Lessons from applying the systematic literature review process within the software engineering domain. A consequence of the growing number of empirical studies in software engineering is the need to adopt systematic approaches to assessing and aggregating research outcomes in order to provide a balanced and objective summary of research evidence for a particular topic. The paper reports experiences with applying one such approach, the practice of systematic literature review, to the published studies relevant to topics within the software engineering domain. The systematic literature review process is summarised, a number of reviews being undertaken by the authors and others are described and some lessons about the applicability of this practice to software engineering are extracted. The basic systematic literature review process seems appropriate to software engineering and the preparation and validation of a review protocol in advance of a review activity is especially valuable. The paper highlights areas where some adaptation of the process to accommodate the domain-specific characteristics of software engineering is needed as well as areas where improvements to current software engineering infrastructure and practices would enhance its applicability. In particular, infrastructure support provided by software engineering indexing databases is inadequate. Also, the quality of abstracts is poor; it is usually not possible to judge the relevance of a study from a review of the abstract alone. 2006 Elsevier Inc. All rights reserved.

Question: What is the systematic literature review process?


Better Word Representations with Recursive Neural Networks for Morphology. Vector-space word representations have been very successful in recent years at improving performance across a variety of NLP tasks. However, common to most existing work, words are regarded as independent entities without any explicit relationship among morphologically related words being modeled. As a result, rare and complex words are often poorly estimated, and all unknown words are represented in a rather crude way using only one or a few vectors. This paper addresses this shortcoming by proposing a novel model that is capable of building representations for morphologically complex words from their morphemes. We combine recursive neural networks (RNNs), where each morpheme is a basic unit, with neural language models (NLMs) to consider contextual information in learning morphologicallyaware word representations. Our learned models outperform existing word representations by a good margin on word similarity tasks across many datasets, including a new dataset we introduce focused on rare words to complement existing ones in an interesting way.

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


Evidence for perceptual deficits in associative visual (prosop)agnosia: a single-case study. Associative visual agnosia is classically defined as normal visual perception stripped of its meaning [Archiv für Psychiatrie und Nervenkrankheiten 21 (1890) 22/English translation: Cognitive Neuropsychol. 5 (1988) 155]: these patients cannot access to their stored visual memories to categorize the objects nonetheless perceived correctly. However, according to an influential theory of visual agnosia [Farah, Visual Agnosia: Disorders of Object Recognition and What They Tell Us about Normal Vision, MIT Press, Cambridge, MA, 1990], visual associative agnosics necessarily present perceptual deficits that are the cause of their impairment at object recognition Here we report a detailed investigation of a patient with bilateral occipito-temporal lesions strongly impaired at object and face recognition. NS presents normal drawing copy, and normal performance at object and face matching tasks as used in classical neuropsychological tests. However, when tested with several computer tasks using carefully controlled visual stimuli and taking both his accuracy rate and response times into account, NS was found to have abnormal performances at high-level visual processing of objects and faces. Albeit presenting a different pattern of deficits than previously described in integrative agnosic patients such as HJA and LH, his deficits were characterized by an inability to integrate individual parts into a whole percept, as suggested by his failure at processing structurally impossible three-dimensional (3D) objects, an absence of face inversion effects and an advantage at detecting and matching single parts. Taken together, these observations question the idea of separate visual representations for object/face perception and object/face knowledge derived from investigations of visual associative (prosop)agnosia, and they raise some methodological issues in the analysis of single-case studie

Question: What is the difference between a prosopagnosia and a visual associative agnosia?


Arabic Diacritization with Gated Recurrent Unit. Arabic and similar languages require the use of diacritics in order to determine the necessary parameters to pronounce and identify every part of the speech correctly. Therefore, when it comes to perform Natural Language Processing (NLP) over Arabic, diacritization is a crucial step. In this paper we use a gated recurrent unit network as a language-independent framework for Arabic diacritization. The end-to-end approach allows to use exclusively vocalized text to train the system without using external resources. Evaluation is performed versus the state-of-the-art literature results. We demonstrate that we achieve state-of-the-art results and enhance the learning process by scoring better performance in the training and testing timing.

Question: What is the state-of-the-art for Arabic diacritization?


Visual Analysis of Large Graphs. The analysis of large graphs plays a prominent role in various fields of research and is relevant in many important application areas. Effective visual analysis of graphs requires appropriate visual presentations in combination with respective user interaction facilities and algorithmic graph analysis methods. How to design appropriate graph analysis systems depends on many factors, including the type of graph describing the data, the analytical task at hand, and the applicability of graph analysis methods. The most recent surveys of graph visualization and navigation techniques were presented by Herman et al. [HMM00] and Diaz [DPS02]. The first work surveyed the main techniques for visualization of hierarchies and graphs in general that had been introduced until 2000. The second work concentrated on graph layouts introduced until 2002. Recently, new techniques have been developed covering a broader range of graph types, such as time-varying graphs. Also, in accordance with ever growing amounts of graph-structured data becoming available, the inclusion of algorithmic graph analysis and interaction techniques becomes increasingly important. In this State-of-the-Art Report, we survey available techniques for the visual analysis of large graphs. Our review firstly considers graph visualization techniques according to the type of graphs supported. The visualization techniques form the basis for the presentation of interaction approaches suitable for visual graph exploration. As an important component of visual graph analysis, we discuss various graph algorithmic aspects useful for the different stages of the visual graph analysis process.

Question: What is the size of the canadian military?


Routing and Wavelength Assignment in Optical WDM Networks. This article discusses the routing and wavelength assignment (RWA) problem in optical networks employing wavelenength division multiplexing (WDM) technology. Two variants of the problem are studied: static RWA, whereby the traffic requirements are known in advance, and dynamic RWA in which connection requests arrive in some random fashion. Both point-topoint and multicast traffic demands are considered.

Question: What is the routing and wavelength assignment problem?


BodyAvatar: creating freeform 3D avatars using first-person body gestures. BodyAvatar is a Kinect-based interactive system that allows users without professional skills to create freeform 3D avatars using body gestures. Unlike existing gesture-based 3D modeling tools, BodyAvatar centers around a first-person "you're the avatar" metaphor, where the user treats their own body as a physical proxy of the virtual avatar. Based on an intuitive body-centric mapping, the user performs gestures to their own body as if wanting to modify it, which in turn results in corresponding modifications to the avatar. BodyAvatar provides an intuitive, immersive, and playful creation experience for the user. We present a formative study that leads to the design of BodyAvatar, the system's interactions and underlying algorithms, and results from initial user trials.

Question: How does BodyAvatar work?


Expression and regulation of lincRNAs during T cell development and differentiation. Although intergenic long noncoding RNAs (lincRNAs) have been linked to gene regulation in various tissues, little is known about lincRNA transcriptomes in the T cell lineages. Here we identified 1,524 lincRNA clusters in 42 T cell samples, from early T cell progenitors to terminally differentiated helper T cell subsets. Our analysis revealed highly dynamic and cell-specific expression patterns for lincRNAs during T cell differentiation. These lincRNAs were located in genomic regions enriched for genes that encode proteins with immunoregulatory functions. Many were bound and regulated by the key transcription factors T-bet, GATA-3, STAT4 and STAT6. We found that the lincRNA LincR-Ccr2-5′AS, together with GATA-3, was an essential component of a regulatory circuit in gene expression specific to the TH2 subset of helper T cells and was important for the migration of TH2 cells.

Question: What is the function of lincRNAs in T cells?


Research on the Matthews Correlation Coefficients Metrics of Personalized Recommendation Algorithm Evaluation. The personalized recommendation systems could better improve the personalized service for network user and alleviate the problem of information overload in the Internet. As we all know, the key point of being a successful recommendation system is the performance of recommendation algorithm. When scholars put forward some new recommendation algorithms, they claim that the new algorithms have been improved in some respects, better than previous algorithm. So we need some evaluation metrics to evaluate the algorithm performance. Due to the scholar didn't fully understand the evaluation mechanism of recommendation algorithms. They mainly emphasized some specific evaluation metrics like Accuracy, Diversity. What's more, the academia did not establish a complete and unified assessment of recommendation algorithms evaluation system which is credibility to do the work of recommendation evaluation. So how to do this work objective and reasonable is still a challengeable task. In this article, we discussed the present evaluation metrics with its respective advantages and disadvantages. Then, we put forward to use the Matthews Correlation Coefficient to evaluate the recommendation algorithm's performance. All this based on an open source projects called mahout which provides a rich set of components to construct the classic recommendation algorithm. The results of the experiments show that the applicability of Matthews correlation coefficient in the relative evaluation work of recommendation algorithm.

Question: What is the Matthews Correlation Coefficient?


Neural networks and principal component analysis: Learning from examples without local minima. We consider the problem of learning from examples in layered linear feed-forward neural networks using optimization methods, such as back propagation, with respect to the usual quadratic error function E of the connection weights. Our main result is a complete description of the landscape attached to E in terms of principal component analysis. We show that E has a unique minimum corresponding to the projection onto the subspace generated by the first principal vectors of a covariance matrix associated with the training patterns. All the additional critical points of E are saddle points (corresponding to projections onto subspaces generated by higher order vectors). The auto-associative case is examined in detail. Extensions and implications for the learning algorithms are discussed.

Question: What is the neural network?


Epidemic dengue/dengue hemorrhagic fever as a public health, social and economic problem in the 21st century.. Dengue fever/dengue hemorrhagic fever is now one of the most important public health problems in tropical developing countries and also has major economic and societal consequences.

Question: What is the 21st century?


Detection of and compensation for shadows in colored urban aerial images. A new method for shadow detection in colored urban aerial images is proposed. First, a new imaging model of shadows is presented, which indicates that hue values of shadowed image areas are larger than those of these areas non-shadowed. Based on this model, a thresholding technique is employed to detect shadowed areas. After detection, the Retinex technique is applied to shadowed and non-shadowed areas individually to compensate for shadows. Experiment results show the effectiveness of the proposed method.

Question: How to detect shadows in colored urban aerial images?


Scene text detection using graph model built upon maximally stable extremal regions. Scene text detection could be formulated as a bi-label (text and non-text regions) segmentation problem. However, due to the high degree of intraclass variation of scene characters as well as the limited number of training samples, single information source or classifier is not enough to segment text from non-text background. Thus, in this paper, we propose a novel scene text detection approach using graph model built upon Maximally Stable Extremal Regions (MSERs) to incorporate various information sources into one framework. Concretely, after detecting MSERs in the original image, an irregular graph whose nodes are MSERs, is constructed to label MSERs as text regions or non-text ones. Carefully designed features contribute to the unary potential to assess the individual penalties for labeling a MSER node as text or non-text, and color and geometric features are used to define the pairwise potential to punish the likely discontinuities. By minimizing the cost function via graph cut algorithm, different information carried by the cost function could be optimally balanced to get the final MSERs labeling result. The proposed method is naturally context-relevant and scale-insensitive. Experimental results on the ICDAR 2011 competition dataset show that the proposed approach outperforms state-of-the-art methods both in recall and

Question: What is the cost function?


Business artifacts: An approach to operational specification. Any business, no matter what physical goods or services it produces, relies on business records. It needs to record details of what it produces in terms of concrete information. Business artifacts are a mechanism to record this information in units that are concrete, identifiable, self-describing, and indivisible. We developed the concept of artifacts, or semantic objects, in the context of a technique for constructing formal yet intuitive operational descriptions of a business. This technique, called OpS (Operational Specification), was developed over the course of many business-transformation and business-process-integration engagements for use in IBM's internal processes as well as for use with customers. Business artifacts (or business records) are the basis for the factorization of knowledge that enables the OpS technique. In this paper we present a comprehensive discussion of business artifacts—what they are, how they are represented, and the role they play in operational business modeling. Unlike the more familiar and popular concept of business objects, business artifacts are pure instances rather than instances of a taxonomy of types. Consequently, the key operation on business artifacts is recognition rather than classification.

Question: What is the difference between business objects and business artifacts?


On spatial smoothing for direction-of-arrival estimation of coherent signals.

Question: How to estimate direction of arrival of a coherent signal?


End-to-End Blind Image Quality Assessment Using Deep Neural Networks. We propose a multi-task end-to-end optimized deep neural network (MEON) for blind image quality assessment (BIQA). MEON consists of two sub-networks—a distortion identification network and a quality prediction network—sharing the early layers. Unlike traditional methods used for training multi-task networks, our training process is performed in two steps. In the first step, we train a distortion type identification sub-network, for which large-scale training samples are readily available. In the second step, starting from the pre-trained early layers and the outputs of the first sub-network, we train a quality prediction sub-network using a variant of the stochastic gradient descent method. Different from most deep neural networks, we choose biologically inspired generalized divisive normalization (GDN) instead of rectified linear unit as the activation function. We empirically demonstrate that GDN is effective at reducing model parameters/layers while achieving similar quality prediction performance. With modest model complexity, the proposed MEON index achieves state-of-the-art performance on four publicly available benchmarks. Moreover, we demonstrate the strong competitiveness of MEON against state-of-the-art BIQA models using the group maximum differentiation competition methodology.

Question: What is the difference between a deep neural network and a deep belief network?


Junior: The Stanford entry in the Urban Challenge.

Question: What is the Stanford entry in the Urban Challenge?


The Design of High-Level Features for Photo Quality Assessment. We propose a principled method for designing high level features forphoto quality assessment. Our resulting system can classify between high quality professional photos and low quality snapshots. Instead of using the bag of low-level features approach, we first determine the perceptual factors that distinguish between professional photos and snapshots. Then, we design high level semantic features to measure the perceptual differences. We test our features on a large and diverse dataset and our system is able to achieve a classification rate of 72% on this difficult task. Since our system is able to achieve a precision of over 90% in low recall scenarios, we show excellent results in a web image search application.

Question: How to design high level features for photo quality assessment?


3D printing bone models extracted from medical imaging data. Additive manufacturing in the form of 3D printing is becoming more common with the rise of low-cost hobby machines which makes research in the field of medicine more accessible. This article explores and experiments with methods of producing three dimensional replicas of body parts as education material from the medical imaging data of Computed Tomography and Magnetic Resonance Imaging.

Question: What is the medical imaging data?


Development of folded dual-polarization dividers for broadband ortho-mode transducers. A waveguide divider with folded lateral arms is presented for separating dual orthogonal linear polarizations in broadband ortho-mode transducers. The structure is based on a well-known double symmetry junction, where the metallic pins have been eliminated and the lateral outputs have been folded to achieve a combined effect: matching for the vertical polarization and a very significant size reduction. In addition, since the path for the lateral branches has been reduced, the insertion losses for the different polarizations are balanced. The isolation between orthogonal polarizations is kept because of the double-symmetry of the junction. From the mechanical point of view, the proposed junction allows a more simple manufacture and assembly of the ortho-mode transducer parts, which has been shown with a Ku-band design, covering the full Ku-band from 12.6 to 18.25 GHz. The experimental prototype has demonstrated a measured return loss better than 28 dB in the design band and insertion loss smaller than 0.15 dB for both polarizations.

Question: What is the return loss of a divider?


Prosody-Based Automatic Segmentation of Speech into Sentences and Topics. A crucial step in processing speech audio data for informati onextraction, topicdetection, or browsing/playback is to segment the input into sentence and topic units. Speech segm entation is challenging, since the cues typically present for segmenting text (headers, paragraphs, punctuation) ar e absent in spoken language. We investigate the use of prosody (informationgleaned from the timing and melody of s peech) for these tasks. Using decision tree and hidden Markov modeling techniques, we combine prosodic cues with w ord-based approaches, and evaluate performance on two speech corpora, Broadcast News and Switchboard. Resu lts show that the prosodic model alone performs on par with, or better than, word-based statistical languag e models—for both true and automatically recognized words in news speech. The prosodic model achieves comparabl e performance with significantly less training data, and requires no hand-labeling of prosodic events. Across ta sks nd corpora, we obtain a significant improvement over word-only models using a probabilistic combination of prosodic and lexical information. Inspection reveals that the prosodic models capture language-independent bou dary indicators described in the literature. Finally, cue usage is task and corpus dependent. For example, pause and pi tch features are highly informative for segmenting news speech, whereas pause, duration and word-based cues do minate for natural conversation.

Question: How to segment speech into sentences and topics?


Enhancement of Images Using Histogram Processing Techniques. Image enhancement is a mean as the improvement of an image appearance by increasing dominance of some features or by decreasing ambiguity between different regions of the image. Image enhancement processes consist of a collection of techniques that seek to improve the visual appearance of an image or to convert the image to a form better suited for analysis by a human or machine. Many images such as medical images, remote sensing images, electron microscopy images and even real life photographic pictures, suffer from poor contrast. Therefore it is necessary to enhance the contrast.The purpose of image enhancement methods is to increase image visibility and details. Enhanced image provide clear image to eyes or assist feature extraction processing in computer vision system. Numerous enhancement methods have been proposed but the enhancement efficiency, computational requirements, noise amplification, user intervention, and application suitability are the common factors to be considered when choosing from these different methods for specific image processing application.

Question: What is the purpose of image enhancement?


Classification of Various Neighborhood Operations for the Nurse Scheduling Problem. Since the nurse scheduling problem (NSP) is a problem of finding a feasible solution, the solution space must include infeasible solutions to solve it using a local search algorithm. However, the solution space consisting of all the solutions is so large that the search requires much CPU time. In the NSP, some constraints have higher priority. Thus, we can define the solution space to be the set of solutions satisfying some of the important constraints, which are called the elementary constraints. The connectivity of the solution space is also important for the performance. However, the connectivity is not obvious when the solution space consists only of solutions satisfying the elementary constraints and is composed of small neighborhoods. This paper gives theoretical support for using 4-opt-type neighborhood operations by discussing the connectivity of its solution space and the size of the neighborhood. Another interesting point in our model is a special case of the NSP corresponds to the bipartite transportation problem, and our result also applies to it.

Question: What is the size of the neighborhood of a solution?


Optimizing 2D Gabor Filters for Iris Recognition. The randomness and richness present in the iris texture make the 2D Gabor filter bank analysis a suitable technique to be used for iris recognition systems. To accurately characterize complex texture structures using 2D Gabor filters it is necessary to use multiple sets of parameters of this type of filters. This paper proposes a technique of optimizing multiple sets of 2D Gabor filter parameters to gradually enhance the accuracy of an iris recognition system. The proposed methodology is suitable to be applied on both near infrared and visible spectrum iris images. To illustrate the efficiency of the filter bank design technique, UBIRISv1 database was used for benchmarking.

Question: What is the iris recognition system?


CamStyle: A Novel Data Augmentation Method for Person Re-Identification. Person re-identification (re-ID) is a cross-camera retrieval task that suffers from image style variations caused by different cameras. The art implicitly addresses this problem by learning a camera-invariant descriptor subspace. In this paper, we explicitly consider this challenge by introducing camera style (CamStyle). CamStyle can serve as a data augmentation approach that reduces the risk of deep network overfitting and that smooths the CamStyle disparities. Specifically, with a style transfer model, labeled training images can be style transferred to each camera, and along with the original training samples, form the augmented training set. This method, while increasing data diversity against overfitting, also incurs a considerable level of noise. In the effort to alleviate the impact of noise, the label smooth regularization (LSR) is adopted. The vanilla version of our method (without LSR) performs reasonably well on few camera systems in which overfitting often occurs. With LSR, we demonstrate consistent improvement in all systems regardless of the extent of overfitting. We also report competitive accuracy compared with the state of the art on Market-1501 and DukeMTMC-re-ID. Importantly, CamStyle can be employed to the challenging problems of one view learning and unsupervised domain adaptation (UDA) in person re-identification (re-ID), both of which have critical research and application significance. The former only has labeled data in one camera view and the latter only has labeled data in the source domain. Experimental results show that CamStyle significantly improves the performance of the baseline in the two problems. Specially, for UDA, CamStyle achieves state-of-the-art accuracy based on a baseline deep re-ID model on Market-1501 and DukeMTMC-reID. Our code is available at: https://github.com/zhunzhong07/CamStyle.

Question: What is the state of the art in person re-identification?


Never-Ending Multiword Expressions Learning. This paper introduces NEMWEL, a system that performs Never-Ending MultiWord Expressions Learning. Instead of using a static corpus and classifier, NEMWEL applies supervised learning on automatically crawled news texts. Moreover, it uses its own results to periodically retrain the classifier, bootstrapping on its own results. In addition to a detailed description of the system's architecture and its modules, we report the results of a manual evaluation. It shows that NEMWEL is capable of learning new expressions over time with improved precision.

Question: What is a never-ending multiword expression?


Learning and Collective Knowledge Construction With Social Media: A Process-Oriented Perspective. Social media are increasingly being used for educational purposes. The first part of this article briefly reviews literature that reports on educational applications of social media tools. The second part discusses theories that may provide a basis for analyzing the processes that are relevant for individual learning and collective knowledge construction. We argue that a systems-theoretical constructivist approach is appropriate to examine the processes of educational social media use, namely, self-organization, the internalization of information, the externalization of knowledge, and the interplay of externalization and internalization providing the basis of a co-evolution of cognitive and social systems. In the third part we present research findings that illustrate and support this systems-theoretical framework. Concluding, we discuss the implications for educational design and for future research on learning and collective knowledge construction with social media.

Question: What is the difference between a theory and a framework?


Yelp Restaurant Photo Classification. Everyday, millions of users upload numerous photos of different businesses to Yelp. Though most of them are tagged with simple information such as business names, the deep-level information typically are not revealed until looked by humans. Such deep-level information contains things like whether the restaurant is good for lunch or dinner, whether it has outdoor seating, etc. This information can be crucial in helping potential customers to determine whether its the ideal restaurant they want to dine in. Hand labeling these restaurants can be not only costly but also tedious since photos are uploaded almost every second to Yelp. In this project, we used machine learning techniques, specifically image classification methods, to identify the context and rich information of different photos on Yelp. We used three different models to train and test the given dataset. The dataset is provided by Yelp on Kaggle.com. In the training set, 234842 arbitrary-sized images submitted by Yelp users are correlated to one of 2000 Yelp businesses. The number of corresponding photos for each business ranges from 2 to 2974, with mean of 117. The image count distribution is shown by Fig1. Every business is tagged with multiple descriptive labels in 9 aspects. The labels includes: 0: good for lunch 1: good for dinner 2: takes reservations 3: outdoor seating 4: restaurant is expensive 5: has alcohol 6: has table service 7: ambience is classy 8: good for kids As an example, Fig2 illustrates the structure of the dataset. Our goal is to predict the labels of a business in these 9 aspects given its photos. A notable challenge is that the labels are not directly correlated to each image. Individual photos can possibly be indicative in one aspect, yet it is tagged with all the labels of the business. As a result, the dataset might exhibit high noise level. The fraction of each label across the training set is shown in Fig3. Though assumed independent, some l

Question: What is the fraction of each label across the training set?


Measurement Science Needs for Real-time Control of Additive Manufacturing Powder Bed Fusion Processes. Additive Manufacturing is increasingly used in the development of new products: from conceptual design to functional parts and tooling. However, today, variability in part quality due to inadequate dimensional tolerances, surface roughness, and defects, limits its broader acceptance for high-value or mission-critical applications. While process control in general can limit this variability, it is impeded by a lack of adequate process measurement methods. Process control today is based on heuristics and experimental data, yielding limited improvement in part quality. The overall goal is to develop the measurement science 1 necessary to make in-process measurement and real-time control possible in additive manufacturing. Traceable dimensional and thermal metrology methods must be developed for real-time closed-loop control of additive manufacturing processes. As a precursor, this report presents a review on the additive manufacturing control schemes, process measurements, and modeling and simulation methods as it applies to the powder bed fusion process, though results from other processes are reviewed where applicable. The aim of the review is to identify and summarize the measurement science needs that are critical to real-time process control. We organize our research findings to identify the correlations between process parameters, process signatures, and product quality. The intention of this report is to serve as a background reference and a go-to place for our work to identify the most suitable measurement methods and corresponding measurands for real-time control. 1 Measurement science broadly includes: development of performance metrics, measurement and testing methods, predictive modeling and simulation tools, knowledge modeling, protocols, technical data, and reference materials and artifacts; conduct of inter-comparison studies and calibrations; evaluatio

Question: What is the measurement science needs for real-time control of additive manufacturing powder bed fusion processes?


Exercise motion classification from large-scale wearable sensor data using convolutional neural networks. The ability to accurately identify human activities is essential for developing automatic rehabilitation and sports training systems. In this paper, large-scale exercise motion data obtained from a forearm-worn wearable sensor are classified with a convolutional neural network (CNN). Time-series data consisting of accelerometer and orientation measurements are formatted as images, allowing the CNN to automatically extract discriminative features. A comparative study on the effects of image formatting and different CNN architectures is also presented. The best performing configuration classifies 50 gym exercises with 92.1% accuracy.

Question: How to classify exercise motion data?


Recurrent Transformer Networks for Semantic Correspondence. We present recurrent transformer networks (RTNs) for obtaining dense correspondences between semantically similar images. Our networks accomplish this through an iterative process of estimating spatial transformations between the input images and using these transformations to generate aligned convolutional activations. By directly estimating the transformations between an image pair, rather than employing spatial transformer networks to independently normalize each individual image, we show that greater accuracy can be achieved. This process is conducted in a recursive manner to refine both the transformation estimates and the feature representations. In addition, a technique is presented for weakly-supervised training of RTNs that is based on a proposed classification loss. With RTNs, state-of-the-art performance is attained on several benchmarks for semantic correspondence.

Question: What is recurrent transformer networks?


Few-shot Learning Using a Small-Sized Dataset of High-Resolution FUNDUS Images for Glaucoma Diagnosis. Deep learning has recently attracted a lot of attention, mainly thanks to substantial gains in terms of effectiveness. However, there is still room for significant improvement, especially when dealing with use cases that come with a limited availability of data, as is often the case in the area of medical image analysis. In this paper, we introduce a novel approach for early diagnosis of glaucoma in high-resolution FUNDUS images, only requiring a small number of training samples. In particular, we developed a predictive model based on a matching neural network architecture, integrating a high-resolution deep convolutional network that allows preserving the high-fidelity nature of the medical images. Our experimental results show that our predictive model is able to obtain higher levels of effectiveness than vanilla deep convolutional neural networks.

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


Interpreting random forest models using a feature contribution method. Model interpretation is one of the key aspects of the model evaluation process. The explanation of the relationship between model variables and outputs is easy for statistical models, such as linear regressions, thanks to the availability of model parameters and their statistical significance. For "black box" models, such as random forest, this information is hidden inside the model structure. This work presents an approach for computing feature contributions for random forest classification models. It allows for the determination of the influence of each variable on the model prediction for an individual instance. Interpretation of feature contributions for two UCI benchmark datasets shows the potential of the proposed methodology. The robustness of results is demonstrated through an extensive analysis of feature contributions calculated for a large number of generated random forest models.

Question: What is the difference between a random forest model and a decision tree model?


Plot Induction and Evolutionary Search for Story Generation. In this paper we develop a story generator that leverages knowledge inherent in corpora without requiring extensive manual involvement. A key feature in our approach is the reliance on a story planner which we acquire automatically by recording events, their participants, and their precedence relationships in a training corpus. Contrary to previous work our system does not follow a generate-and-rank architecture. Instead, we employ evolutionary search techniques to explore the space of possible stories which we argue are well suited to the story generation task. Experiments on generating simple children's stories show that our system outperforms previous data-driven approaches.

Question: How does evolutionary search work?


Efficient Object Instance Search Using Fuzzy Objects Matching. Recently, global features aggregated from local convolutional features of the convolutional neural network have shown to be much more effective in comparison with hand-crafted features for image retrieval. However, the global feature might not effectively capture the relevance between the query object and reference images in the object instance search task, especially when the query object is relatively small and there exist multiple types of objects in reference images. Moreover, the object instance search requires to localize the object in the reference image, which may not be achieved through global representations. In this paper, we propose a Fuzzy Objects Matching (FOM) framework to effectively and efficiently capture the relevance between the query object and reference images in the dataset. In the proposed FOM scheme, object proposals are utilized to detect the potential regions of the query object in reference images. To achieve high search efficiency, we factorize the feature matrix of all the object proposals from one reference image into the product of a set of fuzzy objects and sparse codes. In addition, we refine the feature of the generated fuzzy objects according to its neighborhood in the feature space to generate more robust representation. The experimental results demonstrate that the proposed FOM framework significantly outperforms the state-of-theart methods in precision with less memory and computational cost on three public datasets. The task of object instance search, is to retrieve all the images containing a specific object query and localize the query object in the reference images. It has received a sustained attention over the last decade, leading to many object instance search systems (Meng et al. 2010; Jiang, Meng, and Yuan 2012; Jiang et al. 2015; Tolias, Avrithis, and Jégou 2013; Tao et al. 2014; Razavian et al. 2014a; 2014b; Tolias, Sicre, and Jégou 2016; Meng et al. 2016

Question: What is the task of object instance search?


LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop. While there has been remarkable progress in the performance of visual recognition algorithms, the state-of-the-art models tend to be exceptionally data-hungry. Large labeled training datasets, expensive and tedious to produce, are required to optimize millions of parameters in deep network models. Lagging behind the growth in model capacity, the available datasets are quickly becoming outdated in terms of size and density. To circumvent this bottleneck, we propose to amplify human effort through a partially automated labeling scheme, leveraging deep learning with humans in the loop. Starting from a large set of candidate images for each category, we iteratively sample a subset, ask people to label them, classify the others with a trained model, split the set into positives, negatives, and unlabeled based on the classification confidence, and then iterate with the unlabeled set. To assess the effectiveness of this cascading procedure and enable further progress in visual recognition research, we construct a new image dataset, LSUN. It contains around one million labeled images for each of 10 scene categories and 20 object categories. We experiment with training popular convolutional networks and find that they achieve substantial performance gains when trained on this dataset.

Question: What is the size of the dataset?


Mindfulness and emotion regulation in depression and anxiety: common and distinct mechanisms of action.. BACKGROUND The current study seeks to investigate the mechanisms through which mindfulness is related to mental health in a clinical sample of adults by examining (1) whether specific cognitive emotion regulation strategies (rumination, reappraisal, worry, and nonacceptance) mediate associations between mindfulness and depression and anxiety, respectively, and (2) whether these emotion regulation strategies operate uniquely or transdiagnostically in relation to depression and anxiety.   METHODS Participants were 187 adults seeking treatment at a mood and anxiety disorders clinic in Connecticut. Participants completed a battery of self-report measures that included assessments of depression and anxiety (Mood and Anxiety Symptom Questionnaire), and emotion regulation (Ruminative Response Scale, Penn State Worry Questionnaire, Emotion Regulation Questionnaire, Difficulties in Emotion Regulation Scale).   RESULTS Simple mediation analyses indicated that rumination and worry significantly mediated associations between mindfulness and anxiety symptoms, whereas rumination and reappraisal significantly mediated associations between mindfulness and depressive symptoms. Multiple mediation analyses showed that worry significantly mediated associations between mindfulness and anxiety symptoms and rumination and reappraisal significantly mediated associations between mindfulness and depressive symptoms.   CONCLUSIONS Findings suggest that mindfulness operates through distinct and common mechanisms depending on clinical context.

Question: What is the difference between rumination and worry?


Designing a mobile game to thwarts malicious IT threats: A phishing threat avoidance perspective. Phishing is an online identity theft, which aims to steal sensitive information such as username, password and online banking details from victims. To prevent this, phishing education needs to be considered. Game based education is becoming more and more popular. This paper introduces a mobile game prototype for the android platform based on a story, which simplifies and exaggerates real life. The elements of a game design framework for avoiding phishing attacks were used to address the game design issues and game design principles were used as a set of guidelines for structuring and presenting information. The overall mobile game design was aimed to enhance the user's avoidance behaviour through motivation to protect themselves against phishing threats. The prototype mobile game design was presented on MIT App Inventor Emulator.

Question: How to design a mobile game to thwarts malicious IT threats?


Resilience and Sustainable Development : Theory of resilience , systems thinking and adaptive governance.  Basic information on SD  Country profiles  Quarterly reports  Policy briefs  Case studies  Conference papers  Workshop papers  Getting in touch with us The European Sustainable Development Network (ESDN) is an informal network of public administrators and other experts who deal with sustainable development strategies and policies. The network covers all 27 EU Member States, plus other European countries. The ESDN is active in promoting sustainable development and facilitating the exchange of good practices in Europe and gives advice to policy-makers at the European and national levels. This ESDN Quarterly Report (QR) provides a condensed overview of the concept of resilience. Despite the complexity of the theory behind resilience, this QR tries to communicate the main notions behind this concept in a way that is understandable and not overly technical. The intention of this QR is to serve as a guide through the concept of resilience. The report does not aim at being exhaustive but intends to provide an overview on the links which are particularly relevant for sustainable development (SD) in general and SD governance in particular. A multitude of diverse sources have been used, mainly from the academic literature. It has to be mentioned the significant and decisive role that the Resilience Alliance has in providing extensive knowledge: the website that they are running, is an exceptionally good source of information for those who are interested and want to deepen their knowledge of resilience. Additionally, among all the scientific publications cited throughout the report, a special mention goes to the book by Walker and Salt (2006) entitled " Resilience thinking: sustaining ecosystems and people in a changing world " , which is very much suggested as a practical source of information on resilience. As a first chapter, an executive summary is provided wi

Question: What is resilience?


Characterizing Variation in Crowd-Sourced Data for Training Neural Language Generators to Produce Stylistically Varied Outputs. One of the biggest challenges of endto-end language generation from meaning representations in dialogue systems is making the outputs more natural and varied. Here we take a large corpus of 50K crowd-sourced utterances in the restaurant domain and develop text analysis methods that systematically characterize types of sentences in the training data. We then automatically label the training data to allow us to conduct two kinds of experiments with a neural generator. First, we test the effect of training the system with different stylistic partitions and quantify the effect of smaller, but more stylistically controlled training data. Second, we propose a method of labeling the style variants during training, and show that we can modify the style of the generated utterances using our stylistic labels. We contrast and compare these methods that can be used with any existing large corpus, showing how they vary in terms of semantic quality and

Question: How to characterize the stylistic variation in crowd-sourced data for training neural language generators to produce stylistically varied outputs.


Steatocystoma simplex in penile foreskin: a case report. BACKGROUND Steatocystoma simplex is an uncommon skin lesion with a histological pattern that is identical to that of steatocystoma multiplex. We are reporting this case of steatocystoma simplex for its uncommon location in the penile foreskin, and its occurrence in a Wapishana man.   CASE PRESENTATION A 56-year-old man of Wapishana ethnicity presented with complaints of referred penile discomfort and pain during sexual intercourse for 5 years. A physical examination revealed a mobile, compressible subcutaneous non-tender mass of 4 cm diameter located on the left-side of his penile foreskin. There were no signs of inflammation, no grip on the penile shaft, and no urethral discharge or enlargement of lymph nodes. We found no evidence of other cysts on cutaneous examination. We performed classical excision of the lesion under local anesthesia and confirmed the diagnosis of steatocystoma with the pathological report. As there were no complications, we discharged him the same day.   CONCLUSION Steatocystoma can be considered a differential diagnosis for cystic lesions on and around the penis.

Question: What is steatocystoma simplex?


The Relationship Between Health and Growth : When Lucas. This paper revisits the relationship between health and growth in light of modern endogenous growth theory. We propose a unified framework that encompasses the growth effects of both the rate of improvement of health and the level of health. Based on cross-country regressions over the period 1960-2000, where we instrument for both variables, we find that a higher initial level and a higher rate of improvement in life expectancy both have a significantly positive impact on per capita GDP growth. Then, restricting attention to OECD countries, we find supportive evidence that only the reduction in mortality below age forty generates productivity gains, which in turn may explain why the positive correlation between health and growth in cross-OECD country regressions appears to have weakened since 1960. JEL classification: E0, I10, O10, O15

Question: What is the relationship between health and growth?


Cognitive radio: making software radios more personal. Software radios arc cmcrging as platforms for multiband multimode personal communications systems. Radio etiqucttc is the set of RF bands, air interfaces, protocols, and spatial and temporal patterns that modcrate the use of the radio spectrum. Cognitive radio extends the software radio with radio-domain model-based rcasoning about such ctiquettes. Cognitivc radio enhances the flexibility of personal services through a Radio Knowlcdge Representation Language. This language reprcsents knowledge of radio etiquettc, devices, software modules, propagation, nctworks, user needs, and application scenarios in a way that supports automated reasoning about thc needs of the user. This empowers software radios to conduct expressivc ncgotiations among peers about the use of radio spectrum across fluents of space, time, and uscr context. With RKRL, cognitivc radio agents may actively manipulate the protocol stack to adapt known etiquettes to better satis$ the LISCT'S nccds. This transforms radio nodes from blind executors of predefined protocols to radio-domain-aware intclligent agents that scarch out ways to dclivcr the services thc uscr wants even if that uscr docs not know how to obtain them. Softwarc radio [l] provides an idcal platform for thc rcalization of cognitive radio. Cognitive Radio: Making Software Radios More Personal J O S E P H MITOLA 111 A N D GERALD Q. M A G U I R E , J R . R O Y A L INSTITUTE OF TECHNOLOGY Global System for Mobile Communications (GSM) radio's equalizer taps reflcct the channel multipath structure. A network might want to ask a handset, "How many distinguishable multipath components are you seeing?" Knowledge of thc internal states of the equalizer could be useful bccause in some reception areas, thcrc may be little or no multipath and 20 dB of extra signal-to-noisc ratio (SNR). Software radio processing capacity is wasted running a computationally intcnsive equalizer algorithm when no cqu

Question: How many distinguishable multipath components are you seeing?


Learning Linear Threshold Functions in the Presence of Classification Noise. I show that the linear threshold functions are polynomially learnable in the presence of classification noise, i.e., polynomial in <italic>n</italic>, 1/ε, 1/δ, and 1/&sgr;, where <italic>n</italic> is the number of Boolean attributes, ε and δ are the usual accuracy and confidence parameters, and &sgr; indicates the minimum distance of any example from the target hyperplane, which is assumed to be lower than the average distance of the examples from any hyperplane. This result is achieved by modifying the Perceptron algorithm—for each update, a weighted average of a sample of misclassified examples and a correction vector is added to the current weight vector. Similar modifications are shown for the Weighted Majority algorithm. The correction vector is simply   the mean of the normalized examples. In the special case of Boolean threshold functions, the modified Perceptron algorithm performs <italic>O</italic> (<italic>n</italic><supscrpt>2</supscrpt>ε<supscrpt>−2</supscrpt> ) iterations over <italic>O</italic>(<italic>n</italic><supscrpt>4</supscrpt>ε<supscrpt> −2</supscrpt>ln(<italic>n</italic>/(δε))) examples. This improves on the previous classification-noise result of Angluin and Laird to a much larger concept class with a similar number of examples, but with multiple iterations over the examples.

Question: What is the number of iterations over the examples?


Survey on Energy Consumption Entities on the Smartphone Platform. The full degree of freedom in mobile systems heavily depends on the energy provided by the mobile phone's batteries. Their capacity is in general limited and for sure not keeping pace as the mobile devices are crammed up with new functionalities. The discrepancy of Moore's law, offering twice the processing power at least each second year, and the development in batteries, which did not even double over the last decade, makes a shift in researchers' way of designing networks, protocols, and the mobile device itself. The bottleneck to take care of in the design process of mobile systems is not only the wireless data rate, but even more the energy limitation as the customers ask for new energy-hungry services, e.g., requiring faster connections or even multiple air interfaces, and longer standby or operational times of their mobile devices at the same time. In this survey, the energy consuming entities of a mobile device such as wireless air interfaces, display, mp3 player and others are measured and compared. The presented measurement results allow the reader to understand what the energy hungry parts of a mobile device are and use those findings for the design of future mobile protocols and applications. All results presented in this work and further results are made public on our web page [2].

Question: What is the energy consumption of a mobile device?


Fostering User Engagement: Rhetorical Devices for Applause Generation Learnt from TED Talks. One problem that every presenter faces when delivering a public discourse is how to hold the listeners' attentions or to keep them involved. Therefore, many studies in conversation analysis work on this issue and suggest qualitatively constructions that can effectively lead to audience's applause. To investigate these proposals quantitatively, in this study we analyze the transcripts of 2,135 TED Talks, with a particular focus on the rhetorical devices that are used by the presenters for applause elicitation. Through conducting regression analysis, we identify and interpret 24 rhetorical devices as triggers of audience applauding. We further build models that can recognize applause-evoking sentences and conclude this work with potential implications. Introduction and Motivation Many academic studies have been devoted to the tasks of user engagement characterization. However, applause, as the most direct audience reaction, has till now not been fully investigated and understood. The audience do not just clap whenever they like, they do so only at certain points and are continuously looking for appropriate times when applause can possibly occur (Kuo, 2001). Having a deep understanding of audience's applause is important because it will help the speakers to better design their speech with recognizable calls for conjoined response, and to make their presentation more appealing and engageable. Despite its importance, to date relatively limited work have been conducted on the topic of applause generation, except a few qualitative studies done by social psychologists. Atkinson (1984) first claimed that applause is closely synchronized with a number of actions on the part of the speakers, which he referred to as "rhetorical devices". He identified three rhetorical devices that are effective in evoking applauses, including: contrasts, three-part lists, and projection of names. Herita

Question: How to get applause?


Can we Evaluate the Quality of Generated Text?. Evaluating the output of NLG systems is notoriously difficult, and performin g assessments of text quality even more so. A range of automated and subject-based approaches to the evaluation of text quality h ave been taken, including comparison with a putative gold standard text, analysis of specific linguistic features of the output, expert review and task-based evaluation. In this paper we present the results of a variety of such approaches in the context of a case study application. We discuss the problems encountered in the implementation of each approach in the context of the literature, and propo se that a test based on the Turing test for machine intelligence offers a way forward in the evaluation of the subjective notion of text qua lity.

Question: How can we evaluate the quality of generated text?


Planar Array Antennas with Travelling-Wave Excitation in Millimeter-Wave Band. あらまし ミリ波の周波数帯において指向性走査可能な平面アンテナが望まれている.本論文では,ミリ波の 代表的な二つの平面アンテナである導波管アンテナとマイクロストリップアンテナについて,開発した平面アン テナを紹介する.ペンシルビームが必要な機械走査型アンテナに適した二次元導波管アレーアンテナと,複数の サブアレーの配列からなる電子走査方式のサブアレー平面アンテナ用に,導波管アレーアンテナとマイクロスト リップコムラインアンテナについて開発した例を示す.これらについて,各放射素子で反射特性を低減すること が,特性の向上・設計自由度の拡大に有効であることを示す.また,これらのアンテナについて低損失に給電す る構造を示すと同時に,片端から給電するよりも,中央から給電する方が,周波数帯域幅が広くなることを示す. 更に,これらのアンテナ実現のためのキーとなる進行波励振アレー設計技術について解説する. キーワード ミリ波,アレーアンテナ,平面アンテナ,導波管アンテナ,マイクロストリップアンテナ

Question: What is the Canadian military?


Maneuver-Based Trajectory Planning for Highly Autonomous Vehicles on Real Road With Traffic and Driver Interaction. This paper presents the design and first test on a simulator of a vehicle trajectory-planning algorithm that adapts to traffic on a lane-structured infrastructure such as highways. The proposed algorithm is designed to run on a fail-safe embedded environment with low computational power, such as an engine control unit, to be implementable in commercial vehicles of the near future. The target platform has a clock frequency of less than 150 MHz, 150 kB RAM of memory, and a 3-MB program memory. The trajectory planning is performed by a two-step algorithm. The first step defines the feasible maneuvers with respect to the environment, aiming at minimizing the risk of a collision. The output of this step is a target group of maneuvers in the longitudinal direction (accelerating or decelerating), in the lateral direction (changing lanes), and in the combination of both directions. The second step is a more detailed evaluation of several possible trajectories within these maneuvers. The trajectories are optimized to additional performance indicators such as travel time, traffic rules, consumption, and comfort. The output of this module is a trajectory in the vehicle frame that represents the recommended vehicle state (position, heading, speed, and acceleration) for the following seconds.

Question: How to plan a trajectory for a vehicle?


Visual inertial odometry for quadrotors on SE(3). The combination of on-board sensors measurements with different statistical characteristics can be employed in robotics for localization and control, especially in GPS-denied environments. In particular, most aerial vehicles are packaged with low cost sensors, important for aerial robotics, such as camera, a gyroscope, and an accelerometer. In this work, we develop a visual inertial odometry system based on the Unscented Kalman Filter (UKF) acting on the Lie group SE(3), such to obtain an unique, singularity-free representation of a rigid body pose. We model this pose with the Lie group SE(3) and model the noise on the corresponding Lie algebra. Moreover, we extend the concepts used in the standard UKF formulation, such as state uncertainty and modeling, to correctly incorporate elements that do not belong to an Euclidean space such as the Lie group members. In this analysis, we use the parallel transport, which requires us to explicitly consider SE(3) as representing rigid bodies though the use of the affine connection. We present experimental results to show the effectiveness of the proposed approach for state estimation of a quadrotor platform.

Question: What is the Lie group SE(3)?


High speed operation of electrical machines, a review on technology, benefits and challenges. The High Speed (HS) technology, in electrical machine based drive systems, is reviewed in this paper by investigating around 200 different machine's data that are reported in the literature or open sources. This study includes information regarding clarifying the maximum power vs. speed capability, potential applications, different machine types, design limitation issues consisting of thermal, electro-magnetic, mechanical, control, inverter and power electronics. The limitations of electrical machines in HS technology are classified by introducing HS-index, which is the product of the machine nominal power and nominal speed (= n × Pout). It is shown that HS-index behavior changes by speed, because limitation factors for HS power capability take different physics/nature, as speed increases. It is found that in relatively low speeds, n <; 20 krpm, maximum mechanical stress in the rotor, and in high speeds, n > 20 krpm, additional losses in the machine dictates the maximum power capability. The survey shows that Induction Machine (IM) and Permanent Magnet (PM) machine have relatively superior performance. The maximum rotor surface speed, vmax, is limited to 250-300 m/s (can reach 550 m/s) and is independent of n and output power. The HS-index is a function of vmax2 and almost independent of n for n <; 10-20 krpm. However, for n > 20 krpm is proportional to vmax3/n2 due to thermal limitations. The maximum power is almost cubically (Pout ∝ d2.6) proportional to the rotor diameter.

Question: What is the maximum speed of a machine?


Mapping Unseen Words to Task-Trained Embedding Spaces. Education University of California, Berkeley (2008-2013) Ph.D. in Computer Science Thesis: Surface Web Semantics for Structured Natural Language Processing Advisor: Dan Klein. Committee members: Dan Klein, Marti Hearst, Line Mikkelsen, Nelson Morgan University of California, Berkeley (2012) Master of Science (M.S.) in Computer Science Thesis: An All-Fragments Grammar for Simple and Accurate Parsing Advisor: Dan Klein Indian Institute of Technology, Kanpur (2004-2008) Bachelor of Technology (B.Tech.) in Computer Science and Engineering GPA: 3.96/4.00 (Institute and Department Rank 2) Cornell University (Summer 2007) CS490 (Independent Research and Reading) GPA: 4.00/4.00 Advisors: Lillian Lee, Claire Cardie

Question: What is the name of the university in the document?


Design of a Multi-Sensor Cooperation Travel Environment Perception System for Autonomous Vehicle. This paper describes the environment perception system designed for intelligent vehicle SmartV-II, which won the 2010 Future Challenge. This system utilizes the cooperation of multiple lasers and cameras to realize several necessary functions of autonomous navigation: road curb detection, lane detection and traffic sign recognition. Multiple single scan lasers are integrated to detect the road curb based on Z-variance method. Vision based lane detection is realized by two scans method combining with image model. Haar-like feature based method is applied for traffic sign detection and SURF matching method is used for sign classification. The results of experiments validate the effectiveness of the proposed algorithms and the whole system.

Question: How to design a multi-sensor cooperation travel environment perception system for autonomous vehicle?


Following High-level Navigation Instructions on a Simulated Quadcopter with Imitation Learning. We introduce a method for following high-level navigation instructions by mapping directly from images, instructions and pose estimates to continuous low-level velocity commands for real-time control. The Grounded Semantic Mapping Network (GSMN) is a fully-differentiable neural network architecture that builds an explicit semantic map in the world reference frame by incorporating a pinhole camera projection model within the network. The information stored in the map is learned from experience, while the local-to-world transformation is computed explicitly. We train the model using DAGGERFM, a modified variant of DAGGER that trades tabular convergence guarantees for improved training speed and memory use. We test GSMN in virtual environments on a realistic quadcopter simulator and show that incorporating an explicit mapping and grounding modules allows GSMN to outperform strong neural baselines and almost reach an expert policy performance. Finally, we analyze the learned map representations and show that using an explicit map leads to an interpretable instruction-following model.

Question: How do you train a neural network to follow instructions?


Navigation-related structural change in the hippocampi of taxi drivers.. Structural MRIs of the brains of humans with extensive navigation experience, licensed London taxi drivers, were analyzed and compared with those of control subjects who did not drive taxis. The posterior hippocampi of taxi drivers were significantly larger relative to those of control subjects. A more anterior hippocampal region was larger in control subjects than in taxi drivers. Hippocampal volume correlated with the amount of time spent as a taxi driver (positively in the posterior and negatively in the anterior hippocampus). These data are in accordance with the idea that the posterior hippocampus stores a spatial representation of the environment and can expand regionally to accommodate elaboration of this representation in people with a high dependence on navigational skills. It seems that there is a capacity for local plastic change in the structure of the healthy adult human brain in response to environmental demands.

Question: What is the size of the hippocampus?


Age Progression/Regression by Conditional Adversarial Autoencoder. If I provide you a face image of mine (without telling you the actual age when I took the picture) and a large amount of face images that I crawled (containing labeled faces of different ages but not necessarily paired), can you show me what I would look like when I am 80 or what I was like when I was 5? The answer is probably a No. Most existing face aging works attempt to learn the transformation between age groups and thus would require the paired samples as well as the labeled query image. In this paper, we look at the problem from a generative modeling perspective such that no paired samples is required. In addition, given an unlabeled image, the generative model can directly produce the image with desired age attribute. We propose a conditional adversarial autoencoder (CAAE) that learns a face manifold, traversing on which smooth age progression and regression can be realized simultaneously. In CAAE, the face is first mapped to a latent vector through a convolutional encoder, and then the vector is projected to the face manifold conditional on age through a deconvolutional generator. The latent vector preserves personalized face features (i.e., personality) and the age condition controls progression vs. regression. Two adversarial networks are imposed on the encoder and generator, respectively, forcing to generate more photo-realistic faces. Experimental results demonstrate the appealing performance and flexibility of the proposed framework by comparing with the state-of-the-art and ground truth.

Question: How to age progression and regression?


Application of generalized Bagley-polygon four-port power dividers to designing microwave dual-band bandpass planar filters. A new type of microwave dual-band bandpass planar filter based on signal-interference techniques is reported. The described filter approach consists of transversal filtering sections made up of generalized Bagley-polygon four-port power dividers. This transversal section, by exploiting feedforward signal-interaction concepts, enables dual-band bandpass filtering transfer functions with several transmission zeros to be obtained. A set of closed formulas and guidelines for the analytical synthesis of the dual-passband transversal filtering section are derived. Moreover, its practical usefulness is proven with the development and testing of a 2.75/3.25-GHz dual-band microstrip prototype.

Question: What is a generalized Bagley-polygon four-port power divider?


On IC traceability via blockchain. Traceability of ICs is important for verifying provenance. We present a novel IC traceability scheme based on blockchain. A blockchain is an immutable public record that maintains a continuously-growing list of data records secured from tampering and revision. In the proposed scheme, IC ownership transfer information is recorded and archived in a blockchain. This safe, verifiable method prevents any party from altering or challenging the legitimacy of the information being exchanged. However, we also need to establish correspondence between a record in a public database and the physical device, in an unclonable way. We propose an embedded physically unclonable function (PUF) to establish this correspondence The blockchain ensures the legitimacy of an IC's current owner, while the PUF safeguards against IC counterfeiting and tampering. Our proposed solution combines hardware and software protocols to let supply chain participants authenticate, track, trace, analyze, and provision chips during their entire life cycle.

Question: How does blockchain help in IC traceability?


Authentication of FPGA Bitstreams: Why and How. Encryption of volatile FPGA bitstreams provides confidentiality to the design but does not ensure its authenticity. This paper motivates the need for adding authentication to the configuration process by providing application examples where this functionality is useful. An examination of possible solutions is followed by suggesting a practical one in consideration of the FPGA's configuration environment constraints. The solution presented here involves two symmetric-key encryption cores running in parallel to provide both authentication and confidentiality while sharing resources for efficient implementation.

Question: How to authenticate FPGA bitstreams?


Groundnut leaf disease detection and classification by using back probagation algorithm. Many studies shows that quality of agricultural products may be reduced from many causes. One of the most important factors contributing to low yield is disease attack. The plant disease such as fungi, bacteria and viruses. The leaf disease completely destroys the quality of the leaf. Common ground nut disease is cercospora. It is one of the type of disease in early stage of ground nut leaf. The upgraded processing pattern comprises of four leading steps. Initially a color renovation constitute intended for the input RGB image is formed, This RGB is converted into HSV because RGB is for Color generation and color descriptor. The next step is plane separation. Next performed the color features. Then using back propagation algorithm detection of leaf disease is done.

Question: How to detect leaf disease?


ICrafter: A Service Framework for Ubiquitous Computing Environments. In this paper, we propose ICrafter, a framework for services and their user interfaces in a class of ubiquitous computing environments. The chief objective of ICrafter is to let users flexibly interact with the services in their environment using a variety of modalities and input devices. We extend existing service frameworks in three ways. First, to offload services and user input devices, ICrafter provides infrastructure support for UI selection, generation, and adaptation. Second, ICrafter allows UIs to be associated with service patterns for on-the-fly aggregation of services. Finally, ICrafter facilitates the design of service UIs that are portable but still reflect the context of the local environment. In addition, we also focus on the system properties such as incremental deployability and robustness that are critical for ubiquitous computing environments. We describe the goals and architecture of ICrafter, a prototype implementation that validates its design, and the key lessons learnt from our

Question: What is the difference between a service framework and a service framework for ubiquitous computing environments?


Justice at the millennium: a meta-analytic review of 25 years of organizational justice research.. The field of organizational justice continues to be marked by several important research questions, including the size of relationships among justice dimensions, the relative importance of different justice criteria, and the unique effects of justice dimensions on key outcomes. To address such questions, the authors conducted a meta-analytic review of 183 justice studies. The results suggest that although different justice dimensions are moderately to highly related, they contribute incremental variance explained in fairness perceptions. The results also illustrate the overall and unique relationships among distributive, procedural, interpersonal, and informational justice and several organizational outcomes (e.g., job satisfaction, organizational commitment, evaluation of authority, organizational citizenship behavior, withdrawal, performance). These findings are reviewed in terms of their implications for future research on organizational justice.

Question: What is the size of the relationships among justice dimensions?


Spark: A Big Data Processing Platform Based on Memory Computing. Spark is a memory-based computing framework which has a better ability of computing and fault tolerance, supports batch, interactive, iterative and flow calculations. In this paper, we analyze the Spark's primary framework, core technologies, and point out the advantages and disadvantages of the Spark. In the end, we make a discussion for the future trends of the Spark technologies.

Question: What is the Spark?


metaSEM: an R package for meta-analysis using structural equation modeling. The metaSEM package provides functions to conduct univariate, multivariate, and three-level meta-analyses using a structural equation modeling (SEM) approach via the OpenMx package in the R statistical platform. It also implements the two-stage SEM approach to conducting fixed- and random-effects meta-analytic SEM on correlation or covariance matrices. This paper briefly outlines the theories and their implementations. It provides a summary on how meta-analyses can be formulated as structural equation models. The paper closes with a conclusion on several relevant topics to this SEM-based meta-analysis. Several examples are used to illustrate the procedures in the supplementary material.

Question: How to conduct a meta-analysis using structural equation modeling?


Control methodologies in networked control systems. The use of a data network in a control loop has gained increasing attentions in recent years due to its cost effective and flexible applications. One of the major challenges in this so-called networked control system (NCS) is the network-induced delay effect in the control loop. Network delays degrade the NCS control performance and destabilize the system. A significant emphasis has been on developing control methodologies to handle the network delay effect in NCS. This survey paper presents recent NCS control methodologies. The overview on NCS structures and description of network delays including characteristics and effects are also covered. r 2003 Published by Elsevier Ltd.

Question: What is a networked control system?


Python for Scientific Computing. Python is an excellent "steering" language for scientific codes written in other languages. However, with additional basic tools, Python transforms into a high-level language suited for scientific and engineering code that's often fast enough to be immediately useful but also flexible enough to be sped up with additional extensions.

Question: What is a steering language?


The cultural construction of self-enhancement: an examination of group-serving biases.. Self-serving biases, found routinely in Western samples, have not been observed in Asian samples. Yet given the orientation toward individualism and collectivism in these 2 cultures, respectively, it is imperative to examine whether parallel differences emerge when the target of evaluation is the group. It may be that Asians show a group-serving bias parallel to the Western self-serving bias. In 2 studies, group-serving biases were compared across European Canadian, Asian Canadian, and Japanese students. Study 1 revealed that Japanese students evaluated a family member less positively than did both groups of Canadian students. Study 2 replicated this pattern with students' evaluations of their universities. The data suggest that cultural differences in enhancement biases are robust, generalizing to individuals' evaluations of their groups.

Question: What is the cultural construction of self-enhancement?


Modeling and control active suspension system for a full car model. The purpose of this paper is to investigate the performance of a full car model active suspension system using LQR controller. Dynamic model used in this study is a linear model. A linear model can capture basic performances of vehicle suspension such as body displacement, body acceleration, wheel displacement, wheel deflection, suspension travels, pitch and yawn. Performance of suspension system is determined by the ride comfort and vehicle handling. It can be measured by car body displacement and wheel displacement performance. Two types of road profiles are used as input for the system. Simulation is based on the mathematical model by using MATLAB/SIMULINK software. Results show that the performance of body displacement and wheel displacement can be improved by using Linear Quadratic Regulator control (LQR).

Question: What is the purpose of this paper?


Reverse Engineering of the Interaction Diagrams from C++ Code. In object oriented programming, the functionalities of a system result from the interactions (message exchanges) among the objects allocated by the system. While designing object interactions is far more complex than designing the object structure in forward engineering, the problem of understanding object interactions during code evolution is even harder, because the related information is spread across the code. In this paper, a technique for the automatic extraction of UML interaction diagrams from C++ code is proposed. The algorithm is based on a static, conservative flow analysis, that approximates the behavior of the system in any execution and for any possible input. Applicability of the approach to large software is achieved by means of two mechanisms: partial analysis and focusing. Usage of our method on a real world, large C++ system confirmed its viability.

Question: How to extract interaction diagrams from c++ code?


Real-time object detection and localization with SIFT-based clustering. a r t i c l e i n f o Keywords: Pick-and-place applications Machine vision for industrial applications SIFT This paper presents an innovative approach for detecting and localizing duplicate objects in pick-and-place applications under extreme conditions of occlusion, where standard appearance-based approaches are likely to be ineffective. The approach exploits SIFT keypoint extraction and mean shift clustering to partition the correspondences between the object model and the image onto different potential object instances with real-time performance. Then, the hypotheses of the object shape are validated by a projection with a fast Euclidean transform of some delimiting points onto the current image. Moreover, in order to improve the detection in the case of reflective or transparent objects, multiple object models (of both the same and different faces of the object) are used and fused together. Many measures of efficacy and efficiency are provided on random disposals of heavily-occluded objects, with a specific focus on real-time processing. Experimental results on different and challenging kinds of objects are reported. Information technologies have become in the last decades a fundamental aid for helping the automation of everyday people life and industrial processes. Among the many different disciplines contributing to this process, machine vision and pattern recognition have been widely used for industrial applications and especially for robot vision. A typical need is to automate the pick-and-place process of picking up objects, possibly performing some tasks, and then placing them down on a different location. Most of the pick-and-place systems are basically composed of robotic systems and sensors. The sensors are in charge of driving the robot arms to the right 3D location and possibly orientation of the next object to be picked up, according to the robot's degrees of freedom. Object pick

Question: What is the difference between pick-and-place and pick-and-place?


Synonym set extraction from the biomedical literature by lexical pattern discovery. Although there are a large number of thesauri for the biomedical domain many of them lack coverage in terms and their variant forms. Automatic thesaurus construction based on patterns was first suggested by Hearst [1], but it is still not clear how to automatically construct such patterns for different semantic relations and domains. In particular it is not certain which patterns are useful for capturing synonymy. The assumption of extant resources such as parsers is also a limiting factor for many languages, so it is desirable to find patterns that do not use syntactical analysis. Finally to give a more consistent and applicable result it is desirable to use these patterns to form synonym sets in a sound way. We present a method that automatically generates regular expression patterns by expanding seed patterns in a heuristic search and then develops a feature vector based on the occurrence of term pairs in each developed pattern. This allows for a binary classifications of term pairs as synonymous or non-synonymous. We then model this result as a probability graph to find synonym sets, which is equivalent to the well-studied problem of finding an optimal set cover. We achieved 73.2% precision and 29.7% recall by our method, out-performing hand-made resources such as MeSH and Wikipedia. We conclude that automatic methods can play a practical role in developing new thesauri or expanding on existing ones, and this can be done with only a small amount of training data and no need for resources such as parsers. We also concluded that the accuracy can be improved by grouping into synonym sets.

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


Aggregate G-Buffer Anti-Aliasing -Extended Version-. We present Aggregate G-Buffer Anti-Aliasing (AGAA), a new technique for efficient anti-aliased deferred rendering of complex geometry using modern graphics hardware. In geometrically complex situations where many surfaces intersect a pixel, current rendering systems shade each contributing surface <italic>at least</italic> once per pixel. As the sample density and geometric complexity increase, the shading cost becomes prohibitive for real-time rendering. Under deferred shading, so does the required framebuffer memory. Our goal is to make high per-pixel sampling rates practical for real-time applications by substantially reducing shading costs and per-pixel storage compared to traditional deferred shading. AGAA uses the rasterization pipeline to generate a compact, pre-filtered geometric representation inside each pixel. We shade this representation at a fixed rate, independent of geometric complexity. By decoupling shading rate from geometric sampling rate, the algorithm reduces the storage and bandwidth costs of a geometry buffer, and allows scaling to high visibility sampling rates for anti-aliasing. AGAA with two aggregates per-pixel generates results comparable to 32<inline-formula><tex-math notation="LaTeX">$\times$</tex-math><alternatives> <inline-graphic xlink:type="simple" xlink:href="crassin-ieq1-2586073.gif"/></alternatives></inline-formula> MSAA, but requires 54 percent less memory and is up to 2.6<inline-formula><tex-math notation="LaTeX">$\times$</tex-math><alternatives> <inline-graphic xlink:type="simple" xlink:href="crassin-ieq2-2586073.gif"/></alternatives></inline-formula> faster (<inline-formula> <tex-math notation="LaTeX">$-30$</tex-math><alternatives> <inline-graphic xlink:type="simple" xlink:href="crassin-ieq3-2586073.gif"/></alternatives></inline-formula> percent memory and 1.7 <inline-formula><tex-math notation="LaTeX">$\times$</tex-math><alternatives> <inline-graphic xlink:type="simple" xl

Question: How many times does the rasterization pipeline need to be called per pixel?


Space/Time Trade-offs in Hash Coding with Allowable Errors. In this paper trade-offs among certain computational factors in hash coding are analyzed. The paradigm problem considered is that of testing a series of messages one-by-one for membership in a given set of messages. Two new hash-coding methods are examined and compared with a particular conventional hash-coding method. The computational factors considered are the size of the hash area (space), the time required to identify a message as a nonmember of the given set (reject time), and an allowable error frequency. The new methods are intended to reduce the amount of space required to contain the hash-coded information from that associated with conventional methods. The reduction in space is accomplished by exploiting the possibility that a small fraction of errors of commission may be tolerable in some applications, in particular, applications in which a large amount of data is involved and a core resident hash area is consequently not feasible using conventional methods. In such applications, it is envisaged that overall performance could be improved by using a smaller core resident hash area in conjunction with the new methods and, when necessary, by using some secondary and perhaps time-consuming test to "catch" the small fraction of errors associated with the new methods. An example is discussed which illustrates possible areas of application for the new methods. Analysis of the paradigm problem demonstrates that allowing a small number of test messages to be falsely identified as members of the given set will permit a much smaller hash area to be used without increasing reject time.

Question: What is the size of the hash area?


Usability Dimensions for Mobile Applications-A Review. Usability has been increasingly recognized as a significant quality dimension to determine the success of mobile applications. Due to its importance, a number of usability guidelines have been proposed to direct the design of usable applications. The guidelines are intended particularly for desktop and web-based applications. Mobile applications on the other hand are different in many ways from those applications due to the mobility nature of mobile devices. To date, the usability guidelines for mobile applications are very limited. They in fact are isolated, which makes usability evaluation for mobile devices more difficult. This study aims to address this issue by proposing a set of usability dimensions that should be considered for designing and evaluating mobile applications. The dimensions are illustrated as a model that considers four contextual factors: user, environment, technology and task/activity. The model was proposed based on the reviews of previous related studies, which were analyzed by using content analysis approach. Twenty-five dimensions were found from the analysis. The dimensions however were synthesized and prioritized based on their importance towards designing usable mobile applications. As a result, ten most important dimensions were outlined in the model. The model can be used by practitioners and researchers as a guideline to design usable mobile applications and further research can be conducted in the near future.

Question: What is the usability of mobile applications?


ImageNet Classification with Deep Convolutional Neural Networks. We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively, which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overfitting in the fully connected layers we employed a recently developed regularization method called "dropout" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry.

Question: What is the top-5 error rate of the model?


A Framework for Cloud Forensic Readiness in Organizations. Many have argued that cloud computing is one of the fastest growing and most transformative technologies in the history of computing. It has radically changed the way in which information technologies can manage, access, deliver and create services. It has also brought numerous benefits to end-users and organizations. However, this rapid growth in cloud computing adoption has also seen it become a new arena for cybercrime. This has, in turn, led to new technical, legal and organizational challenges. In addition to the large number of attacks which affect cloud computing and the decentralized nature of data processing in the cloud, many concerns have been raised. One of these concerns is how to conduct a proper digital investigation in cloud environments and be ready to collect data proactively before an incident occurs in order to save time, money and effort. This paper proposes the technical, legal and organizational factors that influence digital forensic readiness for Infrastructure as a Service consumers.

Question: What is the difference between cloud computing and cloud forensic readiness?


Swish: a Self-Gated Activation Function. The choice of activation functions in deep networks has a significant effect on the training dynamics and task performance. Currently, the most successful and widely-used activation function is the Rectified Linear Unit (ReLU). Although various alternatives to ReLU have been proposed, none have managed to replace it due to inconsistent gains. In this work, we propose a new activation function, named Swish, which is simply f(x) = x · sigmoid(x). Our experiments show that Swish tends to work better than ReLU on deeper models across a number of challenging datasets. For example, simply replacing ReLUs with Swish units improves top-1 classification accuracy on ImageNet by 0.9% for Mobile NASNetA and 0.6% for Inception-ResNet-v2. The simplicity of Swish and its similarity to ReLU make it easy for practitioners to replace ReLUs with Swish units in any neural network.

Question: What is a self-gated activation function?


A Job Scheduling Design for Visualization Services Using GPU Clusters. Modern large-scale heterogeneous computers incorporating GPUs offer impressive processing capabilities. It is desirable to fully utilize such systems for serving multiple users concurrently to visualize large data at interactive rates. However, as the disparity between data transfer speed and compute speed continues to increase in heterogeneous systems, data locality becomes crucial for performance. We present a new job scheduling design to support multi-user exploration of large data in a heterogeneous computing environment, achieving near optimal data locality and minimizing I/O overhead. The targeted application is a parallel visualization system which allows multiple users to render large volumetric data sets in both interactive mode and batch mode. We present a cost model to assess the performance of parallel volume rendering and quantify the efficiency of job scheduling. We have tested our job scheduling scheme on two heterogeneous systems with different configurations. The largest test volume data used in our study has over two billion grid points. The timing results demonstrate that our design effectively improves data locality for complex multi-user job scheduling problems, leading to better overall performance of the service.

Question: How to design a job scheduling design for visualization services using GPU clusters?


Physical Discipline and Behavior Problems in African American , European American , and Hispanic Children : Emotional Support as a Moderator. Using data collected over a 6-year period on a sample of 1,039 European American children, 550 African American children, and 401 Hispanic children from the children of the National Longitudinal Survey of Youth, this study assessed whether maternal emotional support of the child moderates the relation between spanking and behavior problems. Children were 4–5 years of age in the first of 4 waves of data used (1988, 1990, 1992, 1994). At each wave, mothers reported their use of spanking and rated their children's behavior problems. Maternal emotional support of the child was based on interviewer observations conducted as part of the Home Observation for Measurement of the Environment. For each of the 3 racial-ethnic groups, spanking predicted an increase in the level of problem behavior over time, controlling for income-needs ratio and maternal emotional support. Maternal emotional support moderated the link between spanking and problem behavior. Spanking was associated with an increase in behavior problems over time in the context of low levels of emotional support, but not in the context of high levels of emotional support. This pattern held for all 3 racial-ethnic groups.

Question: What is the difference between spanking and physical discipline?


Quantity versus quality: A new approach to examine the relationship between technology use and student outcomes. The author argues that to examine the relationship between technology use and student outcomes, the quality of technology use—how, and what, technology is used—is a more significant factor than the quantity of technology use—how much technology is used. This argument was exemplified by an empirical study that used both angles to examine the association between technology use and student outcomes. When only the quantity of technology use was examined, no significant association was observed. However, when the quality of technology was examined by investigating the specific types of technology uses, a significant association was identified between technology use and all student outcomes. Furthermore, different types of technology use showed different influences on specific student outcomes. General technology uses were positively associated with student technology proficiency, while subject-specific technology uses were negatively associated with student technology proficiency. Social-communication technology uses were significantly positively associated with developmental outcomes such as self-esteem and positive attitude towards school. Entertainment/exploration technology use showed significant positive association with student learning habits. None of these technology uses had significant influence on student academic outcome. Specific suggestions for integrating technology into schools and future research were provided. Introduction In the last two decades, generous investments have been made in educational technology around the world. For example, the US had invested more than $66 billion in school technology in just 10 years (Quality Education Data, 2004). By 2004, China had spent 100 billion Yuan (about $13.2 billion) on educational technology (Zhao, 2005), and British Journal of Educational Technology Vol 41 No 3 2010 455–472 doi:10.1111/j.1467-853

Question: What is the relationship between technology use and student outcomes?


Static Scheduling of Synchronous Data Flow Programs for Digital Signal Processing. Large grain data flow (LGDF) programming is natural and convenient for describing digital signal processing (DSP) systems, but its runtime overhead is costly in real time or cost-sensitive applications. In some situations, designers are not willing to squander computing resources for the sake of programmer convenience. This is particularly true when the target machine is a programmable DSP chip. However, the runtime overhead inherent in most LGDF implementations is not required for most signal processing systems because such systems are mostly synchronous (in the DSP sense). Synchronous data flow (SDF) differs from traditional data flow in that the amount of data produced and consumed by a data flow node is specified a priori for each input and output. This is equivalent to specifying the relative sample rates in signal processing system. This means that the scheduling of SDF nodes need not be done at runtime, but can be done at compile time (statically), so the runtime overhead evaporates. The sample rates can all be different, which is not true of most current data-driven digital signal processing programming methodologies. Synchronous data flow is closely related to computation graphs, a special case of Petri nets. This self-contained paper develops the theory necessary to statically schedule SDF programs on single or multiple processors. A class of static (compile time) scheduling algorithms is proven valid, and specific algorithms are given for scheduling SDF systems onto single or multiple processors.

Question: What is the difference between a data flow and a computation graph?