AUGMENTED REALITY VISUALIZATION TOOL FOR KINGS STORMWATER BRIDGE. Augmented Reality (AR) is an emerging visualization technology that promises to change the way people see the world. The main goal of AR is to "enhance" a person's vision of the real world with useful information about the surrounding environment. Primarily this can be done through the use of a portable computer such as a laptop and a head-mounted display (HMD) unit. When such technology is made available to scientists and engineers, Augmented Reality can provide them with the freedom to do scientific analysis at the actual location of an experiment. With this idea in mind, the goal of this paper is to investigate the development of an AR system as a visualization tool for structural engineers. This particular application will focus on monitoring the "health" of the Kings Stormwater Bridge structure. Unfortunately, since AR is still in its infancy, much of the research devoted to it has focused mainly on technical issues rather than on the development of how this technology would be used by scientists. This has led to the development of "functional" AR systems that primarily can only be operated by the developer of the system ([1], [2], [3], [12], [14]). This paper avoids this trend by focusing on the design of the visualization system so that it is useful for structural engineers. As a result, this should produce a more robust Augmented Reality system that can be used by many different users. Thus, it is the goal of this paper to show how scientists would benefit by designing a visualization system with their needs in mind.

Question: How can augmented reality be used to monitor the health of a structure?


Shape driven kernel adaptation in Convolutional Neural Network for robust facial trait recognition. One key challenge of facial trait recognition is the large non-rigid appearance variations due to some irrelevant real world factors, such as viewpoint and expression changes. In this paper, we explore how the shape information, i.e. facial landmark positions, can be explicitly deployed into the popular Convolutional Neural Network (CNN) architecture to disentangle such irrelevant non-rigid appearance variations. First, instead of using fixed kernels, we propose a kernel adaptation method to dynamically determine the convolutional kernels according to the spatial distribution of facial landmarks, which helps learning more robust features. Second, motivated by the intuition that different local facial regions may demand different adaptation functions, we further propose a tree-structured convolutional architecture to hierarchically fuse multiple local adaptive CNN subnetworks. Comprehensive experiments on WebFace, Morph II and MultiPIE databases well validate the effectiveness of the proposed kernel adaptation method and tree-structured convolutional architecture for facial trait recognition tasks, including identity, age and gender recognition. For all the tasks, the proposed architecture consistently achieves the state-of-the-art performances.

Question: How to disentangle non-rigid appearance variations?


Flexible UHF/VHF Vivaldi antenna for broadband and gas balloon applications. A Vivaldi flexible antenna with a −6 dB bandwidth from 150 MHz to 2000 MHz is introduced. The antenna is fabricated on a 60∗60 cm2 silicone substrate. In this paper, we present the design, realization and performances of this wide band and directive antenna. The proposed structure is lightweight, easy to realize and does not require any matching network. The targeted application is the radio-localization of signal source emissions from helium gas balloon. Six antennas are integrated on the bottom side of the balloon and the information is recovered with a cable that serves to its stabilization.

Question: What is a helium gas balloon?


Undefined By Data: A Survey of Big Data Definitions. The term big data has become ubiquitous. Owing to a shared origin between academia, industry and the media there is no single unified definition, and various stakeholders provide diverse and often contradictory definitions. The lack of a consistent definition introduces ambiguity and hampers discourse relating to big data. This short paper attempts to collate the various definitions which have gained some degree of traction and to furnish a clear and concise definition of an otherwise ambiguous term.

Question: What is big data?


A High-Voltage Pulse Generator With Continuously Variable Pulsewidth Based on a Modified PFN. The amount of stored energy is an important issue in the low-droop and high-precision pulse generation. Pulse forming network (PFN) simulating an open-ended transmission line seems to be useful due to its minimum stored energy. The drawbacks of the PFN include low-output pulse quality and constant pulsewidth, which limit their applications. Another limitation of generating rectangular pulses using PFN is that half of the charged voltage appears in the output when delivering the entire stored energy to the matched load. This paper proposes a structure of PFN-based pulse generator that overcomes the above-mentioned limitations. For continuous pulsewidth adjustment, a lossless scheme is presented. By increasing the ratio of the load magnitude to the characteristic impedance, the amplitude of the output pulse voltage would be approximately equal to that of the charged voltage. Therefore, the semiconductor switches can be utilized in the PFN pulse generator easily. In addition, the pulse quality is enhanced and faster rising time is obtained in comparison with the conventional PFNs. In this proposal, the stored energy quantity is limited to three times the energy needed for the output pulse in the case of  $R_{L}=10Z_{0}$ . To verify the proposed structure, a prototype is simulated using PSPICE software, and the experimental results are presented.

Question: What is the difference between a PFN and a PFN-based pulse generator?


And Lead Us (Not) into Persuasion…? Persuasive Technology and the Ethics of Communication. The paper develops ethical guidelines for the development and usage of persuasive technologies (PT) that can be derived from applying discourse ethics to this type of technologies. The application of discourse ethics is of particular interest for PT, since 'persuasion' refers to an act of communication that might be interpreted as holding the middle between 'manipulation' and 'convincing'. One can distinguish two elements of discourse ethics that prove fruitful when applied to PT: the analysis of the inherent normativity of acts of communication ('speech acts') and the Habermasian distinction between 'communicative' and 'strategic rationality' and their broader societal interpretation. This essay investigates what consequences can be drawn if one applies these two elements of discourse ethics to PT.

Question: What is the difference between persuasive technology and persuasive communication?


Feature Selection for Maximizing the Area Under the ROC Curve. Feature selection is an important pre-processing step for solving classification problems. A good feature selection method may not only improve the performance of the final classifier, but also reduce the computational complexity of it. Traditionally, feature selection methods were developed to maximize the classification accuracy of a classifier. Recently, both theoretical and experimental studies revealed that a classifier with the highest accuracy might not be ideal in real-world problems. Instead, the Area Under the ROC Curve (AUC) has been suggested as the alternative metric, and many existing learning algorithms have been modified in order to seek the classifier with maximum AUC. However, little work was done to develop new feature selection methods to suit the requirement of AUC maximization. To fill this gap in the literature, we propose in this paper a novel algorithm, called AUC and Rank Correlation coefficient Optimization (ARCO) algorithm. ARCO adopts the general framework of a well-known method, namely minimal redundancy- maximal-relevance (mRMR) criterion, but defines the terms "relevance" and "redundancy" in totally different ways. Such a modification looks trivial from the perspective of algorithmic design. Nevertheless, experimental study on four gene expression data sets showed that feature subsets obtained by ARCO resulted in classifiers with significantly larger AUC than the feature subsets obtained by mRMR. Moreover, ARCO also outperformed the Feature Assessment by Sliding Thresholds algorithm, which was recently proposed for AUC maximization, and thus the efficacy of ARCO was validated.

Question: What is the area under the ROC curve?


A Softwarization Architecture for UAVs and WSNs as Part of the Cloud Environment. The development of Unmanned Aerial Vehicles (UAV) and wireless sensor network (WSN) applications depends on the availability of resources in UAVs and sensor nodes. In traditional applications, UAVs obtain the required data from specific sensors. However, this tightly coupled architecture restricts the usage of the infrastructure for specific applications. This paper proposes a softwarization architecture for UAVs and WSNs. In addition, the higher layers are proposed to be part of the cloud to benefit from the various cloud opportunities. The proposed architecture is based on the network softwarization concept and the Software Defined Networks (SDN) concept along with the Network Functional Virtualization (NFV) technologies. These concepts are associated with decoupling the hardware infrastructure from the control layer that virtualizes the infrastructure resources for the higher layers. The architecture is illustrated with an agricultural example for a cooperative system of a WSN and UAVs. We implement a prototype system that consists of a sensing node, UAV, WSN controller, UAV controller, and orchestration layer to provide a proof of concept for the proposed architecture.

Question: What is the difference between a UAV and a drone?


Moderating variables of music training-induced neuroplasticity: a review and discussion. A large body of literature now exists to substantiate the long-held idea that musicians' brains differ structurally and functionally from non-musicians' brains. These differences include changes in volume, morphology, density, connectivity, and function across many regions of the brain. In addition to the extensive literature that investigates these differences cross-sectionally by comparing musicians and non-musicians, longitudinal studies have demonstrated the causal influence of music training on the brain across the lifespan. However, there is a large degree of inconsistency in the findings, with discordance between studies, laboratories, and techniques. A review of this literature highlights a number of variables that appear to moderate the relationship between music training and brain structure and function. These include age at commencement of training, sex, absolute pitch (AP), type of training, and instrument of training. These moderating variables may account for previously unexplained discrepancies in the existing literature, and we propose that future studies carefully consider research designs and methodologies that control for these variables.

Question: What is the difference between a review and a discussion?


Fast hyperparameter selection for graph kernels via subsampling and multiple kernel learning. Model selection is one of the most computationally expensive tasks in a machine learning application. When dealing with kernel methods for structures, the choice with the largest impact on the overall performance is the selection of the feature bias, i.e. the choice of the concrete kernel for structures. Each kernel in turn exposes several hyper-parameters which also need to be fine tuned. Multiple Kernel Learning offers a way to approach this computational bottleneck by generating a combination of different kernels under different parametric settings. However, this solution still requires the computation of many large kernel matrices. In this paper we propose a method to efficiently select a small number of kernels on a subset of the original data, gaining a dramatic reduction in the runtime without a significant loss of predictive performance.

Question: How to select a subset of kernels?


"An Odd Kind of Pleasure": Differentiating Emotional Challenge in Digital Games. Recent work introduced the notion of emotional challenge as a means to afford more unique and diverse gaming experiences. However, players' experience of emotional challenge has received little empirical attention. It remains unclear whether players enjoy it and what exactly constitutes the challenge thereof. We surveyed 171 players about a challenging or an emotionally challenging experience, and analyzed their responses with regards to what made the experience challenging, their emotional response, and the relation to core player experience constructs. We found that emotional challenge manifested itself in different ways, by confronting players with difficult themes or decisions, as well as having them deal with intense emotions. In contrast to more'conventional' challenge, emotional challenge evoked a wider range of negative emotions and was appreciated significantly more by players. Our findings showcase the appeal of uncomfortable gaming experiences, and extend current conceptualizations of challenge in games.

Question: What is the appeal of uncomfortable gaming experiences?


Protocol considerations for a prefix-caching proxy for multimedia streams. The increasing popularity of multimedia streaming applications introduces new challenges in content distribution. Web-initiated multimedia streams typically experience high start-up delay, due to large protocol overheads and the poor delay, throughput, and loss properties of the Internet. Internet service providers can improve performance by caching the initial segment (the pre x) of popular streams at proxies near the requesting clients. The proxy can initiate transmission to the client while simultaneously requesting the remainder of the stream from the server. This paper analyzes the challenges of realizing a pre x-caching service in the context of the IETF's Real-Time Streaming Protocol (RTSP), a multimedia streaming protocol that derives from HTTP. We describe how to exploit existing RTSP features, such as the Range header, and how to avoid several round-trip delays by caching protocol information at the proxy. Based on our experiences, we propose extensions to RTSP that would ease the development of new multimedia proxy services. In addition, we discuss how caching the partial contents of multimedia streams introduces new challenges in cache coherency and feedback control. Then, we brie y present our preliminary implementation of pre x caching on a Linux-based PC, and describe how the proxy interoperates with the RealNetworks server and client.

Question: What is the protocol for streaming multimedia?


"Oops, I Did It Again" - Security of One-Time Signatures Under Two-Message Attacks. One-time signatures (OTS) are called one-time, because the accompanying security reductions only guarantee security under single-message attacks. However, this does not imply that efficient attacks are possible under two-message attacks. Especially in the context of hash-based OTS (which are basic building blocks of recent standardization proposals) this leads to the question if accidental reuse of a one-time key pair leads to immediate loss of security or to graceful degradation. In this work we analyze the security of the most prominent hash-based OTS, Lamport's scheme, its optimized variant, and WOTS, under different kinds of two-message attacks. Interestingly, it turns out that the schemes are still secure under two message attacks, asymptotically. However, this does not imply anything for typical parameters. Our results show that for Lamport's scheme, security only slowly degrades in the relevant attack scenarios and typical parameters are still somewhat secure, even in case of a two-message attack. As we move on to optimized Lamport and its generalization WOTS, security degrades faster and faster, and typical parameters do not provide any reasonable level of security under two-message attacks.

Question: What is the security of OTS under two-message attacks?


Analysis and Simulation of Theme Park Queuing System. It has been an important issue to improve customers' satisfaction in theme parks for which become a major role of recreation in our daily life. Waiting for rides has been identified as a factor decreasing satisfaction. A previous study indicated that a virtual queuing system can reduce the total waiting time so the customer's satisfaction is improved. The results from a simulation tool Arena show that an index Satisfaction Value (SV) increases when the queuing system is introduced. In this study, a more complex scenario of theme park queuing system (TPQS) is first designed, followed by comparison of a number of combinations of the rides with various waiting time and distribution factors. Analysis is also carried out.

Question: What is a virtual queuing system?


Detection of Red Tomato on Plants using Image Processing Techniques. Tomatoes are the best-known grown fruit in greenhouses that have been recently attempted to be picked up automatically. Tomato is a plant which its fruit does not ripe simultaneously, therefore it is necessary to develop an algorithm to distinguish red tomatoes. In the current study, a new segmentation algorithm based on region growing was proposed for guiding a robot to pick up red tomatoes. For this purpose, several colour images of tomato plants were acquired in a greenhouse. The colour images of tomato were captured under natural light, without any artificial lighting equipment. To recognize red tomatoes form non-red ones, at first background of images were removed. For removing the background, subtraction of red and green components (R-G) was applied. Usually tomatoes touch together, so separating touching tomatoes was next step. In this step, the watershed algorithm was used that was followed by improving process. Afterwards, red tomato was detected by the region growing approach. Results obtained from testing the developed algorithm showed an encouraging accuracy (82.38%) to develop an expert system for online recognition of red

Question: What is the best way to detect red tomatoes?


Very-Heavy Sled Training for Improving Horizontal-Force Output in Soccer Players.. BACKGROUND Sprint running acceleration is a key feature of physical performance in team sports, and recent literature shows that the ability to generate large magnitudes of horizontal ground-reaction force and mechanical effectiveness of force application are paramount. The authors tested the hypothesis that very-heavy loaded sled sprint training would induce an improvement in horizontal-force production, via an increased effectiveness of application.   METHODS Training-induced changes in sprint performance and mechanical outputs were computed using a field method based on velocity-time data, before and after an 8-wk protocol (16 sessions of 10- × 20-m sprints). Sixteen male amateur soccer players were assigned to either a very-heavy sled (80% body mass sled load) or a control group (unresisted sprints).   RESULTS The main outcome of this pilot study is that very-heavy sled-resisted sprint training, using much greater loads than traditionally recommended, clearly increased maximal horizontal-force production compared with standard unloaded sprint training (effect size of 0.80 vs 0.20 for controls, unclear between-groups difference) and mechanical effectiveness (ie, more horizontally applied force; effect size of 0.95 vs -0.11, moderate between-groups difference). In addition, 5-m and 20-m sprint performance improvements were moderate and small for the very-heavy sled group and small and trivial for the control group, respectively. Practical Applications: This brief report highlights the usefulness of very-heavy sled (80% body mass) training, which may suggest value for practical improvement of mechanical effectiveness and maximal horizontal-force capabilities in soccer players and other team-sport athletes.   RESULTS This study may encourage further research to confirm the usefulness of very-heavy sled in this context.

Question: What is the usefulness of very heavy sled training?


A system model for energy efficient green-IoT network. The notion of IoT has ignited umpteen possibilities of different heterogeneous devices to blend within a network. In IoT, especially sensor devices are mostly deployed in an extremely resource constrained environment and thus pose the necessity to extend the capability and life expectancy of these kind of devices in terms of energy consumption. Since, the devices in IoT have limited energy sources they often run on battery with a certain energy capability, the deployment of green communication and system model in IoT has been a core challenging issue. This paper addresses the energy efficiency issues across diverse IoT driven networks by proposing a system model for G-IoT and energy efficient scheme for the IoT devices to extend the life expectancy of the whole IoT network.

Question: What is the life expectancy of an IoT device?


Design of a Real-Time Micro-Winch Controller for Kite-Power Applications Master ' s Thesis in Embedded. Airborne wind energy is a technology to extract energy from high altitude winds. This technology is under heavy development by several companies and universities. An actual problem with the commercialization of the technology is the reliability and safety of the system. In this thesis a real time environment suitable to perform research and further development of the prototype steering and depower control is proposed. Additionally, the overload prevention of the kite lines is researched. This thesis presents a method to estimate the tension on the kite control tapes using only one tension sensor. Thus, reducing the amount of hardware needed to protect the kite from overloads. The method relies on the characterization of the powertrain efficiency and can be used to estimate the tensions at high loads. An algorithm to limit the forces on the steering lines by depowering the kite is shown; it controls the depower state of the kite based on the desired depower state, the actual tension, and previous tensions on the KCU's tapes. The tensions history is used to calculate a higher depower state to prevent future overloads, this reduces the amount of action needed by the motors and enable the system to use a brake to save energy. The limiter output is used as an input to a position controller, which allows the project to use off the shelf solutions to build the KCU prototype. The controller was implemented in a real time system and is able to run as fast as 20 Hz being the communication protocol the execution time bottleneck. The control algorithms were tested using a mathematical model of the kite, the environment, and trajectory control inputs from FreeKiteSim. Three scenarios were considered for the model test, normal operation, overload operation without tension limitation, and overload operation with tension limitation. The apparent wind speed during the reel out pha

Question: How to design a real time micro-winch controller for kite-power applications?


Inference Over Distribution of Posterior Class Probabilities for Reliable Bayesian Classification and Object-Level Perception. State of the art Bayesian classification approaches typically maintain a posterior distribution over possible classes given available sensor observations (images). Yet, while these approaches fuse all classifier outputs thus far, they do not provide any indication regarding how reliable the posterior classification is, thus limiting its functionality in terms of autonomous systems and robotics. On the other hand, current deep learning based classifiers provide an uncertainty measure, thereby quantifying model uncertainty. However, they do so on a single frame basis and do not consider a sequential framework. In this letter, we develop a novel approach that infers a distribution over posterior class probabilities, while accounting for model uncertainty. This distribution enables reasoning about uncertainty in the posterior classification and, therefore, is of prime importance for robust classification, object-level perception in uncertain and ambiguous scenarios, and for safe autonomy in general. The distribution of the posterior class probability has no known analytical solution; thus, we propose to approximate this distribution via sampling. We evaluate our approach in simulation and using real images fed into a convolutional neural network classifier.

Question: What is the difference between a posterior distribution and a posterior class probability?


Accurate Prediction of Phase Transitions in Compressed Sensing via a Connection to Minimax Denoising. Compressed sensing posits that, within limits, one can undersample a sparse signal and yet reconstruct it accurately. Knowing the precise limits to such undersampling is important both for theory and practice. We present a formula that characterizes the allowed undersampling of generalized sparse objects. The formula applies to approximate message passing (AMP) algorithms for compressed sensing, which are here generalized to employ denoising operators besides the traditional scalar soft thresholding denoiser. This paper gives several examples including scalar denoisers not derived from convex penalization-the firm shrinkage nonlinearity and the minimax nonlinearity-and also nonscalar denoisers-block thresholding, monotone regression, and total variation minimization. Let the variables ε = k/N and δ = n/N denote the generalized sparsity and undersampling fractions for sampling the k-generalized-sparse N-vector x0 according to y=Ax0. Here, A is an n×N measurement matrix whose entries are iid standard Gaussian. The formula states that the phase transition curve δ = δ(ε) separating successful from unsuccessful reconstruction of x0 by AMP is given by δ = M(ε|Denoiser) where M(ε|Denoiser) denotes the per-coordinate minimax mean squared error (MSE) of the specified, optimally tuned denoiser in the directly observed problem y = x + z. In short, the phase transition of a noiseless undersampling problem is identical to the minimax MSE in a denoising problem. We prove that this formula follows from state evolution and present numerical results validating it in a wide range of settings. The above formula generates numerous new insights, both in the scalar and in the nonscalar cases.

Question: What is the minimax MSE of a denoising problem?


Performance Modeling and Scalability Optimization of Distributed Deep Learning Systems. Big deep neural network (DNN) models trained on large amounts of data have recently achieved the best accuracy on hard tasks, such as image and speech recognition. Training these DNNs using a cluster of commodity machines is a promising approach since training is time consuming and compute-intensive. To enable training of extremely large DNNs, models are partitioned across machines. To expedite training on very large data sets, multiple model replicas are trained in parallel on different subsets of the training examples with a global parameter server maintaining shared weights across these replicas. The correct choice for model and data partitioning and overall system provisioning is highly dependent on the DNN and distributed system hardware characteristics. These decisions currently require significant domain expertise and time consuming empirical state space exploration.  This paper develops performance models that quantify the impact of these partitioning and provisioning decisions on overall distributed system performance and scalability. Also, we use these performance models to build a scalability optimizer that efficiently determines the optimal system configuration that minimizes DNN training time. We evaluate our performance models and scalability optimizer using a state-of-the-art distributed DNN training framework on two benchmark applications. The results show our performance models estimate DNN training time with high estimation accuracy and our scalability optimizer correctly chooses the best configurations, minimizing the training time of distributed DNNs.

Question: How to choose the best partitioning and provisioning for a distributed deep learning system?


Security and Privacy in Cloud Computing: Vision, Trends, and Challenges. Cloud computing offers cost-effective solutions via a variety of flexible services. However, security concerns related to managing data, applications, and interactions hamper the rapid deployment of cloud-based services on a large scale. Although many solutions exist, efficiency, scalability, and provable security still have issues that need to be properly addressed. This article explores the various challenges, existing solutions, and limitations of cloud security, with a focus on data utilization management aspects, including data storage, data analytics, and access control. The article concludes with a discussion on future research directions that might lead to more trustworthy cloud security and privacy.

Question: What is the cloud computing security?


Sensor networks for medical care. Sensor networks have the potential to greatly impact many aspects of medical care. By outfitting patients with wireless, wearable vital sign sensors, collecting detailed real-time data on physiological status can be greatly simplified. However, there is a significant gap between existing sensor network systems and the needs of medical care. In particular, medical sensor networks must support multicast routing topologies, node mobility, a wide range of data rates and high degrees of reliability, and security. This paper describes our experiences with developing a combined hardware and software platform for medical sensor networks, called CodeBlue. CodeBlue provides protocols for device discovery and publish/subscribe multihop routing, as well as a simple query interface that is tailored for medical monitoring. We have developed several medical sensors based on the popular MicaZ and Telos mote designs, including a pulse oximeter, EKG and motion-activity sensor. We also describe a new, miniaturized sensor mote designed for medical use. We present initial results for the CodeBlue prototype demonstrating the integration of our medical sensors with the publish/subscribe routing substrate. We have experimentally validated the prototype on our 30-node sensor network testbed, demonstrating its scalability and robustness as the number of simultaneous queries, data rates, and transmitting sensors are varied. We also study the effect of node mobility, fairness across multiple simultaneous paths, and patterns of packet loss, confirming the system's ability to maintain stable routes despite variations in node location and

Question: What is the medical sensor network?


Multiple-resonator-based bandpass filters. This article describes a class of recently developed multiple-mode-resonator-based bandpass filters for ultra-wide-band (UWB) transmission systems. These filters have many attractive features, including a simple design, compact size, low loss and good linearity in the UWB, enhanced out-of-band rejection, and easy integration with other circuits/antennas. In this article, we present a variety of multiple-mode resonators with stepped-impedance or stub-loaded nonuniform configurations and analyze their properties based on the transmission line theory. Along with the frequency dispersion of parallel-coupled transmission lines, we design and implement various filter structures on planar, uniplanar, and hybrid transmission line geometries.

Question: What is a multiple-mode resonator?


Evaluating radial, axial and transverse flux topologies for 'in-wheel' motor. Three different motor topologies, (1) radial, (2) axial and (3) transverse flux, are investigated for 'in-wheel' electric propulsion system. The design constraints are: 350mm/spl times/100mm package limitation, 48 V DC bus restriction and per phase inverter current limited to 500 A/sup pk/. In addition, magnet pole pairs are fixed to 9 due to limitation in 'position sensor' capacity. The paper summarizes results obtained from analytical design followed by 2D and 3D static and transient finite element analysis accompanied with experimental verification for the radial flux topology.

Question: What is the difference between radial, axial and transverse flux topologies?


Three-dimensional menus: A survey and taxonomy. Various interaction techniques have been developed in the field of virtual and augmented reality. Whereas techniques for object selection, manipulation, travel, and wayfinding have already been covered in existing taxonomies in some detail, application control techniques have not yet been sufficiently considered. However, they are needed by almost every mixed reality application, e.g. for choosing from alternative objects or options. For this purpose a great variety of distinct three-dimensional (3D) menu selection techniques is available. This paper surveys existing 3D menus from the corpus of literature and classifies them according to various criteria. The taxonomy introduced here assists developers of interactive 3D applications to better evaluate their options when choosing, optimizing, and implementing a 3D menu technique. Since the taxonomy spans the design space for 3D menu solutions, it also aids researchers in identifying opportunities to improve or create novel virtual menu techniques. r 2006 Elsevier Ltd. All rights reserved.

Question: What is a 3D menu?


Face spoofing detection from single images using texture and local shape analysis. Current face biometric systems are vulnerable to spoofing attacks. A spoofing attack occurs when a person tries to masquerade as someone else by falsifying data and thereby gaining illegitimate access. Inspired by image quality assessment, characterisation of printing artefacts and differences in light reflection, the authors propose to approach the problem of spoofing detection from texture analysis point of view. Indeed, face prints usually contain printing quality defects that can be well detected using texture and local shape features. Hence, the authors present a novel approach based on analysing facial image for detecting whether there is a live person in front of the camera or a face print. The proposed approach analyses the texture and gradient structures of the facial images using a set of low-level feature descriptors, fast linear classification scheme and score level fusion. Compared to many previous works, the authors proposed approach is robust and does not require user-cooperation. In addition, the texture features that are used for spoofing detection can also be used for face recognition. This provides a unique feature space for coupling spoofing detection and face recognition. Extensive experimental analysis on three publicly available databases showed excellent results compared to existing works.

Question: What is the difference between face recognition and face spoofing detection?


Scalable Metadata Management Techniques for Ultra-Large Distributed Storage Systems - A Systematic Review. The provisioning of an efficient ultra-large scalable distributed storage system for expanding cloud applications has been a challenging job for researchers in academia and industry. In such an ultra-large-scale storage system, data are distributed on multiple storage nodes for performance, scalability, and availability. The access to this distributed data is through its metadata, maintained by multiple metadata servers. The metadata carries information about the physical address of data and access privileges. The efficiency of a storage system highly depends on effective metadata management. This research presents an extensive systematic literature analysis of metadata management techniques in storage systems. This research work will help researchers to find the significance of metadata management and important parameters of metadata management techniques for storage systems. Methodical examination of metadata management techniques developed by various industry and research groups is described. The different metadata distribution techniques lead to various taxonomies. Furthermore, the article investigates techniques based on distribution structures and key parameters of metadata management. It also presents strengths and weaknesses of individual existing techniques that will help researchers to select the most appropriate technique for specific applications. Finally, it discusses existing challenges and significant research directions in metadata management for researchers.

Question: What is metadata?


ScienceDirect The 2 nd International Workshop on Communications and Sensor Networks ( ComSense-2014 ) A cloud-based healthcare framework for security and patients ' data privacy using wireless body area networks. The recent developments in remote healthcare systems have witnessed significant interests from IT industry (Microsoft, Google, VMware etc) that provide ubiquitous and easily deployable healthcare systems. These systems provide a platform to share medical information, applications, and infrastructure in a ubiquitous and fully automated manner. Communication security and patients' data privacy are the aspects that would increase the confidence of users in such remote healthcare systems. This paper presents a secure cloud-based mobile healthcare framework using wireless body area networks (WBANs). The research work presented here is twofold: first, it attempts to secure the inter-sensor communication by multi-biometric based key generation scheme in WBANs; and secondly, the electronic medical records (EMRs) are securely stored in the hospital community cloud and privacy of the patients' data is preserved. The evaluation and analysis shows that the proposed multi-biometric based mechanism provides significant security measures due to its highly efficient key generation mechanism.

Question: What is the difference between a cloud-based healthcare framework and a cloud-based healthcare system?


Automatic Conversational Helpdesk Solution using Seq2Seq and Slot-filling Models. Helpdesk is a key component of any large IT organization, where users can log a ticket about any issue they face related to IT infrastructure, administrative services, human resource services, etc. Normally, users have to assign appropriate set of labels to a ticket so that it could be routed to right domain expert who can help resolve the issue. In practice, the number of labels are very large and organized in form of a tree. It is non-trivial to describe the issue completely and attach appropriate labels unless one knows the cause of the problem and the related labels. Sometimes domain experts discuss the issue with the users and change the ticket labels accordingly, without modifying the ticket description. This results in inconsistent and badly labeled data, making it hard for supervised algorithms to learn from. In this paper, we propose a novel approach of creating a conversational helpdesk system, which will ask relevant questions to the user, for identification of the right category and will then raise a ticket on users' behalf. We use attention based seq2seq model to assign the hierarchical categories to tickets. We use a slot filling model to help us decide what questions to ask to the user, if the top-k model predictions are not consistent. We also present a novel approach to generate training data for the slot filling model automatically based on attention in the hierarchical classification model. We demonstrate via a simulated user that the proposed approach can give us a significant gain in accuracy on ticket-data without asking too many questions to users. Finally, we also show that our seq2seq model is as versatile as other approaches on publicly available datasets, as state of the art approaches.

Question: What is a conversational helpdesk?


Oxytocin Attenuates Amygdala Reactivity to Fear in Generalized Social Anxiety Disorder. Patients with generalized social anxiety disorder (GSAD) exhibit heightened activation of the amygdala in response to social cues conveying threat (eg, fearful/angry faces). The neuropeptide oxytocin (OXT) decreases anxiety and stress, facilitates social encounters, and attenuates amygdala reactivity to threatening faces in healthy subjects. The goal of this study was to examine the effects of OXT on fear-related amygdala reactivity in GSAD and matched healthy control (CON) subjects. In a functional magnetic resonance imaging study utilizing a double-blind placebo-controlled within-subjects design, we measured amygdala activation to an emotional face matching task of fearful, angry, and happy faces following acute intranasal administration of OXT (24 IU or 40.32 μg) and placebo in 18 GSAD and 18 CON subjects. Both the CON and GSAD groups activated bilateral amygdala to all emotional faces during placebo, with the GSAD group exhibiting hyperactivity specifically to fearful faces in bilateral amygdala compared with the CON group. OXT had no effect on amygdala activity to emotional faces in the CON group, but attenuated the heightened amygdala reactivity to fearful faces in the GSAD group, such that the hyperactivity observed during the placebo session was no longer evident following OXT (ie, normalization). These findings suggest that OXT has a specific effect on fear-related amygdala activity, particularly when the amygdala is hyperactive, such as in GSAD, thereby providing a brain-based mechanism of the impact of OXT in modulating the exaggerated processing of social signals of threat in patients with pathological anxiety.

Question: What is the effect of oxytocin on amygdala reactivity in generalized social anxiety disorder?


Analysis of a linear series-fed rectangular microstrip antenna array. A new method is proposed to analyze a linear series-fed microstrip antenna array which maintains accuracy while not requiring a full-wave analysis of the entire structure. The method accounts for mutual coupling between patches as well as the patch excitations by the feed lines.

Question: How to analyze a linear series-fed rectangular microstrip antenna array?


An implementation of the earliest deadline first algorithm in Linux. Recently, many projects have been started to introduce some real-time mechanisms into general purpose operating systems (GPOS) in order to make them capable of providing the users with some temporal guarantees. Many of these projects focused especially on Linux for its capillary and widespread adoption throughout many different research and industrial environments.  By tracking the kernel release cycle, we propose an efficient Earliest Deadline First implementation in the form of a patch-set against the 2.6.27 version, that is the latest released one, as of now. Our implementation provides the user with the possibility to choose SCHED_EDF as one of the possible scheduling policies for a task, with an enhanced version of the standard algorithm. In fact, we propose a new approach to shared resources' access which, differently from many other previous existing works, does not require the user to specify any parameters about the critical sections every task will enter during its execution.

Question: What is the earliest deadline first algorithm?


Question: What is the AR face database?


Anchored Correlation Explanation: Topic Modeling with Minimal Domain Knowledge. While generative models such as Latent Dirichlet Allocation (LDA) have proven fruitful in topic modeling, they often require detailed assumptions and careful specification of hyperparameters. Such model complexity issues only compound when trying to generalize generative models to incorporate human input. We introduce Correlation Explanation (CorEx), an alternative approach to topic modeling that does not assume an underlying generative model, and instead learns maximally informative topics through an information-theoretic framework. This framework naturally generalizes to hierarchical and semi-supervised extensions with no additional modeling assumptions. In particular, word-level domain knowledge can be flexibly incorporated within CorEx through anchor words, allowing topic separability and representation to be promoted with minimal human intervention. Across a variety of datasets, metrics, and experiments, we demonstrate that CorEx produces topics that are comparable in quality to those produced by unsupervised and semi-supervised variants of LDA.

Question: What is a topic model?


Towards a Methodology for Building Ontologies. We outline some requirements for a comprehensive methodology for building ontologies, and review some important work that has been done in the area which could contribute to this goal. We describe our own experiences in constructing a signi cant ontology, emphasising the ontology capture phase. We rst consider the very general issue of categorisation in modelling, and relate it to the process of ontology capture. We then describe the procedure that we used to identify the terms and produce de nitions. We describe a successful way to handle ambiguous terms, which can be an enormous obstacle to reaching a shared understanding. Other important ndings include: it may not be necessary to identify competency questions before building the ontology; the meta-ontology can be chosen after detailed text de nitions are produced; de ning terms which are 'cognitively basic' rst can lead to less re-work. AIAI-TR-183 Page 1 of 13

Question: What is a cognitively basic term?


Semantic Granularity in Ontology-Driven Geographic Information Systems. The integration of information of different kinds, such as spatial and alphanumeric at different levels of detail, is a challenge. While a solution is not reached, it is widely recognized that the need to integrate information is so pressing that it does not matter if detail is lost, as long as integration is achieved. This paper shows the potential for information retrieval at different levels of granularity inside the framework of information systems based on ontologies. Ontologies are theories that use a specific vocabulary to describe entities, classes, properties and functions related to a certain view of the world. The use of an ontology, translated into an active information system component, leads to ontology-driven information systems and, in the specific case of GIS, leads to what we call ontology-driven geographic information systems.

Question: What is the potential for information retrieval at different levels of granularity inside the framework of information systems based on ontologies?


Male and Female Spoken Language Differences : Stereotypes and Evidence. Male speech and female speech have been observed to differ in their form, topic, content, and use. Early writers were largely introspective in their analyses; more recent work has begun to provide empirical evidence. Men may be more loquacious and directive; they use more nonstandard forms, talk more about sports, money, and business, and more frequently refer to time, space, quantity, destructive action, perceptual attributes, physical movements, and objects. Women are often more supportive, polite, and expressive, talk more about home and family, and use more words implying feeling, evaluation, interpretation, and psychological state. A comprehensive theory of "genderlect" must include information about linguistic features under a multiplicity of conditions.

Question: What are the differences between male and female speech?


Statistical Phrase-Based Translation. We propose a new phrase-based translation model and decoding algorithm that enables us to evaluate and compare several, previously proposed phrase-based translation models. Within our framework, we carry out a large number of experiments to understand better and explain why phrase-based models outperform word-based models. Our empirical results, which hold for all examined language pairs, suggest that the highest levels of performance can be obtained through relatively simple means: heuristic learning of phrase translations from word-based alignments and lexical weighting of phrase translations. Surprisingly, learning phrases longer than three words and learning phrases from high-accuracy wordlevel alignment models does not have a strong impact on performance. Learning only syntactically motivated phrases degrades the performance of our systems.

Question: What is the highest level of performance that can be obtained through relatively simple means?


Simple sequence repeat markers that identify Claviceps species and strains. Claviceps purpurea is a pathogen that infects most members of Pooideae, a subfamily of Poaceae, and causes ergot, a floral disease in which the ovary is replaced with a sclerotium. When the ergot body is accidently consumed by either man or animal in high enough quantities, there is extreme pain, limb loss and sometimes death. This study was initiated to develop simple sequence repeat (SSRs) markers for rapid identification of C. purpurea. SSRs were designed from sequence data stored at the National Center for Biotechnology Information database. The study consisted of 74 ergot isolates, from four different host species, Lolium perenne, Poa pratensis, Bromus inermis, and Secale cereale plus three additional Claviceps species, C. pusilla, C. paspali and C. fusiformis. Samples were collected from six different counties in Oregon and Washington over a 5-year period. Thirty-four SSR markers were selected, which enabled the differentiation of each isolate from one another based solely on their molecular fingerprints. Discriminant analysis of principle components was used to identify four isolate groups, CA Group 1, 2, 3, and 4, for subsequent cluster and molecular variance analyses. CA Group 1 consisting of eight isolates from the host species P. pratensis, was separated on the cluster analysis plot from the remaining three groups and this group was later identified as C. humidiphila. The other three groups were distinct from one another, but closely related. These three groups contained samples from all four of the host species. These SSRs are simple to use, reliable and allowed clear differentiation of C. humidiphila from C. purpurea. Isolates from the three separate species, C. pusilla, C. paspali and C. fusiformis, also amplified with these markers. The SSR markers developed in this study will be helpful in defining the population structure and genetics of Claviceps strains. They will also pro

Question: What is the difference between ergot and claviceps?


A Particle Swarm Optimization-Based Maximum Power Point Tracking Algorithm for PV Systems Operating Under Partially Shaded Conditions. A photovoltaic (PV) generation system (PGS) is becoming increasingly important as renewable energy sources due to its advantages such as absence of fuel cost, low maintenance requirement, and environmental friendliness. For large PGS, the probability for partially shaded condition (PSC) to occur is also high. Under PSC, the P-V curve of PGS exhibits multiple peaks, which reduces the effectiveness of conventional maximum power point tracking (MPPT) methods. In this paper, a particle swarm optimization (PSO)-based MPPT algorithm for PGS operating under PSC is proposed. The standard version of PSO is modified to meet the practical consideration of PGS operating under PSC. The problem formulation, design procedure, and parameter setting method which takes the hardware limitation into account are described and explained in detail. The proposed method boasts the advantages such as easy to implement, system-independent, and high tracking efficiency. To validate the correctness of the proposed method, simulation, and experimental results of a 500-W PGS will also be provided to demonstrate the effectiveness of the proposed technique.

Question: How to design a PGS for a 500-W PV system?


Design of a Water Environment Monitoring System Based on Wireless Sensor Networks. A water environmental monitoring system based on a wireless sensor network is proposed. It consists of three parts: data monitoring nodes, data base station and remote monitoring center. This system is suitable for the complex and large-scale water environment monitoring, such as for reservoirs, lakes, rivers, swamps, and shallow or deep groundwaters. This paper is devoted to the explanation and illustration for our new water environment monitoring system design. The system had successfully accomplished the online auto-monitoring of the water temperature and pH value environment of an artificial lake. The system's measurement capacity ranges from 0 to 80 °C for water temperature, with an accuracy of ±0.5 °C; from 0 to 14 on pH value, with an accuracy of ±0.05 pH units. Sensors applicable to different water quality scenarios should be installed at the nodes to meet the monitoring demands for a variety of water environments and to obtain different parameters. The monitoring system thus promises broad applicability prospects.

Question: How to design a water environment monitoring system based on wireless sensor networks?


Integration of Standardized and Non-Standardized Product Data. Smart products adapt to environments, process contexts, users and other products. Standardized product data such as BMECat, eCl@ss, and EPC global formats are designed to support the exploitation of product data and therefore contribute to more efficient supply chains. Non-standardized product data mainly target soft benefits which targets the exploration of product data [Su04]. We present an extended product data model (SPDO) that integrates standardized and non-standardized product data. The SPDO model consists of five product information types that receive product data and, if available, associated explicit semantics. Explicit semantics are used for advanced services, such as compatibility testing or product bundling. Exemplified by the GS1 approach, we present how standardized product data is retrieved via RFID-based Electronic Product Codes (EPC) and stored in SPDO instantiations.

Question: What is the difference between standardized and non-standardized product data?


Malware Classification with Deep Convolutional Neural Networks. In this paper, we propose a deep learning framework for malware classification. There has been a huge increase in the volume of malware in recent years which poses a serious security threat to financial institutions, businesses and individuals. In order to combat the proliferation of malware, new strategies are essential to quickly identify and classify malware samples so that their behavior can be analyzed. Machine learning approaches are becoming popular for classifying malware, however, most of the existing machine learning methods for malware classification use shallow learning algorithms (e.g. SVM). Recently, Convolutional Neural Networks (CNN), a deep learning approach, have shown superior performance compared to traditional learning algorithms, especially in tasks such as image classification. Motivated by this success, we propose a CNN-based architecture to classify malware samples. We convert malware binaries to grayscale images and subsequently train a CNN for classification. Experiments on two challenging malware classification datasets, Malimg and Microsoft malware, demonstrate that our method achieves better than the state-of-the-art performance. The proposed method achieves 98.52% and 99.97% accuracy on the Malimg and Microsoft datasets respectively.

Question: What is a malware classification?


Parametric Hidden Markov Models for Gesture Recognition. ÐA new method for the representation, recognition, and interpretation of parameterized gesture is presented. By parameterized gesture we mean gestures that exhibit a systematic spatial variation; one example is a point gesture where the relevant parameter is the two-dimensional direction. Our approach is to extend the standard hidden Markov model method of gesture recognition by including a global parametric variation in the output probabilities of the HMM states. Using a linear model of dependence, we formulate an expectation-maximization (EM) method for training the parametric HMM. During testing, a similar EM algorithm simultaneously maximizes the output likelihood of the PHMM for the given sequence and estimates the quantifying parameters. Using visually derived and directly measured three-dimensional hand position measurements as input, we present results that demonstrate the recognition superiority of the PHMM over standard HMM techniques, as well as greater robustness in parameter estimation with respect to noise in the input features. Last, we extend the PHMM to handle arbitrary smooth (nonlinear) dependencies. The nonlinear formulation requires the use of a generalized expectation-maximization (GEM) algorithm for both training and the simultaneous recognition of the gesture and estimation of the value of the parameter. We present results on a pointing gesture, where the nonlinear approach permits the natural spherical coordinate parameterization of pointing direction. Index TermsÐGesture recognition, hidden Markov models, expectation-maximization algorithm, time-series modeling, computer vision.

Question: How do you recognize gestures?


Component-Based Robotic Engineering (Part II). This article discusses the role of software components as architectural units of large, possibly distributed, software-intensive robotic systems. The focus is on technologies to manage the heterogeneity of hardware, computational, and communication resources and on design techniques to assemble components into systems .A component-based system is a composition of components, and the way components interact with other components and with the computational environment greatly affects the flexibility of the entire system and the reusability of individual functionality.

Question: What is a component-based system?


Massively Parallel NUMA-aware Hash Joins. Driven by the two main hardware trends increasing main memory and massively parallel multi-core processing in the past few years, there has been much research e ort in parallelizing well-known join algorithms. However, the non-uniform memory access (NUMA) of these architectures to main memory has only gained limited attention in the design of these algorithms. We study recent proposals of main memory hash join implementations and identify their major performance problems on NUMA architectures. We then develop a NUMA-aware hash join for massively parallel environments, and show how the speci c implementation details a ect the performance on a NUMA system. Our experimental evaluation shows that a carefully engineered hash join implementation outperforms previous high performance hash joins by a factor of more than two, resulting in an unprecedented throughput of 3/4 billion join argument tuples per second.

Question: What is a hash join?


Comparison of Feedback Control Techniques for Torque-Vectoring Control of Fully Electric Vehicles. Fully electric vehicles (FEVs) with individually controlled powertrains can significantly enhance vehicle response to steering-wheel inputs in both steady-state and transient conditions, thereby improving vehicle handling and, thus, active safety and the fun-to-drive element. This paper presents a comparison between different torque-vectoring control structures for the yaw moment control of FEVs. Two second-order sliding-mode controllers are evaluated against a feedforward controller combined with either a conventional or an adaptive proportional-integral-derivative (PID) controller. Furthermore, the potential performance and robustness benefits arising from the integration of a body sideslip controller with the yaw rate feedback control system are assessed. The results show that all the evaluated controllers are able to significantly change the understeer behavior with respect to the baseline vehicle. The PID-based controllers achieve very good vehicle performance in steady-state and transient conditions, whereas the controllers based on the sliding-mode approach demonstrate a high level of robustness against variations in the vehicle parameters. The integrated sideslip controller effectively maintains the sideslip angle within acceptable limits in the case of an erroneous estimation of the tire-road friction coefficient.

Question: What is the difference between a feedforward controller and a PID controller?


3D Soft Tissue Analysis – Part 1: Sagittal Parameters. The aim of this study was to develop a reliable threedimensional (3D) analysis of facial soft tissues. We determined the mean sagittal 3D values and relationships between sagittal skeletal parameters, and digitally recorded 3D soft tissue parameters. A total of 100 adult patients (nª = 53, nº = 47) of Caucasian ethnic origin were included in this study. Patients with syndromes, cleft lip and palate, noticeable asymmetry or anomalies in the number of teeth were excluded. Arithmetic means for seven sagittal 3D soft tissue parameters were calculated. The parameters were analyzed biometrically in terms of their reliability and gender-specific differences. The 3D soft tissue values were further analyzed for any correlations with sagittal cephalometric values. Reproducible 3D mean values were defined for 3D soft tissue parameters. We detected gender-specific differences among the parameters. Correlations between the sagittal 3D soft tissue and cephalometric measurements were statistically significant. 3D soft tissue analysis provides additional information on the sagittal position of the jaw bases and on intermaxillary sagittal relations. Further studies are needed to integrate 3D soft tissue analysis in future treatment planning and assessment as a supportive diagnostic tool. Ziel dieser Untersuchung war die Entwicklung einer reliablen dreidimensionalen (3D) Analyse der Gesichtsweichteile. Es sollten sagittale 3D-Durchschnittswerte bestimmt werden und Beziehungen zwischen sagittalen skelettalen Parametern und digital erfassten 3D-Weichteilparametern dargestellt werden. In die Studie eingeschlossen wurden 100 erwachsene Patienten (nª = 53, nº = 47) kaukasischer Herkunft. Ausgeschlossen wurden Patienten mit Syndromen, LKGSSpalten, auffälligen Asymmetrien oder Anomalien der Zahnzahl. Es wurden arithmetische Mittelwerte für sieben sagittale 3DWeichteilparameter ermittelt. Die Parameter wurden biometrisch hinsichtlich ihr

Question: What is the difference between the sagittal 3D soft tissue and the sagittal 3D skeletal parameters?


Is Proof More Cost-Effective Than Testing?. ÐThis paper describes the use of formal development methods on an industrial safety-critical application. The Z notation was used for documenting the system specification and part of the design, and the SPARK subset of Ada was used for coding. However, perhaps the most distinctive nature of the project lies in the amount of proof that was carried out: proofs were carried out both at the Z levelÐapproximately 150 proofs in 500 pagesÐand at the SPARK code levelÐapproximately 9,000 verification conditions generated and discharged. The project was carried out under UK Interim Defence Standards 00-55 and 00-56, which require the use of formal methods on safety-critical applications. It is believed to be the first to be completed against the rigorous demands of the 1991 version of these standards. The paper includes comparisons of proof with the various types of testing employed, in terms of their efficiency at finding faults. The most striking result is that the Z proof appears to be substantially more efficient at finding faults than the most efficient testing phase. Given the importance of early fault detection, we believe this helps to show the significant benefit and practicality of large-scale proof on projects of this kind. Index TermsÐSafety-critical software, formal specification, SPARK, specification proof, code proof, proof vs. testing, industrial case study.

Question: What is the difference between proof and testing?


Exploring steganography: Seeing the unseen. Steganography is the art of hiding information in ways that prevent the detection of hidden messages. It includes a vast array of secret communications methods that conceal the message's very existence. These methods include invisible inks, microdots, character arrangement, digital signatures, covert channels, and spread spectrum communications. Steganography and cryptography are cousins in the spycraft family: cryptography scrambles a message so it cannot be understood while steganography hides the message so it cannot be seen. In this article the authors discuss image files and how to hide information in them, and discuss results obtained from evaluating available steganographic software. They argue that steganography by itself does not ensure secrecy, but neither does simple encryption. If these methods are combined, however, stronger encryption methods result. If an encrypted message is intercepted, the interceptor knows the text is an encrypted message. But with steganography, the interceptor may not know that a hidden message even exists. For a brief look at how steganography evolved, there is included a sidebar titled "Steganography: Some History."

Question: What is steganography?


Modular bases for fluid dynamics. We present a new approach to fluid simulation that balances the speed of model reduction with the flexibility of grid-based methods. We construct a set of composable reduced models, or tiles, which capture spatially localized fluid behavior. We then precompute coupling terms so that these models can be rearranged at runtime. To enforce consistency between tiles, we introduce constraint reduction. This technique modifies a reduced model so that a given set of linear constraints can be fulfilled. Because dynamics and constraints can be solved entirely in the reduced space, our method is extremely fast and scales to large domains.

Question: How to precompute coupling terms?


Parallel online exact sum for Java8. Java8 introduced the notion of streams that is a new data structure and supports multi-core processors. When the sum method is called for a stream of floating-point numbers, the summation is calculated at high-speed by applying MapReduce, which distributes computations to cores. However, since floating-point calculation causes an error, simple adaptation of this method can not determine the result uniquely. Then, in this study, we develop a summation program that can be applied to a stream with MapReduce. Our method can calculate at high-speed with keeping correctly rounded.

Question: How to calculate the sum of a stream of floating-point numbers?


EMBROIDERED FULLY TEXTILE WEARABLE AN- TENNA FOR MEDICAL MONITORING APPLICATIONS. Telecommunication systems integrated within garments and wearable products are such methods by which medical devices are making an impact on enhancing healthcare provisions around the clock. These garments when fully developed will be capable of alerting and demanding attention if and when required along with minimizing hospital resources and labour. Furthermore, they can play a major role in preventative ailments, health irregularities and unforeseen heart or brain disorders in apparently healthy individuals. This work presents the feasibility of investigating an Ultra-WideBand (UWB) antenna made from fully textile materials that were used for the substrate as well as the conducting parts of the designed antenna. Simulated and measured results show that the proposed antenna design meets the requirements of wide working bandwidth and provides 17GHz bandwidth with compact size, washable and flexible materials. Results in terms of return loss, bandwidth, radiation pattern, current distribution as well as gain and efficiency are presented to validate the usefulness of the current manuscript design. The work presented here has profound implications for future studies of a standalone suite that may one day help to provide wearer (patient) with such reliable and comfortable medical monitoring techniques. Received 12 April 2011, Accepted 23 May 2011, Scheduled 10 June 2011 * Corresponding author: Mai A. Rahman Osman (mai.rahman@fkegraduate.utm.my).

Question: What is the purpose of this work?


Computational analysis of the safe zone for the antegrade lag screw in posterior column fixation with the anterior approach in acetabular fracture: A cadaveric study.. OBJECTIVE The fluoroscopically-guided procedure of antegrade posterior lag screw in posterior column fixation through anterior approach is technique-dependent and requires an experienced surgeon. The purpose of this study was to establish the safe zone for the antegrade posterior lag screw by using computational analysis.   METHOD The virtual three-dimensional model of 178 hemi-pelvises was created from the CT data (DICOM format) by using Mimics® program, and were used to measure the safe zone of antegrade lag screw fixation on the inner table of the iliac wing, and the largest diameter of cylindrical implant inside safe zone. The central point (point A) of the cylinder was assessed and was compared with the intersection point (point B) between the linea terminalis and the anterior border of the sacroiliac articulation.   RESULTS The safe zone was triangular with an average area of 670.4mm2 (range, 374.8-1084.5mm2). The largest diameter of the cylinder was a mean 7.4mm (range, 5.0-10.0mm). When height was under 156.3cm, the diameter of the cylindrical implant was smaller than 7.0mm (p<0.001, regression coefficient=0.09). The linear distance between points A and B was 32.5mm (range, 19.2-49.3mm). Point A was far enough away from the well-positioned anterior column plate to prevent collision between the two.   CONCLUSION The safe zone was shaped like a triangle, and was large enough for multiple screws. Considering the straight-line distance between points A and B, the central screw can be fixed without overlapping with the well-positioned anterior column plate at the point between holes 2 and 3.

Question: What is the safe zone for the antegrade lag screw in posterior column fixation with the anterior approach in acetabular fracture?


A study of phase noise in CMOS oscillators. This paper presents a study of phase noise in two inductorless CMOS oscillators. First-order analysis of a linear oscillatory system leads to a noise shaping function and a new definition of Q. A linear model of CMOS ring oscillators is used to calculate their phase noise, and three phase noise phenomena, namely, additive noise, high-frequency multiplicative noise, and low-frequency multiplicative noise, are identified and formulated. Based on the same concepts, a CMOS relaxation oscillator is also analyzed. Issues and techniques related to simulation of noise in the time domain are described, and two prototypes fabricated in a 0.5m CMOS technology are used to investigate the accuracy of the theoretical predictions. Compared with the measured results, the calculated phase noise values of a 2-GHz ring oscillator and a 900-MHz relaxation oscillator at 5 MHz offset have an error of approximately 4 dB.

Question: What is the phase noise of a CMOS oscillator?


A Study of Fonts Designed for Screen Display. This study examined the readabiity.and subjective preferences of a set of fonts designed for screen display. Two new binary bitmap fonts performed well, suggesting that designers hould consider incorporating similar attributes into default fonts for online type.

Question: What is the readability of a font?


SmartPaste: Learning to Adapt Source Code. Deep Neural Networks have been shown to succeed at a range of natural language tasks such as machine translation and text summarization. While tasks on source code (ie, formal languages) have been considered recently, most work in this area does not attempt to capitalize on the unique opportunities offered by its known syntax and structure. In this work, we introduce SmartPaste, a first task that requires to use such information. The task is a variant of the program repair problem that requires to adapt a given (pasted) snippet of code to surrounding, existing source code. As first solutions, we design a set of deep neural models that learn to represent the context of each variable location and variable usage in a data flow-sensitive way. Our evaluation suggests that our models can learn to solve the SmartPaste task in many cases, achieving 58.6% accuracy, while learning meaningful representation of variable usages.

Question: What is a data flow-sensitive way?


Parallel AES algorithm for fast Data Encryption on GPU. With the improvement of cryptanalysis, More and more applications are starting to use Advanced Encryption Standard (AES) instead of Data Encryption Standard (DES) to protect their information security. However, current implementations of AES algorithm suffer from huge CPU resource consumption and low throughput. In this paper, we studied the technologies of GPU parallel computing and its optimized design for cryptography. Then, we proposed a new algorithm for AES parallel encryption, and designed and implemented a fast data encryption system based on GPU. The test proves that our approach can accelerate the speed of AES encryption significantly.

Question: What is AES?


Interest flooding attack and countermeasures in Named Data Networking. Distributed Denial of Service (DDoS) attacks are an ongoing problem in today's Internet, where packets from a large number of compromised hosts thwart the paths to the victim site and/or overload the victim machines. In a newly proposed future Internet architecture, Named Data Networking (NDN), end users request desired data by sending Interest packets, and the network delivers Data packets upon request only, effectively eliminating many existing DDoS attacks. However, an NDN network can be subject to a new type of DDoS attack, namely Interest packet flooding. In this paper we investigate effective solutions to mitigate Interest flooding. We show that NDN's inherent properties of storing per packet state on each router and maintaining flow balance (i.e., one Interest packet retrieves at most one Data packet) provides the basis for effective DDoS mitigation algorithms. Our evaluation through simulations shows that the solution can quickly and effectively respond and mitigate Interest flooding.

Question: What is the difference between a DDoS attack and a flooding attack?


CertiCoq : A verified compiler for Coq. CertiCoq is a mechanically verified, optimizing compiler for Coq that bridges the gap between certified high-level programs and their translation to machine language. We outline its design as well as the main foundational and engineering challenges involved in building and certifying a compiler for Coq in Coq.

Question: What is a verified compiler?


EdgeConnect: Generative Image Inpainting with Adversarial Edge Learning. Over the last few years, deep learning techniques have yielded significant improvements in image inpainting. However, many of these techniques fail to reconstruct reasonable structures as they are commonly over-smoothed and/or blurry. This paper develops a new approach for image inpainting that does a better job of reproducing filled regions exhibiting fine details. We propose a two-stage adversarial model EdgeConnect that comprises of an edge generator followed by an image completion network. The edge generator hallucinates edges of the missing region (both regular and irregular) of the image, and the image completion network fills in the missing regions using hallucinated edges as a priori. We evaluate our model end-to-end over the publicly available datasets CelebA, Places2, and Paris StreetView, and show that it outperforms current state-ofthe-art techniques quantitatively and qualitatively.

Question: What is the difference between generative image inpainting and image completion?


Toward Computational Fact-Checking. Our news are saturated with claims of "facts" made from data. Database research has in the past focused on how to answer queries, but has not devoted much attention to discerning more subtle qualities of the resulting claims, e.g., is a claim "cherry-picking"? This paper proposes a framework that models claims based on structured data as parameterized queries. A key insight is that we can learn a lot about a claim by perturbing its parameters and seeing how its conclusion changes. This framework lets us formulate practical fact-checking tasks—reverse-engineering (often intentionally) vague claims, and countering questionable claims—as computational problems. Along with the modeling framework, we develop an algorithmic framework that enables efficient instantiations of "meta" algorithms by supplying appropriate algorithmic building blocks. We present real-world examples and experiments that demonstrate the power of our model, efficiency of our algorithms, and usefulness of their results.

Question: What is a "fact-checking" algorithm?


Evaluation: from Precision, Recall and F-measure to ROC, Informedness, Markedness and Correlation. Commonly used evaluation measures including Recall, Precision, F-Measure and Rand Accuracy are biased and should not be used without clear understanding of the biases, and corresponding identification of chance or base case levels of the statistic. Using these measures a system that performs worse in the objective sense of Informedness, can appear to perform better under any of these commonly used measures. We discuss several concepts and measures that reflect the probability that prediction is informed versus chance. Informedness and introduce Markedness as a dual measure for the probability that prediction is marked versus chance. Finally we demonstrate elegant connections between the concepts of Informedness, Markedness, Correlation and Significance as well as their intuitive relationships with Recall and Precision, and outline the extension from the dichotomous case to the general multi-class case.

Question: What is the difference between precision and recall?


A Dual-Fed Aperture-Coupled Microstrip Antenna With Polarization Diversity. This communication presents a dual-port reconfigurable square patch antenna with polarization diversity for 2.4 GHz. By controlling the states of four p-i-n diodes on the patch, the polarization of the proposed antenna can be switched among linear polarization (LP), left- or right-hand circular polarization (CP) at each port. The air substrate and aperture-coupled feed structure are employed to simplify the bias circuit of p-i-n diodes. With high isolation and low cross-polarization level in LP modes, both ports can work simultaneously as a dual linearly polarized antenna for polarimetric radars. Different CP waves are obtained at each port, which are suitable for addressing challenges ranging from mobility, adverse weather conditions and non-line-of-sight applications. The antenna has advantages of simple biasing network, easy fabrication and adjustment, which can be widely applied in polarization diversity applications.

Question: What is a dual-fed aperture-coupled microstrip antenna?


An interface for targeted collection of common sense knowledge using a mixture model. We present a game-based interface for acquiring common sense knowledge. In addition to being interactive and entertaining, our interface guides the knowledge acquisition process to learn about the most salient characteristics of a particular concept. We use statistical classification methods to discover the most informative characteristics in the Open Mind Common Sense knowledge base, and use these characteristics to play a game of 20 Questions with the user. Our interface also allows users to enter knowledge more quickly than a more traditional knowledge-acquisition interface. An evaluation showed that users enjoyed the game and that it increased the speed of knowledge acquisition.

Question: What is a game-based interface?


Stress and hippocampal plasticity.. The hippocampus is a target of stress hormones, and it is an especially plastic and vulnerable region of the brain. It also responds to gonadal, thyroid, and adrenal hormones, which modulate changes in synapse formation and dendritic structure and regulate dentate gyrus volume during development and in adult life. Two forms of structural plasticity are affected by stress: Repeated stress causes atrophy of dendrites in the CA3 region, and both acute and chronic stress suppresses neurogenesis of dentate gyrus granule neurons. Besides glucocorticoids, excitatory amino acids and N-methyl-D-aspartate (NMDA) receptors are involved in these two forms of plasticity as well as in neuronal death that is caused in pyramidal neurons by seizures and by ischemia. The two forms of hippocampal structural plasticity are relevant to the human hippocampus, which undergoes a selective atrophy in a number of disorders, accompanied by deficits in declarative episodic, spatial, and contextual memory performance. It is important, from a therapeutic standpoint, to distinguish between a permanent loss of cells and a reversible atrophy.

Question: What is the difference between atrophy and atrophy?


A hot/cool-system analysis of delay of gratification: dynamics of willpower.. A 2-system framework is proposed for understanding the processes that enable--and undermine--self-control or "willpower" as exemplified in the delay of gratification paradigm. A cool, cognitive "know" system and a hot, emotional "go" system are postulated. The cool system is cognitive, emotionally neutral, contemplative, flexible, integrated, coherent, spatiotemporal, slow, episodic, and strategic. It is the seat of self-regulation and self-control. The hot system is the basis of emotionality, fears as well as passions--impulsive and reflexive--initially controlled by innate releasing stimuli (and, thus, literally under "stimulus control"): it is fundamental for emotional (classical) conditioning and undermines efforts at self-control. The balance between the hot and cool systems is determined by stress, developmental level, and the individual's self-regulatory dynamics. The interactions between these systems allow explanation of findings on willpower from 3 decades of research.

Question: What is a hot/cool-system analysis of delay of gratification?


Universal approximation bounds for superpositions of a sigmoidal function. Approximation properties of a class of artificial neural networks are established. It is shown that feedforward networks with one layer of sigmoidal nonlinearities achieve inte grated squared error of order O(l/n), where n is the number of nodes. The function appruximated is assumed to have a bound on the first moment of the magnitude distribution of the Fourier transform. The nonlinear parameters associated with the sigmoidal nodes, as well as the parameters of linear combination, are adjusted in the approximation. In contrast, it is shown that for series expansions with n terms, in which only the parameters of linear combination are adjusted, the integrated squared approximation error cannot be made smaller than order 1/n2/d uniformly for functions satisfying the same smoothness assumption, where d is the dimension of the input to the function. For the class of functions examined here, the approximation rate and the parsimony of the parameterization of the networks are surprisingly advantageous in high-dimensional settings.

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


Confronting the Challenge of Quality Diversity. In contrast to the conventional role of evolution in evolutionary computation (EC) as an optimization algorithm, a new class of evolutionary algorithms has emerged in recent years that instead aim to accumulate as diverse a collection of discoveries as possible, yet where each variant in the collection is as fit as it can be. Often applied in both neuroevolution and morphological evolution, these new quality diversity (QD) algorithms are particularly well-suited to evolution's inherent strengths, thereby offering a promising niche for EC within the broader field of machine learning. However, because QD algorithms are so new, until now no comprehensive study has yet attempted to systematically elucidate their relative strengths and weaknesses under different conditions. Taking a first step in this direction, this paper introduces a new benchmark domain designed specifically to compare and contrast QD algorithms. It then shows how the degree of alignment between the measure of quality and the behavior characterization (which is an essential component of all QD algorithms to date) impacts the ultimate performance of different such algorithms. The hope is that this initial study will help to stimulate interest in QD and begin to unify the disparate ideas in the area.

Question: What is a benchmark domain?


Subdermal neo-umbilicoplasty in abdominoplasty. Umbilicoplasty is an important surgical procedure in abdominoplasty, regardless of the technique used. An unaesthetic umbilicus often irreversibly affects surgical outcomes. This study describes the experience of our team with the subdermal neo-umbilicoplasty technique and assesses patient satisfaction with the appearance of the new umbilicus. Fifty-eight patients with abdominal deformity underwent abdominoplasty with subdermal neo-umbilicoplasty. Patients were followed up for at least 1 year with photographic documentation, assessment of patient satisfaction, and evaluation of eventual postoperative complications. Postoperative complications included one case of shallow umbilicus, four cases of superficial necrosis, and one case of midline deviation. No patient required surgical revision. There was a high level of patient satisfaction with the natural-looking umbilicus. Subdermal neo-umbilicoplasty resulted in low postoperative complications and provided a new, natural-looking umbilicus without external scars. Level of evidence: Level IV, therapeutic study

Question: What is the subdermal neo-umbilicoplasty technique?


The Past, Present, and Future of Silicon Photonics. The pace of the development of silicon photonics has quickened since 2004 due to investment by industry and government. Commercial state-of-the-art CMOS silicon-on-insulator (SOI) foundries are now being utilized in a crucial test of 1.55-mum monolithic optoelectronic (OE) integration, a test sponsored by the Defense Advanced Research Projects Agency (DARPA). The preliminary results indicate that the silicon photonics are truly CMOS compatible. R&D groups have now developed 10-100-Gb/s electro-optic modulators, ultrafast Ge-on-Si photodetectors, efficient fiber-to-waveguide couplers, and Si Raman lasers. Electrically pumped silicon lasers are under intense investigation, with several approaches being tried; however, lasing has not yet been attained. The new paradigm for the Si-based photonic and optoelectric integrated circuits is that these chip-scale networks, when suitably designed, will operate at a wavelength anywhere within the broad spectral range of 1.2-100 mum, with cryocooling needed in some cases

Question: What is the future of silicon photonics?


Scholarly communication and bibliometrics. Why devote an ARIST chapter to scholarly communication and bibliometrics, and why now? Bibliometrics already is a frequently covered ARIST topic, with chapters such as that by White and McCain (1989) on bibliometrics generally, White and McCain (1997) on visualization of literatures, Wilson and Hood (2001) on informetric laws, and Tabah (2001) on literature dynamics. Similarly, scholarly communication has been addressed in other ARIST chapters such as Bishop and Star (1996) on social informatics and digital libraries, Schamber (1994) on relevance and information behavior, and many earlier chapters on information needs and uses. More than a decade ago, the first author addressed the intersection of scholarly communication and bibliometrics with a journal special issue and an edited book (Borgman, 1990; Borgman & Paisley, 1989), and she recently examined interim developments (Borgman, 2000a, 2000c). This review covers the decade (1990–2000) since the comprehensive 1990 volume, citing earlier works only when necessary to explain the foundation for recent developments. Given the amount of attention these topics have received, what is new and exciting enough to warrant a full chapter in 2001? What is new is that electronic scholarly communication is reaching critical mass, and CHAPTER 1

Question: What is new and exciting enough to warrant a full chapter in 2001?


Lyapunov Approach for the stabilization of the Inverted Spherical Pendulum. A nonlinear controller is presented for the stabilization of the spherical inverted pendulum system. The control strategy is based on the Lyapunov approach in conjunction with LaSalle's invariance principle. The proposed controller is able to bring the pendulum to the unstable upright equilibrium point with the position of the movable base at the origin. The obtained closed-loop system has a very large domain of attraction, that can be as large as desired, for any initial position of the pendulum which lies above the horizontal plane

Question: How to stabilize the spherical pendulum?


The relationship between trauma and beliefs about hearing voices: a study of psychiatric and non-psychiatric voice hearers.. BACKGROUND Cognitive models suggest that distress associated with auditory hallucinations is best understood in terms of beliefs about voices. What is less clear is what factors govern such beliefs. This study aimed to explore the way in which traumatic life events contribute towards beliefs about voices and any associated distress.   METHOD The difference in the nature and prevalence of traumatic life events and associated psychological sequelae was compared in two groups of voice hearers: psychiatric voice hearers with predominantly negative beliefs about voices (PVH) and non-psychiatric voice hearers with predominantly positive beliefs about voices (NPVH). The data from the two groups were then combined in order to examine which factors could significantly account for the variance in beliefs about voices and therefore levels of distress.   RESULTS Both groups reported a high prevalence of traumatic life events although significantly more PVH reported trauma symptoms sufficient for a diagnosis of post-traumatic stress disorder (PTSD). Furthermore, significantly more PVH reported experiencing childhood sexual abuse. Current trauma symptoms (re-experiencing, avoidance and hyperarousal) were found to be a significant predictor of beliefs about voices. Trauma variables accounted for a significant proportion of the variance in anxiety and depression.   CONCLUSIONS The results suggest that beliefs about voices may be at least partially understood in the context of traumatic life events.

Question: What is the difference between a psychiatric and non-psychiatric voice hearer?


An effective approach for active tracking with a PTZ camera. The concept of active tracking is presented to simulate the characteristics of human vision in intelligent visual surveillance. The Pan/Tilt/Zoom (PTZ) camera is generally used for active tracking. In this paper, we present a novel and effective approach for active object tracking with a PTZ camera, and construct a near real-time system for indoor and outdoor scenes. The tracking algorithm of our system is based on the feature matching, with the PID control to drive the camera. The feature extracted from moving people is described as a region covariance matrix which combines the spatial and statistical properties of the targets (e.g. coordinates, color, and gradient). Results from indoor and outdoor experiments demonstrate the effectiveness and accuracy of our approach.

Question: What is the pan/tilt/zoom camera?


Fast in-memory transaction processing using RDMA and HTM. We present DrTM, a fast in-memory transaction processing system that exploits advanced hardware features (i.e., RDMA and HTM) to improve latency and throughput by over one order of magnitude compared to state-of-the-art distributed transaction systems. The high performance of DrTM are enabled by mostly offloading concurrency control within a local machine into HTM and leveraging the strong consistency between RDMA and HTM to ensure serializability among concurrent transactions across machines. We further build an efficient hash table for DrTM by leveraging HTM and RDMA to simplify the design and notably improve the performance. We describe how DrTM supports common database features like read-only transactions and logging for durability. Evaluation using typical OLTP workloads including TPC-C and SmallBank show that DrTM scales well on a 6-node cluster and achieves over 5.52 and 138 million transactions per second for TPC-C and SmallBank Respectively. This number outperforms a state-of-the-art distributed transaction system (namely Calvin) by at least 17.9X for TPC-C.

Question: What is the name of the company that developed DrTM?


The Web Never Forgets: Persistent Tracking Mechanisms in the Wild. We present the first large-scale studies of three advanced web tracking mechanisms - canvas fingerprinting, evercookies and use of "cookie syncing" in conjunction with evercookies. Canvas fingerprinting, a recently developed form of browser fingerprinting, has not previously been reported in the wild; our results show that over 5% of the top 100,000 websites employ it. We then present the first automated study of evercookies and respawning and the discovery of a new evercookie vector, IndexedDB. Turning to cookie syncing, we present novel techniques for detection and analysing ID flows and we quantify the amplification of privacy-intrusive tracking practices due to cookie syncing.  Our evaluation of the defensive techniques used by privacy-aware users finds that there exist subtle pitfalls --- such as failing to clear state on multiple browsers at once - in which a single lapse in judgement can shatter privacy defenses. This suggests that even sophisticated users face great difficulties in evading tracking techniques.

Question: What is the first large-scale study of evercookies?


CS Teacher Experiences with Educational Technology, Problem-BasedLearning, and a CS Principles Curriculum. Little is known about how K-12 Computer Science (CS) teachers use technology and problem-based learning (PBL) to teach CS content in the context of CS Principles curricula. Significantly, little qualitative research has been conducted in these areas in computer science education, so we lack an in-depth understanding of the complicated realities of CS teachers' experiences. This paper describes the practices and experiences of six teachers' use of technology that was implemented to support PBL in the context of a dual enrollment CS Principles course.  Results from an early offering of this course suggest that (1) while CS teachers used technology, they did not appear to use it to support student inquiry, (2) local adaptations to the curriculum were largely teacher-centric, and (3) the simultaneous adoption of new instructional practices, technologies, and curricula was overwhelming to teachers. This paper then describes how these results were used to modify the curriculum and professional development, leading to increased teacher satisfaction and student success in the course.

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


Multiscale Rotation-Invariant Convolutional Neural Networks for Lung Texture Classification. We propose a new multiscale rotation-invariant convolutional neural network (MRCNN) model for classifying various lung tissue types on high-resolution computed tomography. MRCNN employs Gabor-local binary pattern that introduces a good property in image analysis—invariance to image scales and rotations. In addition, we offer an approach to deal with the problems caused by imbalanced number of samples between different classes in most of the existing works, accomplished by changing the overlapping size between the adjacent patches. Experimental results on a public interstitial lung disease database show a superior performance of the proposed method to state of the art.

Question: What is a convolutional neural network?


SCA-CNN: Spatial and Channel-Wise Attention in Convolutional Networks for Image Captioning. Visual attention has been successfully applied in structural prediction tasks such as visual captioning and question answering. Existing visual attention models are generally spatial, i.e., the attention is modeled as spatial probabilities that re-weight the last conv-layer feature map of a CNN encoding an input image. However, we argue that such spatial attention does not necessarily conform to the attention mechanism — a dynamic feature extractor that combines contextual fixations over time, as CNN features are naturally spatial, channel-wise and multi-layer. In this paper, we introduce a novel convolutional neural network dubbed SCA-CNN that incorporates Spatial and Channel-wise Attentions in a CNN. In the task of image captioning, SCA-CNN dynamically modulates the sentence generation context in multi-layer feature maps, encoding where (i.e., attentive spatial locations at multiple layers) and what (i.e., attentive channels) the visual attention is. We evaluate the proposed SCA-CNN architecture on three benchmark image captioning datasets: Flickr8K, Flickr30K, and MSCOCO. It is consistently observed that SCA-CNN significantly outperforms state-of-the-art visual attention-based image captioning methods.

Question: What is the difference between spatial and channel-wise attention?


Competing Values in the Era of Digitalization. This case study examines three different digital innovation projects within Auto Inc -- a large European automaker. By using the competing values framework as a theoretical lens we explore how dynamic capabilities occur in a firm trying to meet increasing demands in originating and innovating from digitalization. In this digitalization process, our study indicates that established socio-technical congruences are being challenged. More so, we pinpoint the need for organizations to find ways to embrace new experimental learning processes in the era of digitalization. While such a change requires long-term commitment and vision, this study presents three informal enablers for such experimental processes these enablers are timing, persistence, and contacts.

Question: How can we use the competing values framework to understand the digitalization process?