Acquiring evolvability through adaptive representations. Adaptive representations allow evolution to explore the space of phenotypes by choosing the most suitable set of genotypic parameters. Although such an approach is believed to be efficient on complex problems, few empirical studieshave been conducted in such domains. In this paper, three neural network representations, a direct encoding, a complexifying encoding, and an implicit encoding capable of adapting the genotype-phenotype mapping are compared on Nothello, a complex game playing domain from the AAAI General Game Playing Competition. Implicit encoding makes the search more efficient and uses several times fewer parameters. Random mutation leads to highly structured phenotypic variation that is acquired during the course of evolution rather than built into the representation itself. Thus, adaptive representations learn to become evolvable, and furthermore do so in a way that makes search efficient on difficult coevolutionary problems.

Question: What is the difference between a direct encoding, a complexifying encoding, and an implicit encoding?


Insufficient effort responding: examining an insidious confound in survey data.. Insufficient effort responding (IER; Huang, Curran, Keeney, Poposki, & DeShon, 2012) to surveys has largely been assumed to be a source of random measurement error that attenuates associations between substantive measures. The current article, however, illustrates how and when the presence of IER can produce a systematic bias that inflates observed correlations between substantive measures. Noting that inattentive responses as a whole generally congregate around the midpoint of a Likert scale, we propose that Mattentive, defined as the mean score of attentive respondents on a substantive measure, will be negatively related to IER's confounding effect on substantive measures (i.e., correlations between IER and a given substantive measure will become less positive [or more negative] as Mattentive increases). Results from a personality questionnaire (Study 1) and a simulation (Study 2) consistently support the hypothesized confounding influence of IER. Using an employee sample (Study 3), we demonstrated how IER can confound bivariate relationships between substantive measures. Together, these studies indicate that IER can inflate the strength of observed relationships when scale means depart from the scale midpoints, resulting in an inflated Type I error rate. This challenges the traditional view that IER attenuates observed bivariate correlations. These findings highlight situations where IER may be a methodological nuisance, while underscoring the need for survey administrators and researchers to deter and detect IER in surveys. The current article serves as a wake-up call for researchers and practitioners to more closely examine IER in their data.

Question: How can I detect and deter IER?


Analysis of Software Defined Network firewall (SDF). Software Defined Networks (SDN) technology can alternate the network, unlock critical intelligence, and help deliver the new services and analytics needed to run on-demand applications for today's businesses and consumers. So, SDN has been much request for adoption in mostly data centre. SDN provides network administrators to clear overview of the entire network architecture. It decouples a network's control and forwarding functions such that the physical and logical networks can be treated independently. This method allows network traffic flows to be programmatically and effectively reallocated to meet changing demands. That's what SDN does. Software defined network have too many scarcity like throughput, packet loss and network attack defense. Software defined firewalls helps to overcome lack of SDN and also reduce ratios of attack traffic. Here we analyze some SDF techniques and challenges related to software defined network.

Question: What is the difference between a software defined network and a software defined firewall?


A Calibration Technique for Bang-Bang ADPLLs Using Jitter Distribution Monitoring. This brief presents a built-in self-calibration (BISC) technique for minimization of the total jitter in bang-bang all-digital phase-locked loops (ADPLLs). It is based on the addition of a monitoring phase-frequency detector (PFD) with tunable delay cells for the reference clock and the divider clock and a counter for this PFD output signal. This allows for on-chip binary comparison of the jitter distribution widths at the ADPLL PFD input, when ADPLL filter parameters are altered. Since only a relative comparison is performed, no accurate delay calibration is required. The statistical properties of this comparison of two random distributions are analyzed theoretically, and guidelines for circuit dimensioning are derived. The proposed method is used for BISC by adaption of the ADPLL filter coefficients. This allows for jitter minimization under process, voltage and temperature variations as well as gain and period jitter of the digitally controlled oscillator. The proposed calibration technique is verified by system simulations and measurements of a silicon prototype implementation in 28-nm CMOS technology.

Question: How to calibrate bang-bang ADPLLs?


Forensics examination of volatile system data using virtual introspection. While static examination of computer systems is an important part of many digital forensics investigations, there are often important system properties present only in volatile memory that cannot be effectively recovered using static analysis techniques, such as offline hard disk acquisition and analysis. An alternative approach, involving the live analysis of target systems to uncover this volatile data, presents significant risks and challenges to forensic investigators as observation techniques are generally intrusive and can affect the system being observed. This paper provides a discussion of live digital forensics analysis through virtual introspection and presents a suite of virtual introspection tools developed for Xen (VIX tools). The VIX tools suite can be used for unobtrusive digital forensic examination of volatile system data in virtual machines, and addresses a key research area identified in the virtualization in digital forensics research agenda [22].

Question: What is the difference between static and live analysis?


Energy-Brushes: Interactive Tools for Illustrating Stylized Elemental Dynamics. Dynamic effects such as waves, splashes, fire, smoke, and explosions are an integral part of stylized animations. However, such dynamics are challenging to produce, as manually sketching key-frames requires significant effort and artistic expertise while physical simulation tools lack sufficient expressiveness and user control. We present an interactive interface for designing these elemental dynamics for animated illustrations. Users draw with coarse-scale energy brushes which serve as control gestures to drive detailed flow particles which represent local velocity fields. These fields can convey both realistic and artistic effects based on user specification. This painting metaphor for creating elemental dynamics simplifies the process, providing artistic control, and preserves the fluidity of sketching. Our system is fast, stable, and intuitive. An initial user evaluation shows that even novice users with no prior animation experience can create intriguing dynamics using our system.

Question: How do you create stylized animations?


Automatic detection of motion artifacts in MR images using CNNS. Considerable practical interest exists in being able to automatically determine whether a recorded magnetic resonance image is affected by motion artifacts caused by patient movements during scanning. Existing approaches usually rely on the use of navigators or external sensors to detect and track patient motion during image acquisition. In this work, we present an algorithm based on convolutional neural networks that enables fully automated detection of motion artifacts in MR scans without special hardware requirements. The approach is data driven and uses the magnitude of MR images in the spatial domain as input. We evaluate the performance of our algorithm on both synthetic and real data and observe adequate performance in terms of accuracy and generalization to different types of data. Our proposed approach could potentially be used in clinical practice to tag an MR image as motion-free or motion-corrupted immediately after a scan is finished. This process would facilitate the acquisition of high-quality MR images that are often indispensable for accurate medical diagnosis.

Question: How to detect motion artifacts in MR images?


Halide: a language and compiler for optimizing parallelism, locality, and recomputation in image processing pipelines. Image processing pipelines combine the challenges of stencil computations and stream programs. They are composed of large graphs of different stencil stages, as well as complex reductions, and stages with global or data-dependent access patterns. Because of their complex structure, the performance difference between a naive implementation of a pipeline and an optimized one is often an order of magnitude. Efficient implementations require optimization of both parallelism and locality, but due to the nature of stencils, there is a fundamental tension between parallelism, locality, and introducing redundant recomputation of shared values.  We present a systematic model of the tradeoff space fundamental to stencil pipelines, a schedule representation which describes concrete points in this space for each stage in an image processing pipeline, and an optimizing compiler for the Halide image processing language that synthesizes high performance implementations from a Halide algorithm and a schedule. Combining this compiler with stochastic search over the space of schedules enables terse, composable programs to achieve state-of-the-art performance on a wide range of real image processing pipelines, and across different hardware architectures, including multicores with SIMD, and heterogeneous CPU+GPU execution. From simple Halide programs written in a few hours, we demonstrate performance up to 5x faster than hand-tuned C, intrinsics, and CUDA implementations optimized by experts over weeks or months, for image processing applications beyond the reach of past automatic compilers.

Question: What is the tradeoff space of stencil pipelines?


More on the fragility of performance: choking under pressure in mathematical problem solving.. In 3 experiments, the authors examined mathematical problem solving performance under pressure. In Experiment 1, pressure harmed performance on only unpracticed problems with heavy working memory demands. In Experiment 2, such high-demand problems were practiced until their answers were directly retrieved from memory. This eliminated choking under pressure. Experiment 3 dissociated practice on particular problems from practice on the solution algorithm by imposing a high-pressure test on problems practiced 1, 2, or 50 times each. Infrequently practiced high-demand problems were still performed poorly under pressure, whereas problems practiced 50 times each were not. These findings support distraction theories of choking in math, which contrasts with considerable evidence for explicit monitoring theories of choking in sensorimotor skills. This contrast suggests a skill taxonomy based on real-time control structures.

Question: What is the difference between choking under pressure and distraction?


Multi-View Stereo Revisited. We present an extremely simple yet robust multi-view stereo algorithm and analyze its properties. The algorithm first computes individual depth maps using a window-based voting approach that returns only good matches. The depth maps are then merged into a single mesh using a straightforward volumetric approach. We show results for several datasets, showing accuracy comparable to the best of the current state of the art techniques and rivaling more complex algorithms.

Question: How to merge multiple depth maps into a single mesh?


Design guidelines for wireless sensor networks: communication, clustering and aggregation. When sensor nodes are organized in clusters, they could use either single hop or multi-hop mode of communication to send their data to their respective cluster heads. We present a systematic cost-based analysis of both the modes, and provide results that could serve as guidelines to decide which mode should be used for given settings. We determine closed form expressions for the required number of cluster heads and the required battery energy of nodes for both the modes. We also propose a hybrid communication mode which is a combination of single hop and multi-hop modes, and which is more cost-effective than either of the two modes. Our problem formulation also allows for the application to be taken into account in the overall design problem through a data aggregation model. 2003 Elsevier B.V. All rights reserved.

Question: What is the cost of a wireless sensor network?


TwitterReporter: Breaking News Detection and Visualization through the Geo-Tagged Twitter Network. The Twitter social network provides a constant stream of concise data, useful within both geospatial and temporal domains. Previous studies have attempted to mine and find recent news topics within the data set. Although the efforts were a step in the right direction, we believe more is needed to accomplish a useful and interesting task: using live Twitter data to automatically identify breaking news events in near real-time. In this paper, we present methods to collect data, identify breaking news topics, and display results in a geo-temporal visualization. Our goal is to provide a solution that discovers breaking news faster than traditional reporting mediums.

Question: What is a breaking news topic?


Understanding the Effects of Mobile Gamification on Learning Performance. With the development of mobile technology, mobile learning, which is not bound by time and space, has become one of the most essential learning methods for modern people. However, previous studies have suggested that improvements are still needed in mobile learning performance. One of the means to achieve this goal is situated learning, which enhances learners' autonomous motivation and make them more enthusiastic in learning. Nonetheless, few studies have attempted to increase learning performance through situated learning. This study attempts to explore the development of a productive learning atmosphere in the context of mobile situated learning. An experiment is conducted with university-level students having homogenous background and coursework by applying heterogeneous pedagogies, including textual pedagogy, video pedagogy, collaborative pedagogy, and gamification pedagogy. Moreover, the uses and gratification theory and the cognitive load theory were adopted to identify the cognitive factors that influence the learning performance in situated learning. It is hoped that education service providers can benefit from the insights discovered from this study and implement more effective learning strategies in mobile education.

Question: What is the difference between gamification and gamification?


Personalized Dialogue Generation with Diversified Traits. Endowing a dialogue system with particular personality traits is essential to deliver more human-like conversations. However, due to the challenge of embodying personality via language expression and the lack of large-scale persona-labeled dialogue data, this research problem is still far from well-studied. In this paper, we investigate the problem of incorporating explicit personality traits in dialogue generation to deliver personalized dialogues. To this end, firstly, we construct PersonalDialog, a large-scale multi-turn dialogue dataset containing various traits from a large number of speakers. The dataset consists of 20.83M sessions and 56.25M utterances from 8.47M speakers. Each utterance is associated with a speaker who is marked with traits like Age, Gender, Location, Interest Tags, etc. Several anonymization schemes are designed to protect the privacy of each speaker. This large-scale dataset will facilitate not only the study of personalized dialogue generation, but also other researches on sociolinguistics or social science. Secondly, to study how personality traits can be captured and addressed in dialogue generation, we propose persona-aware dialogue generation models within the sequence to sequence learning framework. Explicit personality traits (structured by key-value pairs) are embedded using a trait fusion module. During the decoding process, two techniques, namely persona-aware attention and persona-aware bias, are devised to capture and address trait-related information. Experiments demonstrate that our model is able to address proper traits in different contexts. Case studies also show interesting results for this challenging research problem.

Question: How large is the canadian military?


Deep learning traffic sign detection, recognition and augmentation. Driving is a complex, continuous, and multitask process that involves driver's cognition, perception, and motor movements. The way road traffic signs and vehicle information is displayed impacts strongly driver's attention with increased mental workload leading to safety concerns. Drivers must keep their eyes on the road, but can always use some assistance in maintaining their awareness and directing their attention to potential emerging hazards. Research in perceptual and human factors assessment is needed for relevant and correct display of this information for maximal road traffic safety as well as optimal driver comfort. In-vehicle contextual Augmented Reality (AR) has the potential to provide novel visual feedbacks to drivers for an enhanced driving experience. In this paper, we present a new real-time approach for fast and accurate framework for traffic sign recognition, based on Cascade Deep learning and AR, which superimposes augmented virtual objects onto a real scene under all types of driving situations, including unfavorable weather conditions. Experiments results show that, by combining the Haar Cascade and deep convolutional neural networks show that the joint learning greatly enhances the capability of detection and still retains its realtime performance.

Question: What is the difference between deep learning and cascade deep learning?


Bandwidth Enhancement of a Single-Feed Circularly Polarized Antenna Using a Metasurface: Metamaterial-based wideband CP rectangular microstrip antenna.. In this article, the authors use a 7 x 7 rectangular-ring unit metasurface and a single-feed, CP, rectangular, slotted patch antenna to enhance bandwidth. The antenna consists of a rectangular, slotted patch radiator, a metasurface with an array of rectangular-ring cells, and a coaxial feed. Using the metasurface results in a wide CP bandwidth sevenfold the size of a conventional antenna. The measured 3-dB-AR bandwidth of an antenna prototype printed onto an FR4 substrate is 28.3% (3.62 GHz-4.75 GHz) with 2:1-voltage standing wave ratio (VSWR) bandwidth of 36%. A 6.0-dBic, boresight gain with a variation of 1.5 dB is achieved across the frequency band of 3.35 GHz-4.75 GHz, with the maximum gain of 7.4 dBic at 4.1 GHz. We measure and compare the antenna prototype with a simulation using CST Microwave Studio. Parametric studies aid in understanding the proposed antenna's operation mechanism.

Question: How to design a circularly polarized antenna?


Software-Based Resolver-to-Digital Conversion Using a DSP. A simple and cost-effective software-based resolver-to-digital converter using a digital signal processor is presented. The proposed method incorporates software generation of the resolver carrier using a digital filter for synchronous demodulation of the resolver outputs in such a way that there is a substantial savings on hardware like the costly carrier oscillator and associated digital and analog circuits for amplitude demodulators. In addition, because the method does not cause any time delay, the dynamics of the servo control using the scheme are not affected. Furthermore, the method enables the determination of the angle for a complete 360deg shaft rotation with reasonable accuracy using a lookup table that contains entries of only up to 45deg. Computer simulations and experimental results demonstrate the effectiveness and applicability of the proposed scheme.

Question: What is a software-based resolver-to-digital converter?


A generic visual perception domain randomisation framework for Gazebo. The impressive results of applying deep neural networks in tasks such as object recognition, language translation, and solving digital games are largely attributed to the availability of massive amounts of high quality labelled data. However, despite numerous promising steps in incorporating these techniques in robotic tasks, the cost of gathering data with a real robot has halted the proliferation of deep learning in robotics. In this work, a plugin for the Gazebo simulator is presented, which allows rapid generation of synthetic data. By introducing variations in simulator-specific or irrelevant aspects of a task, one can train a model which exhibits some degree of robustness against those aspects, and ultimately narrow the reality gap between simulated and real-world data. To show a use-case of the developed software, we build a new dataset for detection and localisation of three object classes: box, cylinder and sphere. Our results in the object detection and localisation task demonstrate that with small datasets generated only in simulation, one can achieve comparable performance to that achieved when training on real-world images.

Question: What is a gazebo simulator?


Incivility in nursing education: An intervention.. Incivility in nursing education is an unfortunate phenomenon affecting nursing students in all aspects of their educational experience. Students and their instructors are often ill equipped to deal with academic incivility and their lack of ability to handle such behaviors has proven detrimental to the future of the nursing profession. Nursing instructors need tools to help educate nursing students on how to recognize uncivil behaviors within themselves as well as others and ways to combat it. This research project addressed these aspects of academic incivility and implemented an e-learning module that was developed to educate students on incivility. The data was collected through a pre-test, post-test model with resulting statistical analysis using the McNemar's test. Results showed the nursing students obtained increased self-efficacy in regards to their ability to define, detect, and combat academic incivility after viewing the e-learning module. In conclusion, the successful implementation of the e-learning module provides further incentive for schools of nursing to consider implementing incivility education in their curriculums.

Question: What is the difference between a pre-test and a post-test?


Using argumentation logic for firewall configuration management. Firewalls remain the main perimeter security protection for corporate networks. However, network size and complexity make firewall configuration and maintenance notoriously difficult. Tools are needed to analyse firewall configurations for errors, to verify that they correctly implement security requirements and to generate configurations from higher-level requirements. In this paper we extend our previous work on the use of formal argumentation and preference reasoning for firewall policy analysis and develop means to automatically generate firewall policies from higher-level requirements. This permits both analysis and generation to be done within the same framework, thus accommodating a wide variety of scenarios for authoring and maintaining firewall configurations. We validate our approach by applying it to both examples from the literature and real firewall configurations of moderate size (≈ 150 rules).

Question: What is the size of a firewall?


Teechain: Scalable Blockchain Payments using Trusted Execution Environments. Blockchain protocols such as Bitcoin are gaining traction for exchanging payments in a secure and decentralized manner. Their need to achieve consensus across a large number of participants, however, fundamentally limits their performance. We describe Teechain, a new off-chain payment protocol that utilizes trusted execution environments (TEEs) to perform secure, efficient and scalable fund transfers on top of a blockchain, with asynchronous blockchain access. Teechain introduces secure payment chains to route payments across multiple payment channels. Teechain mitigates failures of TEEs with two strategies: (i) backups to persistent storage and (ii) a novel variant of chain-replication. We evaluate an implementation of Teechain using Intel SGX as the TEE and the operational Bitcoin blockchain. Our prototype achieves orders of magnitude improvement in most metrics compared to existing implementations of payment channels: with replicated Teechain nodes in a trans-atlantic deployment, we measure a throughput of over 33, 000 transactions per second with 0.1 second latency.

Question: What is a trusted execution environment?


Clustering-based Approach for Anomaly Detection in XACML Policies. The development of distributed applications arises multiple security issues such as access control. AttributeBased Access Control has been proposed as a generic access control model, which provides more flexibility and promotes information and security sharing. eXtensible Access Control Markup Language (XACML) is the most convenient way to express ABAC policies. However, in distributed environments, XACML policies become more complex and hard to manage. In fact, an XACML policy in distributed applications may be aggregated from multiple parties and can be managed by more than one administrator. Therefore, it may contain several anomalies such as conflicts and redundancies, which may affect the performance of the policy execution. In this paper, we propose an anomaly detection method based on the decomposition of a policy into clusters before searching anomalies within each cluster. Our evaluation results demonstrate the efficiency of the suggested approach.

Question: How to detect anomalies in XACML policies?


Force of Habit and Information Systems Usage: Theory and Initial Validation. Over the last two decades, information systems (IS) research has primarily focused on people's conscious (intentional) behavior when trying to explain and predict IS usage. Consequently, almost no research has investigated the potential importance of subconscious (automatic) behaviors, also known as habits. This study represents a first step toward validating the idea that one can add explanatory power to a behavioral model such as Ajzen's [1985] theory of planned behavior (TPB) by including the habit construct. We conducted a two-stage questionnaire-based survey involving two different groups of students who had access to a sophisticated internet-based communication tool (IBCT). These data were used to test a behavioral model integrating theoretical constructs of TPB and a relevant subset of Triandis' [1980] behavioral framework. Our findings highlight the importance of considering both conscious (intentions) and subconscious (habits) factors in explaining usage behavior. Furthermore, we share our observations about antecedents of IBCT usage in the educational context. Implications for practice and research are discussed.

Question: What is the difference between a habit and a behavior?


End-fire Quasi-Yagi antennas with pattern diversity on LTCC technology for 5G mobile communications. We have designed two end-fire antennas on LTCC with horizontal and vertical polarizations respectively. The antennas operate at 38GHz, a potential frequency for 5G applications. The horizontally-polarized antenna provide a broadband performance about 27% and 6dB end-fire gain and the vertically-polarized one provide a 12.5% bandwidth and 5dB gain. Both antenna are integrated under a compact substrate. Excellent isolation is achieved between the nearby elements making these antennas suitable for corner elements in 5G mobile system.

Question: What is the difference between horizontal and vertical polarizations?


Small-footprint Keyword Spotting Using Deep Neural Network and Connectionist Temporal Classifier. Mainly for the sake of solving the lack of keyword-specific data, we propose one Keyword Spotting (KWS) system using Deep Neural Network (DNN) and Connectionist Temporal Classifier (CTC) on power-constrained small-footprint mobile devices, taking full advantage of general corpus from continuous speech recognition which is of great amount. DNN is to directly predict the posterior of phoneme units of any personally customized key-phrase, and CTC to produce a confidence score of the given phoneme sequence as responsive decision-making mechanism. The CTC-KWS has competitive performance in comparison with purely DNN based keyword specific KWS, but not increasing any computational complexity.

Question: What is the difference between deep neural network and connectionist temporal classifier?


Spatio-Temporal Anomaly Detection for Industrial Robots through Prediction in Unsupervised Feature Space. Spatio-temporal anomaly detection by unsupervised learning have applications in a wide range of practical settings. In this paper we present a surveillance system for industrial robots using a monocular camera. We propose a new unsupervised learning method to train a deep feature extractor from unlabeled images. Without any data augmentation, the algorithm co-learns the network parameters on different pseudo-classes simultaneously to create unbiased feature representation. Combining the learned features with a prediction system, we can detect irregularities in high dimensional data feed (e.g. video of a robot performing pick and place task). The results show how the proposed approach can detect previously unseen anomalies in the robot surveillance video. Although the technique is not designed for classification, we show the use of the learned features in a more traditional classification application for CIFAR-10 dataset.

Question: What is the difference between supervised and unsupervised learning?


Learning Internet-of-Things Security "Hands-On". What can you glean from using inexpensive, off-the-shelf parts to create Internet of Things (IoT) use cases? As it turns out, a lot. The fast productization of IoT technologies is leaving users vulnerable to security and privacy risks.

Question: What can you glean from using inexpensive, off-the-shelf parts to create Internet of Things (IoT) use cases?


A prioritized grid long short-term memory RNN for speech recognition. Recurrent neural networks (RNNs) are naturally suitable for speech recognition because of their ability of utilizing dynamically changing temporal information. Deep RNNs have been argued to be able to model temporal relationships at different time granularities, but suffer vanishing gradient problems. In this paper, we extend stacked long short-term memory (LSTM) RNNs by using grid LSTM blocks that formulate computation along not only the temporal dimension, but also the depth dimension, in order to alleviate this issue. Moreover, we prioritize the depth dimension over the temporal one to provide the depth dimension more updated information, since the output from it will be used for classification. We call this model the prioritized Grid LSTM (pGLSTM). Extensive experiments on four large datasets (AMI, HKUST, GALE, and MGB) indicate that the pGLSTM outperforms alternative deep LSTM models, beating stacked LSTMs with 4% to 7% relative improvement, and achieve new benchmarks among uni-directional models on all datasets.

Question: What is the difference between a stacked LSTM and a grid LSTM?


On accelerating pair-HMM computations in programmable hardware. This paper explores hardware acceleration to significantly improve the runtime of computing the forward algorithm on Pair-HMM models, a crucial step in analyzing mutations in sequenced genomes. We describe 1) the design and evaluation of a novel accelerator architecture that can efficiently process real sequence data without performing wasteful work; and 2) aggressive memoization techniques that can significantly reduce the number of invocations of, and the amount of data transferred to the accelerator. We describe our demonstration of the design on a Xilinx Virtex 7 FPGA in an IBM Power8 system. Our design achieves a 14.85× higher throughput than an 8-core CPU baseline (that uses SIMD and multi-threading) and a 147.49 × improvement in throughput per unit of energy expended on the NA12878 sample.

Question: How much does it cost to accelerate pair-HMM computations in programmable hardware?


Ad Exchanges: Research Issues. An emerging way to sell and buy display ads on the Internet is via ad exchanges. RightMedia [1], AdECN [2] and DoubleClick Ad Exchange [3] are examples of such real-time two-sided markets. We describe an abstraction of ad exchanges. Based on that abstraction, we present several research directions and discuss some insights.

Question: What is an ad exchange?


Organizational Learning and Management Information Systems. T he articles in this issue ofDATA BASE were chosen b y Anthony G . Hopwood, who is a professor of accounting and financial reporting at the London Graduate Schoo l of Business Studies . The articles contain important ideas , Professor Hopwood wrote, of significance to all intereste d in information systems, be they practitioners or academics . The authors, with their professional affiliations at th e time, were Chris Argyris, Graduate School of Education , Harvard University; Bo Hedberg and Sten Jonsson, Department of Business Administration, University o f Gothenburg; J . Frisco den Hertog, N . V. Philips' Gloeilampenfabrieken, The Netherlands, and Michael J . Earl, Oxford Centre for Management Studies . The articles appeared originally in Accounting, Organizations and Society, a publication of which Professor Hopwood is editor-in-chief. AOS exists to monitor emergin g developments and to actively encourage new approaches and perspectives .

Question: What is the name of the journal?


A TOR-based anonymous communication approach to secure smart home appliances. Digital information has become a social infrastructure and with the expansion of the Internet, network infrastructure has become an indispensable part of social life and industrial activity for mankind. The idea of using existing electronics in smart home appliances and connecting them to the Internet is a new dimension along which technologies continue to grow, and in recent years mankind has witnessed an upsurge of usage of devices such as smart phone, smart television, home health-care device, smart LED light bulbs system, etc. Their build-in internet-controlled function has made them quite attractive to many segments of consumers and smart phone has become a common gadget for social networking. There are, however, serious challenges which need to be addressed as these tiny devices are designed for specific functions and lack processing capacity required for most security software. This research explores how these internet-enabled smart devices can be turned into very dangerous spots for distributed attacks purposes by cybercriminals for various ill intensions in a pinpointed manner. It then introduces a new approach to deal with such problems by taking advantage of the anonymous communication of the Onion Router (hereafter: TOR). It compares pros and cons of using anonymous communication scheme and justifies it be an efficient countermeasure to most attack scenarios.

Question: What is the difference between TOR and VPN?


Radiometric calibration from a single image. Photometric methods in computer vision require calibration of the camera's radiometric response, and previous works have addressed this problem using multiple registered images captured under different camera exposure settings. In many instances, such an image set is not available, so we propose a method that performs radiometric calibration from only a single image, based on measured RGB distributions at color edges. This technique automatically selects appropriate edge information for processing, and employs a Bayesian approach to compute the calibration. Extensive experimentation has shown that accurate calibration results can be obtained using only a single input image.

Question: How to calibrate a camera from a single image?


AudioDAQ: turning the mobile phone's ubiquitous headset port into a universal data acquisition interface. We present AudioDAQ, a new platform for continuous data acquisition using the headset port of a mobile phone. AudioDAQ differs from existing phone peripheral interfaces by drawing all necessary power from the microphone bias voltage, encoding all data as analog audio, and leveraging the phone's built-in voice memo application (or a custom application) for continuous data collection. These properties make the AudioDAQ design more universal, so it works across a broad range of phones including sophisticated smart phones and simpler feature phones, enables simple analog peripherals without requiring a microcontroller, requires no hardware or software modifications on the phone itself, uses significantly less power than prior approaches, and allows continuous data capture over an extended period of time. The AudioDAQ design is efficient because it draws all necessary power from the microphone bias voltage, and it is general because this voltage and a voice memo application are present on most mobile phones in use today. We show the viability of our architecture by evaluating an end-to-end system that can capture EKG signals continuously for hours and send the data to the cloud for storage, processing, and visualization.

Question: How does AudioDAQ work?


DivQ: diversification for keyword search over structured databases. Keyword queries over structured databases are notoriously ambiguous. No single interpretation of a keyword query can satisfy all users, and multiple interpretations may yield overlapping results. This paper proposes a scheme to balance the relevance and novelty of keyword search results over structured databases. Firstly, we present a probabilistic model which effectively ranks the possible interpretations of a keyword query over structured data. Then, we introduce a scheme to diversify the search results by re-ranking query interpretations, taking into account redundancy of query results. Finally, we propose α-nDCG-W and WS-recall, an adaptation of α-nDCG and S-recall metrics, taking into account graded relevance of subtopics. Our evaluation on two real-world datasets demonstrates that search results obtained using the proposed diversification algorithms better characterize possible answers available in the database than the results of the initial relevance ranking.

Question: What is the meaning of diversification?


Intrinsically Motivated Reinforcement Learning: A Promising Framework for Developmental Robot Learning. One of the primary challenges of developmental robotics is the question of how to learn and represent increasingly complex behavior in a self-motivated, open-ended way. Barto, Singh, and Chentanez (Barto, Singh, & Chentanez 2004; Singh, Barto, & Chentanez 2004) have recently presented an algorithm for intrinsically motivated reinforcement learning that strives to achieve broad competence in an environment in a tasknonspecific manner by incorporating internal reward to build a hierarchical collection of skills. This paper suggests that with its emphasis on task-general, self-motivated, and hierarchical learning, intrinsically motivated reinforcement learning is an obvious choice for organizing behavior in developmental robotics. We present additional preliminary results from a gridworld abstraction of a robot environment and advocate a lay-ion of a robot environment and advocate a layered learning architecture for applying the algorithm on a physically embodied system.

Question: What is intrinsically motivated reinforcement learning?


An experimental comparison of ensemble of classifiers for bankruptcy prediction and credit scoring. In this paper, we investigate the performance of several systems based on ensemble of classifiers for bankruptcy prediction and credit scoring. The obtained results are very encouraging, our results improved the performance obtained using the stand-alone classifiers. We show that the method ''Random Subspace" outperforms the other ensemble methods tested in this paper. Moreover, the best stand-alone method is the multi-layer perceptron neural net, while the best method tested in this work is the Random Subspace of Levenberg–Marquardt neural net. In this work, three financial datasets are chosen for the experiments: Australian credit, German credit, and Japanese credit. 2008 Elsevier Ltd. All rights reserved.

Question: What is the best method for bankruptcy prediction?


Web Site Usability, Design, and Performance Metrics. Websites provide the key interface for consumer use of the Internet. This research reports on a series of three studies that develop and validate Web site usability, design and performance metrics, including download delay, navigability, site content, interactivity, and responsiveness. The performance metric that was developed includes the subconstructs user satisfaction, the likelihood of return, and the frequency of use. Data was collected in 1997, 1999, and 2000 from corporate Web sites via three methods, namely, a jury, third-party ratings, and a software agent. Significant associations betweenWeb site design elements and Web site performance indicate that the constructs demonstrate good nomological validity. Together, the three studies provide a set of measures with acceptable validity and reliability. The findings also suggest lack of significant common methods biases across the jury-collected data, third-party data, and agent-collected data. Results suggest that Web site success is a first-order construct. Moreover, Web site success is significantly associated with Web site download delay (speed of access and display rate within the Web site), navigation (organization, arrangement, layout, and sequencing), content (amount and variety of product information), interactivity (customization and interactivity), and responsiveness (feedback options and FAQs). (e-Commerce, Web Metrics, or Measurement;Web Site Usability;Design and Performance Constructs; Construct Validity; Nomological Validity)

Question: What is the difference between a web site and a web page?


Autonomous local path planning for a mobile robot using a genetic algorithm. This work presents results of our work in development of a genetic algorithm based path-planning algorithm for local obstacle avoidance (local feasible path) of a mobile robot in a given search space. The method tries to find not only a valid path but also an optimal one. The objectives are to minimize the length of the path and the number of turns. The proposed path-planning method allows a free movement of the robot in any direction so that the path-planner can handle complicated search spaces.

Question: What is a genetic algorithm?


Automatic Ontology Matching via Upper Ontologies: A Systematic Evaluation. ¿Ontology matching¿ is the process of finding correspondences between entities belonging to different ontologies. This paper describes a set of algorithms that exploit upper ontologies as semantic bridges in the ontology matching process and presents a systematic analysis of the relationships among features of matched ontologies (number of simple and composite concepts, stems, concepts at the top level, common English suffixes and prefixes, and ontology depth), matching algorithms, used upper ontologies, and experiment results. This analysis allowed us to state under which circumstances the exploitation of upper ontologies gives significant advantages with respect to traditional approaches that do no use them. We run experiments with SUMO-OWL (a restricted version of SUMO), OpenCyc, and DOLCE. The experiments demonstrate that when our ¿structural matching method via upper ontology¿ uses an upper ontology large enough (OpenCyc, SUMO-OWL), the recall is significantly improved while preserving the precision obtained without upper ontologies. Instead, our ¿nonstructural matching method¿ via OpenCyc and SUMO-OWL improves the precision and maintains the recall. The ¿mixed method¿ that combines the results of structural alignment without using upper ontologies and structural alignment via upper ontologies improves the recall and maintains the F-measure independently of the used upper ontology.

Question: What is the difference between ontology matching and ontology alignment?


Topic- and Time-Oriented Visual Text Analysis. In this article, we first present the benefit of combing text analysis (topic models in particular) with interactive visualization. We then highlight examples from prior work on topic and time-oriented visual text analysis. Lastly, we present four challenges that warrant additional future research in the field of visual text analysis.

Question: What is topic- and time-oriented visual text analysis?


Cyberbullying and Primary-School Aged Children : The Psychological Literature and the Challenge for Sociology. Cyberbullying is an international issue for schools, young people and their families. Whilst many research domains have explored this phenomenon, and bullying more generally, the majority of reported studies appear in the psychological and educational literatures, where bullying, and more recently, cyberbullying has been examined primarily at the individual level: amongst adolescents and young people, with a focus on the definition, its prevalence, behaviours, and impact. There also is growing evidence that younger children are increasingly accessing technology and engaging with social media, yet there is limited research dedicated to this younger age group. The purpose of this paper is to report on a systematic literature review from the psychological and educational research domains related to this younger age group, to inform future research across the disciplines. Younger children require different methods of engagement. This review highlights the methodological challenges associated with this age group present in the psychological literature, and argues for a greater use of sociological, child-centred approaches to data collection. This review examined studies published in English, between 2009 and 2014, and conducted with children aged 5–12 years, about their experiences with cyberbullying. Searches were conducted on seven key databases using keywords associated with cyberbullying and age of children. A Google Scholar search also examined published and unpublished reports. A total of 966 articles and reports were retrieved. A random peer review process was employed to establish inter-rater reliability and veracity of the review. Findings revealed 38 studies reported specifically on children aged 5–12 years. The dominant focus of these articles was on prevalence of cyberbullying, established through survey methodology. Few studies noted OPEN ACCESS Soc

Question: What is the age of children who are cyberbullied?


Abstract Semantic Differencing for Numerical Programs. Semantic Differencing for Numerical Programs Nimrod Partush Eran Yahav

Question: What is the abstract of this paper?


eyond virtual museums : Experiencing immersive virtual reality in eal museums. Contemporary museums are much more than places devoted to the placement and the exhibition of collections and artworks; indeed, they are nowadays considered as a privileged means for communication and play a central role in making culture accessible to the mass audience. One of the keys to approach the general public is the use of new technologies and novel interaction paradigms. These means, which bring with them an undeniable appeal, allow curators to modulate the cultural proposal by structuring different courses for different user profiles. Immersive Virtual reality (VR) is probably one of the most appealing and potentially effective technologies to serve this purpose; nevertheless, it is still quite uncommon to find immersive installations in museums. Starting from our 10 years' experience in this topic, and following an in-depth survey about these technologies and their use in cultural contexts, we propose a classification of VR installations, specifically oriented to cultural heritage applications, based on their features in terms of interaction and immersion. On the basis of this classification, aiming to provide a tool for framing VR systems which would hopefully suggest indications related to costs, usability and quality of the sensorial experience, we analyze a series of live examples of which we point out strengths and weak points. We then summarize the current state and the very next future, identifying the major issues that prevent these technologies from being actually widespread, and outline proposals for a more se of pervasive and effective u

Question: What is the difference between a museum and a virtual museum?


Real-Time Recognition of Physical Activities and Their Intensities Using Wireless Accelerometers and a Heart Rate Monitor. In this paper, we present a real-time algorithm for automatic recognition of not only physical activities, but also, in some cases, their intensities, using five triaxial wireless accelerometers and a wireless heart rate monitor. The algorithm has been evaluated using datasets consisting of 30 physical gymnasium activities collected from a total of 21 people at two different labs. On these activities, we have obtained a recognition accuracy performance of 94.6% using subject-dependent training and 56.3% using subject-independent training. The addition of heart rate data improves subject-dependent recognition accuracy only by 1.2% and subject-independent recognition only by 2.1%. When recognizing activity type without differentiating intensity levels, we obtain a subject-independent performance of 80.6%. We discuss why heart rate data has such little discriminatory power.

Question: How do you recognize physical activities?


The Agile Requirements Refinery: Applying SCRUM Principles to Software Product Management. Although agile software development methods such as SCRUM and DSDM are gaining popularity, the consequences of applying agile principles to software product management have received little attention until now. In this paper, this gap is filled by the introduction of a method for the application of SCRUM principles to software product management. For this purpose, the 'agile requirements refinery' is presented, an extension to the SCRUM process that enables product managers to cope with large requirements in an agile development environment. A real-life case study is presented to illustrate how agile methods can be applied to software product management. The experiences of the case study company are provided as a set of lessons learned that will help others to apply agile principles to their software product management process.

Question: What is the agile requirements refinery?


A Constrained, Weighted-L1 Minimization Approach for Joint Discovery of Heterogeneous Neural Connectivity Graphs. Determining functional brain connectivity is crucial to understanding the brain and neural differences underlying disorders such as autism. Recent studies have used Gaussian graphical models to learn brain connectivity via statistical dependencies across brain regions from neuroimaging. However, previous studies often fail to properly incorporate priors tailored to neuroscience, such as preferring shorter connections. To remedy this problem, the paper here introduces a novel, weighted-`1, multi-task graphical model (W-SIMULE). This model elegantly incorporates a flexible prior, along with a parallelizable formulation. Additionally, W-SIMULE extends the often-used Gaussian assumption, leading to considerable performance increases. Here, applications to fMRI data show that W-SIMULE succeeds in determining functional connectivity in terms of (1) log-likelihood, (2) finding edges that differentiate groups, and (3) classifying different groups based on their connectivity, achieving 58.6% accuracy on the ABIDE dataset. Having established W-SIMULE's effectiveness, it links four key areas to autism, all of which are consistent with the literature. Due to its elegant domain adaptivity, W-SIMULE can be readily applied to various data types to effectively estimate connectivity.

Question: How does this paper relate to autism?


Efficient Time-Domain Image Formation with Precise Topography Accommodation for General Bistatic SAR Configurations. Due to the lack of an appropriate symmetry in the acquisition geometry, general bistatic synthetic aperture radar (SAR) cannot benefit from the two main properties of low-to-moderate resolution monostatic SAR: azimuth-invariance and topography-insensitivity. The precise accommodation of azimuth-variance and topography is a real challenge for efficent image formation algorithms working in the Fourier domain, but can be quite naturally handled by time-domain approaches. We present an efficient and practical implementation of a generalised bistatic SAR image formation algorithm with an accurate accommodation of these two effects. The algorithm has a common structure with the monostatic fast-factorised backprojection (FFBP), and is therefore based on subaperture processing. The images computed over the different subapertures are displayed in an advantageous elliptical coordinate system capable of incorporating the topographic information of the imaged scene in an analogous manner as topography-dependent monostatic SAR algorithms do. Analytical expressions for the Nyquist requirements using this coordinate system are derived. The overall discussion includes practical implementation hints and a realistic computational burden estimation. The algorithm is tested with both simulated and actual bistatic SAR data. The actual data correspond to the spaceborne-airborne experiment between TerraSAR-X and F-SAR performed in 2007 and to the DLR-ONERA airborne experiment carried out in 2003. The presented approach proves its suitability for the precise SAR focussing of the data acquired in general bistatic configurations.

Question: How to calculate the Nyquist requirements for a general bistatic SAR configuration?


A brain-computer interface for high-level remote control of an autonomous, reinforcement-learning-based robotic system for reaching and grasping. We present an Internet-based brain-computer interface (BCI) for controlling an intelligent robotic device with autonomous reinforcement-learning. BCI control was achieved through dry-electrode electroencephalography (EEG) obtained during imaginary movements. Rather than using low-level direct motor control, we employed a high-level control scheme of the robot, acquired via reinforcement learning, to keep the users cognitive load low while allowing control a reaching-grasping task with multiple degrees of freedom. High-level commands were obtained by classification of EEG responses using an artificial neural network approach utilizing time-frequency features and conveyed through an intuitive user interface. The novel ombination of a rapidly operational dry electrode setup, autonomous control and Internet connectivity made it possible to conveniently interface subjects in an EEG laboratory with remote robotic devices in a closed-loop setup with online visual feedback of the robots actions to the subject. The same approach is also suitable to provide home-bound patients with the possibility to control state-of-the-art robotic devices currently confined to a research environment. Thereby, our BCI approach could help severely paralyzed patients by facilitating patient-centered research of new means of communication, mobility and independence.

Question: How does a brain-computer interface work?


Co-change Clusters: Extraction and Application on Assessing Software Modularity. The traditional modular structure defined by the package hierarchy suffers from the dominant decomposition problem and it is widely accepted that alternative forms of modularization are necessary to increase developer's productivity. In this paper, we propose an alternative form to understand and assess package modularity based on co-change clusters, which are highly inter-related classes considering cochange relations. We evaluate how co-change clusters relate to the package decomposition of four real-world systems. The results show that the projection of co-change clusters to packages follows different patterns in each system. Therefore, we claim that modular views based on co-change clusters can improve developers' understanding on how well-modularized are their systems, considering that modularity is the ability to confine changes and evolve components in parallel.

Question: How to assess software modularity?


An arousal-based neural model of infant attachment. We develop an arousal-based neural model of infant attachment using a deep learning architecture. We show how our model can differentiate between attachment classifications during strange situation-like separation and reunion episodes, in terms of both signalling behaviour and patterns of autonomic arousal, according to the sensitivity of previous interaction.

Question: What is an arousal-based neural model of infant attachment?


Tracking and Connecting Topics via Incremental Hierarchical Dirichlet Processes. Much research has been devoted to topic detection from text, but one major challenge has not been addressed: revealing the rich relationships that exist among the detected topics. Finding such relationships is important since many applications are interested in how topics come into being, how they develop, grow, disintegrate, and finally disappear. In this paper, we present a novel method that reveals the connections between topics discovered from the text data. Specifically, our method focuses on how one topic splits into multiple topics, and how multiple topics merge into one topic. We adopt the hierarchical Dirichlet process (HDP) model, and propose an incremental Gibbs sampling algorithm to incrementally derive and refine the labels of clusters. We then characterize the splitting and merging patterns among clusters based on how labels change. We propose a global analysis process that focuses on cluster splitting and merging, and a finer granularity analysis process that helps users to better understand the content of the clusters and the evolution patterns. We also develop a visualization process to present the results.

Question: How to track topics?


Spatial augmented reality — A tool for 3D data visualization. This paper presents a proposal for the use of Spatial Augmented Reality as a tool for 3D data visualizations. The use of traditional display technologies such as LCD monitors provides a fish tank metaphor for the user, i.e. the information is behind a plane of glass. The use of VR technologies allows the user to be immersed in the 3D volume and remove this fish tank problem, but can limit the set of techniques that allow users to interact with spatial data. Spatial Augmented Reality employed in conjunction with a highresolution monitor provides an elegant blend of spatial reasoning, tangible interaction, and detailed information viewing. This paper purposes a range of usages for SAR in 3D visualizations.

Question: What is spatial augmented reality?


Sliding-Window Superposition Coding: Two-User Interference Channels. A low-complexity coding scheme is developed to achieve the r at egion of maximum likelihood decoding for interference channels. As in the classical rate-splitting mu ltiple access scheme by Grant, Rimoldi, Urbanke, and Whitin g, the proposed coding scheme uses superposition of multiple c od words with successive cancellation decoding, which can be implemented using standard point-to-point encoders and decoders. Unlike rate-splitting multiple access, which is not rate-optimal for multiple receivers, the proposed coding s cheme transmits codewords over multiple blocks in a stagger ed manner and recovers them successively over sliding decodin g wi dows, achieving the single-stream optimal rate region as well as the more general Han–Kobayashi inner bound for the tw o-user interference channel. The feasibility of this schem e in practice is verified by implementing it using commercial c hannel codes over the two-user Gaussian interference chann el. I. I NTRODUCTION For high data rates and massive connectivity, next-generat ion cellular networks are expected to deploy many small base stations. While such dense deployment provides the ben fit of bringing radio closer to end users, it also increases the amount of interference from neighboring cells. Consequ ently, efficient and effective management of interference is expected to become one of the main challenges for high-spe ctral-efficiency, low-power, broad-coverage wireless communications. Over the past few decades, several techniques at different p otocol layers [1]–[3] have been proposed to mitigate adver se effects of interference in wireless networks. One importan t conceptual technique at the physical layer is simultaneous decoding[4, Section 6.2], [5]. In this decoding method, each receive r attempts to recover both the intended and a subset of the interfering codewords at the same time. When the inter fer nce is strong [6], [7] and weak [8]–[11],

Question: What is the difference between simultaneous decoding and successive decoding?


Hedonism , Achievement , and Power : Universal values that characterize the Dark Triad. Using a sample of Swedes and Americans (N = 385), we attempted to understand the Dark Triad traits (i.e., Machiavellianism, narcissism, and psychopathy) in terms of universal social values. The Dark Triad traits correlated significantly with all 10 value types, forming a sinusoid pattern corresponding to the value model circumplex. In regression analyses, Machiavellianism and narcissism were positively associated with the values Achievement and Power, while psychopathy was positively associated with the values Hedonism, and Power. In addition, the Dark Triad traits explained significant variance over the Big Five traits in accounting for individual differences in social values. Differences between the Swedish and the US sample in the social value Achievement was mediated by the Dark Triad traits, as well as age. Given the unique complex of values accounted for by the Dark Triad traits compared to the Big Five traits, we argue that the former account for a system of self-enhancing ''dark values'', often hidden but constantly contributing in evaluations of others. 2015 Elsevier Ltd. All rights reserved.

Question: What is the dark triad?


Personality traits associated with problematic and non-problematic massively multiplayer online role playing game use. This research investigated the associations between personality traits and both normal and problematic massively multiplayer online role playing game (MMORPGs) play, as measured by a self report scale. A total of 225 participants completed the online questionnaire, 66 of whom played MMORPGs. Correlational analyses indicated that low levels of functional impulsivity and agreeableness alongside high levels of verbal aggression and video game dependency were associated with greater amount of time spent playing MMORPGs. When comparing problematic and non-problematic MMORPG players directly, problematic players were found to be lower in self regulation, dysfunctional impulsivity and agreeableness, suggesting that these traits may be important in the development and maintenance of problematic

Question: What personality traits are associated with problematic MMORPG use?


Image-based Airborne LiDAR Point Cloud Encoding for 3 D Building Model Retrieval. With the development of Web 2.0 and cyber city modeling, an increasing number of 3D models have been available on web-based model-sharing platforms with many applications such as navigation, urban planning, and virtual reality. Based on the concept of data reuse, a 3D model retrieval system is proposed to retrieve building models similar to a user-specified query. The basic idea behind this system is to reuse these existing 3D building models instead of reconstruction from point clouds. To efficiently retrieve models, the models in databases are compactly encoded by using a shape descriptor generally. However, most of the geometric descriptors in related works are applied to polygonal models. In this study, the input query of the model retrieval system is a point cloud acquired by Light Detection and Ranging (LiDAR) systems because of the efficient scene scanning and spatial information collection. Using Point clouds with sparse, noisy, and incomplete sampling as input queries is more difficult than that by using 3D models. Because that the building roof is more informative than other parts in the airborne LiDAR point cloud, an image-based approach is proposed to encode both point clouds from input queries and 3D models in databases. The main goal of data encoding is that the models in the database and input point clouds can be consistently encoded. Firstly, top-view depth images of buildings are generated to represent the geometry surface of a building roof. Secondly, geometric features are extracted from depth images based on height, edge and plane of building. Finally, descriptors can be extracted by spatial histograms and used in 3D model retrieval system. For data retrieval, the models are retrieved by matching the encoding coefficients of point clouds and building models. In experiments, a database including about 900,000 3D models collected from the Internet is used for evaluati

Question: What is the size of the canadian military?


Handling class imbalance in customer churn prediction. 0957-4174/$ see front matter 2008 Elsevier Ltd. A doi:10.1016/j.eswa.2008.05.027 * Corresponding author. Tel.: +32 9 264 89 80; fax: E-mail address: dirk.vandenpoel@UGent.be (D. Va URL: http://www.crm.UGent.be (D. Van den Poel). Customer churn is often a rare event in service industries, but of great interest and great value. Until recently, however, class imbalance has not received much attention in the context of data mining [Weiss, G. M. (2004). Mining with rarity: A unifying framework. SIGKDD Explorations, 6 (1), 7–19]. In this study, we investigate how we can better handle class imbalance in churn prediction. Using more appropriate evaluation metrics (AUC, lift), we investigated the increase in performance of sampling (both random and advanced under-sampling) and two specific modelling techniques (gradient boosting and weighted random forests) compared to some standard modelling techniques. AUC and lift prove to be good evaluation metrics. AUC does not depend on a threshold, and is therefore a better overall evaluation metric compared to accuracy. Lift is very much related to accuracy, but has the advantage of being well used in marketing practice [Ling, C., & Li, C. (1998). Data mining for direct marketing problems and solutions. In Proceedings of the fourth international conference on knowledge discovery and data mining (KDD-98). New York, NY: AAAI Press]. Results show that under-sampling can lead to improved prediction accuracy, especially when evaluated with AUC. Unlike Ling and Li [Ling, C., & Li, C. (1998). Data mining for direct marketing problems and solutions. In Proceedings of the fourth international conference on knowledge discovery and data mining (KDD98). New York, NY: AAAI Press], we find that there is no need to under-sample so that there are as many churners in your training set as non churners. Results show no increase in predictive performance when using the advanced sampling technique CU

Question: How to handle class imbalance in churn prediction?


Conceptual Integration of Enterprise Architecture Management and Security Risk Management. Enterprise Architecture Management (EAM) is considered to provide the mechanism for, amongst others, governing enterprise transformations required by changes in the environment. In this paper, we focus on changes that result from the analysis of information security risks and of their impacts on the services delivered by an enterprise. We present how the concepts of an information system security risks management domain can be mapped into the ArchiMate enterprise architecture modeling language. We illustrate the application of the proposed approach through the handling of a lab case.

Question: What is the difference between enterprise architecture and enterprise risk management?


Broadcast Encryption. We introduce new theoretical measures for the qualitative and quantitative assessment of encryption schemes designed for broadcast transmissions. The goal is to allow a central broadcast site to broadcast secure transmissions to an arbitrary set of recipients while minimizing key management related transmissions. We present several schemes that allow a center to broadcast a secret to any subset of privileged users out of a universe of size so that coalitions of users not in the privileged set cannot learn the secret. The most interesting scheme requires every user to store keys and the center to broadcast messages regardless of the size of the privileged set. This scheme is resilient to any coalition of users. We also present a scheme that is resilient with probability against a random subset of users. This scheme requires every user to store keys and the center to broadcast messages. Preliminary version appeared in Advances in Cryptology CRYPTO '93 Proceedings, Lecture Notes in Computer Science, Vol. 773, 1994, pp. 480–491. Dept. of Computer Science, School of Mathematics, Tel Aviv University, Tel Aviv, Israel, and Algorithmic Research Ltd. E-mail fiat@math.tau.ac.il. Incumbent of the Morris and Rose Goldman Career Development Chair, Dept. of Applied Mathematics and Computer Science, Weizmann Institute of Science, Rehovot 76100, Israel. Research supported by an Alon Fellowship and a grant from the Israel Science Foundation administered by the Israeli Academy of Sciences. E-mail: naor@wisdom.weizmann.ac.il.

Question: What is broadcast encryption?


When is a linguistic metaphor a conceptual metaphor ?. Volume 24 New Directions in Cognitive Linguistics Edited by Vyvyan Evans and Stéphanie Pourcel Editorial Advisory Board Melissa F. Bowerman Nijmegen Wallace Chafe Santa Barbara, CA Philip R. Cohen Portland, OR Antonio Damasio Iowa City, IA Morton Ann Gernsbacher Madison, WI David McNeill Chicago, IL Eric Pederson Eugene, OR François Recanati Paris Sally Rice Edmonton, Alberta Benny Shanon Jerusalem Lokendra Shastri Berkeley, CA Paul !agard Waterloo, Ontario Human Cognitive Processing (HCP)

Question: What is a conceptual metaphor?


PV panel model based on datasheet values. This work presents the construction of a model for a PV panel using the single-diode five-parameters model, based exclusively on data-sheet parameters. The model takes into account the series and parallel (shunt) resistance of the panel. The equivalent circuit and the basic equations of the PV cell/panel in Standard Test Conditions (STC)1 are shown, as well as the parameters extraction from the data-sheet values. The temperature dependence of the cell dark saturation current is expressed with an alternative formula, which gives better correlation with the datasheet values of the power temperature dependence. Based on these equations, a PV panel model, which is able to predict the panel behavior in different temperature and irradiance conditions, is built and tested.

Question: How to build a PV panel model based on datasheet values?


Presentation Attack Detection Methods for Face Recognition Systems: A Comprehensive Survey. The vulnerability of face recognition systems to presentation attacks (also known as direct attacks or spoof attacks) has received a great deal of interest from the biometric community. The rapid evolution of face recognition systems into real-time applications has raised new concerns about their ability to resist presentation attacks, particularly in unattended application scenarios such as automated border control. The goal of a presentation attack is to subvert the face recognition system by presenting a facial biometric artifact. Popular face biometric artifacts include a printed photo, the electronic display of a facial photo, replaying video using an electronic display, and 3D face masks. These have demonstrated a high security risk for state-of-the-art face recognition systems. However, several presentation attack detection (PAD) algorithms (also known as countermeasures or antispoofing methods) have been proposed that can automatically detect and mitigate such targeted attacks. The goal of this survey is to present a systematic overview of the existing work on face presentation attack detection that has been carried out. This paper describes the various aspects of face presentation attacks, including different types of face artifacts, state-of-the-art PAD algorithms and an overview of the respective research labs working in this domain, vulnerability assessments and performance evaluation metrics, the outcomes of competitions, the availability of public databases for benchmarking new PAD algorithms in a reproducible manner, and finally a summary of the relevant international standardization in this field. Furthermore, we discuss the open challenges and future work that need to be addressed in this evolving field of biometrics.

Question: What is the difference between a presentation attack and a spoof attack?


AN OVERVIEW OF MASSIVE MIMO SYSTEM IN 5 G. 4G is proving good speeds up to 1Gbps. Then why do we need anything more. The problem is that it is not able to provide real time applications. 5G is the name given to the next generation of mobile data connectivity. It will definitely provide great speeds between 10Gbps to 100Gbps and it will have enough capacity. But the thing that separated 5G from 4G is latency; the latency provided by 4G is between 40ms to 60ms, whereas in 5G it will provide ultra latency between 1ms to 10ms. The standards for 5G will be set till 2020 and it will be applicable by 2022/23. In this paper we have discussed about 5G and its advantages, a probable architecture for 5G and some challenges in 5G. The main technology that maybe used in 5G are massive MIMO, millimetre wave communication, device to device communication, beam division multiple access etc. In this paper we have discussed about massive MIMO, channel estimate in massive MIMO, beam division multiple access technique to be used in massive MIMO, antenna selection in massive MIMO, capacity and energy efficiency in massive MIMO. In future 5G is going to be a technology which will be invisible, I will be just there everywhere just like electricity. It is a very good area for research as standards and frequency band for 5G are yet to be standardised.

Question: What is the difference between 4G and 5G?


Depth Transfer: Depth Extraction from Video Using Non-Parametric Sampling. We describe a technique that automatically generates plausible depth maps from videos using non-parametric depth sampling. We demonstrate our technique in cases where past methods fail (non-translating cameras and dynamic scenes). Our technique is applicable to single images as well as videos. For videos, we use local motion cues to improve the inferred depth maps, while optical flow is used to ensure temporal depth consistency. For training and evaluation, we use a Kinect-based system to collect a large data set containing stereoscopic videos with known depths. We show that our depth estimation technique outperforms the state-of-the-art on benchmark databases. Our technique can be used to automatically convert a monoscopic video into stereo for 3D visualization, and we demonstrate this through a variety of visually pleasing results for indoor and outdoor scenes, including results from the feature film Charade.

Question: How do we extract depth from a video?


A systematic review of neurobiological and clinical features of mindfulness meditations.. BACKGROUND Mindfulness meditation (MM) practices constitute an important group of meditative practices that have received growing attention. The aim of the present paper was to systematically review current evidence on the neurobiological changes and clinical benefits related to MM practice in psychiatric disorders, in physical illnesses and in healthy subjects.   METHOD A literature search was undertaken using Medline, ISI Web of Knowledge, the Cochrane collaboration database and references of retrieved articles. Controlled and cross-sectional studies with controls published in English up to November 2008 were included.   RESULTS Electroencephalographic (EEG) studies have revealed a significant increase in alpha and theta activity during meditation. Neuroimaging studies showed that MM practice activates the prefrontal cortex (PFC) and the anterior cingulate cortex (ACC) and that long-term meditation practice is associated with an enhancement of cerebral areas related to attention. From a clinical viewpoint, Mindfulness-Based Stress Reduction (MBSR) has shown efficacy for many psychiatric and physical conditions and also for healthy subjects, Mindfulness-Based Cognitive Therapy (MBCT) is mainly efficacious in reducing relapses of depression in patients with three or more episodes, Zen meditation significantly reduces blood pressure and Vipassana meditation shows efficacy in reducing alcohol and substance abuse in prisoners. However, given the low-quality designs of current studies it is difficult to establish whether clinical outcomes are due to specific or non-specific effects of MM.   DISCUSSION Despite encouraging findings, several limitations affect current studies. Suggestions are given for future research based on better designed methodology and for future directions of investigation.

Question: What is the effect of mindfulness meditation on depression?


Robust Federated Learning Using ADMM in the Presence of Data Falsifying Byzantines. In this paper, we consider the problem of federated (or decentralized) learning using ADMM with multiple agents. We consider a scenario where a certain fraction of agents (referred to as Byzantines) provide falsified data to the system. In this context, we study the convergence behavior of the decentralized ADMM algorithm. We show that ADMM converges linearly to a neighborhood of the solution to the problem under certain conditions. We next provide guidelines for network structure design to achieve faster convergence. Next, we provide necessary conditions on the falsified updates for exact convergence to the true solution. To tackle the data falsification problem, we propose a robust variant of ADMM. We also provide simulation results to validate the analysis and show the resilience of the proposed algorithm to Byzantines.

Question: How to design a network for federated learning?


Towards More Confident Recommendations : Improving Recommender Systems Using Filtering Approach Based on Rating Variance. In the present age of information overload, it is becoming increasingly harder to find relevant content. Recommender systems have been introduced to help people deal with these vast amounts of information and have been widely used in research as well as e-commerce applications. In this paper, we propose several new approaches to improve the accuracy of recommender systems by using rating variance to gauge the confidence of recommendations. We then empirically demonstrate how these approaches work with various recommendation techniques. We also show how these approaches can generate more personalized recommendations, as measured by the coverage metric. As a result, users can be given a better control to choose whether to receive recommendations with higher coverage or higher accuracy.

Question: How to improve recommender systems?


Dynamic hand gesture recognition using hidden Markov models. Hand gesture has become a powerful means for human-computer interaction. Traditional gesture recognition just consider hand trajectory. For some specific applications, such as virtual reality, more natural gestures are needed, which are complex and contain movement in 3-D space. In this paper, we introduce an HMM-based method to recognize complex single hand gestures. Gesture images are gained by a common web camera. Skin color is used to segment hand area from the image to form a hand image sequence. Then we put forward a state-based spotting algorithm to split continuous gestures. After that, feature extraction is executed on each gesture. Features used in the system contain hand position, velocity, size, and shape. We raise a data aligning algorithm to align feature vector sequences for training. Then an HMM is trained alone for each gesture. The recognition results demonstrate that our methods are effective and accurate.

Question: What is the difference between a hand gesture and a hand gesture?


Polyphase filters - A model for teaching the art of discovery in DSP. By its very nature DSP is a mathematically heavy topic and to fully understand it students need to understand the mathematical developments underlying DSP topics. However, relying solely on mathematical developments often clouds the true nature of the foundation of a result. It is likely that students who master the mathematics may still not truly grasp the key ideas of a topic. Furthermore, teaching DSP topics by merely "going through the mathematics" deprives students of learning the art of discovery that will make them good researchers. This paper uses the topic of polyphase decimation and interpolation to illustrate how it is possible to maintain rigor yet teach using less mathematical approaches that show students how researchers think when developing new ideas.

Question: What is the art of discovery?


The Effects of Intervention Using a Robot on the Levels of Compliance of Four Children with Autism to Requests Given by T heir Mothers. The Effects of Intervention Using a Robot on the Levels of Compliance of Four Children with Autism to Requests Given by Their Mothers Holly J. Nelson Department of Communication Disorders Master of Science The current study presents the use of a humanoid robot to facilitate compliant behaviors to two types of directives in four children with autism. The children participated in a three-month intervention program that incorporated core components of the SCERTS model in order to facilitate social communication (Prizant, Wetherby, Rubin, & Laurent, 2003). Treatment sessions were comprised of 40 minutes of traditional treatment and 10 minutes of interaction with a humanoid robot. Preand post-intervention assessment were conducted, each involving a 5 minute interaction with the child's mother, in which they were presented with directives in the form of physical manipulation and verbal requests accompanied by a gesture or model. These preand post-intervention sessions were recorded, analyzed, and coded for compliant and noncompliant behavior to the directives, as well as any eye contact, language, or reciprocal action that accompanied their behavior. The overall results were variable, revealing that two participants made notable gains, one child remained consistent, and another participant showed a decrease in compliant behavior in the post-intervention sessions. Further research should be conducted to include a longer period of baseline and intervention, more systematic identification of the most effective probes for the child, and documentation of the child's physical and emotional state.

Question: What is the effect of a robot on the levels of compliance of four children with autism to requests given by their mothers?


A Study of Attribute-based Proxy Re-encryption Scheme in Cloud Environments. Attribute-based proxy re-encryption (ABPRE) scheme is one of the proxy cryptography, which can delegate the reencryption capability to the proxy and re-encrypt the encrypted data by using the re-encryption key. ABPRE extending the traditional proxy cryptography and attributes plays an important role. In ABPRE, users are identified by attributes, and the access policy is designed to control the user's access. Using ABPRE can have these advantages: (i) The proxy can be delegated to execute the re-encryption operation, which reduces the computation overhead of the data owner; (ii) The authorized user just uses his own secret key to decrypt the encrypted data, and he doesn't need to store an additional decryption key for deciphering; (iii) The sensitive information cannot be revealed to the proxy in re-encryption, and the proxy only complies to the data owner's command. In this paper, we survey two various access policy attribute-based proxy re-encryption schemes and analyze these schemes. Thereafter, we list the comparisons of them by some criteria.

Question: What is the difference between attribute-based proxy re-encryption and traditional proxy re-encryption?


Bubble-up: Increasing utilization in modern warehouse scale computers via sensible co-locations. As much of the world's computing continues to move into the cloud, the overprovisioning of computing resources to ensure the performance isolation of latency-sensitive tasks, such as web search, in modern datacenters is a major contributor to low machine utilization. Being unable to accurately predict performance degradation due to contention for shared resources on multicore systems has led to the heavy handed approach of simply disallowing the co-location of high-priority, latency-sensitive tasks with other tasks. Performing this precise prediction has been a challenging and unsolved problem.  In this paper, we present Bubble-Up, a characterization methodology that enables the accurate prediction of the performance degradation that results from contention for shared resources in the memory subsystem. By using a bubble to apply a tunable amount of "pressure" to the memory subsystem on processors in production datacenters, our methodology can predict the performance interference between co-locate applications with an accuracy within 1% to 2% of the actual performance degradation. Using this methodology to arrive at "sensible" co-locations in Google's production datacenters with real-world large-scale applications, we can improve the utilization of a 500-machine cluster by 50% to 90% while guaranteeing a high quality of service of latency-sensitive applications.

Question: How to predict the performance degradation of co-locating applications?


Preventing man-in-the-middle attack in Diffie-Hellman key exchange protocol. The acceleration in developments in communication technology has led to a consequent increase in the vulnerability of data due to penetration attacks. These attacks often came from outside where non-qualified companies develop IT projects. Cryptography can offer high levels of security but has recently shown vulnerabilities such as the man-in-the-middle (MITM) attack in areas of key exchange protocols, especially in the Diffie-Hellman (DH) protocol. Firstly, this paper presents an overview of MITM attacks targeted at the DH protocol then discusses some of the shortcomings of current defenses. A proposed method to secure DH, which helps secure systems against MITM attacks, is then presented. This method involves the use of Geffe generation of binary sequences. The use of Geffe generator offers high levels of randomness. Data hashed and encrypted using this proposed method will be so difficult to intercept and decrypt without the appropriate keys. This offers high levels of security and helps prevent MITM attacks.

Question: What is a man-in-the-middle attack?


Hyperbolic centroidal Voronoi tessellation. The centroidal Voronoi tessellation (CVT) has found versatile applications in geometric modeling, computer graphics, and visualization. In this paper, we extend the concept of the CVT from Euclidean space to hyperbolic space. A novel hyperbolic CVT energy is defined, and the relationship between minimizing this energy and the hyperbolic CVT is proved. We also show by our experimental results that the hyperbolic CVT has the similar property as its Euclidean counterpart where the sites are uniformly distributed according to given density values. Two algorithms -- Lloyd's algorithm and the L-BFGS algorithm -- are adopted to compute the hyperbolic CVT, and the convergence of Lloyd's algorithm is proved. As an example of the application, we utilize the hyperbolic CVT to compute uniform partitions and high-quality remeshing results for high-genus (genus>1) surfaces.

Question: What is the hyperbolic CVT?


Web-scale information extraction in knowitall: (preliminary results). Manually querying search engines in order to accumulate a large bodyof factual information is a tedious, error-prone process of piecemealsearch. Search engines retrieve and rank potentially relevantdocuments for human perusal, but do not extract facts, assessconfidence, or fuse information from multiple documents. This paperintroduces KnowItAll, a system that aims to automate the tedious process ofextracting large collections of facts from the web in an autonomous,domain-independent, and scalable manner.The paper describes preliminary experiments in which an instance of KnowItAll, running for four days on a single machine, was able to automatically extract 54,753 facts. KnowItAll associates a probability with each fact enabling it to trade off precision and recall. The paper analyzes KnowItAll's architecture and reports on lessons learned for the design of large-scale information extraction systems.

Question: What is the largest number of facts that can be extracted by KnowItAll?


A conceptual framework to develop Green IT – going beyond the idea of environmental sustainability. This paper presents a conceptual framework aiming to better research, understand and develop Green IT within organizations. Based on a literature review on Green IT, regarding the concepts of responsibility and sustainability, we propose an initial framework with five dimensions: ethical, technological, economical, social and environmental. These dimensions compose Green IT strategies and practices. Additionally, the framework considers that environmental changing requirements, strategic requirements and dynamic capabilities are the forces which move organizations toward green practices and foster innovation. This framework is part of a five-year horizon project that began in 2009 and aims to contribute to the theory in the field, having as initial goal to identify the constructs associated with Green IT.

Question: What is a conceptual framework?


MHz-frequency operation of flyback converter with monolithic self-synchronized rectifier (SSR). Synchronous rectification offers an effective solution to improve efficiency of low-output-voltage flyback converters. Conventional synchronous flyback topologies using discrete synchronous rectifier face challenges in precise timing control on the secondary side. In this paper, MHz-frequency operation of a flyback DC-DC converter using a new monolithic self-synchronized rectifier (SSR) is investigated and demonstrated. The SSR IC, acting solely on its own drain-source voltage instead of external control signals, considerably simplifies converter design, improves system efficiency, and enables MHz operating frequency. Analysis, modeling, and experimental results are presented.

Question: How to design a flyback converter with a monolithic self-synchronized rectifier?


Flexible HALS algorithms for sparse non-negative matrix/tensor factorization. In this paper we propose a family of new algorithms for non-negative matrix/tensor factorization (NMF/NTF) and sparse nonnegative coding and representation that has many potential applications in computational neuroscience, multi-sensory, multidimensional data analysis and text mining. We have developed a class of local algorithms which are extensions of hierarchical alternating least squares (HALS) algorithms proposed by us in . For these purposes, we have performed simultaneous constrained minimization of a set of robust cost functions called alpha and beta divergences. Our algorithms are locally stable and work well for the NMF blind source separation (BSS) not only for the over-determined case but also for an under-determined (over-complete) case (i.e., for a system which has less sensors than sources) if data are sufficiently sparse. The NMF learning rules are extended and generalized for N-th order nonnegative tensor factorization (NTF). Moreover, new algorithms can be potentially accommodated to different noise statistics by just adjusting a single parameter. Extensive experimental results confirm the validity and high performance of the developed algorithms, especially, with usage of the multi-layer hierarchical approach .

Question: What is the difference between NMF and NTF?


Data transmission via GSM voice channel for end to end security. Global System for Mobile Communications (GSM) technology still plays a key role because of its availability, reliability and robustness. Recently, additional consumer applications are proposed in which GSM is used as a backup or data transmission service. Unfortunately sending data via GSM channel is a challenging task since it is speech sensitive and suppresses other forms of signals. In this paper, a systematic method is proposed to develop a modem that transmits data over GSM voice channel (DoGSMV) using speech like (SL) symbols. Unlike the previous approaches an artificial search space is produced to find best SL symbols and analyses by synthesis (AbyS) method is introduced for parameter decoding. As a result 1.6 kbps simulation data rate is achieved when wireless communication errors are ignored.

Question: How to send data over GSM voice channel?


Topic-link LDA: joint models of topic and author community. Given a large-scale linked document collection, such as a collection of blog posts or a research literature archive, there are two fundamental problems that have generated a lot of interest in the research community. One is to identify a set of high-level topics covered by the documents in the collection; the other is to uncover and analyze the social network of the authors of the documents. So far these problems have been viewed as separate problems and considered independently from each other. In this paper we argue that these two problems are in fact inter-dependent and should be addressed together. We develop a Bayesian hierarchical approach that performs topic modeling and author community discovery in one unified framework. The effectiveness of our model is demonstrated on two blog data sets in different domains and one research paper citation data from CiteSeer.

Question: What is the topic of this paper?


Simple topologies of PWM AC-AC converters. This letter proposes a new family of simple topologies of PWM AC-AC converters with minimal switches. With extension from the basic DC-DC converters, a series of AC-AC converters such as buck, boost, buck-boost, Cuk, and isolated converters are obtained. By PWM duty ratio control, they become a "solid-state transformer" with a continuously variable turns ratio. All the proposed AC-AC converters in this paper employ only two switches. Compared to the existing circuits that use six switches or more, they can reduce cost and improve reliability. The operating principle and control method of the proposed topologies are presented. Analysis and simulation results are given using the Cuk AC-AC converter as an example. The analysis can be easily extended to other converters of the proposed family.

Question: What is a solid-state transformer?


Parsing Natural Scenes and Natural Language with Recursive Neural Networks. Recursive structure is commonly found in the inputs of different modalities such as natural scene images or natural language sentences. Discovering this recursive structure helps us to not only identify the units that an image or sentence contains but also how they interact to form a whole. We introduce a max-margin structure prediction architecture based on recursive neural networks that can successfully recover such structure both in complex scene images as well as sentences. The same algorithm can be used both to provide a competitive syntactic parser for natural language sentences from the Penn Treebank and to outperform alternative approaches for semantic scene segmentation, annotation and classification. For segmentation and annotation our algorithm obtains a new level of state-of-theart performance on the Stanford background dataset (78.1%). The features from the image parse tree outperform Gist descriptors for scene classification by 4%.

Question: What is the max-margin structure prediction architecture based on recursive neural networks?


1 Mobile Landscapes : using location data from cellphones for urban analysis. The technology for determining the geographic location of cell phones and other hand-held devices is becoming increasingly available. It is opening the way to a wide range of applications, collectively referred to as Location Based Services (LBS), that are primarily aimed at individual users. However, if deployed to retrieve aggregated data in cities, LBS could become a powerful tool for urban analysis. This paper aims to review and introduce the potential of this technology to the urban planning community. In addition, it presents the 'Mobile Landscapes' project: an application in the metropolitan area of Milan, Italy, based on the geographical mapping of cell phone usage at different times of the day. The results enable a graphic representation of the intensity of urban activities and their evolution through space and time. Finally, a number of future applications are discussed and their potential for urban studies and planning is assessed.

Question: What is the potential of using cell phone data for urban analysis?


ESTIMATION METHODS FOR STOCHASTIC VOLATILITY MODELS : A SURVEY. Although stochastic volatility (SV) models have an intuitive appeal, their empirical application has been limited mainly due to difficulties involved in their estimation. The main problem is that the likelihood function is hard to evaluate. However, recently, several new estimation methods have been intro duced and the literature on SV models has grown substantially. In this article, we review this literature. We describe the main estimators of the parameters and the underlying volatilities focusing on their advantages and limitations both from the theoretical and empirical point of view. We complete the survey with an application of the most important procedures to the S&P 500 stock price index.

Question: What is the stochastic volatility model?