Saliency-based Sequential Image Attention with Multiset Prediction. Humans process visual scenes selectively and sequentially using attention. Central to models of human visual attention is the saliency map. We propose a hierarchical visual architecture that operates on a saliency map and uses a novel attention mechanism to sequentially focus on salient regions and take additional glimpses within those regions. The architecture is motivated by human visual attention, and is used for multi-label image classification on a novel multiset task, demonstrating that it achieves high precision and recall while localizing objects with its attention. Unlike conventional multi-label image classification models, the model supports multiset prediction due to a reinforcement-learning based training process that allows for arbitrary label permutation and multiple instances per label.

Question: What is saliency-based sequential image attention with multiset prediction?


Cubrick : A Scalable Distributed MOLAP Database for Fast Analytics. This paper describes the architecture and design of Cubrick, a distributed multidimensional in-memory database that enables real-time data analysis of large dynamic datasets. Cubrick has a strictly multidimensional data model composed of dimensions, dimensional hierarchies and metrics, supporting sub-second MOLAP operations such as slice and dice, roll-up and drill-down over terabytes of data. All data stored in Cubrick is chunked in every dimension and stored within containers called bricks in an unordered and sparse fashion, providing high data ingestion ratios and indexed access through every dimension. In this paper, we describe details about Cubrick's internal data structures, distributed model, query execution engine and a few details about the current implementation. Finally, we present some experimental results found in a first Cubrick deployment inside Facebook.

Question: What is the architecture of Cubrick?


Mining asynchronous periodic patterns in time series data. Periodicy detection in time series data is a challenging pro blem of great importance in many applications. Most previous work focused on mining synchronous p eriodic patterns and did not recognize misaligned presence of a pattern due to the intervention of r andom noise. In this paper, we propose a more flexible model of asynchronous periodic pattern that ma y be present only within a subsequence and whose occurrences may be shifted due to disturbance. Two parametersmin rep andmax dis are employed to specify the minimum number of repetitions th at is required within each segment of non-disrupted pattern occurrences and the maximum allowed disturbance between any two successive valid segments. Upon satisfying these two requirements, th e longest valid subsequence of a pattern is returned. A two phase algorithm is devised to first generate p o ntial periods by distance-based pruning followed by an iterative procedure to derive and validat e candidate patterns and locate the longest valid subsequence. We also show that this algorithm can not o nly provide linear time complexity with respect to the length of the sequence but also achieve space e ffici ncy.

Question: How to detect asynchronous periodic patterns in time series data?


Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes.. Importance A deep learning system (DLS) is a machine learning technology with potential for screening diabetic retinopathy and related eye diseases.   Objective To evaluate the performance of a DLS in detecting referable diabetic retinopathy, vision-threatening diabetic retinopathy, possible glaucoma, and age-related macular degeneration (AMD) in community and clinic-based multiethnic populations with diabetes.   Design, Setting, and Participants Diagnostic performance of a DLS for diabetic retinopathy and related eye diseases was evaluated using 494 661 retinal images. A DLS was trained for detecting diabetic retinopathy (using 76 370 images), possible glaucoma (125 189 images), and AMD (72 610 images), and performance of DLS was evaluated for detecting diabetic retinopathy (using 112 648 images), possible glaucoma (71 896 images), and AMD (35 948 images). Training of the DLS was completed in May 2016, and validation of the DLS was completed in May 2017 for detection of referable diabetic retinopathy (moderate nonproliferative diabetic retinopathy or worse) and vision-threatening diabetic retinopathy (severe nonproliferative diabetic retinopathy or worse) using a primary validation data set in the Singapore National Diabetic Retinopathy Screening Program and 10 multiethnic cohorts with diabetes.   Exposures Use of a deep learning system.   Main Outcomes and Measures Area under the receiver operating characteristic curve (AUC) and sensitivity and specificity of the DLS with professional graders (retinal specialists, general ophthalmologists, trained graders, or optometrists) as the reference standard.   Results In the primary validation dataset (n = 14 880 patients; 71 896 images; mean [SD] age, 60.2 [2.2] years; 54.6% men), the prevalence of referable diabetic retinopathy was 3.0%; vision-threatenin

Question: What is the prevalence of diabetic retinopathy in Singapore?


SPIKING NEURAL NETWORKS SPIKING NEURAL NETWORKS. Background: Infectious complications are the major cause of death in acute pancreatitis. Small bowel bacterial overgrowth and subsequent bacterial translocation are held responsible for the vast majority of these infections. Goal of this study is to determine whether selected probiotics are capable of preventing infectious complications without the disadvantages of antibiotic prophylaxis; antibiotic resistance and fungal overgrowth. Methods/design: PROPATRIA is a double-blind, placebo-controlled randomised multicenter trial in which 200 patients will be randomly allocated to a multispecies probiotic preparation (Ecologic 641) or placebo. The study is performed in all 8 Dutch University Hospitals and 7 non-University hospitals. The study-product is administered twice daily through a nasojejunal tube for 28 days or until discharge. Patients eligible for randomisation are adult patients with a first onset of predicted severe acute pancreatitis: Imrie criteria 3 or more, CRP 150 mg/L or more, APACHE II score 8 or more. Exclusion criteria are post-ERCP pancreatitis, malignancy, infection/sepsis caused by a second disease, intra-operative diagnosis of pancreatitis and use of probiotics during the study. Administration of the study product is started within 72 hours after onset of abdominal pain. The primary endpoint is the total number of infectious complications. Secondary endpoints are mortality, necrosectomy, antibiotic resistance, hospital stay and adverse events. To demonstrate that probiotic prophylaxis reduces the proportion of patients with infectious complications from 50% to 30%, with alpha 0,05 and power 80%, a total sample size of 200 patients was calculated. Conclusion: The PROPATRIA study is aimed to show a reduction in infectious complications due to early enteral use of multispecies probiotics in severe acute pancreatitis. Published: 29 September 2004 BMC Surgery 2004, 4:12 doi:10.1186/1471-2482-4-12 Receive

Question: What is the difference between a probiotic and a prebiotic?


Point-of-Interest Recommendation Using Heterogeneous Link Prediction. Venue recommendation in location-based social networks is among the more important tasks that enhances user participation on the social network. Despite its importance, earlier research have shown that the accurate recommendation of appropriate venues for users is a difficult task specially given the highly sparse nature of user check-in information. In this paper, we show how a comprehensive set of user and venue related information can be methodically incorporated into a heterogeneous graph representation based on which the problem of venue recommendation can be efficiently formulated as an instance of the heterogeneous link prediction problem on the graph. We systematically compare our proposed approach with several strong baselines and show that our work, which is computationally less-intensive compared to the baselines, is able to shows improved performance in terms of precision and f-measure.

Question: What is the name of the venue recommendation algorithm?


Automatic Acquisition of Linguistic Patterns for Conceptual Modeling. Conceptual modeling aims to capture the knowledge of a domain as perceived by the modeler and represent it in a way that will help further system analysis and design. Object-oriented methodology has been taken as a common practice for software system analysis and design because of its obvious advantages of handling the "software crisis" (Booch, 1994). Object-oriented analysis (OOA) is "a method of analysis that examines requirements from the perspectives of the classes and objects" (Booch, 1994). Object-oriented design (OOD) is a method of design "encompassing the process of OO decomposition and a notation for depicting both logical and physical as well as static and dynamic models of the system" (Booch, 1994).

Question: What is the difference between OOA and OOD?


SemEval-2016 Task 5: Aspect Based Sentiment Analysis. This paper describes the SemEval 2016 shared task on Aspect Based Sentiment Analysis (ABSA), a continuation of the respective tasks of 2014 and 2015. In its third year, the task provided 19 training and 20 testing datasets for 8 languages and 7 domains, as well as a common evaluation procedure. From these datasets, 25 were for sentence-level and 14 for text-level ABSA; the latter was introduced for the first time as a subtask in SemEval. The task attracted 245 submissions from 29 teams.

Question: What is the task of the semeval 2016?


Context-assisted face clustering framework with human-in-the-loop. Automatic face clustering, which aims to group faces referring to the same people together, is a key component for face tagging and image management. Standard face clustering approaches that are based on analyzing facial features can already achieve high-precision results. However, they often suffer from low recall due to the large variation of faces in pose, expression, illumination, occlusion, etc. To improve the clustering recall without reducing the high precision, we leverage the heterogeneous context information to iteratively merge the clusters referring to same entities. We first investigate the appropriate methods to utilize the context information at the cluster level, including using of "common scene", people co-occurrence, human attributes, and clothing. We then propose a unified framework that employs bootstrapping to automatically learn adaptive rules to integrate this heterogeneous contextual information, along with facial features, together. Finally, we discuss a novel methodology for integrating human-in-the-loop feedback mechanisms that leverage human interaction to achieve the high-quality clustering results. Experimental results on two personal photo collections and one real-world surveillance dataset demonstrate the effectiveness of the proposed approach in improving recall while maintaining very high precision of face clustering.

Question: What is the context-assisted face clustering framework with human-in-the-loop?


Designing and implementing an integrated technological pedagogical science knowledge framework for science teachers professional development. This paper reports on the design and the implementation of the Technological Pedagogical Science Knowledge (TPASK), a new model for science teachers professional development built on an integrated framework determined by the Technological Pedagogical Content Knowledge (TPACK) model and the authentic learning approach. The TPASK curriculum dimensions and the related course sessions are also elaborated and applied in the context of a teacher trainers' preparation program aiming at ICT integration in science classroom practice. A brief description of the project, its accomplishments, and perceptions of the participants, through the lens of TPASK professional development model, are presented. This is followed by the presentation of the evaluation results on the impact of the program which demonstrates that science teachers reported meaningful TPASK knowledge and increased willingness to adopt and apply this framework in their instruction. Finally, we draw on the need to expand TPACK by incorporating a fourth dimension, the Educational Context within Pedagogy, Content and Technology mutually interact, in order to address future policy models concerning teacher preparation to integrate ICT in education. 2010 Elsevier Ltd. All rights reserved.

Question: How to design a new model for science teachers professional development?


Social Interactions in Online Gaming. This paper briefly overviews five studies examining massively multiplayer online role-playing games (MMORPGs). The first study surveyed 540 gamers and showed that the social aspects of the game were the most important factor for many gamers. The second study explored the social interactions of 912 MMORPG players and showed they created strong friendships and emotional relationships. A third study examined the effect of online socializing in the lives of 119 online gamers. Significantly more male gamers than female gamers said that they found it easier to converse online than offline, and 57% of gamers had engaged in gender swapping. A fourth study surveyed 7,069 gamers and found that 12% of gamers fulfilled at least three diagnostic criteria of addiction. Finally, an interview study of 71 gamers explored attitudes, experiences, and feelings about online gaming. They provided detailed descriptions of personal problems that had arisen due to playing MMORPGs. an individualistic character (Griffiths, Davies, & Chappell, 2004). This is the only setting where millions of users voluntarily immerse themselves in a graphical virtual environment and interact with each other through avatars on a daily basis (Yee, 2007). Research suggests that the game play within these virtual worlds is enhanced because players use them as traditional games as well as arenas in which to explore DOI: 10.4018/ijgbl.2011100103 International Journal of Game-Based Learning, 1(4), 20-36, October-December 2011 21 Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. new relationships, new places and themselves (Krotoski, 2004). Despite the massive amounts of money spent on online gaming, very little research has been carried out regarding the positive social aspects of these games. Much of the debate over the last 30 years has focused on the dangers of computer gaming in the adol

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


The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. , doi: 10.1098/rspa.1998.0193 454 1998 Proc. R. Soc. Lond. A Yen, Chi Chao Tung and Henry H. Liu Norden E. Huang, Zheng Shen, Steven R. Long, Manli C. Wu, Hsing H. Shih, Quanan Zheng, Nai-Chyuan nonlinear and non-stationary time series analysis The empirical mode decomposition and the Hilbert spectrum for References http://rspa.royalsocietypublishing.org/content/454/1971/903#related-urls Article cited in: Email alerting service here corner of the article or click Receive free email alerts when new articles cite this article sign up in the box at the top right-hand

Question: What is the empirical mode decomposition?


The Dreaming Variational Autoencoder for Reinforcement Learning Environments. Reinforcement learning has shown great potential in generalizing over raw sensory data using only a single neural network for value optimization. There are several challenges in the current state-of-the-art reinforcement learning algorithms that prevent them from converging towards the global optima. It is likely that the solution to these problems lies in shortand long-term planning, exploration and memory management for reinforcement learning algorithms. Games are often used to benchmark reinforcement learning algorithms as they provide a flexible, reproducible, and easy to control environment. Regardless, few games feature a state-space where results in exploration, memory, and planning are easily perceived. This paper presents The Dreaming Variational Autoencoder (DVAE), a neural network based generative modeling architecture for exploration in environments with sparse feedback. We further present Deep Maze, a novel and flexible maze engine that challenges DVAE in partial and fully-observable state-spaces, long-horizon tasks, and deterministic and stochastic problems. We show initial findings and encourage further work in reinforcement learning driven by generative exploration.

Question: What is reinforcement learning?


Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations. A key challenge for neural modeling is to explain how a continuous stream of multimodal input from a rapidly changing environment can be processed by stereotypical recurrent circuits of integrate-and-fire neurons in real time. We propose a new computational model for real-time computing on time-varying input that provides an alternative to paradigms based on Turing machines or attractor neural networks. It does not require a task-dependent construction of neural circuits. Instead, it is based on principles of high-dimensional dynamical systems in combination with statistical learning theory and can be implemented on generic evolved or found recurrent circuitry. It is shown that the inherent transient dynamics of the high-dimensional dynamical system formed by a sufficiently large and heterogeneous neural circuit may serve as universal analog fading memory. Readout neurons can learn to extract in real time from the current state of such recurrent neural circuit information about current and past inputs that may be needed for diverse tasks. Stable internal states are not required for giving a stable output, since transient internal states can be transformed by readout neurons into stable target outputs due to the high dimensionality of the dynamical system. Our approach is based on a rigorous computational model, the liquid state machine, that, unlike Turing machines, does not require sequential transitions between well-defined discrete internal states. It is supported, as the Turing machine is, by rigorous mathematical results that predict universal computational power under idealized conditions, but for the biologically more realistic scenario of real-time processing of time-varying inputs. Our approach provides new perspectives for the interpretation of neural coding, the design of experiments and data analysis in neurophysiology, and the solution of problems i

Question: What is the difference between a Turing machine and a liquid state machine?


Sequential Self-Folding Structures by 3D Printed Digital Shape Memory Polymers. Folding is ubiquitous in nature with examples ranging from the formation of cellular components to winged insects. It finds technological applications including packaging of solar cells and space structures, deployable biomedical devices, and self-assembling robots and airbags. Here we demonstrate sequential self-folding structures realized by thermal activation of spatially-variable patterns that are 3D printed with digital shape memory polymers, which are digital materials with different shape memory behaviors. The time-dependent behavior of each polymer allows the temporal sequencing of activation when the structure is subjected to a uniform temperature. This is demonstrated via a series of 3D printed structures that respond rapidly to a thermal stimulus, and self-fold to specified shapes in controlled shape changing sequences. Measurements of the spatial and temporal nature of self-folding structures are in good agreement with the companion finite element simulations. A simplified reduced-order model is also developed to rapidly and accurately describe the self-folding physics. An important aspect of self-folding is the management of self-collisions, where different portions of the folding structure contact and then block further folding. A metric is developed to predict collisions and is used together with the reduced-order model to design self-folding structures that lock themselves into stable desired configurations.

Question: What is a self-folding structure?


Video parsing for abnormality detection. Detecting abnormalities in video is a challenging problem since the class of all irregular objects and behaviors is infinite and thus no (or by far not enough) abnormal training samples are available. Consequently, a standard setting is to find abnormalities without actually knowing what they are because we have not been shown abnormal examples during training. However, although the training data does not define what an abnormality looks like, the main paradigm in this field is to directly search for individual abnormal local patches or image regions independent of another. To address this problem we parse video frames by establishing a set of hypotheses that jointly explain all the foreground while, at same time, trying to find normal training samples that explain the hypotheses. Consequently, we can avoid a direct detection of abnormalities. They are discovered indirectly as those hypotheses which are needed for covering the foreground without finding an explanation by normal samples for themselves. We present a probabilistic model that localizes abnormalities using statistical inference. On the challenging dataset of [15] it outperforms the state-of-the-art by 7% to achieve a frame-based abnormality classification performance of 91% and the localization performance improves by 32% to 76%.

Question: How to detect abnormalities in video?


An evolutionary artificial neural networks approach for breast cancer diagnosis. This paper presents an evolutionary artificial neural network (EANN) approach based on the pareto-differential evolution (PDE) algorithm augmented with local search for the prediction of breast cancer. The approach is named memetic pareto artificial neural network (MPANN). Artificial neural networks (ANNs) could be used to improve the work of medical practitioners in the diagnosis of breast cancer. Their abilities to approximate nonlinear functions and capture complex relationships in the data are instrumental abilities which could support the medical domain. We compare our results against an evolutionary programming approach and standard backpropagation (BP), and we show experimentally that MPANN has better generalization and much lower computational cost.

Question: What is the difference between a neural network and an artificial neural network?


Computer security technology planning study. Approved for pubJic reJease; distribution unri mited. When U.S. Government drawings, specifications or other data are used for any purpose other than a definitely related government procurement operation, the government thereby incurs no responsibility nor any obligation whatsoever; and the fact that the government may have formulated, fui·nished, or in any way sup plied the said drawings, specifications, or other data is not to be regarded by implication or otherwise as in any manner licensing the holder or any other person or conveying any rights or permission to manufacture, use, or sell any patented invention that may in any way be related thereto.

Question: What is the difference between a patent and a copyright?


Social Media and Firm Equity Value. Companies have increasingly advocated social media technologies and platforms to improve and ultimately transform business performance. This study examines whether social media has a predictive relationship with firm equity value, which metric of social media has the strongest relationship, and the dynamics of the relationship. The results derived from the vector autoregressive models suggest that social media-based metrics (blogs and reviews) are significant leading indicators of firm stock market performance. We also find a faster predictive value of social media, i.e., a shorter "wear-in" effect compared with conventional online behavior metrics, such as web traffic and search. Interestingly, conventional digital user metrics (Google search and web traffic), which have been widely adopted to measure online consumer behavior, are found to have significant yet substantially weaker predictive relationships with firm equity value than social media. The results provide new insights for social media managers and firm equity valuations.

Question: What is the relationship between social media and firm equity value?


Embodied Construction Grammar. Construction-based approaches to grammar are driven by the insight that meaning plays a crucial role in answering this question: the meanings of both words and grammatical structures can affect grammaticality, and they should therefore be incorporated into grammatical representations. To wit, the various approaches presented in this volume take this tack in predicting the grammaticality of utterances of all sorts, from the creative to the conventional. But focusing on utterance grammaticality removes utterances from their physical and social contexts of use. The result is an approach to language that resembles traditional anatomy, in which utterances are dissected, categorized, and explained in terms of their structural and distributional properties. Insights from such an approach can be both useful and informative in accounting for what utterances are or potentially could be. They do not, however, explain human language use—that is, the mechanisms and representations that human beings use to learn, produce, and understand utterances. If we are interested in these latter issues, issues more properly situated within cognitive science than analytical linguistics, then we find our investigations of grammar (and language more generally) guided by a different sort of question:

Question: What is the difference between construction grammar and embodied construction grammar?


LAYER RECURRENT NEURAL NETWORKS. In this paper, we propose a Layer-RNN (L-RNN) module that is able to learn contextual information adaptively using within-layer recurrence. Our contributions are three-fold: (i) we propose a hybrid neural network architecture that interleaves traditional convolutional layers with L-RNN module for learning longrange dependencies at multiple levels; (ii) we show that a L-RNN module can be seamlessly inserted into any convolutional layer of a pre-trained CNN, and the entire network then fine-tuned, leading to a boost in performance; (iii) we report experiments on the CIFAR-10 classification task, showing that a network with interleaved convolutional layers and L-RNN modules, achieves comparable results (5.39% top1 error) using only 15 layers and fewer parameters to ResNet-164 (5.46%); and on the PASCAL VOC2012 semantic segmentation task, we show that the performance of a pre-trained FCN network can be boosted by 5% (mean IOU) by simply inserting Layer-RNNs.

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


Netradar - Measuring the wireless world. There exists a number of network measurement tools and web-based services for end users. Almost all of them focus on network bandwidth and possibly latency, and simply report the results to the user. This paper presents Netradar, a somewhat different measurement service. We analyze network bandwidth and latency but also over ten other parameters. We dwell into the data and show various coverage maps and statistics on our web site for the benefit of the end users. Our long term goal is to be able to answer the simple question: why did I get the indicated result at a given time and place?

Question: What is the difference between a network measurement tool and a network monitoring tool?


Ways to React: Comparing Reactive Languages and Complex Event Processing. Reactive applications demand for detecting the changes that occur in a domain of interest and for timely reactions. Examples range from simple interactive applications to complex monitoring tasks involving distributed and heterogeneous systems. Over the last years, different programming paradigms and solutions have been proposed to support such applications. In this paper, we focus on two prominent approaches: event-based programming, specifically Complex Event Processing (CEP), and Reactive Languages (RLs). CEP systems enable the definition of high level situations of interest from low level primitive events detected in the external environment. On the other hand, RLs support time-changing values and their composition as dedicated language abstractions. These research fields have been investigated by different communities, belonging respectively to the database and the distributed systems areas and to the programming language area. It is our belief that a deeper understanding of these research fields, including their benefits and limitations, their similarities and differences, could drive further developments in supporting reactive applications. For this reason, we propose a first comparison of the two fields. Despite huge differences, we believe that such a comparison can trigger an interesting discussion across the communities, favor knowledge sharing, and let new ideas emerge.

Question: What is the difference between reactive languages and complex event processing?


CGBoost : Conjugate Gradient in Function Space. The superior out-of-sample performance of AdaBoost has been attributed to the fact that it minimizes a cost function based on margin, in that it can be viewed as a special case of AnyBoost, an abstract gradient descent algorithm. In this paper, we provide a more sophisticated abstract boosting algorithm, CGBoost, based on conjugate gradient in function space. When the AdaBoost exponential cost function is optimized, CGBoost generally yields much lower cost and training error but higher test error, which implies that the exponential cost is vulnerable to overfitting. With the optimization power of CGBoost, we can adopt more "regularized" cost functions that have better out-of-sample performance but are difficult to optimize. Our experiments demonstrate that CGBoost generally outperforms AnyBoost in cost reduction. With suitable cost functions, CGBoost can have better out-of-sample performance.

Question: What is the difference between AdaBoost and CGBoost?


Power to the people? The evolving recognition of human aspects of security. It is perhaps unsurprising to find much of the focus in IT and computer security being drawn towards the technical aspects of the discipline. However, it is increasingly recognised that technology alone cannot deliver a complete solution, and there is also a tangible need to address human aspects. At the core, people must understand the threats they face and be able to use the protection available to them, and although this has not been entirely ignored, it has not received the level of attention that it merits either. Indeed, security surveys commonly reveal that the more directly user-facing aspects such as policy, training and education are prone to receiving significantly less attention than technical controls such as firewalls, antivirus and intrusion detection. The underlying reason for such disparity is that the human aspects are in many ways a more challenging problem to approach, not least because they cannot be easily targeted with a product-based solution. There is also a direct overlap into the technical area, with issues such as the usability and acceptability of technology solutions having a direct impact upon the actual protection that they are able to deliver. This paper explores these themes, highlighting the need for human aspects to form part of a holistic security strategy alongside the necessary technologies. Taking the specific examples of security awareness and two user-facing technical controls (user authentication and antivirus), the discussion examines how things have evolved to the present day and considers how they need to be positioned for the future. a 2012 Elsevier Ltd. All rights reserved.

Question: What is the difference between a firewall and an antivirus?


Question: What is the shape of focus system?


Open data kit: tools to build information services for developing regions. This paper presents Open Data Kit (ODK), an extensible, open-source suite of tools designed to build information services for developing regions. ODK currently provides four tools to this end: Collect, Aggregate, Voice, and Build. Collect is a mobile platform that renders application logic and supports the manipulation of data. Aggregate provides a "click-to-deploy" server that supports data storage and transfer in the "cloud" or on local servers. Voice renders application logic using phone prompts that users respond to with keypad presses. Finally, Build is a application designer that generates the logic used by the tools. Designed to be used together or independently, ODK core tools build on existing open standards and are supported by an open-source community that has contributed additional tools. We describe four deployments that demonstrate how the decisions made in the system architecture of ODK enable services that can both push and pull information in developing regions.

Question: What is the open data kit?


Real-time 3D scene reconstruction with dynamically moving object using a single depth camera. Online 3D reconstruction of real-world scenes has been attracting increasing interests from both the academia and industry, especially with the consumer-level depth cameras becoming widely available. Recent most online reconstruction systems take live depth data from a moving Kinect camera and incrementally fuse them to a single high-quality 3D model in real time. Although most real-world scenes have static environment, the daily objects in a scene often move dynamically, which are non-trivial to reconstruct especially when the camera is also not still. To solve this problem, we propose a single depth camera-based real-time approach for simultaneous reconstruction of dynamic object and static environment, and provide solutions for its key issues. In particular, we first introduce a robust optimization scheme which takes advantage of raycasted maps to segment moving object and background from the live depth map. The corresponding depth data are then fused to the volumes, respectively. These volumes are raycasted to extract views of the implicit surface which can be used as a consistent reference frame for the next iteration of segmentation and tracking. Particularly, in order to handle fast motion of dynamic object and handheld camera in the fusion stage, we propose a sequential 6D pose prediction method which largely increases the registration robustness and avoids registration failures occurred in conventional methods. Experimental results show that our approach can reconstruct moving object as well as static environment with rich details, and outperform conventional methods in multiple aspects.

Question: How to reconstruct a moving object?


The Love/Hate Relationship with the C Preprocessor: An Interview Study (Artifact). The C preprocessor has received strong criticism in academia, among others regarding separation of concerns, error proneness, and code obfuscation, but is widely used in practice. Many (mostly academic) alternatives to the preprocessor exist, but have not been adopted in practice. Since developers continue to use the preprocessor despite all criticism and research, we ask how practitioners perceive the C preprocessor. We performed interviews with 40 developers, used grounded theory to analyze the data, and cross-validated the results with data from a survey among 202 developers, repository mining, and results from previous studies. In particular, we investigated four research questions related to why the preprocessor is still widely used in practice, common problems, alternatives, and the impact of undisciplined annotations. Our study shows that developers are aware of the criticism the C preprocessor receives, but use it nonetheless, mainly for portability and variability. Many developers indicate that they regularly face preprocessorrelated problems and preprocessor-related bugs. The majority of our interviewees do not see any current C-native technologies that can entirely replace the C preprocessor. However, developers tend to mitigate problems with guidelines, even though those guidelines are not enforced consistently. We report the key insights gained from our study and discuss implications for practitioners and researchers on how to better use the C preprocessor to minimize its negative impact. 1998 ACM Subject Classification D.3.4 Processors

Question: What is the C preprocessor?


Robust misinterpretation of confidence intervals.. Null hypothesis significance testing (NHST) is undoubtedly the most common inferential technique used to justify claims in the social sciences. However, even staunch defenders of NHST agree that its outcomes are often misinterpreted. Confidence intervals (CIs) have frequently been proposed as a more useful alternative to NHST, and their use is strongly encouraged in the APA Manual. Nevertheless, little is known about how researchers interpret CIs. In this study, 120 researchers and 442 students-all in the field of psychology-were asked to assess the truth value of six particular statements involving different interpretations of a CI. Although all six statements were false, both researchers and students endorsed, on average, more than three statements, indicating a gross misunderstanding of CIs. Self-declared experience with statistics was not related to researchers' performance, and, even more surprisingly, researchers hardly outperformed the students, even though the students had not received any education on statistical inference whatsoever. Our findings suggest that many researchers do not know the correct interpretation of a CI. The misunderstandings surrounding p-values and CIs are particularly unfortunate because they constitute the main tools by which psychologists draw conclusions from data.

Question: What is the difference between a confidence interval and a confidence level?


Meta BCI : Hippocampus-striatum network inspired architecture towards flexible BCI. Classifying neural signals is a crucial step in the brain-computer interface (BCI). Although Deep Neural Network (DNN) has been shown to be surprisingly good at classification, DNN suffers from long training time and catastrophic forgetting. Catastrophic forgetting refers to a phenomenon in which a DNN tends to forget previously learned task when it learns a new task. Here we argue that the solution to this problem may be found in the human brain, specifically, by combining functions of the two regions: the striatum and the hippocampus, which is pivotal for reinforcement learning and memory recall relevant to the current context, respectively. The mechanism of these brain regions provides insights into resolving catastrophic forgetting and long training time of DNNs. Referring to the hippocampus-striatum network we discuss design principles of combining different types of DNNs for building a new BCI architecture, called "Meta BCI".

Question: What is the hippocampus-striatum network?


Reinforcement Learning in Continuous State and Action Spaces. Many traditional reinforcement-learning algorithms have been designed for problems with small finite state and action spaces. Learn ing in such discrete problems can been difficult, due to noise and delayed reinfor cements. However, many real-world problems have continuous state or action sp aces, which can make learning a good decision policy even more involved. In this c apter we discuss how to automatically find good decision policies in continuous d omains. Because analytically computing a good policy from a continuous model c an be infeasible, in this chapter we mainly focus on methods that explicitly up date a representation of a value function, a policy or both. We discuss conside rations in choosing an appropriate representation for these functions and disc uss gradient-based and gradient-free ways to update the parameters. We show how to a pply these methods to reinforcement-learning problems and discuss many speci fic algorithms. Amongst others, we cover gradient-based temporal-difference lear ning, evolutionary strategies, policy-gradient algorithms and (natural) actor-cri ti methods. We discuss the advantages of different approaches and compare the perform ance of a state-of-theart actor-critic method and a state-of-the-art evolutiona ry strategy empirically.

Question: Reinforcement Learning in Continuous State and Action Spaces.


Decision trees and forests: a probabilistic perspective. Decision trees and ensembles of decision trees are very popular in machine learning and often achieve state-of-the-art performance on black-box prediction tasks. However, popular variants such as C4.5, CART, boosted trees and random forests lack a probabilistic interpretation since they usually just specify an algorithm for training a model. We take a probabilistic approach where we cast the decision tree structures and the parameters associated with the nodes of a decision tree as a probabilistic model; given labeled examples, we can train the probabilistic model using a variety of approaches (Bayesian learning, maximum likelihood, etc). The probabilistic approach allows us to encode prior assumptions about tree structures and share statistical strength between node parameters; furthermore, it offers a principled mechanism to obtain probabilistic predictions which is crucial for applications where uncertainty quantification is important. Existing work on Bayesian decision trees relies on Markov chain Monte Carlo which can be computationally slow and suffer from poor mixing. We propose a novel sequential Monte Carlo algorithm that computes a particle approximation to the posterior over trees in a top-down fashion. We also propose a novel sampler for Bayesian additive regression trees by combining the above top-down particle filtering algorithm with the Particle Gibbs (Andrieu et al., 2010) framework. Finally, we propose Mondrian forests (MFs), a computationally efficient hybrid solution that is competitive with non-probabilistic counterparts in terms of speed and accuracy, but additionally produces well-calibrated uncertainty estimates. MFs use the Mondrian process (Roy and Teh, 2009) as the randomization mechanism and hierarchically smooth the node parameters within each tree (using a hierarchical probabilistic model and approximate Bayesian updates), but combine the trees in a non-Bayesian fashion. MFs can b

Question: What is a probabilistic approach to decision trees?


An automatic vision-based malaria diagnosis system.. Malaria is a worldwide health problem with 225 million infections each year. A fast and easy-to-use method, with high performance is required to differentiate malaria from non-malarial fevers. Manual examination of blood smears is currently the gold standard, but it is time-consuming, labour-intensive, requires skilled microscopists and the sensitivity of the method depends heavily on the skills of the microscopist. We propose an easy-to-use, quantitative cartridge-scanner system for vision-based malaria diagnosis, focusing on low malaria parasite densities. We have used special finger-prick cartridges filled with acridine orange to obtain a thin blood film and a dedicated scanner to image the cartridge. Using supervised learning, we have built a Plasmodium falciparum detector. A two-step approach was used to first segment potentially interesting areas, which are then analysed in more detail. The performance of the detector was validated using 5,420 manually annotated parasite images from malaria parasite culture in medium, as well as using 40 cartridges of 11,780 images containing healthy blood. From finger prick to result, the prototype cartridge-scanner system gave a quantitative diagnosis in 16 min, of which only 1 min required manual interaction of basic operations. It does not require a wet lab or a skilled operator and provides parasite images for manual review and quality control. In healthy samples, the image analysis part of the system achieved an overall specificity of 99.999978% at the level of (infected) red blood cells, resulting in at most seven false positives per microlitre. Furthermore, the system showed a sensitivity of 75% at the cell level, enabling the detection of low parasite densities in a fast and easy-to-use manner. A field trial in Chittagong (Bangladesh) indicated that future work should primarily focus on improving the filling process of the cartridge and the focus control part of the

Question: What is the canadian military?


The effect of glycolic acid on the treatment of acne in Asian skin.. BACKGROUND Glycolic acid has become important and popular for treating acne.   OBJECTIVE To evaluate the efficacy and safety of serial glycolic acid peels with glycolic acid home care products on facial acne lesions and other associated skin problems.   METHODS We collected 40 Asian candidates with moderate to moderately severe acne. They were divided into two groups according to the degree of greasiness of their facial skin. The two groups' members were treated with four series of 35% and 50% glycolic acid peels, respectively. They also used 15% glycolic acid home care products during this study period. The improvement of acne as well as other associated problems were assessed by both the physicians and the patient themselves.   RESULTS Significant resolution of comedones, papules, and pustules was found. The skin texture of each candidate was dramatically rejuvenated. Consistent and repetitive treatment with glycolic acid was needed for the apparent improvement of acne scars and cystic lesions. The follicular pores also became comparatively smaller. Furthermore, most of the candidates had much brighter and lighter looking skin. Only small percentage of patients (5.6%) developed side effects.   CONCLUSION Glycolic acid has considerable therapeutic value for acne with minimal side effects even in Asian skin. It may be an ideal adjunctive treatment of acne.

Question: What is the difference between glycolic acid and alpha hydroxy acid?


PEMOGEN: Automatic adaptive performance modeling during program runtime. Traditional means of gathering performance data are tracing, which is limited by the available storage, and profiling, which has limited accuracy. Performance modeling is often used to interpret the tracing data and generate performance predictions. We aim to complement the traditional data collection mechanisms with online performance modeling, a method that generates performance models while the application is running. This allows us to greatly reduce the storage overhead while still producing accurate predictions. We present PEMOGEN, our compilation and modeling framework that automatically instruments applications to generate performance models during program execution. We demonstrate the ability of PEMOGEN to both reduce storage cost and improve the prediction accuracy compared to traditional techniques such as least squares fitting. With our tool, we automatically detect 3,370 kernels from fifteen NAS and Mantevo applications and model their execution time with a median coefficient of variation R2 of 0.81. These automatically generated performance models can be used to quickly assess the scaling and potential bottlenecks with regards to any input parameter and the number of processes of a parallel application.

Question: What is the name of the tool?


Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. Simultaneous tracking of multiple persons in real-world environments is an active research field and several approaches have been proposed, based on a variety of features and algorithms. Recently, there has been a growing interest in organizing systematic evaluations to compare the various techniques. Unfortunately, the lack of common metrics for measuring the performance of multiple object trackers still makes it hard to compare their results. In this work, we introduce two intuitive and general metrics to allow for objective comparison of tracker characteristics, focusing on their precision in estimating object locations, their accuracy in recognizing object configurations and their ability to consistently label objects over time. These metrics have been extensively used in two large-scale international evaluations, the 2006 and 2007 CLEAR evaluations, to measure and compare the performance of multiple object trackers for a wide variety of tracking tasks. Selected performance results are presented and the advantages and drawbacks of the presented metrics are discussed based on the experience gained during the evaluations.

Question: What is the difference between the CLEAR MOT metrics and the CLEAR MOT metrics?


Classification and Regression using SAS. K-Nearest Neighbor (KNN) classification and regression are two widely used analytic methods in predictive modeling and data mining fields. They provide a way to model highly nonlinear decision boundaries, and to fulfill many other analytical tasks such as missing value imputation, local smoothing, etc. In this paper, we discuss ways in SAS R © to conduct KNN classification and KNN Regression. Specifically, PROC DISCRIM is used to build multi-class KNN classification and PROC KRIGE2D is used for KNN regression tasks. Technical details such as tuning parameter selection, etc are discussed. We also discuss tips and tricks in using these two procedures for KNN classification and regression. Examples are presented to demonstrate full process flow in applying KNN classification and regression in real world business projects.

Question: How to use SAS R © to conduct KNN classification and KNN regression?


The Future of Memory: Remembering, Imagining, and the Brain. During the past few years, there has been a dramatic increase in research examining the role of memory in imagination and future thinking. This work has revealed striking similarities between remembering the past and imagining or simulating the future, including the finding that a common brain network underlies both memory and imagination. Here, we discuss a number of key points that have emerged during recent years, focusing in particular on the importance of distinguishing between temporal and nontemporal factors in analyses of memory and imagination, the nature of differences between remembering the past and imagining the future, the identification of component processes that comprise the default network supporting memory-based simulations, and the finding that this network can couple flexibly with other networks to support complex goal-directed simulations. This growing area of research has broadened our conception of memory by highlighting the many ways in which memory supports adaptive functioning.

Question: What is the future of memory?


A hybridization of cuckoo search and particle swarm optimization for solving optimization problems. A new hybrid optimization algorithm, a hybridization of cuckoo search and particle swarm optimization (CSPSO), is proposed in this paper for the optimization of continuous functions and engineering design problems. This algorithm can be regarded as some modifications of the recently developed cuckoo search (CS). These modifications involve the construction of initial population, the dynamic adjustment of the parameter of the cuckoo search, and the incorporation of the particle swarm optimization (PSO). To cover search space with balance dispersion and neat comparability, the initial positions of cuckoo nests are constructed by using the principle of orthogonal Lation squares. To reduce the influence of fixed step size of the CS, the step size is dynamically adjusted according to the evolutionary generations. To increase the diversity of the solutions, PSO is incorporated into CS using a hybrid strategy. The proposed algorithm is tested on 20 standard benchmarking functions and 2 engineering optimization problems. The performance of the CSPSO is compared with that of several meta-heuristic algorithms based on the best solution, worst solution, average solution, standard deviation, and convergence rate. Results show that in most cases, the proposed hybrid optimization algorithm performs better than, or as well as CS, PSO, and some other exiting meta-heuristic algorithms. That means that the proposed hybrid optimization algorithm is competitive to other optimization algorithms.

Question: What is a hybridization of cuckoo search and particle swarm optimization?


Discriminative Multi-View Interactive Image Re-Ranking. Given an unreliable visual patterns and insufficient query information, content-based image retrieval is often suboptimal and requires image re-ranking using auxiliary information. In this paper, we propose a discriminative multi-view interactive image re-ranking (DMINTIR), which integrates user relevance feedback capturing users' intentions and multiple features that sufficiently describe the images. In DMINTIR, heterogeneous property features are incorporated in the multi-view learning scheme to exploit their complementarities. In addition, a discriminatively learned weight vector is obtained to reassign updated scores and target images for re-ranking. Compared with other multi-view learning techniques, our scheme not only generates a compact representation in the latent space from the redundant multi-view features but also maximally preserves the discriminative information in feature encoding by the large-margin principle. Furthermore, the generalization error bound of the proposed algorithm is theoretically analyzed and shown to be improved by the interactions between the latent space and discriminant function learning. Experimental results on two benchmark data sets demonstrate that our approach boosts baseline retrieval quality and is competitive with the other state-of-the-art re-ranking strategies.

Question: How large is the canadian military?


The Anti-vaccination Movement: A Regression in Modern Medicine. There have been recent trends of parents in Western countries refusing to vaccinate their children due to numerous reasons and perceived fears. While opposition to vaccines is as old as the vaccines themselves, there has been a recent surge in the opposition to vaccines in general, specifically against the MMR (measles, mumps, and rubella) vaccine, most notably since the rise in prominence of the notorious British ex-physician, Andrew Wakefield, and his works. This has caused multiple measles outbreaks in Western countries where the measles virus was previously considered eliminated. This paper evaluates and reviews the origins of the anti-vaccination movement, the reasons behind the recent strengthening of the movement, role of the internet in the spread of anti-vaccination ideas, and the repercussions in terms of public health and safety.

Question: What is the anti-vaccination movement?


Bat-Inspired Optimization Approach for the Brushless DC Wheel Motor Problem. This paper presents a metaheuristic algorithm inspired in evolutionary computation and swarm intelligence concepts and fundamentals of echolocation of micro bats. The aim is to optimize the mono and multiobjective optimization problems related to the brushless DC wheel motor problems, which has 5 design parameters and 6 constraints for the mono-objective problem and 2 objectives, 5 design parameters, and 5 constraints for multiobjective version. Furthermore, results are compared with other optimization approaches proposed in the recent literature, showing the feasibility of this newly introduced technique to high nonlinear problems in electromagnetics.

Question: What is the brushless DC wheel motor problem?


Integral imaging using color multiplexed holographic optical element. We propose a novel integral imaging system using holographic optical element (HOE) which can provide multi-color 3-dimensional (3D) images. The optical properties of a lens array are recorded on a photopolymer using color multiplexing method with two different wavelength lasers (473 nm and 532 nm). The lens array HOE recorded on the photopolymer functions as 3D imaging HOE for integral imaging instead of the conventional lens array or concave mirror array. Recording scheme for color multiplexed HOE is presented and the proposed system is experimentally verified.

Question: What is the difference between a lens array and a holographic optical element?


The myth of the visual word form area. Recent functional imaging studies have referred to a posterior region of the left midfusiform gyrus as the "visual word form area" (VWFA). We review the evidence for this claim and argue that neither the neuropsychological nor neuroimaging data are consistent with a cortical region specialized for visual word form representations. Specifically, there are no reported cases of pure alexia who have deficits limited to visual word form processing and damage limited to the left midfusiform. In addition, we present functional imaging data to demonstrate that the so-called VWFA is activated by normal subjects during tasks that do not engage visual word form processing such as naming colors, naming pictures, reading Braille, repeating auditory words, and making manual action responses to pictures of meaningless objects. If the midfusiform region has a single function that underlies all these tasks, then it does not correspond to visual word form processing. On the other hand, if the region participates in several functions as defined by its interactions with other cortical areas, then identifying the neural system sustaining visual word form representations requires identification of the set of regions involved. We conclude that there is no evidence that visual word form representations are subtended by a single patch of neuronal cortex and it is misleading to label the left midfusiform region as the visual word form area.

Question: What is the visual word form area?


Big data reduction framework for value creation in sustainable enterprises.

Question: What is a big data reduction framework for value creation in sustainable enterprises?


Automated plant Watering system. In daily operations related to farming or gardening Watering is the most important cultural practice and the most labor-intensive task. No matter whichever weather it is, either too hot and dry or too cloudy and wet, you want to be able to control the amount of water that reaches your plants. Modern watering systems could be effectively used to water plants when they need it. But this manual process of watering requires two important aspects to be considered: when and how much to water. In order to replace manual activities and making gardener's work easier, we have create automatic plant watering system. By adding automated plant watering system to your garden or agricultural field, you will help all of your plants reach their fullest potential as well as conserving water. Using sprinklers drip emitters, or a combination of both, we can design a system that is ideal for every plant in our yard. For implementation of automatic plant watering system, we have used combination of sprinkler systems, pipes, and nozzles. This project uses the ATmega328 microcontroller. It is programmed to sense moisture level of plants at particular instance of time, if the moisture content is less than specified threshold which is predefined according to particular plant's water need then desired amount of water is supplied till it reaches threshold. Generally, plants need to be watered twice a day, morning and evening. Thus, the microcontroller is programmed to water plants two times per day. System is designed in such a way that it reports its current state as well as remind the user to add water to the tank. All this notifications are made through mobile application. We hope that through this prototype we all can enjoy having plants, without being worried about absent or forgetfulness.

Question: How to design a plant watering system?


Multilingual OCR (MOCR): An Approach to Classify Words to Languages. There are immense efforts to design a complete OCR for most of the world's leading languages, however, multilingual documents either of handwritten or of printed form. As a united attempt, Unicode based OCRs were studied mostly with some positive outcomes, despite the fact that a large character set slows down the recognition significantly. In this paper, we come out with a method to classify words to a language as the word segmentation is complete. For the purpose, we identified the characteristics of writings of several languages and utilized projecting method combined with some other feature extraction methods. In addition, this paper intends a modified statistical approach to correct the skewness before processing a segmented document. The proposed procedure, evaluated for a collection of both handwritten and printed documents, came with excellent outcomes in assigning words to languages. General Terms Pattern Recognition, Document Processing, Optical Character Recognition.

Question: What is the difference between a handwritten and a printed document?


Supervised clustering of streaming data for email batch detection. We address the problem of detecting batches of emails that have been created according to the same template. This problem is motivated by the desire to filter spam more effectively by exploiting collective information about entire batches of jointly generated messages. The application matches the problem setting of supervised clustering, because examples of correct clusterings can be collected. Known decoding procedures for supervised clustering are cubic in the number of instances. When decisions cannot be reconsidered once they have been made --- owing to the streaming nature of the data --- then the decoding problem can be solved in linear time. We devise a sequential decoding procedure and derive the corresponding optimization problem of supervised clustering. We study the impact of collective attributes of email batches on the effectiveness of recognizing spam emails.

Question: What is the impact of collective attributes of email batches on the effectiveness of recognizing spam emails?


Self-Oscillating Flyback Converter for Mobile Batteries Charging Applications. The self-oscillating flyback converter is a popular circuit for cost-sensitive applications due to its simplicity and low component count. This paper presents self-oscillating fly-back converter for mobile batteries charging applications. The converter is designed to step-down the high voltage input to an acceptable level from 220 VAC to 7VDC. By adding indictor and capacitor the power factor and input currant increased, and became 500mA, where the old input currant was 250mA, Diode was connected between Emitter and collector, so the output voltage was improved, and has less ripple, in addition to the soft switching was obtained, simulation model is developed by using MULTISIM software. The operation principle of this converter is described using some operating waveforms based on simulation results and a detailed circuit operation analysis. Analytical results are verified by laboratory experiment. With an input of 220 VAC, 500mA and an output of 7V DC, the efficiency of circuit was75%.

Question: How to design a self-oscillating flyback converter for mobile batteries charging applications?


Sign language recognition : Generalising to more complex corpora. The aim of this thesis is to find new approaches to Sign Language Recognition (SLR) which are suited to working with the limited corpora currently available. Data available for SLR is of limited quality; low resolution and frame rates make the task of recognition even more complex. The content is rarely natural, concentrating on isolated signs and filmed under labo ratory conditions. In addition, the amount of accurately labelled data is minimal. To this end, several contributions are made: Tracking the hands is eschewed in favour of detection based techniques more robust to noise; for both signs and for linguistically-motivated sign sub-units are investigated, to make best use of limited data sets. Finally, an algorithm is proposed to learn signs fiom the inset signers on TV, with the aid of the accompanying subtitles, thus increasing the corpus of data available. Tracking fast moving hands under laboratory conditions is a complex task, move this to real world data and the challenge is even greater. When using tracked data as a base for SLR, the er rors in the tracking are compounded at the classification stage. Proposed instead, is a novel sign detection method, which views space-time as a 3D volume and the sign within it as an object to be located. Features are combined into strong classifiers using a novel boosting implementation designed to create optimal classifiers over sparse datasets. Using boosted volumetric features, on a robust frame differenced input, average classification rates reach 71% on seen signers and 66% on a mixture of seen and unseen signers, with individual sign classification rates gaining 95%. Using a classifier per sign approach to SLR, means that data sets need to contain numerous examples of the signs to be learnt. Instead, this thesis proposes lear nt classifiers to detect the common sub-units of sign. The responses of these classifiers can then be combined for recogni

Question: What is the difference between a sign and a sign sub-unit?


Organic Photodiodes: The Future of Full Color Detection and Image Sensing.. Major growth in the image sensor market is largely as a result of the expansion of digital imaging into cameras, whether stand-alone or integrated within smart cellular phones or automotive vehicles. Applications in biomedicine, education, environmental monitoring, optical communications, pharmaceutics and machine vision are also driving the development of imaging technologies. Organic photodiodes (OPDs) are now being investigated for existing imaging technologies, as their properties make them interesting candidates for these applications. OPDs offer cheaper processing methods, devices that are light, flexible and compatible with large (or small) areas, and the ability to tune the photophysical and optoelectronic properties - both at a material and device level. Although the concept of OPDs has been around for some time, it is only relatively recently that significant progress has been made, with their performance now reaching the point that they are beginning to rival their inorganic counterparts in a number of performance criteria including the linear dynamic range, detectivity, and color selectivity. This review covers the progress made in the OPD field, describing their development as well as the challenges and opportunities.

Question: What is the future of full color detection and image sensing?


Who Do I Look Like? Determining Parent-Offspring Resemblance via Gated Autoencoders. Recent years have seen a major push for face recognition technology due to the large expansion of image sharing on social networks. In this paper, we consider the difficult task of determining parent-offspring resemblance using deep learning to answer the question "Who do I look like?" Although humans can perform this job at a rate higher than chance, it is not clear how they do it [2]. However, recent studies in anthropology [24] have determined which features tend to be the most discriminative. In this study, we aim to not only create an accurate system for resemblance detection, but bridge the gap between studies in anthropology with computer vision techniques. Further, we aim to answer two key questions: 1) Do offspring resemble their parents? and 2) Do offspring resemble one parent more than the other? We propose an algorithm that fuses the features and metrics discovered via gated autoencoders with a discriminative neural network layer that learns the optimal, or what we call genetic, features to delineate parent-offspring relationships. We further analyze the correlation between our automatically detected features and those found in anthropological studies. Meanwhile, our method outperforms the state-of-the-art in kinship verification by 3-10% depending on the relationship using specific (father-son, mother-daughter, etc.) and generic models.

Question: How do I determine parent-offspring resemblance?


What is Your Organization 'Like'?: A Study of Liking Activity in the Enterprise. The 'like' button, introduced by Facebook several years ago, has become one of the most prominent icons of social media. Similarly to other popular social media features on the web, enterprises have also recently adopted it. In this paper, we present a first comprehensive study of liking activity in the enterprise. We studied the logs of an enterprise social media platform within a large global organization along a period of seven months, in which 393,720 'likes' were performed. In addition, we conducted a survey of 571 users of the platform's 'like' button. Our evaluation combines quantitative and qualitative analysis to inspect what employees like, why they use the 'like' button, and to whom they give their 'likes'.

Question: What is your organization like?


Software and Algorithms for Graph Queries on Multithreaded Architectures. Search-based graph queries, such as finding short paths and isomorphic subgraphs, are dominated by memory latency. If input graphs can be partitioned appropriately, large cluster-based computing platforms can run these queries. However, the lack of compute-bound processing at each vertex of the input graph and the constant need to retrieve neighbors implies low processor utilization. Furthermore, graph classes such as scale-free social networks lack the locality to make partitioning clearly effective. Massive multithreading is an alternative architectural paradigm, in which a large shared memory is combined with processors that have extra hardware to support many thread contexts. The processor speed is typically slower than normal, and there is no data cache. Rather than mitigating memory latency, multithreaded machines tolerate it. This paradigm is well aligned with the problem of graph search, as the high ratio of memory requests to computation can be tolerated via multithreading. In this paper, we introduce the multithreaded graph library (MTGL), generic graph query software for processing semantic graphs on multithreaded computers. This library currently runs on serial machines and the Cray MTA-2, but Sandia is developing a run-time system that will make it possible to run MTGL-based code on symmetric multiprocessors. We also introduce a multithreaded algorithm for connected components and a new heuristic for inexact subgraph isomorphism We explore the performance of these and other basic graph algorithms on large scale-free graphs. We conclude with a performance comparison between the Cray MTA-2 and Blue Gene/Light for s-t connectivity.

Question: What is the performance of graph algorithms on large scale-free graphs?


A Short-term Traffic Flow Forecasting Method Based on the Hybrid PSO-SVR. Accurate short-term flow forecasting is important for the real-time traffic control, but due to its complex nonlinear data pattern, getting a high precision is difficult. The support vector regression model (SVR) has been widely used to solve nonlinear regression and time series predicting problems. To get a higher precision with less learning time, this paper presents a Hybrid PSO-SVR forecasting method, which uses particle swarm optimization (PSO) to search optimal SVR parameters. In order to find a PSO that is more proper for SVR parameters searching, this paper proposes three kinds of strategies to handle the particles flying out of the searching space Through the comparison of three strategies, we find one of the strategies can make PSO get the optimal parameters more quickly. The PSO using this strategy is called fast PSO. Furthermore, aiming at the problem about the decrease of prediction accuracy caused by the noises in the original data, this paper proposes a hybrid PSO-SVR method with historical momentum based on the similarity of historical short-term flow data. The results of extensive comparison experiments indicate that the proposed model can get more accurate forecasting results than other state-of-the-art algorithms, and when the data contain noises, the method with historical momentum still gets accurate forecasting results.

Question: What is the hybrid PSO-SVR method?


On the Use of College Students in Social Science Research : Insights from a Second-Order Meta-analysis. A second-order meta-analysis was conducted to assess the implications of using college student subjects in social science research. Four meta-analyses investigating response homogeneity (cumulative N 1 650,000) and 30 meta-analyses reporting effect sizes for 65 behavioral or psychological relationships (cumulative N 1 350,000) provided comparative data for college student subjects and nonstudent (adult) subjects for the present research. In general, responses of college student subjects were found to be slightly more homogeneous than those of nonstudent subjects.Moreover, effect sizes derived from college student subjects frequently differed from those derived from nonstudent subjects both directionally and in magnitude. Because there was no systematic pattern to the differences observed, caution must be exercised when attempting to extend any relationship found using college student subjects to a nonstudent (adult) population. The results augur in favor of, and emphasize the importance of, replicating research based on college student subjects withnonstudent subjects before attempting any generalizations.

Question: What is a second-order meta-analysis?


Metal nanogrids , nanowires , and nanofi bers for transparent electrodes. VOLUME 36 • OCTOBER 2011 • www.mrs.org/bulletin © 2011 Materials Research Society Introduction Transparent electrodes are ubiquitously used in optoelectronic devices where light must pass through a section, and current or voltage needs to be applied. These devices include thin-fi lm solar cells (organic, inorganic, hybrid), displays (liquid-crystal display, plasma display, organic light-emitting diode [OLED]), touch screens (resistive, capacitive), electronic books, smart windows, fl exible lighting, and passive devices (such as transparent heating, transparent speakers). 1 The properties of transparent electrodes are extremely important for device performance, and the specifi cations vary based on types of devices. For example, while source roughness and work function are insignifi cant for resistive touch screens, they are crucial for OLEDs and organic solar cells. 1 Moreover, the optical haze of particular transparent electrodes is benefi cial for high-effi ciency solar cells but problematic for certain types of displays. The mechanical properties of the electrode are crucial for fl exible devices but not important for devices on rigid substrates. Across all devices, however, the optical transparency ( T ) and electrical sheet resistance ( Rs ) are the two most important parameters. Unfortunately, these two parameters are in constant competition with one another. Since Rs values are often reported at different transparencies, it is hard to compare two transparent electrodes directly, and hence a fi gure of merit ( σ dc / σ op , where σ dc is the dc conductivity, and σ op is the optical conductivity) Metal nanogrids, nanowires, and nanofi bers for transparent electrodes

Question: What is the fi gure of merit?


Put your money where your mouth is! Explaining collective action tendencies through group-based anger and group efficacy.. Insights from appraisal theories of emotion are used to integrate elements of theories on collective action. Three experiments with disadvantaged groups systematically manipulated procedural fairness (Study 1), emotional social support (Study 2), and instrumental social support (Study 3) to examine their effects on collective action tendencies through group-based anger and group efficacy. Results of structural equation modeling showed that procedural fairness and emotional social support affected the group-based anger pathway (reflecting emotion-focused coping), whereas instrumental social support affected the group efficacy pathway (reflecting problem-focused coping), constituting 2 distinct pathways to collective action tendencies. Analyses of the means suggest that collective action tendencies become stronger the more fellow group members "put their money where their mouth is." The authors discuss how their dual pathway model integrates and extends elements of current approaches to collective action.

Question: How does the dual pathway model integrate and extend current approaches to collective action?


The heroism of women and men.. Heroism consists of actions undertaken to help others, despite the possibility that they may result in the helper's death or injury. The authors examine heroism by women and men in 2 extremely dangerous settings: the emergency situations in which Carnegie medalists rescued others and the holocaust in which some non-Jews risked their lives to rescue Jews. The authors also consider 3 risky but less dangerous prosocial actions: living kidney donations, volunteering for the Peace Corps, and volunteering for Doctors of the World. Although the Carnegie medalists were disproportionately men, the other actions yielded representations of women that were at least equal to and in most cases higher than those of men. These findings have important implications for the psychology of heroism and of gender.

Question: What is the difference between heroism and heroism?


IEEE 802.15.7 visible light communication: modulation schemes and dimming support. Visible light communication refers to shortrange optical wireless communication using visible light spectrum from 380 to 780 nm. Enabled by recent advances in LED technology, IEEE 802.15.7 supports high-data-rate visible light communication up to 96 Mb/s by fast modulation of optical light sources which may be dimmed during their operation. IEEE 802.15.7 provides dimming adaptable mechanisms for flicker-free high-data-rate visible light communication.

Question: What is the visible light spectrum?


Supply Chain Sourcing Under Asymmetric Information. We study a supply chain with two suppliers competing over a contract to supply components to a manufacturer. One of the suppliers is a big company for whom the manufacturer's business constitutes a small part of his business. The other supplier is a small company for whom the manufacturer's business constitutes a large portion of his business. We analyze the problem from the perspective of the big supplier and address the following questions: What is the optimal contracting strategy that the big supplier should follow? How does the information about the small supplier's production cost affect the profits and contracting decision? How does the existence of the small supplier affect profits? By studying various information scenarios regarding the small supplier's and the manufacturer's production cost, we show, for example, that the big supplier benefits when the small supplier keeps its production cost private. We quantify the value of information for the big supplier and the manufacturer. We also quantify the cost (value) of the alternative-sourcing option for the big supplier (the manufacturer). We determine when an alternative-sourcing option has more impact on profits than information. We conclude with extensions and numerical examples to shed light on how system parameters affect this supply chain.

Question: What is the optimal contracting strategy that the big supplier should follow?


E-government implementation: A bird's eye view of issues relating to costs, opportunities, benefits and risks. After more than a decade of comprehensive research work in the area of electronic government (e-government), no attempt has yet been made to undertake a systematic literature review on the costs, opportunities, benefits and risks that influence the implementation of e-government. This is particularly significant given the various related challenges that governments have faced over the years when implementing e-government initiatives. Hence, the aim of this paper is to undertake a comprehensive analysis of relevant literature addressing these issues using a systematic review of 132 studies identified from the Scopus online database and Google Scholar together with a manual review of relevant papers from journals dedicated to electronic government research such as Electronic Government, an International Journal (EGIJ), International Journal of Electronic Government Research (IJEGR) and Transforming Government: People, Process, and Policy (TGPPP). The overall review indicated that although a large number of papers discuss costs, opportunities, benefits and risks, treatment of these issues have tended to be superficial.Moreover, there is a lack of empirical studies which can statistically evaluate the performance of these constructs in relation to the various egovernment systems. Therefore, this research would help governments to better analyse the impact of costs, opportunities, benefits and risks on the success of e-government systems and its pre-adoption from an implementation perspective.

Question: What are the benefits of e-government?


A ripple voltage sensing MPPT circuit for ultra-low power microsystems. We propose a maximum power point tracking (MPPT) circuit for micro-scale sensor systems that measures ripple voltages in a switched capacitor energy harvester. Compared to conventional current mirror type MPPT circuits, this design incurs no voltage drop and does not require high bandwidth amplifiers. Using correlated double sampling, high accuracy is achieved with a power overhead of 5%, even at low harvested currents of 1.4uA based on measured results in 180nm CMOS.

Question: What is a microsystem?


WreckWatch: Automatic Traffic Accident Detection and Notification with Smartphones. Traffic accidents are one of the leading causes of fatalities in the US. An important indicator of survival rates after an accident is the time between the accident and when emergency medical personnel are dispatched to the scene. Eliminating the time between when an accident occurs and when first responders are dispatched to the scene decreases mortality rates by 6%. One approach to eliminating the delay between accident occurrence and first responder dispatch is to use in-vehicle automatic accident detection and notification systems, which sense when traffic accidents occur and immediately notify emergency personnel. These in-vehicle systems, however, are not available in all cars and are expensive to retrofit for older vehicles. This paper describes how smartphones, such as the iPhone and Google Android platforms, can automatically detect traffic accidents using accelerometers and accoustic data, immediately notify a central emergency dispatch server after an accident, and provide situational awareness through photographs, GPS coordinates, VOIP communication channels, and accident data recording. This paper provides the following contributions to the study of detecting traffic accidents via smartphones: (1) we present a formal model for accident detection that combines sensors and context data, (2) we show how smartphone sensors, network connections, and web services can be used to provide situational awarenss to first responders, and (3) we provide empirical results demonstrating the efficacy of different approaches employed by smartphone accident detection systems to prevent false positives. J. White and H. Turner Dept. of Electrical and Computer Engineering Virginia Tech E-mail: {julesw,hturner0}@vt.edu C. Thompson, B. Dougherty, and D.C. Schmidt Dept. of Electrical Engineering and Computer Science Vanderbilt University E-mail: {cthompson,briand,schmidt}@dre.vanderbilt.edu

Question: How to detect traffic accidents with smartphones?


HOW THE EYE MEASURES REALITY AND VIRTUAL REALITY. tures throughout our history. The paintings at Niaux, Altamira, and Lascaux (Clottes, 1995; Ruspoli, 1986), for example, are known to be about 14,000 years old, but with the recently discovered paintings in the Grotte Chauvet, the origin of representational art appears to have been pushed back even further (Chauvet, Brunel Deschamps, & Hillaire, 1995; Clottes, 1996), to 20,000 years ago if not longer.1 Thus, these paintings date from about the time at which homo sapiens sapiens first appeared in Europe (Nougier, 1969). We should remember these paintings in the context of virtual reality; our fascination with pictures is by no means recent. My intent is threefold: first, to discuss our perception of the cluttered layout, or space, that we normally find around us; second, to discuss the development of representational art up to our current appreciation of it; and third, to apply this knowledge to virtual reality systems. The first discussion focuses on the use of multiple sources of information specifying ordinal depth relations, within the theoretical framework that I have called directed perception (Cutting, 1986, 1991). The second discussion is embedded within the first, but it is steeped in neither history nor art history; instead, it is offered through the peculiar eyes of optics and psychology, particularly psychophysics. The third is addressed to one of the pressing problems of graphics and of virtual reality—how we perceive the layout of the environments that we simulate.

Question: How do we perceive the cluttered layout of the real world?


A Mobile-Cloud Collaborative Traffic Lights Detector for Blind Navigation. Context-awareness is a critical aspect of safe navigation especially for the blind and visually impaired in unfamiliar environments. Existing mobile devices for context-aware navigation fall short in many cases due to their dependence on specific infrastructure requirements as well as having limited access to resources that could provide a wealth of contextual clues. In this paper, we propose a mobile-cloud collaborative approach for context-aware navigation by exploiting the computational power of resources made available by Cloud Computing providers as well as the wealth of location-specific resources available on the Internet. We propose an extensible system architecture that minimizes reliance on infrastructure, thus allowing for wide usability. We present a traffic light detector that we developed as an initial application component of the proposed system. We present preliminary results of experiments performed to test the appropriateness for the real-time nature of the application.

Question: What is a traffic light detector?


Turn-on Performance of Reverse Blocking IGBT (RB IGBT) and Optimization Using Advanced Gate Driver. Turn-on performance of a reverse blocking insulated gate bipolar transistor (RB IGBT) is discussed in this paper. The RB IGBT is a specially designed IGBT having ability to sustain blocking voltage of both the polarities. Such a switch shows superior conduction but much worst switching (turn- on) performances compared to a combination of an ordinary IGBT and blocking diode. Because of that, optimization of the switching performance is a key issue that makes the RB IGB not well accepted in the real applications. In this paper, the RB IGBT turn-on losses and reverse recovery current are analyzed for different gate driver techniques, and a new gate driver is proposed. Commonly used conventional gate drivers do not have capability for the switching dynamics optimization. In contrast to this, the new proposed gate driver provides robust and simple way to control and optimize the reverse recovery current and turn-on losses. The collector current slope and reverse recovery current are controlled by the means of the gate emitter voltage control in feedforward manner. In addition, the collector emitter voltage slope is controlled during the voltage falling phase by the means of inherent increase of the gate current. Therefore, the collector emitter voltage tail and the total turn- on losses are reduced, independently on the reverse recovery current. The proposed gate driver was experimentally verified and the results presented and discussed.

Question: What is the difference between a reverse blocking IGBT and a normal IGBT?


Optimal Reconstruction with a Small Number of Views. Estimating positions of world points from features observed in images is a key problem in 3D reconstruction, image mosaicking, simultaneous localization and mapping and structure from motion. We consider a special instance in which there is a dominant ground plane G viewed from a parallel viewing plane S above it. Such instances commonly arise, for example, in aerial photography. Consider a world point g ∈ G and its worst case reconstruction uncertainty ε(g,S) obtained by merging all possible views of g chosen from S. We first show that one can pick two views sp and sq such that the uncertainty ε(g, {sp, sq}) obtained using only these two views is almost as good as (i.e. within a small constant factor of) ε(g,S). Next, we extend the result to the entire ground plane G and show that one can pick a small subset of S ′ ⊆ S (which grows only linearly with the area of G) and still obtain a constant factor approximation, for every point g ∈ G, to the minimum worst case estimate obtained by merging all views in S. Our results provide a view selection mechanism with provable performance guarantees which can drastically increase the speed of scene reconstruction algorithms. In addition to theoretical results, we demonstrate their effectiveness in an application where aerial imagery is used for monitoring farms and orchards.

Question: How many views are needed to reconstruct a point?


Experiences of integration and performance testing of multilingual OCR for printed Indian scripts. This paper presents integration and testing scheme for managing a large Multilingual OCR Project. The project is an attempt to implement an integrated platform for OCR of different Indian languages. Software engineering, workflow management and testing processes have been discussed in this paper. The OCR has now been experimentally deployed for some specific applications and currently is being enhanced for handling the space and time constraints, achieving higher recognition accuracies and adding new functionalities.

Question: What is the size of the Indian language?


Assessing mental health issues on college campuses: preliminary findings from a pilot study. A significant fraction of college students suffer from serious mental health issues including depression, anxiety, self-harm and suicidal thought. The prevalence and severity of these issues among college students also appear to increase over time. However, most of these issues often remain undiagnosed, and as a result, untreated. One of the main reasons of this gap between illness and treatment results from the lack of reliable data over time. While health care services in college campuses have been focusing on detection of illness onset and appropriate interventions, their tools are mostly manual surveys which often fail to capture the granular details of contexts and behaviors which might provide important cues about illness onset. To overcome the limitations of these manual tools, we deployed a smartphone based tool or unobtrusive and continuous data collection from 22 students during an academic semester. In this paper, we present the preliminary findings from our study about assessing mental health on college campuses using passively sensed smartphone data.

Question: What is the prevalence of mental health issues on college campuses?


Targeted Employee Retention : Performance-Based and Job-Related Differences in Reported Reasons for Staying. A content model of 12 retention factors is developed in the context of previous theory and research. Coding of open-ended responses from 24,829 employees in the leisure and hospitality industry lends support to the identified framework and reveals that job satisfaction, extrinsic rewards, constituent attachments, organizational commitment, and organizational prestige were the most frequently mentioned reasons for staying. Advancement opportunities and organizational prestige were more common reasons for staying among high performers and non-hourly workers, and extrinsic rewards was more common among low performers and hourly employees, providing support for ease/desirability of movement and psychological contract rationales. The findings highlight the importance of differentiating human resource management practices when the goal is to retain those employees valued most by the organization.

Question: What are the most common reasons for staying?


W-net: Bridged U-net for 2D Medical Image Segmentation. In this paper, we focus on three problems in deep learning based medical image segmentation. Firstly, U-net, as a popular model for medical image segmentation, is difficult to train when convolutional layers increase even though a deeper network usually has a better generalization ability because of more learnable parameters. Secondly, the exponential ReLU (ELU), as an alternative of ReLU, is not much different from ReLU when the network of interest gets deep. Thirdly, the Dice loss, as one of the pervasive loss functions for medical image segmentation, is not effective when the prediction is close to ground truth and will cause oscillation during training. To address the aforementioned three problems, we propose and validate a deeper network that can fit medical image datasets that are usually small in the sample size. Meanwhile, we propose a new loss function to accelerate the learning process and a combination of different activation functions to improve the network performance. Our experimental results suggest that our network is comparable or superior to state-of-the-art methods.

Question: What is the difference between ELU and ReLU?


Inertial properties of the human trunk of males determined from magnetic resonance imaging. The purpose of this study was to evaluate the segmental parameters of the human trunk of malesin vivo using magnetic resonance imaging (MRI). In addition, the efficacy of volumetric estimation and existing prediction formulas to produce segmental properties similar to those produced by MRI was evaluated. As opposed to finding one representative normal value for these parameters, a range of normal values was defined. For instance, the average trunk mass was 42.2%±3.5% (x±SD) of body mass, but values ranged from 35.8% to 48.0%. To account for segment parameters more accurately, specific anthropometric measures need to be considered in addition to overall measures of body height and mass. These specific measures included segment length, circumference, width, and depth. Studies reporting general percentages based on height and/or mass were found to be inadequate predictors of segmental parameters of the trunk compared with MRI estimates. Volumebased estimates, which assume a uniform density distribution within a segment, were found to correspond closely to MRI values except for the thorax. However, the use of density values reflective of the livingin vivo state would likely alleviate this disparity, thus indicating that the volumetric technique may be effective for deriving segmental parameters for large segments of the trunk. Future research should adopt noninvasive techniques such as MRI and/or volumetric estimation to enhance the predictability of segmental parameters of the body for specific population groups characterized by gender, developmental age, body type, and fitness level. Further efforts should be made to establish standardized boundary definitions for trunk segments to avoid unnecessary confusion, from which substantial errors may be introduced into biomechanical linked-segment analyses of human movement.

Question: What is the average trunk mass of males?


Bayesian Point Cloud Reconstruction. In this paper, we propose a novel surface reconstruction technique based on Bayesian statistics: The measurement process as well as prior assumptions on the measured objects are modeled as probability distributions and Bayes' rule is used to infer a reconstruction of maximum probability. The key idea of this paper is to define both measurements and reconstructions as point clouds and describe all statistical assumptions in terms of this finite dimensional representation. This yields a discretization of the problem that can be solved using numerical optimization techniques. The resulting algorithm reconstructs both topology and geometry in form of a well-sampled point cloud with noise removed. In a final step, this representation is then converted into a triangle mesh. The proposed approach is conceptually simple and easy to extend. We apply the approach to reconstruct piecewise-smooth surfaces with sharp features and examine the performance of the algorithm on different synthetic and real-world

Question: What is the difference between a point cloud and a triangle mesh?


Brain—Computer Interface. Human-computer interfaces (HCIs) have become ubiquitous. Interfaces such as keyboards and mouses are used daily while interacting with computing devices (Ebrahimi et al., 2003). There is a developing need, however, for HCIs that can be used in situations where these typical interfaces are not viable. Direct brain-computer interfaces (BCI) is a developing field that has been adding this new dimension of functionality to HCI. BCI has created a novel communication channel, especially for those users who are unable to generate necessary muscular movements to use typical HCI devices.

Question: What is a brain-computer interface?