Sentence Selection and Weighting for Neural Machine Translation Domain Adaptation. Neural machine translation NMT has been prominent in many machine translation tasks. However, in some domain-specific tasks, only the corpora from similar domains can improve translation performance. If out-of-domain corpora are directly added into the in-domain corpus, the translation performance may even degrade. Therefore, domain adaptation techniques are essential to solve the NMT domain problem. Most existing methods for domain adaptation are designed for the conventional phrase-based machine translation. For NMT domain adaptation, there have been only a few studies on topics such as fine tuning, domain tags, and domain features. In this paper, we have four goals for sentence level NMT domain adaptation. First, the NMT's internal sentence embedding is exploited and the sentence embedding similarity is used to select out-of-domain sentences that are close to the in-domain corpus. Second, we propose three sentence weighting methods, i.e., sentence weighting, domain weighting, and batch weighting, to balance the data distribution during NMT training. Third, in addition, we propose dynamic training methods to adjust the sentence selection and weighting during NMT training. Fourth, to solve the multidomain problem in a real-world NMT scenario where the domain distributions of training and testing data often mismatch, we proposed a multidomain sentence weighting method to balance the domain distributions of training data and match the domain distributions of training and testing data. The proposed methods are evaluated in international workshop on spoken language translation IWSLT English-to-French/German tasks and a multidomain English-to-French task. Empirical results show that the sentence selection and weighting methods can significantly improve the NMT performance, outperforming the existing baselines.

Question: How to select sentences for domain adaptation?


Novel Online Methods for Time Series Segmentation. To efficiently and effectively mine massive amounts of data in the time series, approximate representation of the data is one of the most commonly used strategies. Piecewise linear approximation is such an approach, which represents a time series by dividing it into segments and approximating each segment with a straight line. In this paper, we first propose a new segmentation criterion that improves computing efficiency. Based on this criterion, two novel online piecewise linear segmentation methods are developed, the feasible space window method and the stepwise feasible space window method. The former usually produces much fewer segments and is faster and more reliable in the running time than other methods. The latter can reduce the representation error with fewer segments. It achieves the best overall performance on the segmentation results compared with other methods. Extensive experiments on a variety of real-world time series have been conducted to demonstrate the advantages of our methods.

Question: What is the best online method for time series segmentation?


Ensemble Named Entity Recognition (NER): Evaluating NER Tools in the Identification of Place Names in Historical Corpora. The field of Spatial Humanities has advanced substantially in the past years. The identification and extraction of toponyms and spatial information mentioned in historical text collections has allowed its use in innovative ways, making possible the application of spatial analysis and the mapping of these places with geographic information systems. For instance, automated place name identification is possible with Named Entity Recognition (NER) systems. Statistical NER methods based on supervised learning, in particular, are highly successful with modern datasets. However, there are still major challenges to address when dealing with historical corpora. These challenges include language changes over time, spelling variations, transliterations, OCR errors, and sources written in multiple languages among others. In this article, considering a task of place name recognition over two collections of historical correspondence, we report an evaluation of five NER systems and an approach that combines these through a voting system. We found that although individual performance of each NER system was corpus dependent, the ensemble combination was able to achieve consistent measures of precision and recall, outperforming the individual NER systems. In addition, the results showed that these NER systems are not strongly dependent on preprocessing and translation to Modern English.

Question: What is the difference between a NER system and a Named Entity Recognition system?


Learning Dependency-Based Compositional Semantics. Suppose we want to build a system that answers a natural language question by representing its semantics as a logical forxm and computing the answer given a structured database of facts. The core part of such a system is the semantic parser that maps questions to logical forms. Semantic parsers are typically trained from examples of questions annotated with their target logical forms, but this type of annotation is expensive.Our goal is to instead learn a semantic parser from question–answer pairs, where the logical form is modeled as a latent variable. We develop a new semantic formalism, dependency-based compositional semantics (DCS) and define a log-linear distribution over DCS logical forms. The model parameters are estimated using a simple procedure that alternates between beam search and numerical optimization. On two standard semantic parsing benchmarks, we show that our system obtains comparable accuracies to even state-of-the-art systems that do require annotated logical forms.

Question: What is a dependency-based compositional semantics?


Searching for the elusive gift: advances in talent identification in sport.. The incentives for sport organizations to identify talented athletes from a young age continue to grow, yet effective talent identification remains a challenging task. This opinion paper examines recent advances in talent identification, focusing in particular on the emergence of new approaches that may offer promise to identify talent (e.g., small-sided games, genetic testing, and advanced statistical analyses). We appraise new multi-disciplinary and large-scale population studies of talent identification, provide a consideration of the most recent psychological predictors of performance, examine the emergence of new approaches that strive to diminish biases in talent identification, and look at the rise in interest in talent identification in Paralympic sport.

Question: What is the most recent psychological predictor of performance?


Federated Learning for Keyword Spotting. We propose a practical approach based on federated learning to solve out-of-domain issues with continuously running embedded speech-based models such as wake word detectors. We conduct an extensive empirical study of the federated averaging algorithm for the "Hey Snips" wake word based on a crowdsourced dataset that mimics a federation of wake word users. We empirically demonstrate that using an adaptive averaging strategy inspired from Adam in place of standard weighted model averaging highly reduces the number of communication rounds required to reach our target performance. The associated upstream communication costs per user are estimated at 8 MB, which is a reasonable in the context of smart home voice assistants. Additionally, the dataset used for these experiments is being open sourced with the aim of fostering further transparent research in the application of federated learning to speech data.

Question: What is the size of the dataset?


Dynamic Modeling and Performance Analysis of a Grid-Connected Current-Source Inverter-Based Photovoltaic System. Voltage-source inverter (VSI) topology is widely used for grid interfacing of distributed generation (DG) systems. However, when employed as the power conditioning unit in photovoltaic (PV) systems, VSI normally requires another power electronic converter stage to step up the voltage, thus adding to the cost and complexity of the system. To make the proliferation of grid-connected PV systems a successful business option, the cost, performance, and life expectancy of the power electronic interface need to be improved. The current-source inverter (CSI) offers advantages over VSI in terms of inherent boosting and short-circuit protection capabilities, direct output current controllability, and ac-side simpler filter structure. Research on CSI-based DG is still in its infancy. This paper focuses on modeling, control, and steady-state and transient performances of a PV system based on CSI. It also performs a comparative performance evaluation of VSI-based and CSI-based PV systems under transient and fault conditions. Analytical expectations are verified using simulations in the Power System Computer Aided Design/Electromagnetic Transient Including DC (PSCAD/EMTDC) environment, based on a detailed system model.

Question: What is the difference between a current source inverter and a voltage source inverter?


Mobile-assisted localization in wireless sensor networks. The localization problem is to determine an assignment of coordinates to nodes in a wireless ad-hoc or sensor network that is consistent with measured pairwise node distances. Most previously proposed solutions to this problem assume that the nodes can obtain pairwise distances to other nearby nodes using some ranging technology. However, for a variety of reasons that include obstructions and lack of reliable omnidirectional ranging, this distance information is hard to obtain in practice. Even when pairwise distances between nearby nodes are known, there may not be enough information to solve the problem uniquely. This paper describes MAL, a mobile-assisted localization method which employs a mobile user to assist in measuring distances between node pairs until these distance constraints form a "globally rigid'* structure that guarantees a unique localization. We derive the required constraints on the mobile's movement and the minimum number of measurements it must collect; these constraints depend on the number of nodes visible to the mobile in a given region. We show how to guide the mobile's movement to gather a sufficient number of distance samples for node localization. We use simulations and measurements from an indoor deployment using the Cricket location system to investigate the performance of MAL, finding in real-world experiments that MAL's median pairwise distance error is less than 1.5% of the true node distance.

Question: What is a globally rigid structure?


Robust Physical-World Attacks on Machine Learning Models. Deep neural network-based classifiers are known to be vulnerable to adversarial examples that can fool them into misclassifying their input through the addition of small-magnitude perturbations. However, recent studies have demonstrated that such adversarial examples are not very effective in the physical world—they either completely fail to cause misclassification or only work in restricted cases where a relatively complex image is perturbed and printed on paper. In this paper we propose a new attack algorithm—Robust Physical Perturbations (RP2)— that generates perturbations by taking images under different conditions into account. Our algorithm can create spatiallyconstrained perturbations that mimic vandalism or art to reduce the likelihood of detection by a casual observer. We show that adversarial examples generated by RP2 achieve high success rates under various conditions for real road sign recognition by using an evaluation methodology that captures physical world conditions. We physically realized and evaluated two attacks, one that causes a Stop sign to be misclassified as a Speed Limit sign in 100% of the testing conditions, and one that causes a Right Turn sign to be misclassified as either a Stop or Added Lane sign in 100% of the testing conditions.

Question: What is the difference between a stop sign and a speed limit sign?


Open Information Extraction on Scientific Text: An Evaluation. Open Information Extraction (OIE) is the task of the unsupervised creation of structured information from text. OIE is often used as a starting point for a number of downstream tasks including knowledge base construction, relation extraction, and question answering. While OIE methods are targeted at being domain independent, they have been evaluated primarily on newspaper, encyclopedic or general web text. In this article, we evaluate the performance of OIE on scientific texts originating from 10 different disciplines. To do so, we use two state-of-the-art OIE systems using a crowd-sourcing approach. We find that OIE systems perform significantly worse on scientific text than encyclopedic text. We also provide an error analysis and suggest areas of work to reduce errors. Our corpus of sentences and judgments are made available.

Question: What is the difference between scientific and encyclopedic text?


Genetic Algorithms + Data Structures = Evolution Programs. If you are looking for Genetic Algorithms Data Structures Evolution Programs in pdf file you can find it here. This is the best place for you where you can find the genetic algorithms data structures evolution programs document. Sign up for free to get the download links. There are 3 sources of download links that you can download and save it in your desktop.

Question: What is the best place for me to find genetic algorithms data structures evolution programs?


Behavioral malware detection approaches for Android. Android, the fastest growing mobile operating system released in November 2007, boasts of a staggering 1.4 billion active users. Android users are susceptible to malicious applications that can hack into their personal data due to the lack of careful monitoring of their in-device security. There have been numerous works on devising malware detection methods. However, none of earlier works are conclusive enough for direct application and lack experimental validation. In this paper, we have investigated the natures and identities of malicious applications and devised two novel detection approaches for detection: network-based detection and system call based detection approaches. To evaluate our proposed approaches, we performed experiments on a subset of 1260 malwares, acquired from Android Malware Genome Project, a malware database created by Y. Zhou et al. [1] and 227 non-malware (benign) applications. Results show that our system call based approach is able to detect malwares with an accuracy of 87% which is quite significant in general malware detection context. Our proposed detection approaches along with the experimental results will provide security professionals with more precise and quantitative approaches in their investigations of mobile malwares on Android systems.

Question: What is the difference between a malware and a benign application?


Advanced Wireless LAN Technologies: IEEE 802.11AC and Beyond. In 2013, global mobile data traffic grew 81% and it is projected to increase 11-fold between 2013 and 2018. Further, it is predicted that by 2018, over two-thirds of the world's mobile traffic will be video and more than half of all traffic from wireless connected devices will be offloaded to Wi-Fi networks and femto-cells [1]. Consequently, wireless LANs need major upgrades to improve both throughput and efficiency. IEEE 802.11ac is an amendment to the 802.11 standard that was just ratified by IEEE 802.11. Promising up to gigabit data rates, many Wi-Fi products are being built based on this specification. In addition to technologies that improve throughput, IEEE 802.11ax is investigating and evaluating advanced wireless technologies that enable more efficient utilization of the existing spectrum.

Question: What is the difference between IEEE 802.11ac and IEEE 802.11ax?


A vision-based robotic grasping system using deep learning for 3D object recognition and pose estimation. Pose estimation of object is one of the key problems for the automatic-grasping task of robotics. In this paper, we present a new vision-based robotic grasping system, which can not only recognize different objects but also estimate their poses by using a deep learning model, finally grasp them and move to a predefined destination. The deep learning model demonstrates strong power in learning hierarchical features which greatly facilitates the recognition mission. We apply the Max-pooling Convolutional Neural Network (MPCNN), one of the most popular deep learning models, in this system, and assign different poses of objects as different classes in MPCNN. Besides, a new object detection method is also presented to overcome the disadvantage of the deep learning model. We have built a database comprised of 5 objects with different poses and illuminations for experimental performance evaluation. The experimental results demonstrate that our system can achieve high accuracy on object recognition as well as pose estimation. And the vision-based robotic system can grasp objects successfully regardless of different poses and illuminations.

Question: What is a vision-based robotic grasping system?


Dissociation between face perception and face memory in adults, but not children, with developmental prosopagnosia. Cognitive models propose that face recognition is accomplished through a series of discrete stages, including perceptual representation of facial structure, and encoding and retrieval of facial information. This implies that impaired face recognition can result from failures of face perception, face memory, or both. Studies of acquired prosopagnosia, autism spectrum disorders, and the development of normal face recognition support the idea that face perception and face memory are distinct processes, yet this distinction has received little attention in developmental prosopagnosia (DP). To address this issue, we tested the face perception and face memory of children and adults with DP. By definition, face memory is impaired in DP, so memory deficits were present in all participants. However, we found that all children, but only half of the adults had impaired face perception. Thus, results from adults indicate that face perception and face memory are dissociable, while the results from children provide no evidence for this division. Importantly, our findings raise the possibility that DP is qualitatively different in childhood versus adulthood. We discuss theoretical explanations for this developmental pattern and conclude that longitudinal studies are necessary to better understand the developmental trajectory of face perception and face memory deficits in DP.

Question: What is the difference between face perception and face memory?


Implementation Challenges for Information Security Awareness Initiatives in E-Government. With the widespread adoption of electronic government services, there has been a need to ensure a seamless flow of information across public sector organizations, while at the same time, maintaining confidentiality, integrity and availability. Governments have put in place various initiatives and programs including information security awareness to provide the needed understanding on how public sector employees can maintain security and privacy. Nonetheless, the implementation of such initiatives often faces a number of challenges that impede further take-up of e-government services. This paper aims to provide a better understanding of the challenges contributing towards the success of information security awareness initiatives implementation in the context of e-government. Political, organizational, social as well as technological challenges have been utilized in a conceptual framework to signify such challenges in e-government projects. An empirical case study conducted in a public sector organization in Greece was exploited in this research to reflect on these challenges. While, the results from this empirical study confirm the role of the identified challenges for the implementation of security awareness programs in e-government, it has been noticed that awareness programmers often pursue different targets of preserving security and privacy, which sometimes results in adding more complexity to the organization.

Question: What are the challenges of implementing information security awareness initiatives in e-government?


: Past , Present , Future. We thank Steve Burks, Dick Thaler, and especially Matthew Rabin (who collaborated during part of the process) for helpful comments.

Question: What is the future of the past?


Molecular and Morphological Characterization of Aphelenchoides fuchsi sp. n. (Nematoda: Aphelenchoididae) Isolated from Pinus eldarica in Western Iran.. Aphelenchoides fuchsi sp. n. is described and illustrated from bark and wood samples of a weakened Mondell pine in Kermanshah Province, western Iran. The new species has body length of 332 to 400 µm (females) and 365 to 395 µm (males). Lip region set off from body contour. The cuticle is weakly annulated, and there are four lines in the lateral field. The stylet is 8 to 10 μm long and has small basal swellings. The excretory pore is located ca one body diam. posterior to metacorpus valve or 51 to 62 μm from the head. The postuterine sac well developed (60-90 µm). Spicules are relatively short (15-16 μm in dorsal limb) with apex and rostrum rounded, well developed, and the end of the dorsal limb clearly curved ventrad like a hook. The male tail has usual three pairs of caudal papillae (2+2+2) and a well-developed mucro. The female tail is conical, terminating in a complicated step-like projection, usually with many tiny nodular protuberances. The new species belongs to the Group 2 sensu Shahina, category of Aphelenchoides species. Phylogenetic analysis based on small subunit (SSU) and partial large subunit (LSU) sequences of rRNA supported the morphological results.

Question: What is the new species?


The Kaldi Speech Recognition Toolkit. We describe the design of Kaldi, a free, open-source toolkit for speech recognition research. Kaldi provides a speech recognition system based on finite-state transducers (using the freely available OpenFst), together with detailed documentation and scripts for building complete recognition systems. Kaldi is written is C++, and the core library supports modeling of arbitrary phonetic-context sizes, acoustic modeling with subspace Gaussian mixture models (SGMM) as well as standard Gaussian mixture models, together with all commonly used linear and affine transforms. Kaldi is released under the Apache License v2.0, which is highly nonrestrictive, making it suitable for a wide community of users.

Question: What is the kaldi speech recognition toolkit?


Trust or consequences? Causal effects of perceived risk and subjective norms on cloud technology adoption. Cloud computing has become a popular alternative information curation solution for organizations. As more corporate proprietary information is stored in the Cloud, concerns about Cloud information security have also increased. This study investigates the causal effect of perceived risk and subjective norms on users' trust intention to adopt Cloud technology. A partial least squares structural equation modeling (PLS-SEM) analysis was performed to assess latent variables and examine moderating effects to Cloud technology adoption. Our findings suggest that a user's perceived risk and subjective norms have a significant effect on their trust intention vis-a-vis Cloud adoption, which leads to their decision on whether to adopt the Cloud technology. While a user's attitudes do influence their intention to trust the Cloud, their attitude is not moderated by either perceived risk or subjective norms. On the other hand, a user's perceived risk of the Cloud environment predominately moderates their knowledge and perceived behavioral control, which results in their knowledge and perceived behavioral control not having a direct effect on their intention to trust the Cloud. Moreover, a user's

Question: What is the difference between a trust and a consequence?


Style Finder : Fine-Grained Clothing Style Recognition and Retrieval. With the rapid proliferation of smartphones and tablet computers, search has moved beyond text to other modalities like images and voice. For many applications like Fashion, visual search offers a compelling interface that can capture stylistic visual elements beyond color and pattern that cannot be as easily described using text. However, extracting and matching such attributes remains an extremely challenging task due to high variability and deformability of clothing items. In this paper, we propose a fine-grained learning model and multimedia retrieval framework to address this problem. First, an attribute vocabulary is constructed using human annotations obtained on a novel finegrained clothing dataset. This vocabulary is then used to train a fine-grained visual recognition system for clothing styles. We report benchmark recognition and retrieval results on Women's Fashion Coat Dataset and illustrate potential mobile applications for attribute-based multimedia retrieval of clothing items and image annotation.

Question: What is the fine-grained clothing style recognition and retrieval framework?


Design of Broadband Constant-Beamwidth Conical Corrugated-Horn Antennas [Antenna Designer's Notebook]. In this paper, a new design procedure is proposed for the design of wideband constant-beamwidth conical corrugated-horn antennas, with minimum design and construction complexity. The inputs to the procedure are the operating frequency band, the required minimum beamwidth in the entire frequency band, and the frequency in which the maximum gain is desired to occur. Based on these values, the procedure gives a relatively good design with a relative bandwidth of up to 2.5:1. Based on the proposed procedure, a corrugated-horn antenna with a constant beamwidth over the frequencies of 8 to 18 GHz was designed and simulated using commercial software. The designed antenna was also constructed, and its electromagnetic performance was measured. The measured results of the constructed prototype antenna confirmed the simulation results and satisfied the design requirements, validating the proposed design procedure.

Question: How to design a wideband antenna?


A bandwidth-tunable bioamplifier with voltage-controlled symmetric pseudo-resistors. This paper describes a bioamplifier that employs a voltage-controlled-pseudo-resistor to achieve tunable bandwidth and wide operating voltage range for biomedical applications. The versatile pseudo-resistor employed provides ultra-high resistance for ac coupling to cancel the dc offset from electrode–tissue interface. The voltage-controlled-pseudo-resistor consists of serial-connected PMOS transistors working at the subthreshold region and an auto-tuning circuit that makes sure the constant (time-invariant) control-voltage of the pseudo-resistor. This bandwidth-tunable bioamplifier is designed in a 0.18-μm standard CMOS process, achieving a gain of 40.2 dB with 10.35-μW power consumption. The designed chip was also used to develop the proof-of-concept prototype. An operation bandwidth of 9.5 kHz, inputreferred noise of 5.2 μVrms from 6.3 Hz to 9.5 kHz and 5.54 μVrms from 250 Hz to 9.5 kHz, and a tunable cutoff-frequency from 6.3–600 Hz were demonstrated to prove our design. & 2015 Elsevier Ltd. All rights reserved.

Question: What is the bandwidth of the bioamplifier?


Stock Trend Prediction Using Regression Analysis – A Data Mining Approach. Organizations have been collecting data for decades, building massive data warehouses in which to store the data. Even though this data is available, very few of these organizations have been able to realize the actual value stored in it. The question these organizations are asking is how to extract meaningful data and uncover patterns and relationship from their databases. This paper presents a study of regression analysis for use in stock price prediction. Data were obtained from the daily official list of the prices of all shares traded on the stock exchange published by the Nigerian Stock Exchange using banking sector of Nigerian economy with three banks namely:First Bank of Nigeria Plc, Zenith Bank Plc, and Skye Bank Plc to build a database. A data mining software tool was used to uncover patterns and relationships and also to extract values of variables from the database to predict the future values of other variables through the use of time series data that employed moving average method. The tools were found capable technique to describe the trends of stock market prices and predict the future stock market prices of three banks sampled.

Question: How to predict stock prices?


Autoencoder-based holographic image restoration. We propose a holographic image restoration method using an autoencoder, which is an artificial neural network. Because holographic reconstructed images are often contaminated by direct light, conjugate light, and speckle noise, the discrimination of reconstructed images may be difficult. In this paper, we demonstrate the restoration of reconstructed images from holograms that record page data in holographic memory and quick response codes by using the proposed method.

Question: How to restore holographic images?


A method for normalizing histology slides for quantitative analysis. Inconsistencies in the preparation of histology slides make it difficult to perform quantitative analysis on their results. In this paper we provide two mechanisms for overcoming many of the known inconsistencies in the staining process, thereby bringing slides that were processed or stored under very different conditions into a common, normalized space to enable improved quantitative analysis.

Question: How to normalize histology slides?


Efficient Private ERM for Smooth Objectives. In this paper, we consider efficient differentially private empirical risk minimization from the viewpoint of optimization algorithms. For strongly convex and smooth objectives, we prove that gradient descent with output perturbation not only achieves nearly optimal utility, but also significantly improves the running time of previous state-of-the-art private optimization algorithms, for both ǫ-DP and (ǫ, δ)-DP. For non-convex but smooth objectives, we propose an RRPSGD (Random Round Private Stochastic Gradient Descent) algorithm, which provably converges to a stationary point with privacy guarantee. Besides the expected utility bounds, we also provide guarantees in high probability form. Experiments demonstrate that our algorithm consistently outperforms existing method in both utility and running time.

Question: What is the difference between the gradient descent and the stochastic gradient descent?


What to expect from teleconferencing.. Like other marvels of the electronic age, teleconferencing has been both oversold and underused. Though it has many potential uses, what managers know, or think they know, about it is generally based on misconceptions. Rather than relying only on vendors of teleconferencing, potential purchasers should first decide what their communication needs are, then choose the system that suits them best. These authors explain the new teleconferencing options and give guidelines for adapting them to a particular company.

Question: What is the difference between teleconferencing and videoconferencing?


Cultivating Competence , Self-Efficacy , and Intrinsic Interest Through Proximal Self-Motivation. The present experiment tested the hypothesis that self-motivation through proximal goal setting serves as an effective mechanism for cultivating competencies, self-percepts of efficacy, and intrinsic interest. Children who exhibited gross deficits and disinterest in mathematical tasks pursued a program of self-directed learning under conditions involving either proximal subgoals, distal goals, or no goals. Results of the multifaceted assessment provide support for the superiority of proximal self-influence. Under proximal subgoals, children progressed rapidly in self-directed learning, achieved substantial mastery of mathematical operations, and developed a sense of personal efficacy and intrinsic interest in arithmetic activities that initially held little attraction for them. Distal goals had no demonstrable effects. In addition to its other benefits, goal proximity fostered veridical self-knowledge of capabilities as reflected in high congruence between judgments of mathematical self-efficacy and subsequent mathematical performance. Perceived self-efficacy was positively related to accuracy of mathematical performance and to intrinsic interest in arithmetic activities. Article: Much human behavior is directed and sustained over long periods, even though the external inducements for it may be few and far between. Under conditions in which external imperatives are minimal and discontinuous, people must partly serve as agents of their own motivation and action. In social learning theory (Bandura, 1977b, in press), self-directedness operates through a self system that comprises cognitive structures and subfunctions for perceiving, evaluating, motivating, and regulating behavior. An important, cognitively based source of self-motivation relies on the intervening processes of goal setting and self-evaluative re-actions to one's own behavior. This form of self-motivation, w

Question: What is the difference between self-efficacy and self-motivation?


Coverage-Guided Fuzzing for Deep Neural Networks. In company with the data explosion over the past decade, deep neural network (DNN) based software has experienced unprecedented leap and is becoming the key driving force of many novel industrial applications, including many safety-critical scenarios such as autonomous driving. Despite great success achieved in various human intelligence tasks, similar to traditional software, DNNs could also exhibit incorrect behaviors caused by hidden defects causing severe accidents and losses. In this paper, we propose DeepHunter, an automated fuzz testing framework for hunting potential defects of general-purpose DNNs. DeepHunter performs metamorphic mutation to generate new semantically preserved tests, and leverages multiple plugable coverage criteria as feedback to guide the test generation from different perspectives. To be scalable towards practical-sized DNNs, DeepHunter maintains multiple tests in a batch, and prioritizes the tests selection based on active feedback. The effectiveness of DeepHunter is extensively investigated on 3 popular datasets (MNIST, CIFAR-10, ImageNet) and 7 DNNs with diverse complexities, under large set of 6 coverage criteria as feedback. The large-scale experiments demonstrate that DeepHunter can (1) significantly boost the coverage with guidance; (2) generate useful tests to detect erroneous behaviors and facilitate the DNN model quality evaluation; (3) accurately capture potential defects during DNN quantization for platform migration.

Question: What is the coverage of deep neural networks?


Fitting Tweedie ' s Compound Poisson Model to Insurance Claims Data : Dispersion Modelling. We reconsider the problem of producing fair and accurate tariffs based on aggregated insurance data giving numbers of claims and total costs for the claims. Jørgensen and de Souza (Scand. Actuarial J., 1994) assumed Poisson arrival of claims and gamma distributed costs for individual claims. Jørgensen and de Souza (1994) directly modelled the risk or expected cost of claims per insured unit, μ say. They observed that the dependence of the likelihood function on μ is as for a linear exponential family, so that modelling similar to that of generalized linear models is possible. In this paper we observe that, when modelling the cost of insurance claims, it is generally necessary to model the dispersion of the costs as well as their mean. In order to model the dispersion we use the framework of double generalized linear models. Modelling the dispersion increases the precision of the estimated tariffs. The use of double generalized linear models also allows us to handle the case where only the total cost of claims and not the number of claims has been recorded.

Question: How to model the dispersion of the costs?


MC64: A Web Platform to Test Bioinformatics Algorithms in a Many-Core Architecture. New analytical methodologies, like the so-called "next-generation sequencing" (NGS), allow the sequencing of full genomes with high speed and reduced price. Yet, such technologies generate huge amounts of data that demand large raw computational power. Many-core technologies can be exploited to overcome the involved bioinformatics bottleneck. Indeed, such hardware is currently in active development. We have developed parallel bioinformatics algorithms for many-core microprocessors containing 64 cores each. Thus, the MC64 web platform allows executing high-performance alignments (NeedlemanWunsch, Smith-Waterman and ClustalW) of long sequences. The MC64 platform can be accessed via web browsers, allowing easy resource integration into thirdparty tools. Furthermore, the results obtained from the MC64 include timeperformance statistics that can be compared with other platforms.

Question: What is the name of the web platform?


A multiscale random field model for Bayesian image segmentation. Many approaches to Bayesian image segmentation have used maximum a posteriori (MAP) estimation in conjunction with Markov random fields (MRF). Although this approach performs well, it has a number of disadvantages. In particular, exact MAP estimates cannot be computed, approximate MAP estimates are computationally expensive to compute, and unsupervised parameter estimation of the MRF is difficult. The authors propose a new approach to Bayesian image segmentation that directly addresses these problems. The new method replaces the MRF model with a novel multiscale random field (MSRF) and replaces the MAP estimator with a sequential MAP (SMAP) estimator derived from a novel estimation criteria. Together, the proposed estimator and model result in a segmentation algorithm that is not iterative and can be computed in time proportional to MN where M is the number of classes and N is the number of pixels. The also develop a computationally efficient method for unsupervised estimation of model parameters. Simulations on synthetic images indicate that the new algorithm performs better and requires much less computation than MAP estimation using simulated annealing. The algorithm is also found to improve classification accuracy when applied to the segmentation of multispectral remotely sensed images with ground truth data.

Question: What is a multiscale random field?


The Vivaldi Aerial. The Vivaldi Aerial is a new member of the class of aperiodic continuously scaled antenna structures and, as such, it has theoretically unlimited instantaneous frequency bandwidth. This aerial has significant gain and linear polarisation and can be made to conform to a constant gain vs. frequency performance. One such design has been made with approximately 10 dBI gain and ¿20 dB sidelobe level over an instantaneous frequency bandwidth extending from below 2 GHz to above 40 GHz.

Question: What is aperiodic continuously scaled antenna structures?


Event Tactic Analysis Based on Broadcast Sports Video. Most existing approaches on sports video analysis have concentrated on semantic event detection. Sports professionals, however, are more interested in tactic analysis to help improve their performance. In this paper, we propose a novel approach to extract tactic information from the attack events in broadcast soccer video and present the events in a tactic mode to the coaches and sports professionals. We extract the attack events with far-view shots using the analysis and alignment of web-casting text and broadcast video. For a detected event, two tactic representations, aggregate trajectory and play region sequence, are constructed based on multi-object trajectories and field locations in the event shots. Based on the multi-object trajectories tracked in the shot, a weighted graph is constructed via the analysis of temporal–spatial interaction among the players and the ball. Using the Viterbi algorithm, the aggregate trajectory is computed based on the weighted graph. The play region sequence is obtained using the identification of the active field locations in the event based on line detection and competition network. The interactive relationship of aggregate trajectory with the information of play region and the hypothesis testing for trajectory temporal–spatial distribution are employed to discover the tactic patterns in a hierarchical coarse-to-fine framework. Extensive experiments on FIFA World Cup 2006 show that the proposed approach is highly effective.

Question: What is the aggregate trajectory?


Nonlinear Modeling of the Self-Oscillating Fluxgate Current Sensor. An accurate mathematical model has been developed in order to describe electrical and geometrical factors affecting operation and DC transfer characteristics of the self-oscillating fluxgate current sensor. The mathematical description of the sensor circuit is based on a continuous function that approximates magnetization curve of the used transformer core. The circuit differential equation based on the adopted transformer model is analytically solved offering a fundamental basis for understanding factors that affect the operation of the sensor. All principle parameters affecting sensor operation are clearly demonstrated by using the proposed model, and good agreements with laboratory measurements has been obtained.

Question: What is a self-oscillating fluxgate current sensor?


Embedded System for Motion Control of an Omnidirectional Mobile Robot. In this paper, an embedded system for motion control of omnidirectional mobile robots is presented. An omnidirectional mobile robot is a type of holonomic robots. It can move simultaneously and independently in translation and rotation. The RoboCup small-size league, a robotic soccer competition, is chosen as the research platform in this paper. The first part of this research is to design and implement an embedded system that can communicate with a remote server using a wireless link, and execute received commands. Second, a fuzzy-tuned proportional–integral (PI) path planner and a related low-level controller are proposed to attain optimal input for driving a linear discrete dynamic model of the omnidirectional mobile robot. To fit the planning requirements and avoid slippage, velocity, and acceleration filters are also employed. In particular, low-level optimal controllers, such as a linear quadratic regulator (LQR) for multiple-input-multiple-output acceleration and deceleration of velocity are investigated, where an LQR controller is running on the robot with feedback from motor encoders or sensors. Simultaneously, a fuzzy adaptive PI is used as a high-level controller for position monitoring, where an appropriate vision system is used as a source of position feedback. A key contribution presented in this research is an improvement in the combined fuzzy-PI LQR controller over a traditional PI controller. Moreover, the efficiency of the proposed approach and PI controller are also discussed. Simulation and experimental evaluations are conducted with and without external disturbance. An optimal result to decrease the variances between the target trajectory and the actual output is delivered by the onboard regulator controller in this paper. The modeling and experimental results confirm the claim that utilizing the new approach in trajectory-planning controllers results in more precise motion o

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


DCnet: A new data center network architecture. The migration of computation and storage to cloud data centers has imposed new communication requirements and a need for fundamental changes in infrastructure. The cloud paradigm means that significant network communication will occur between servers in data centers. Furthermore, the migration of virtual machines and containers across physical servers means that in addition to traditional requirements of efficiency, scalability, and easy manageability, future data center networks must also support efficient mobility. To solve the problem, the paper proposes a radical redesign for data center networks, using a new architectural approach that we call DCnet. The DCnet approach changes the addressing and routing schemes at layers 2 and 3 completely. The new addressing mechanism allows an organization to assign addresses that span multiple data centers and permits VM mobility across data centers. Despite radical changes in addressing and routing, the DCnet architecture retains basics from Ethernet and IP, allowing existing hardware building blocks to be reused. The paper uses IPv6 in examples, but the new addressing model and address mobility approach can be applied to IPv4. By assigning each addressable entity a unique ID, and using the ID as the host suffix in an IP address, DCnet allows legacy operating systems and applications to be used without changes. The paper reports a simulation study that uses mininet and SDN to demonstrate the feasibility of the DCnet approach.

Question: What is the new data center network architecture?


VizTree: a Tool for Visually Mining and Monitoring Massive Time Series Databases. Moments before the launch of every space vehicle, engineering discipline specialists must make a critical go/no-go decision. The cost of a false positive, allowing a launch in spite of a fault, or a false negative, stopping a potentially successful launch, can be measured in the tens of millions of dollars, not including the cost in morale and other more intangible detriments. The Aerospace Corporation is responsible for providing engineering assessments critical to the go/no-go decision for every Department of Defense (DoD) launch vehicle. These assessments are made by constantly monitoring streaming telemetry data in the hours before launch. For this demonstration, we will introduce VizTree, a novel time-series visualization tool to aid the Aerospace analysts who must make these engineering assessments. VizTree was developed at the University of California, Riverside and is unique in that the same tool is used for mining archival data and monitoring incoming live telemetry. Unlike other time series visualization tools, VizTree can scale to very large databases, giving it the potential to be a generally useful data mining and database tool.

Question: What is the cost of a false positive?


PCANet: A Simple Deep Learning Baseline for Image Classification?. In this paper, we propose a very simple deep learning network for image classification that is based on very basic data processing components: 1) cascaded principal component analysis (PCA); 2) binary hashing; and 3) blockwise histograms. In the proposed architecture, the PCA is employed to learn multistage filter banks. This is followed by simple binary hashing and block histograms for indexing and pooling. This architecture is thus called the PCA network (PCANet) and can be extremely easily and efficiently designed and learned. For comparison and to provide a better understanding, we also introduce and study two simple variations of PCANet: 1) RandNet and 2) LDANet. They share the same topology as PCANet, but their cascaded filters are either randomly selected or learned from linear discriminant analysis. We have extensively tested these basic networks on many benchmark visual data sets for different tasks, including Labeled Faces in the Wild (LFW) for face verification; the MultiPIE, Extended Yale B, AR, Facial Recognition Technology (FERET) data sets for face recognition; and MNIST for hand-written digit recognition. Surprisingly, for all tasks, such a seemingly naive PCANet model is on par with the state-of-the-art features either prefixed, highly hand-crafted, or carefully learned [by deep neural networks (DNNs)]. Even more surprisingly, the model sets new records for many classification tasks on the Extended Yale B, AR, and FERET data sets and on MNIST variations. Additional experiments on other public data sets also demonstrate the potential of PCANet to serve as a simple but highly competitive baseline for texture classification and object recognition.

Question: What is a simple deep learning network for image classification?


A high-precision time-to-digital converter for pulsed time-of-flight laser radar applications. A time-to-digital converter (TDC) has been designed, and six units have been constructed and tested. It consists of integrated digital time-interval measurement electronics with a 10-ns resolution that can be improved to 10 ps by an analog interpolation method. Identical construction of the interpolation electronics leads to stable performance of the TDC. The drifts of all six TDC units remain within 10 ps over a temperature range from 10 C to+50 C. The stability can be improved further by a real-time calibration procedure developed here. The single-shot precision of the TDC is better than 15 ps (standard deviation), but precision can be improved to below 0.1 ps by averaging about 10 000 measurements at the maximum measurement speed of 100 kHz. The time range of the TDC is currently 2.55 s, but this can be increased by adding an external digital counter. The TDC suffers from a periodic nonlinearity over the measured time range from 0 to 1.3 s, the amplitude and period of the nonlinearity being 20 ps and 40 ns, respectively. The reason lies in the integrated digital part of the electronics. The linearity of interpolation in the 10-ns time range improves, however, as more results are averaged.

Question: What is a time-to-digital converter?


The extraction of neural strategies from the surface EMG.. This brief review examines some of the methods used to infer central control strategies from surface electromyogram (EMG) recordings. Among the many uses of the surface EMG in studying the neural control of movement, the review critically evaluates only some of the applications. The focus is on the relations between global features of the surface EMG and the underlying physiological processes. Because direct measurements of motor unit activation are not available and many factors can influence the signal, these relations are frequently misinterpreted. These errors are compounded by the counterintuitive effects that some system parameters can have on the EMG signal. The phenomenon of crosstalk is used as an example of these problems. The review describes the limitations of techniques used to infer the level of muscle activation, the type of motor unit recruited, the upper limit of motor unit recruitment, the average discharge rate, and the degree of synchronization between motor units. Although the global surface EMG is a useful measure of muscle activation and assessment, there are limits to the information that can be extracted from this signal.

Question: What is the difference between the surface EMG and the EMG?


MapReduce as a programming model for association rules algorithm on Hadoop. As association rules widely used, it needs to study many problems, one of which is the generally larger and multi-dimensional datasets, and the rapid growth of the mount of data. Single-processor's memory and CPU resources are very limited, which makes the algorithm performance inefficient. Recently the development of network and distributed technology makes cloud computing a reality in the implementation of association rules algorithm. In this paper we describe the improved Apriori algorithm based on MapReduce mode, which can handle massive datasets with a large number of nodes on Hadoop platform.

Question: What is the improved Apriori algorithm based on MapReduce mode?


Patch-based Image Correlation with Rapid Filtering. This paper describes a patch-based approach for rapid image correlation or template matching. By representing a template image with an ensemble of patches, the method is robust with respect to variations such as local appearance variation, partial occlusion, and scale changes. Rectangle filters are applied to each image patch for fast filtering based on the integral image representation. A new method is developed for feature dimension reduction by detecting the "salient" image structures given a single image. Experiments on a variety images show the success of the method in dealing with different variations in the test images. In terms of computation time, the approach is faster than traditional methods by up to two orders of magnitude and is at least three times faster than a fast implementation of normalized cross correlation.

Question: What is a patch-based approach for image correlation?


The diffusion of misinformation on social media: Temporal pattern, message, and source. This study examines dynamic communication processes of political misinformation on social media focusing on three components: the temp oral attern, content mutation, and sources of misinformation. We traced the lifecycle of 17 po pular political rumors that circulated on Twitter over 13 months during the 2012 U.S. preside ntial election. Using text analysis based on time series, we found that while false rumors (misi nformation) tend to come back multiple times after the initial publication, true rumors (facts) do not. Rumor resurgence continues, often accompanying textual changes, until the tension aro und the target dissolves. We observed that rumors resurface by partisan news websites that rep ckage the old rumor into news and, gain visibility by influential Twitter users who introdu ce such rumor into the Twittersphere. In this paper, we argue that media scholars should consider the mutability of diffusing information, temporal recurrence of such messages, and the mecha nism by which these messages evolve over time.

Question: What is the lifecycle of a rumor?


Neural Network-Based Spectrum Estimation for Online WPE Dereverberation. In this paper, we propose a novel speech dereverberation framework that utilizes deep neural network (DNN)-based spectrum estimation to construct linear inverse filters. The proposed dereverberation framework is based on the state-of-the-art inverse filter estimation algorithm called weighted prediction error (WPE) algorithm, which is known to effectively reduce reverberation and greatly boost the ASR performance in various conditions. In WPE, the accuracy of the inverse filter estimation, and thus the deverberation performance, is largely dependent on the estimation of the power spectral density (PSD) of the target signal. Therefore, the conventional WPE iteratively performs the inverse filter estimation, actual dereverberation and the PSD estimation to gradually improve the PSD estimate. However, while such iterative procedure works well when sufficiently long acoustically-stationary observed signals are available, WPE's performance degrades when the duration of observed/accessible data is short, which typically is the case for real-time applications using online block-batch processing with small batches. To solve this problem, we incorporate the DNN-based spectrum estimator into the framework ofWPE, because a DNN can estimate the PSD robustly even from very short observed data. We experimentally show that the proposed framework outperforms the conventional WPE, and improves the ASR performance in real noisy reverberant environments in both single-channel and multichannel cases.

Question: How does the neural network-based spectrum estimation improve the performance of the WPE algorithm?


A Survey of Collaborative Filtering Techniques. As one of the most successful approaches to building recommender systems, collaborative filtering (CF) uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users. In this paper, we first introduce CF tasks and their main challenges, such as data sparsity, scalability, synonymy, gray sheep, shilling attacks, privacy protection, etc., and their possible solutions. We then present three main categories of CF techniques: memory-based, modelbased, and hybrid CF algorithms (that combine CF with other recommendation techniques), with examples for representative algorithms of each category, and analysis of their predictive performance and their ability to address the challenges. From basic techniques to the state-of-the-art, we attempt to present a comprehensive survey for CF techniques, which can be served as a roadmap for research and practice in this area.

Question: What is a collaborative filtering algorithm?


Workflow Automation: Overview and Research Issues. Workflow management systems, a relatively recent technology, are designed to make work more efficient, integrate heterogeneous application systems, and support interorganizational processes in electronic commerce applications. In this paper, we introduce the field of workflow automation, the subject of this special issue of Information Systems Frontiers. In the first part of the paper, we provide basic definitions and frameworks to aid understanding of workflow management technologies. In the remainder of the paper, we discuss technical and management research opportunities in this field and discuss the other contributions to the special issue.

Question: What is workflow automation?


Complex Sequential Question Answering: Towards Learning to Converse Over Linked Question Answer Pairs with a Knowledge Graph. While conversing with chatbots, humans typically tend to ask many questions, a significant portion of which can be answered by referring to large-scale knowledge graphs (KG). While Question Answering (QA) and dialog systems have been studied independently, there is a need to study them closely to evaluate such real-world scenarios faced by bots involving both these tasks. Towards this end, we introduce the task of Complex Sequential QA which combines the two tasks of (i) answering factual questions through complex inferencing over a realistic-sized KG of millions of entities, and (ii) learning to converse through a series of coherently linked QA pairs. Through a labor intensive semi-automatic process, involving in-house and crowdsourced workers, we created a dataset containing around 200K dialogs with a total of 1.6M turns. Further, unlike existing large scale QA datasets which contain simple questions that can be answered from a single tuple, the questions in our dialogs require a larger subgraph of the KG. Specifically, our dataset has questions which require logical, quantitative, and comparative reasoning as well as their combinations. This calls for models which can: (i) parse complex natural language questions, (ii) use conversation context to resolve coreferences and ellipsis in utterances, (iii) ask for clarifications for ambiguous queries, and finally (iv) retrieve relevant subgraphs of the KG to answer such questions. However, our experiments with a combination of state of the art dialog and QA models show that they clearly do not achieve the above objectives and are inadequate for dealing with such complex real world settings. We believe that this new dataset coupled with the limitations of existing models as reported in this paper should encourage further research in Complex Sequential QA.

Question: What is the largest peacekeeping mission in canada?


Affordance detection of tool parts from geometric features. As robots begin to collaborate with humans in everyday workspaces, they will need to understand the functions of tools and their parts. To cut an apple or hammer a nail, robots need to not just know the tool's name, but they must localize the tool's parts and identify their functions. Intuitively, the geometry of a part is closely related to its possible functions, or its affordances. Therefore, we propose two approaches for learning affordances from local shape and geometry primitives: 1) superpixel based hierarchical matching pursuit (S-HMP); and 2) structured random forests (SRF). Moreover, since a part can be used in many ways, we introduce a large RGB-Depth dataset where tool parts are labeled with multiple affordances and their relative rankings. With ranked affordances, we evaluate the proposed methods on 3 cluttered scenes and over 105 kitchen, workshop and garden tools, using ranked correlation and a weighted F-measure score [26]. Experimental results over sequences containing clutter, occlusions, and viewpoint changes show that the approaches return precise predictions that could be used by a robot. S-HMP achieves high accuracy but at a significant computational cost, while SRF provides slightly less accurate predictions but in real-time. Finally, we validate the effectiveness of our approaches on the Cornell Grasping Dataset [25] for detecting graspable regions, and achieve state-of-the-art performance.

Question: What is the canadian military?


REDD : A Public Data Set for Energy Disaggregation Research. Energy and sustainability issues raise a large number of problems that can be tackled using approaches from data mining and machine learning, but traction of such problems has been slow due to the lack of publicly available data. In this paper we present the Reference Energy Disaggregation Data Set (REDD), a freely available data set containing detailed power usage information from several homes, which is aimed at furthering research on energy disaggregation (the task of determining the component appliance contributions from an aggregated electricity signal). We discuss past approaches to disaggregation and how they have influenced our design choices in collecting data, we describe the hardware and software setups for the data collection, and we present initial benchmark disaggregation results using a well-known Factorial Hidden Markov Model (FHMM) technique.

Question: What is the size of the data set?


Learning Symbolic Models of Stochastic Domains. In this article, we work towards the goal of developing agents that can learn to act in complex worlds. We develop a probabilistic, relational planning rule representation that compactly models noisy, nondeterministic action effects, and show how such rules can be effectively learned. Through experiments in simple planning domains and a 3D simulated blocks world with realistic physics, we demonstrate that this learning algorithm allows agents to effectively model world dynamics.

Question: How can we learn to act in complex worlds?


Simple and Space-Efficient Minimal Perfect Hash Functions. A perfect hash function (PHF) h : U → [0, m − 1] for a key set S is a function that maps the keys of S to unique values. The minimum amount of space to represent a PHF for a given set S is known to be approximately 1.44n/m bits, where n = |S|. In this paper we present new algorithms for construction and evaluation of PHFs of a given set (for m = n and m = 1.23n), with the following properties: 1. Evaluation of a PHF requires constant time. 2. The algorithms are simple to describe and implement, and run in linear time. 3. The amount of space needed to represent the PHFs is around a factor 2 from the information theoretical minimum. No previously known algorithm has these properties. To our knowledge, any algorithm in the literature with the third property either: – Requires exponential time for construction and evaluation, or – Uses near-optimal space only asymptotically, for extremely large n. Thus, our main contribution is a scheme that gives low space usage for realistic values of n. The main technical ingredient is a new way of basing PHFs on random hypergraphs. Previously, this approach has been used to design simple PHFs with superlinear space usage. ⋆ This work was supported in part by GERINDO Project–grant MCT/CNPq/CTINFO 552.087/02-5, and CNPq Grants 30.5237/02-0 (Nivio Ziviani) and 142786/2006-3 (Fabiano C. Botelho) 3 This version of the paper is identical to the one published in the WADS 2007 proceedings. Unfortunately, it does not give reference and credit to the paper The Bloomier Filter: An Efficient Data Structure for Static Support Lookup Tables, by Chazelle et al., Proceedings of SODA 2004. They present a way of constructing PHFs that is equivalent to ours. It is explained as a modification of the "Bloomier Filter" data structure at the end of Section 3.3, but they do not make explicit that a PHF is constructed. Thus, the simple construction of a PHF described must be attributed to Chazelle e

Question: What is the minimum amount of space needed to represent a perfect hash function?


An OFDMA based multiple access protocol with QoS guarantee for next generation WLAN. To provide better QoS guarantee for the next generation WLAN, IEEE 802.11ax task group is founded in March 2014. As a promising technology to accommodate multiple nodes concurrent transmissions in dense deployment scenario, orthogonal frequency division multiple access (OFDMA) will be adopted in IEEE 802.11ax with great possibility. In this paper, an OFDMA based multiple access protocol with QoS guarantee is proposed for the next generation WLAN. Firstly, a redundant access mechanism is given to increase the access success probability of the video traffic where the video stations can concurrently send multiple RTS packets in multiple subchannels. Secondly, a priority based resource allocation scheme is presented to let AP allocate more resources to the video stations. Simulation results show that our protocol outperforms the existing OFDMA based multiple access for IEEE 802.11ax (OMAX) protocol in terms of delay and delay jitter of video traffic in dense deployment scenario.

Question: What is the OFDMA based multiple access protocol for IEEE 802.11ax?


Context Prediction for Unsupervised Deep Learning on Point Clouds. Point clouds provide a flexible and natural representation usable in countless applications such as robotics or self-driving cars. Recently, deep neural networks operating on raw point cloud data have shown promising results on supervised learning tasks such as object classification and semantic segmentation. While massive point cloud datasets can be captured using modern scanning technology, manually labelling such large 3D point clouds for supervised learning tasks is a cumbersome process. This necessitates effective unsupervised learning methods that can produce representations such that downstream tasks require significantly fewer annotated samples. We propose a novel method for unsupervised learning on raw point cloud data in which a neural network is trained to predict the spatial relationship between two point cloud segments. While solving this task, representations that capture semantic properties of the point cloud are learned. Our method outperforms previous unsupervised learning approaches in downstream object classification and segmentation tasks and performs on par with fully supervised methods.

Question: What is unsupervised learning on point clouds?


Video Google: A Text Retrieval Approach to Object Matching in Videos. We describe an approach to object and scene retrieval which searches for and localizes all the occurrences of a user outlined object in a video. The object is represented by a set of viewpoint invariant region descriptors so that recognition can proceed successfully despite changes in viewpoint, illumination and partial occlusion. The temporal continuity of the video within a shot is used to track the regions in order to reject unstable regions and reduce the effects of noise in the descriptors. The analogy with text retrieval is in the implementation where matches on descriptors are pre-computed (using vector quantization), and inverted file systems and document rankings are used. The result is that retrieval is immediate, returning a ranked list of key frames/shots in the manner of Google. The method is illustrated for matching on two full length feature films.

Question: How to retrieve a video?


45° Linearly Polarized Resonant Slot Array Antenna Based on Substrate Integrated Waveguide. In this paper, a new configuration of integrated 45deg linearly polarized slot array antenna based on substrate integrated waveguide (SIW) technology is proposed, designed and fabricated with normal printed circuit board (PCB) process. The antenna consists of eight 45deg inclined slots and the measured gain is higher than 10.27 dBi at X-band. The measured radiation patterns and the return loss are in agreement with the simulated results.

Question: What is the gain of the antenna?


Robust Object Detection with Interleaved Categorization and Segmentation. This paper presents a novel method for detecting and localizing objects of a visual category in cluttered real-world scenes. Our approach considers object categorization and figure-ground segmentation as two interleaved processes that closely collaborate towards a common goal. As shown in our work, the tight coupling between those two processes allows them to benefit from each other and improve the combined performance. The core part of our approach is a highly flexible learned representation for object shape that can combine the information observed on different training examples in a probabilistic extension of the Generalized Hough Transform. The resulting approach can detect categorical objects in novel images and automatically infer a probabilistic segmentation from the recognition result. This segmentation is then in turn used to again improve recognition by allowing the system to focus its efforts on object pixels and to discard misleading influences from the background. Moreover, the information from where in the image a hypothesis draws its support is employed in an MDL based hypothesis verification stage to resolve ambiguities between overlapping hypotheses and factor out the effects of partial occlusion. An extensive evaluation on several large data sets shows that the proposed system is applicable to a range of different object categories, including both rigid and articulated objects. In addition, its flexible representation allows it to achieve competitive object detection performance already from training sets that are between one and two orders of magnitude smaller than those used in comparable systems.

Question: What is the name of the algorithm?


The impact of non-cognitive skills on outcomes for young people Literature review 21 November 2013 Institute of Education.

Question: What is the impact of non-cognitive skills on outcomes for young people?


Combining qualitative and quantitative sampling, data collection, and analysis techniques in mixed-method studies.. Researchers have increasingly turned to mixed-method techniques to expand the scope and improve the analytic power of their studies. Yet there is still relatively little direction on and much confusion about how to combine qualitative and quantitative techniques. These techniques are neither paradigm- nor method-linked; researchers' orientations to inquiry and their methodological commitments will influence how they use them. Examples of sampling combinations include criterion sampling from instrument scores, random purposeful sampling, and stratified purposeful sampling. Examples of data collection combinations include the use of instruments for fuller qualitative description, for validation, as guides for purposeful sampling, and as elicitation devices in interviews. Examples of data analysis combinations include interpretively linking qualitative and quantitative data sets and the transformation processes of qualitizing and quantitizing.

Question: How to combine qualitative and quantitative techniques?


Basic aspects of food preservation by hurdle technology.. Hurdle technology is used in industrialized as well as in developing countries for the gentle but effective preservation of foods. Previously hurdle technology, i.e., a combination of preservation methods, was used empirically without much knowledge of the governing principles. Since about 20 years the intelligent application of hurdle technology became more prevalent, because the principles of major preservative factors for foods (e.g., temperature, pH, a(w), Eh, competitive flora), and their interactions, became better known. Recently, the influence of food preservation methods on the physiology and behaviour of microorganisms in foods, i.e. their homeostasis, metabolic exhaustion, stress reactions, are taken into account, and the novel concept of multitarget food preservation emerged. In the present contribution a brief introduction is given on the potential hurdles for foods, the hurdle effect, and the hurdle technology. However, emphasis is placed on the homeostasis, metabolic exhaustion, and stress reactions of microorganisms related to hurdle technology, and the prospects of the future goal of a multitarget preservation of foods.

Question: What is hurdle technology?


Electronic model of human brain using verilog. Computers are man-made machines which work according to the given set of inputs and perform some operation on the input to generate a new set of outputs. They can be programmed to perform huge and complex task yet they lack imagination and ability to understand things. On the hand human brain is a machine which can learn new tasks by process of acquiring knowledge and understanding through thought, experience and the senses. To make computer work or think like human, human brain modeling of machine is necessary. We need to know how human brain works. The basic component of human brain is neuron, which make us think and do smart things. Brains have billions of neurons and they communicate with each other using electrical impulse. These impulses are responsible for making brain think and to have a consciousness. This paper proposes such a model of a part of the brain.

Question: How does the brain work?


Visual Interactive Creation, Customization, and Analysis of Data Quality Metrics. During data preprocessing, analysts spend a significant part of their time and effort profiling the quality of the data along with cleansing and transforming the data for further analysis. While quality metrics—ranging from general to domain-specific measures—support assessment of the quality of a dataset, there are hardly any approaches to visually support the analyst in customizing and applying such metrics. Yet, visual approaches could facilitate users' involvement in data quality assessment. We present MetricDoc, an interactive environment for assessing data quality that provides customizable, reusable quality metrics in combination with immediate visual feedback. Moreover, we provide an overview visualization of these quality metrics along with error visualizations that facilitate interactive navigation of the data to determine the causes of quality issues present in the data. In this article, we describe the architecture, design, and evaluation of MetricDoc, which underwent several design cycles, including heuristic evaluation and expert reviews as well as a focus group with data quality, human-computer interaction, and visual analytics experts.

Question: What is the architecture of MetricDoc?


An Analysis of Buck Converter Efficiency in PWM / PFM Mode with Simulink. This technical paper takes a study into efficiency comparison between PWM and PFM control modes in DC-DC buck converters. Matlab Simulink Models are built to facilitate the analysis of various effects on power loss and converting efficiency, including different load conditions, gate switching frequency, setting of voltage and current thresholds, etc. From efficiency vs. load graph, a best switching frequency is found to achieve a good efficiency throughout the wide load range. This simulation point is then compared to theoretical predictions, justifying the effectiveness of computer based simulation. Efficiencies at two different control modes are compared to verify the improvement of PFM scheme.

Question: How to improve buck converter efficiency?


A CORDIC processor for FFT computation and its implementation using gallium arsenide technology. In this paper, the architecture and the implementation of a complex fast Fourier transform (CFFT) processor using 0.6 m gallium arsenide (GaAs) technology are presented. This processor computes a 1024-point FFT of 16 bit complex data in less than 8 s, working at a frequency beyond 700 MHz, with a power consumption of 12.5 W. The architecture of the processor is based on the COordinate Rotation DIgital Computer (CORDIC) algorithm, which avoids the use of conventional multiplicationand-accumulation (MAC) units, but evaluates the trigonometric functions using only add and shift operations. Improvements to the basic CORDIC architecture are introduced in order to reduce the area and power of the processor. This together with the use of pipelining and carry save adders produces a very regular and fast processor. The CORDIC units were fabricated and tested in order to anticipate the final performance of the processor. This work also demonstrates the maturity of GaAs technology for implementing ultrahigh-performance signal processors.

Question: What is a CORDIC processor?


Reidentification by Relative Distance Comparison. Matching people across nonoverlapping camera views at different locations and different times, known as person reidentification, is both a hard and important problem for associating behavior of people observed in a large distributed space over a prolonged period of time. Person reidentification is fundamentally challenging because of the large visual appearance changes caused by variations in view angle, lighting, background clutter, and occlusion. To address these challenges, most previous approaches aim to model and extract distinctive and reliable visual features. However, seeking an optimal and robust similarity measure that quantifies a wide range of features against realistic viewing conditions from a distance is still an open and unsolved problem for person reidentification. In this paper, we formulate person reidentification as a relative distance comparison (RDC) learning problem in order to learn the optimal similarity measure between a pair of person images. This approach avoids treating all features indiscriminately and does not assume the existence of some universally distinctive and reliable features. To that end, a novel relative distance comparison model is introduced. The model is formulated to maximize the likelihood of a pair of true matches having a relatively smaller distance than that of a wrong match pair in a soft discriminant manner. Moreover, in order to maintain the tractability of the model in large scale learning, we further develop an ensemble RDC model. Extensive experiments on three publicly available benchmarking datasets are carried out to demonstrate the clear superiority of the proposed RDC models over related popular person reidentification techniques. The results also show that the new RDC models are more robust against visual appearance changes and less susceptible to model overfitting compared to other related existing models.

Question: How to reidentify people from a distance?


Skew detection of scanned document images. —Skewing of the scanned image is an inevitable process and its detection is an important issue for document recognition systems. The skew of the scanned document image specifies the deviation of the text lines from the horizontal or vertical axis. This paper surveys methods to detect this skew in

Question: How to detect skew in scanned document images?


Predictors of physical restraint use in Canadian intensive care units. INTRODUCTION Physical restraint (PR) use in the intensive care unit (ICU) has been associated with higher rates of self-extubation and prolonged ICU length of stay. Our objectives were to describe patterns and predictors of PR use.   METHODS We conducted a secondary analysis of a prospective observational study of analgosedation, antipsychotic, neuromuscular blocker, and PR practices in 51 Canadian ICUs. Data were collected prospectively for all mechanically ventilated adults admitted during a two-week period. We tested for patient, treatment, and hospital characteristics that were associated with PR use and number of days of use, using logistic and Poisson regression respectively.   RESULTS PR was used on 374 out of 711 (53%) patients, for a mean number of 4.1 (standard deviation (SD) 4.0) days. Treatment characteristics associated with PR were higher daily benzodiazepine dose (odds ratio (OR) 1.05, 95% confidence interval (CI) 1.00 to 1.11), higher daily opioid dose (OR 1.04, 95% CI 1.01 to 1.06), antipsychotic drugs (OR 3.09, 95% CI 1.74 to 5.48), agitation (Sedation-Agitation Scale (SAS) >4) (OR 3.73, 95% CI 1.50 to 9.29), and sedation administration method (continuous and bolus versus bolus only) (OR 3.09, 95% CI 1.74 to 5.48). Hospital characteristics associated with PR indicated patients were less likely to be restrained in ICUs from university-affiliated hospitals (OR 0.32, 95% CI 0.17 to 0.61). Mainly treatment characteristics were associated with more days of PR, including: higher daily benzodiazepine dose (incidence rate ratio (IRR) 1.07, 95% CI 1.01 to 1.13), daily sedation interruption (IRR 3.44, 95% CI 1.48 to 8.10), antipsychotic drugs (IRR 15.67, 95% CI 6.62 to 37.12), SAS <3 (IRR 2.62, 95% CI 1.08 to 6.35), and any adverse event including accidental device removal (IRR 8.27, 95% CI 2.07 to 33.08). Patient characteristics (age, gender, Acute Physiology and Chronic Health Evaluatio

Question: What is the difference between a predictor and a predictor variable?


Global Linear Neighborhoods for Efficient Label Propagation. Graph-based semi-supervised learning improves classification by combining labeled and unlabeled data through label propagation. It was shown that the sparse representation of graph by weighted local neighbors provides a better similarity measure between data points for label propagation. However, selecting local neighbors can lead to disjoint components and incorrect neighbors in graph, and thus, fail to capture the underlying global structure. In this paper, we propose to learn a nonnegative low-rank graph to capture global linear neighborhoods, under the assumption that each data point can be linearly reconstructed from weighted combinations of its direct neighbors and reachable indirect neighbors. The global linear neighborhoods utilize information from both direct and indirect neighbors to preserve the global cluster structures, while the low-rank property retains a compressed representation of the graph. An efficient algorithm based on a multiplicative update rule is designed to learn a nonnegative low-rank factorization matrix minimizing the neighborhood reconstruction error. Large scale simulations and experiments on UCI datasets and high-dimensional gene expression datasets showed that label propagation based on global linear neighborhoods captures the global cluster structures better and achieved more accurate classification results.

Question: What is the difference between the global linear neighborhoods and the local linear neighborhoods?


Detection of high variability in gene expression from single-cell RNA-seq profiling. The advancement of the next-generation sequencing technology enables mapping gene expression at the single-cell level, capable of tracking cell heterogeneity and determination of cell subpopulations using single-cell RNA sequencing (scRNA-seq). Unlike the objectives of conventional RNA-seq where differential expression analysis is the integral component, the most important goal of scRNA-seq is to identify highly variable genes across a population of cells, to account for the discrete nature of single-cell gene expression and uniqueness of sequencing library preparation protocol for single-cell sequencing. However, there is lack of generic expression variation model for different scRNA-seq data sets. Hence, the objective of this study is to develop a gene expression variation model (GEVM), utilizing the relationship between coefficient of variation (CV) and average expression level to address the over-dispersion of single-cell data, and its corresponding statistical significance to quantify the variably expressed genes (VEGs). We have built a simulation framework that generated scRNA-seq data with different number of cells, model parameters, and variation levels. We implemented our GEVM and demonstrated the robustness by using a set of simulated scRNA-seq data under different conditions. We evaluated the regression robustness using root-mean-square error (RMSE) and assessed the parameter estimation process by varying initial model parameters that deviated from homogeneous cell population. We also applied the GEVM on real scRNA-seq data to test the performance under distinct cases. In this paper, we proposed a gene expression variation model that can be used to determine significant variably expressed genes. Applying the model to the simulated single-cell data, we observed robust parameter estimation under different conditions with minimal root mean square errors. We also examined the

Question: What is the difference between the Canadian Armed Forces and the Canadian Army?


Exploring the factors affecting MOOC retention: A survey study. Massive Open Online Courses (MOOCs) hold the potential to open up educational opportunities to a global audience. However, evidence suggests that only a small proportion of MOOC participants go on to complete their courses and relatively little is understood about the MOOC design and implementation factors that influence retention. This paper reports a survey study of 379 participants enrolled at university in Cairo who were encouraged to take a MOOC of their own choice as part of their development. 122 participants (32.2%) went onto to complete an entire course. There were no significant differences in completion rates by gender, level of study (undergraduate or postgraduate) or MOOC platform. A post-MOOC survey of students' perceptions found that MOOC Course Content was a significant predictor of MOOC retention, with the relationship mediated by the effect of content on the Perceived Effectiveness of the course. Interaction with the instructor of the MOOC was also found to be significant predictor of MOOC retention. Overall these constructs explained 79% of the variance in MOOC retention. © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Question: What are the factors affecting MOOC retention?


An inductorless linear optical receiver for 20Gbaud/s (40Gb/s) PAM-4 modulation using 28nm CMOS. This paper1 presents a linear optical receiver designed using a 28nm CMOS technology suitable for 20Gbaud/s (40Gb/s) PAM-4 modulation. The optical receiver consists of a transimpedance amplifier (gain adjustable from 40dBΩ to 56dBO) followed by a variable gain amplifier (gain adjustable from 6dB to 17dB). Capacitive peaking is used to achieve a bandwidth of ~10GHz, thus avoiding the use of on-chip inductors which require large die area. A robust automatic gain control loop is used to ensure a constant differential output voltage swing of ~100mV for an input dynamic range of 20μA to 500μA (peak current). Over this same range, high linearity (total harmonic distortion less than 5%, 250MHz sinewave, 10harmonics taken into account) is obtained. The rms input referred noise current (integrated from 10MHz to 20GHz) was 2.5μArms. The linear optical receiver consumes 56mW from a 1.5V supply voltage.

Question: What is the purpose of the optical receiver?


A latent factor model for highly multi-relational data. Many data such as social networks, movie preferences or knowledge bases are multi-relational, in that they describe multiple relations between entities. While there is a large body of work focused on modeling these data, modeling these multiple types of relations jointly remains challenging. Further, existing approaches tend to breakdown when the number of these types grows. In this paper, we propose a method for modeling large multi-relational datasets, with possibly thousands of relations. Our model is based on a bilinear structure, which captures various orders of interaction of the data, and also shares sparse latent factors across different relations. We illustrate the performance of our approach on standard tensor-factorization datasets where we attain, or outperform, state-of-the-art results. Finally, a NLP application demonstrates our scalability and the ability of our model to learn efficient and semantically meaningful verb representations.

Question: What is a latent factor model for highly multi-relational data?


Brainport: an alternative input to the brain.. Brain Computer Interface (BCI) technology is one of the most rapidly developing areas of modern science; it has created numerous significant crossroads between Neuroscience and Computer Science. The goal of BCI technology is to provide a direct link between the human brain and a computerized environment. The objective of recent BCI approaches and applications have been designed to provide the information flow from the brain to the computerized periphery. The opposite or alternative direction of the flow of information (computer to brain interface, or CBI) remains almost undeveloped. The BrainPort is a CBI that offers a complementary technology designed to support a direct link from a computerized environment to the human brain - and to do so non-invasively. Currently, BrainPort research is pursuing two primary goals. One is the delivery of missing sensory information critical for normal human behavior through an additional artificial sensory channel around the damaged or malfunctioning natural sensory system. The other is to decrease the risk of sensory overload in human-machine interactions by providing a parallel and supplemental channel for information flow to the brain. In contrast, conventional CBI strategies (e.g., Virtual Reality), are usually designed to provide additional or substitution information through pre-existing sensory channels, and unintentionally aggravate the brain overload problem.

Question: What is the difference between BrainPort and Virtual Reality?


Greenhouse Temperature and Humidity Intelligent Control System. The article based on embedded database of greenhouse temperature and humidity control system intelligent. Put forward by embedded database system set up in an ideal environment for data greenhouse temperature and humidity control, greenhouse crops in the process of growth under control. To address the growth of greenhouse crops in the process of temperature and humidity controlled environment is not ideal, at the same time improve the efficiency of control and cost-effective system. The article focuses on the structure of the control system, hardware, software design and system control strategy. The control system has a simple hardware structure, cost-effective, easy to use and maintenance, temperature and humidity data, and other advantages of good stability. Key-Words: Embedded database, Temperature and Humidity, Control ; data filtering, Greenhouse,

Question: What is the structure of the control system?


Establishing the computer-patient working alliance in automated health behavior change interventions.. Current user interfaces for automated patient and consumer health care systems can be improved by leveraging the results of several decades of research into effective patient-provider communication skills. A research project is presented in which several such "relational" skills - including empathy, social dialogue, nonverbal immediacy behaviors, and other behaviors to build and maintain good working relationships over multiple interactions - are explicitly designed into a computer interface within the context of a longitudinal health behavior change intervention for physical activity adoption. Results of a comparison among 33 subjects interacting near-daily with the relational system and 27 interacting near-daily with an identical system with the relational behaviors ablated, each for 30 days indicate, that the use of relational behaviors by the system significantly increases working alliance and desire to continue working with the system. Comparison of the above groups to another group of 31 subjects interacting with a control system near-daily for 30 days also indicated a significant increase in proactive viewing of health information.

Question: How can we improve the user interface of a computer system?


Constructing inferences during narrative text comprehension.. The authors describe a constructionist theory that accounts for the knowledge-based inferences that are constructed when readers comprehend narrative text. Readers potentially generate a rich variety of inferences when they construct a referential situation model of what the text is about. The proposed constructionist theory specifies that some, but not all, of this information is constructed under most conditions of comprehension. The distinctive assumptions of the constructionist theory embrace a principle of search (or effort) after meaning. According to this principle, readers attempt to construct a meaning representation that addresses the reader's goals, that is coherent at both local and global levels, and that explains why actions, events, and states are mentioned in the text. This study reviews empirical evidence that addresses this theory and contrasts it with alternative theoretical frameworks.

Question: What is the constructionist theory of narrative text comprehension?


Face Detection and Gesture Recognition for Human-Computer Interaction. Reading is a hobby to open the knowledge windows. Besides, it can provide the inspiration and spirit to face this life. By this way, concomitant with the technology development, many companies serve the e-book or book in soft file. The system of this book of course will be much easier. No worry to forget bringing the face detection and gesture recognition for human computer interaction book. You can open the device and get the book by on-line.

Question: What is the book about?


Business Analytics in (a) Blink. The Blink project's ambitious goal is to answer all Business Intelligence (BI) queries in mere seconds, regardless of the database size, with an extremely low total cost of ownership. Blink is a new DBMS aimed primarily at read-mostly BI query processing that exploits scale-out of commodity multi-core processors and cheap DRAM to retain a (copy of a) data mart completely in main memory. Additionally, it exploits proprietary compression technology and cache-conscious algorithms that reduce memory bandwidth consumption and allow most SQL query processing to be performed on the compressed data. Blink always scans (portions of) the data mart in parallel on all nodes, without using any indexes or materialized views, and without any query optimizer to choose among them. The Blink technology has thus far been incorporated into two IBM accelerator products generally available since March 2011. We are now working on the next generation of Blink, which will significantly expand the "sweet spot" of the Blink technology to much larger, disk-based warehouses and allow Blink to "own" the data, rather

Question: What is the next generation of blink?


Adaptive Scale Selection for Multiscale Segmentation of Satellite Images. With dramatically increasing of the spatial resolution of satellite imaging sensors, object-based image analysis (OBIA) has been gaining prominence in remote sensing applications. Multiscale image segmentation is a prerequisite step that splits an image into hierarchical homogeneous segmented objects for OBIA. However, scale selection remains a challenge in multiscale segmentation. In this study, we presented an adaptive approach for defining and estimating the optimal scale in the multiscale segmentation process. Central to our method is the combined use of image features from segmented objects and prior knowledge from historical thematic maps in a top-down segmentation procedure. Specifically, the whole image was first split into segmented objects, with the largest scale in a presupposition segmentation scale sequence. Second, based on segmented object features and prior knowledge in the local region of thematic maps, we calculated complexity values for each segmented object. Third, if the complexity values of an object were large enough, this object would be further split into multiple segmented objects with a smaller scale in the scale sequence. Then, in the similar manner, complex segmented objects were split into the simplest objects iteratively. Finally, the final segmentation result was obtained and evaluated. We have applied this method on a GF-1 multispectral satellite image and a ZY-3 multispectral satellite image to produce multiscale segmentation maps and further classification maps, compared with the state-of-the-art and the traditional mean shift algorithm. The experimental results illustrate that the proposed method is practically helpful and efficient to produce the appropriate segmented image objects with optimal scales.

Question: What is the difference between the mean shift algorithm and the proposed method?


Geodesic flow kernel for unsupervised domain adaptation. In real-world applications of visual recognition, many factors - such as pose, illumination, or image quality - can cause a significant mismatch between the source domain on which classifiers are trained and the target domain to which those classifiers are applied. As such, the classifiers often perform poorly on the target domain. Domain adaptation techniques aim to correct the mismatch. Existing approaches have concentrated on learning feature representations that are invariant across domains, and they often do not directly exploit low-dimensional structures that are intrinsic to many vision datasets. In this paper, we propose a new kernel-based method that takes advantage of such structures. Our geodesic flow kernel models domain shift by integrating an infinite number of subspaces that characterize changes in geometric and statistical properties from the source to the target domain. Our approach is computationally advantageous, automatically inferring important algorithmic parameters without requiring extensive cross-validation or labeled data from either domain. We also introduce a metric that reliably measures the adaptability between a pair of source and target domains. For a given target domain and several source domains, the metric can be used to automatically select the optimal source domain to adapt and avoid less desirable ones. Empirical studies on standard datasets demonstrate the advantages of our approach over competing methods.

Question: What is a kernel?