWL. The framework supports mappings and fully automated instance transformation from syntactic data sources in XML format to a common shared global model defined by an ontology using Semantic Web technologies. The presented framework, JXML2OWL, allows organizations to automatically convert their XML data sources to a semantic model defined in OWL.

Question: How can I convert my XML data to OWL?


Deconstructing the Blockchain to Approach Physical Limits. The concept of a blockchain was invented by Satoshi Nakamoto to maintain a distributed ledger for an electronic payment system, Bitcoin. In addition to its security, important performance measures of a blockchain protocol are its transaction throughput, confirmation latency and confirmation reliability. Existing systems operate far away from these physical limits. In this work we introduce Prism , a new proof-of-work blockchain protocol, which can achieve 1) security against up to 50% adversarial hashing power; 2) optimal throughput up to the capacity C of the network; 3) confirmation latency for honest transactions proportional to the propagation delay D, with confirmation error probability exponentially small in the bandwidth-delay product CD; 4) eventual total ordering of all transactions. Our approach to the design of this protocol is based on deconstructing the blockchain into its basic functionalities and systematically scaling up these functionalities to approach their physical limits.

Question: How does a blockchain work?


Real-time wireless vibration monitoring system using LabVIEW. Vibration analysis provides relevant information about abnormal working condition of machine parts. Vibration measurement is prerequisite for vibration analysis which is used for condition monitoring of machinery. Also, wireless vibration monitoring has many advantages over wired monitoring. This Paper presents, implementation of a reliable and low cost wireless vibration monitoring system. Vibration measurement has been done using 3-Axis digital output MEMS Accelerometer sensor. This sensor can sense vibrations in the range 0.0156g to 8g where, 1g is 9.81m/s2. Accelerometer Sensor is interfaced with Arduino-derived microcontroller board having Atmel's AT-mega328p microcontroller. The implemented system uses ZigBee communication protocol i.e. standard IEEE 802.15.4, for wireless communication between Sensor Unit and Vibration Monitoring Unit. The wireless communication has been done using XBee RF modules. National Instruments's LabVIEW software has been used for development of graphical user interface, data-logging and alarm indication on the PC. Experimental results show continuous real-time monitoring of machine's vibrations on charts. These results, along with data-log file have been used for vibration analysis. This analysis is used to ensure safe working condition of machinery and used in predictive maintenance.

Question: What is vibration analysis?


Eclectic extraction of propositional rules from Neural Networks. Artificial Neural Network is among the most popular algorithm for supervised learning. However, Neural Networks have a well-known drawback of being a "Black Box" learner that is not comprehensible to the Users. This lack of transparency makes it unsuitable for many high risk tasks such as medical diagnosis that requires a rational justification for making a decision. Rule Extraction methods attempt to curb this limitation by extracting comprehensible rules from a trained Network. Many such extraction algorithms have been developed over the years with their respective strengths and weaknesses. They have been broadly categorized into three types based on their approach to use internal model of the Network. Eclectic Methods are hybrid algorithms that combine the other approaches to attain more performance. In this paper, we present an Eclectic method called HERETIC. Our algorithm uses Inductive Decision Tree learning combined with information of the neural network structure for extracting logical rules. Experiments and theoretical analysis show HERETIC to be better in terms of speed and performance.

Question: What is the difference between neural networks and rule extraction?


Development of vehicle tracking system using GPS and GSM modem. The ability to track vehicles is useful in many applications including security of personal vehicles, public transportation systems, fleet management and others. Furthermore, the number of vehicles on the road globally is also expected to increase rapidly. Therefore, the development of vehicle tracking system using the Global Positioning System (GPS) and Global System for Mobile Communications (GSM) modem is undertaken with the aim of enabling users to locate their vehicles with ease and in a convenient manner. The system will provide users with the capability to track vehicle remotely through the mobile network. This paper presents the development of the vehicle tracking system's hardware prototype. Specifically, the system will utilize GPS to obtain a vehicle's coordinate and transmit it using GSM modem to the user's phone through the mobile network. The main hardware components of the system are u-blox NEO-6Q GPS receiver module, u-blox LEON-G100 GSM module and Arduino Uno microcontroller. The developed vehicle tracking system demonstrates the feasibility of near real-time tracking of vehicles and improved customizability, global operability and cost when compared to existing solutions.

Question: What is the development of vehicle tracking system using GPS and GSM modem?


Facial Expression Recognition Using Enhanced Deep 3D Convolutional Neural Networks. Deep Neural Networks (DNNs) have shown to outperform traditional methods in various visual recognition tasks including Facial Expression Recognition (FER). In spite of efforts made to improve the accuracy of FER systems using DNN, existing methods still are not generalizable enough in practical applications. This paper proposes a 3D Convolutional Neural Network method for FER in videos. This new network architecture consists of 3D Inception-ResNet layers followed by an LSTM unit that together extracts the spatial relations within facial images as well as the temporal relations between different frames in the video. Facial landmark points are also used as inputs to our network which emphasize on the importance of facial components rather than the facial regions that may not contribute significantly to generating facial expressions. Our proposed method is evaluated using four publicly available databases in subject-independent and cross-database tasks and outperforms state-of-the-art methods.

Question: How to improve the accuracy of facial expression recognition using deep neural networks?


Millimeter-wave antennas for radio access and backhaul in 5G heterogeneous mobile networks. Millimeter-wave communications are expected to play a key role in future 5G mobile networks to overcome the dramatic traffic growth expected over the next decade. Such systems will severely challenge antenna technologies used at mobile terminal, access point or backhaul/fronthaul levels. This paper provides an overview of the authors' recent achievements in the design of integrated antennas, antenna arrays and high-directivity quasi-optical antennas for high data-rate 60-GHz communications.

Question: What is the difference between a millimeter-wave antenna and a quasi-optical antenna?


A Framework for the Automatic Extraction of Rules from Online Text.

Question: What is a framework for the automatic extraction of rules from online text?


Secure and light IoT protocol (SLIP) for anti-hacking. In the elemental technologies, it is necessary to realize the Internet service of things (IoT), sensors and devices, network, platform (hardware platforms, open software platform, such as specific OS platforms). Web services, data analysis and prediction, big data processing, such as security and privacy protection technology, there are a variety of techniques. These elements technology provide a specific function. The element technology is integrated with each other. However, by several techniques are integrated, it can be problems with integration of security technologies that existed for each element technology. Even if individual technologies basic security features are constituting Internet Services (CIA: Confidentiality, integrity, authentication or authorization). It also offers security technology not connected to each other. Therefore, I will look at the security technology and proposes a lightweight routing protocol indispensable for realizing a secure Internet services things.

Question: What is the difference between the security technology and the Internet service of things?


Ontology Based SMS Controller for Smart Phones. Text analysis includes lexical analysis of the text and has been widely studied and used in diverse applications. In the last decade, researchers have proposed many efficient solutions to analyze / classify large text dataset, however, analysis / classification of short text is still a challenge because 1) the data is very sparse 2) It contains noise words and 3) It is difficult to understand the syntactical structure of the text. Short Messaging Service (SMS) is a text messaging service for mobile/smart phone and this service is frequently used by all mobile users. Because of the popularity of SMS service, marketing companies nowadays are also using this service for direct marketing also known as SMS marketing.In this paper, we have proposed Ontology based SMS Controller which analyze the text message and classify it using ontology aslegitimate or spam. The proposed system has been tested on different scenarios and experimental results shows that the proposed solution is effective both in terms of efficiency and time. Keywords—Short Text Classification; SMS Spam; Text Analysis; Ontology based SMS Spam; Text Analysis and Ontology

Question: What is ontology based SMS controller?


A low-cost smart sensor for non intrusive load monitoring applications. Next generation Smart Cities have the potential to enhance the citizens quality of life and to reduce the overall energy expenditure. In particular, emerging smart metering technologies promise to greatly increase energy efficiency in the residential and industrial areas. In this context, new power metering methods such as Non-Intrusive Load Monitoring (NILM) can provide important real-time information about the contribution of any single appliance. In this paper, we present a complete hardware-software design that concentrates locally an efficient event-based supervised NILM algorithm for load disaggregation. This new kind of power analysis, which usually requires high computing capability, is executed real-time on a low-cost and easy-to-install smart meter ready for the Internet of Things (IoT).

Question: What is a smart meter?


GUSS: Solving Collections of Data Related Models Within GAMS. In many applications, optimization of a collection of problems is required where each problem is structurally the same, but in which some or all of the data defining the instance is updated. Such models are easily specified within modern modeling systems, but have often been slow to solve due to the time needed to regenerate the instance, and the inability to use advance solution information (such as basis factorizations) from previous solves as the collection is processed. We describe a new language extension, GUSS, that gathers data from different sources/symbols to define the collection of models (called scenarios), updates a base model instance with this scenario data and solves the updated model instance and scatters the scenario results to symbols in the GAMS database. We demonstrate the utility of this approach in three applications, namely data envelopment analysis, cross validation and stochastic dual dynamic programming. The language extensions are available for general use in all versions of GAMS starting with release 23.7.

Question: How to solve a collection of models in GAMS?


GestureGAN for Hand Gesture-to-Gesture Translation in the Wild. Hand gesture-to-gesture translation in the wild is a challenging task since hand gestures can have arbitrary poses, sizes, locations and self-occlusions. Therefore, this task requires a high-level understanding of the mapping between the input source gesture and the output target gesture. To tackle this problem, we propose a novel hand Gesture Generative Adversarial Network (GestureGAN). GestureGAN consists of a single generator G and a discriminator D, which takes as input a conditional hand image and a target hand skeleton image. GestureGAN utilizes the hand skeleton information explicitly, and learns the gesture-to-gesture mapping through two novel losses, the color loss and the cycle-consistency loss. The proposed color loss handles the issue of "channel pollution" while back-propagating the gradients. In addition, we present the Frechet ResNet Distance (FRD) to evaluate the quality of generated images. Extensive experiments on two widely used benchmark datasets demonstrate that the proposed GestureGAN achieves state-of-the-art performance on the unconstrained hand gesture-to-gesture translation task. Meanwhile, the generated images are in high-quality and are photo-realistic, allowing them to be used as data augmentation to improve the performance of a hand gesture classifier. Our model and code are available at https://github.com/Ha0Tang/GestureGAN.

Question: How to evaluate the quality of generated images?


Real-time 3D object pose estimation and tracking for natural landmark based visual servo. A real-time solution for estimating and tracking the 3D pose of a rigid object is presented for image-based visual servo with natural landmarks. The many state-of-the-art technologies that are available for recognizing the 3D pose of an object in a natural setting are not suitable for real-time servo due to their time lags. This paper demonstrates that a real-time solution of 3D pose estimation become feasible by combining a fast tracker such as KLT [7] [8] with a method of determining the 3D coordinates of tracking points on an object at the time of SIFT based tracking point initiation, assuming that a 3D geometric model with SIFT description of an object is known a-priori. Keeping track of tracking points with KLT, removing the tracking point outliers automatically, and reinitiating the tracking points using SIFT once deteriorated, the 3D pose of an object can be estimated and tracked in real-time. This method can be applied to both mono and stereo camera based 3D pose estimation and tracking. The former guarantees higher frame rates with about 1 ms of local pose estimation, while the latter assures of more precise pose results but with about 16 ms of local pose estimation. The experimental investigations have shown the effectiveness of the proposed approach with real-time performance.

Question: What is a natural landmark based visual servo?


Quantitative function for community detection.. We propose a quantitative function for community partition -- i.e., modularity density or D value. We demonstrate that this quantitative function is superior to the widely used modularity Q and also prove its equivalence with the objective function of the kernel k means. Both theoretical and numerical results show that optimizing the new criterion not only can resolve detailed modules that existing approaches cannot achieve, but also can correctly identify the number of communities.

Question: What is the objective function of the kernel k means?


The Smart Car Parking System Based on Iot Commanded by Android Application. Parking the car is one of the difficult task that we are facing in our day to day life. The main issue is providing the sufficient parking system. Now a days it is very hard to find the availability of parking slots. The various places(public) that is shopping mall, cinema hall etc finds it difficult to search the available parking area. This calls for the situations of an Smart car parking system which is based on IoT and commanded by Android application which are equipped with IR sensors and microcontroller (arduinouno).In this paper a small prototype of smart car parking system which is based on IoT is implemented. The paper proposed a system that the user will automatically find the parking space throught an android application via server. In addition to this we can say that the its a new way of communication between humans and the things with the help of new technology based on IoT.

Question: What is IoT?


Experienced Grey Wolf Optimizer through Reinforcement Learning and Neural Networks. In this paper, a variant of Grey Wolf Optimizer (GWO) that uses reinforcement learning principles combined with neural networks to enhance the performance is proposed. The aim is to overcome, by reinforced learning, the common challenges of setting the right parameters for the algorithm. In GWO, a single parameter is used to control the exploration/exploitation rate which influences the performance of the algorithm. Rather than using a global way to change this parameter for all the agents, we use reinforcement learning to set it on an individual basis. The adaptation of the exploration rate for each agent depends on the agent's own experience and the current terrain of the search space. In order to achieve this, an experience repository is built based on the neural network to map a set of agents' states to a set of corresponding actions that specifically influence the exploration rate. The experience repository is updated by all the search agents to reflect experience and to enhance the future actions continuously. The resulted algorithm is called Experienced Grey Wolf Optimizer (EGWO) and its performance is assessed on solving feature selection problems and on finding optimal weights for neural networks algorithm. We use a set of performance indicators to evaluate the efficiency of the method. Results over various datasets demonstrate an advance of the EGWO over the original GWO and other meta-heuristics such as genetic algorithms and particle swarm optimization.

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


Ranking Attacks Based on Vulnerability Analysis. Now that multiple-known attacks can affect one software product at the same time, it is necessary to rank and prioritize those attacks in order to establish a better defense. The purpose of this paper is to provide a set of security metrics to rank attacks based on vulnerability analysis. The vulnerability information is retrieved from a vulnerability management ontology, which integrates commonly used standards like CVE, CWE, CVSS, and CAPEC. Among the benefits of ranking attacks through the method proposed here are: a more effective mitigation or prevention of attack patterns against systems, a better foundation to test software products, and a better understanding of vulnerabilities and attacks.

Question: What is the purpose of ranking attacks?


Economic dispatch for a microgrid considering renewable energy cost functions. Microgrids are operated by a customer or a group of customers for having a reliable, clean and economic mode of power supply to meet their demand. Understanding the economics of system is a prime factor which really depends on the cost/kWh of electricity supplied. This paper presents an easy and simple method for analyzing the dispatch rate of power. An isolated microgrid with solar and wind is considered in this paper. Generation cost functions are modeled with the inclusion of investment cost and maintenance cost of resources. Economic dispatch problem is solved using the reduced gradient method. The effects on total generation cost, with the inclusion of wind energy and solar energy into a microgrid is studied and found the most profitable solution by considering different practical scenarios. The paper gives a detailed correlation between the cost function, investment cost, lifetime and the fluctuant energy forecasting of wind and solar resources. It also discusses the advantages of including the renewable energy credits for the solar panel.

Question: What is the cost of a microgrid?


Coupling design and performance analysis of rim-driven integrated motor propulsor. Coupling design of rim-driven integrated motor propulsor for vessels and underwater vehicle is presented in this paper. The main characteristic of integrated motor propulsor is that the motor is integrated in the duct of propulsor. So the coupling design of motor and propulsor is the key to the overall design. Considering the influence of the motor and duct size, the propeller was designed, and the CFD Method was used to analyze the hydrodynamic performance of the propulsor. Based on the air-gap magnetic field of permanent magnet motor and the equivalent magnetic circuit, the integrated motor electromagnetic model was proposed, and the finite element method was used to analyze the motor electromagnetic field. Finally, the simulation of the integrated motor starting process with the load of propulsor torque was carried out, and the results meets the design specifications.

Question: How to design a motor for a propulsor?


Learning representations by back-propagating errors. We describe a new learning procedure, back-propagation, for networks of neurone-like units. The procedure repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector. As a result of the weight adjustments, internal 'hidden' units which are not part of the input or output come to represent important features of the task domain, and the regularities in the task are captured by the interactions of these units. The ability to create useful new features distinguishes back-propagation from earlier, simpler methods such as the perceptron-convergence procedure1.

Question: What is back-propagation?


Cache in the air: exploiting content caching and delivery techniques for 5G systems. The demand for rich multimedia services over mobile networks has been soaring at a tremendous pace over recent years. However, due to the centralized architecture of current cellular networks, the wireless link capacity as well as the bandwidth of the radio access networks and the backhaul network cannot practically cope with the explosive growth in mobile traffic. Recently, we have observed the emergence of promising mobile content caching and delivery techniques, by which popular contents are cached in the intermediate servers (or middleboxes, gateways, or routers) so that demands from users for the same content can be accommodated easily without duplicate transmissions from remote servers; hence, redundant traffic can be significantly eliminated. In this article, we first study techniques related to caching in current mobile networks, and discuss potential techniques for caching in 5G mobile networks, including evolved packet core network caching and radio access network caching. A novel edge caching scheme based on the concept of content-centric networking or information-centric networking is proposed. Using trace-driven simulations, we evaluate the performance of the proposed scheme and validate the various advantages of the utilization of caching content in 5G mobile networks. Furthermore, we conclude the article by exploring new relevant opportunities and challenges.

Question: What is the difference between the 5G mobile networks and the current mobile networks?


Forecasting and Trading Currency Volatility : An Application of Recurrent Neural Regression and Model Combination by. In this paper, we examine the use of GARCH models, Neural Network Regression (NNR), Recurrent Neural Network (RNN) regression and model combinations for forecasting and trading currency volatility, with an application to the GBP/USD and USD/JPY exchange rates. Both the results of the NNR/RNN models and the model combination results are benchmarked against the simpler GARCH alternative. The idea of developing a nonlinear nonparametric approach to forecast FX volatility, identify mispriced options and subsequently develop a trading strategy based upon this process is intuitively appealing. Using daily data from December 1993 through April 1999, we develop alternative FX volatility forecasting models. These models are then tested out-of-sample over the period April 1999-May 2000, not only in terms of forecasting accuracy, but also in terms of trading efficiency: In order to do so, we apply a realistic volatility trading strategy using FX option straddles once mispriced options have been identified. Allowing for transaction costs, most trading strategies retained produce positive returns. RNN models appear as the best single modelling approach yet, somewhat surprisingly, model combination which has the best overall performance in terms of forecasting accuracy, fails to improve the RNN-based volatility trading results. Another conclusion from our results is that, for the period and currencies considered, the currency option market was inefficient and/or the pricing formulae applied by market participants were inadequate. * Christian Dunis is Girobank Professor of Banking and Finance at Liverpool Business School and Director of CIBEF (E-mail: cdunis@globalnet.co.uk). The opinions expressed herein are not those of Girobank. ** Xuehuan Huang is an Associate Researcher with CIBEF. *** CIBEF – Centre for International Banking, Economics and Finance, JMU, John

Question: Forecasting and Trading Currency Volatility : An Application of Recurrent Neural Regression and Model Combination by. In this paper, we examine the use of GARCH models, Neural Network Regression (NNR), Recurrent Neural Network (RNN) regression and model combinations for forecasting and trading currency volatility, with an


A wireless slanted optrode array with integrated micro leds for optogenetics. This paper presents a wireless-enabled, flexible optrode array with multichannel micro light-emitting diodes (μ-LEDs) for bi-directional wireless neural interface. The array integrates wirelessly addressable μ-LED chips with a slanted polymer optrode array for precise light delivery and neural recording at multiple cortical layers simultaneously. A droplet backside exposure (DBE) method was developed to monolithically fabricate varying-length optrodes on a single polymer platform. In vivo tests in rat brains demonstrated that the μ-LEDs were inductively powered and controlled using a wireless switched-capacitor stimulator (SCS), and light-induced neural activity was recorded with the optrode array concurrently.

Question: What is an optrode?


Gist: A Solver for Probabilistic Games. Gist is a tool that (a) solves the qualitative analysis problem of turn-based probabilistic games with ω-regular objectives; and (b) synthesizes reasonable environment assumptions for synthesis of unrealizable specifications. Our tool provides the first and efficient implementations of several reduction-based techniques to solve turn-based probabilistic games, and uses the analysis of turn-based probabilistic games for synthesizing environment assumptions for unrealizable specifications.

Question: What is a solver for probabilistic games?